Meltome Atlas of Arabidopsis thaliana Proteome: A Melting Temperature-Based Identification of Heat & Cold Resistant Proteins

Meltome Atlas of Arabidopsis thaliana Proteome: A Melting Temperature-Based Identification of Heat & Cold Resistant Proteins | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Meltome Atlas of Arabidopsis thaliana Proteome: A Melting Temperature-Based Identification of Heat & Cold Resistant Proteins Karan Martens Mohanta, Tapan Kumar Mohanta This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6629178/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Nov, 2025 Read the published version in BMC Genomics → Version 1 posted 10 You are reading this latest preprint version Abstract Background Plants are always exposed to a variety of stressful environment including heat and drought stress, which severely impacts the growth, development, and productivity of the plants. To overcome such challenges, plants have evolved diverse arrays of defense mechanisms. From several defense strategies, expression and evolution of heat stress-tolerant proteins are crucial. They protect the cellular structures, maintain cellular homeostasis, and overcome the stress condition. Although several studies are conducted to identify the heat-and cold-stress tolerant proteins, studies using the physiochemical properties of the proteins remain scarce. Therefore, we used melting temperature-based identification of heat-and col- tolerant proteins in A. thaliana . Results The study elucidated the thermal properties of the entire Arabidopsis thaliana proteome by considering the melting temperature (Tm) and the melting temperature index (TI). In total, 48359 protein sequences were analyzed, and the melting temperature of the proteins was recorded in three groups (Tm 65°C). The Tm index of the A. thaliana proteome ranged from − 15.6008 ( 65°C). At least 22826 proteins were found in the Tm group of 55°C to 65°C, 20640 proteins were found in the Tm group of > 65°C, and only 4893 proteins were found in the Tm group of < 55°C. The mediator of RNA polymerase II transcription subunit-like protein was found to possess the highest Tm index (9.60), while the NADH dehydrogenase 5B subunit was found to contain the lowest TI (-15.60). The amino acid composition analysis of the A. thaliana proteome revealed that the frequency of Ala, Asp, Glu, Gly, Lys, Gln, and Val increased with the increase in Tm, while the amino acids Cys, Phe, and Trp decreased with the increase in the Tm of the A. thaliana proteome. The molecular mass of the A. thaliana proteome ranged from 0.149 to 611.888 kDa, and protein in the Tm group at 55–65°C showed the highest average molecular mass. The machine learning analysis revealed an increase in the molecular mass positively correlated with the increase in the Tm of the proteins. The codon usage pattern revealed, the codon pair prefer the Tm group specific occurrence where ATG-ATG, CAA-CAA codon pairs were predominated. Relative synonymous codon usage of the three Tm groups revealed AGA (Arg) and CCA (Pro) were the preferred codons for the low and high Tm group DNA sequences, respectively. Codon context analysis revealed the presence of preferences of the Tm group specific codon pairing. There was a variation in the nucleotide position of the codons in different Tm groups. Evolutionary study revealed, gene duplication was the predominant evolutionary feature and all of the studied genes in the three Tm group undergone duplication. Codon context analysis revealed distinct clustering pattern in high Tm protein group. The study underscores the role of amino acid composition, molecular mass, and codon usage in determining the thermal stability of the proteins in the A. thaliana . Conclusion The study reflected the evolution of high Tm-adapting genes through gene duplication, highlighting the role of gene and genome evolution towards encoding high Tm proteins for stress resilience. Protein Proteome Melting temperature Heat stress Arabidopsis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Background Plants are ubiquitous sessile organisms, and hence they face a plethora of stresses and environmental challenges. These challenges adversely affect the growth, development, and productivity of the plants [ 1 , 2 ]. However, they have established a tremendous ability to respond and adapt to these stresses for their survival [ 3 , 4 ]. The translation product, proteins, is crucial to these events, and they play a fundamental role in these stress responses [ 5 – 7 ]. They act as key signaling molecules and key regulators of plant defence responses [ 8 – 10 ]. They perceive, respond, regulate, and adapt to various stress conditions. The roles of receptor-like kinase (RLK) and receptor-like proteins (RLP) are tremendous in perceiving external stress signals [ 8 , 11 – 13 ]. Some proteins act as second messengers like calcium-binding proteins (calcium-dependent protein kinase, calmodulin, calmodulin-like protein, calcineurin B-like proteins) [ 14 – 18 ], and phospholipases [ 19 , 20 ]. Transcription factors like NAC, WRKY, MYB, AP2/ERF, and bZIP regulate the expression of several stress-responsive genes in plants [ 21 – 24 ]. These TFs bind to the specific promoter region and regulate the gene expression, thus adapting to the stress responses [ 25 , 26 ]. Kinases (MAPKs) and phosphatases (PP2C) play phosphorylation and dephosphorylation events to alter the protein function [ 27 , 28 ]. Ubiquitin ligase conducts ubiquitination through the ubiquitin-proteasome complex and removes the damaged proteins and thus regulates the function of key stress proteins [ 29 , 30 ]. During the stress event, the generation of reactive oxygen species occurs, and the oxidative stress gets mitigated by superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT) [ 31 ]. Similarly, osmotic adjustment in cells is regulated by proline dehydrogenase, trehalose 6-phosphate, and other proteins and balances the osmotic stress condition [ 32 ]. During heat stress, heat shock protein (HSP) helps in protein folding and prevents aggregation of denatured proteins resulting from the heat stress [ 33 ]. The role of HSP protein is extraordinary in maintaining protein homeostasis [ 34 , 35 ]. Like HSP, aquaporin protein regulates drought stress in plants [ 36 – 38 ]. From several environmental stresses, heat and cold stress are the most common types of stresses. Cold stress includes chilling stress (0–15°C) and freezing stress (below 0°C) [ 39 ]. The chilling and freezing stress arrest the proteomic and enzymatic activities and thus slow down the metabolic process [ 39 , 40 ]. The cold stress leads to the production of Osmo protectants like proline through the enzyme pyrroline-5-carboxylase synthetase (P5CS) [ 41 – 43 ]. Similarly, sucrose-phosphate synthetase (SPS) and fructan fructosyltransferase (FFT) help in sugar metabolism [ 44 ]. Unlike cold stress, when temperature rises above 35°C, plants encounter heat stress. The heat stress greatly impacts the protein stability and membrane permeability [ 45 , 46 ]. The heat stress is sensed by protein denaturation and changes in membrane permeability [ 47 , 48 ]. The HSPs help in protein folding and prevent the aggregation of denatured and damaged proteins [ 33 , 49 ]. Sometimes plants encounter a combined stress response of heat and drought stress [ 50 ]. During this event, proteins associated with crosstalk get involved, and their signaling mechanism helps to mitigate the stresses. Thus, proteins are the pivotal elements involved in heat and cold stress mitigation by orchestrating complex networks of signaling pathways and metabolic adjustment [ 51 , 52 ]. A lot of studies are being conducted to identify and implement different genes and proteins involved in such stress tolerance for the development of stress-tolerant crops. Indeed, this is also pivotal for the development of sustainable agriculture in the face of global warming. So that plants can thrive in challenging environmental conditions. More specifically, when a plant undergoes extreme heat or chilling stress, the protein either undergoes denaturation or strong folding/misfolding [ 53 , 54 ] thus impacting the protein function. Proteins with a higher melting temperature tend to be more stable and can uphold their structure and function at the higher temperature [ 55 – 57 ]. This can be quite crucial to withstand the heat stress. Similarly, proteins with cold stress can also undergo denaturation (less common) and can lead to disruption in cellular and metabolic activities [ 58 , 59 ]. Cold stress can alter the secondary and tertiary structures of the protein, making them quite rigid and nonfunctional [ 60 – 62 ]. The proteins associated with the membrane and structural components of cells will be greatly impacted due to such chilling stress. Prolonged cold stress can lead to protein aggregation, leading to the formation of insoluble protein complexes and ultimately cellular death. Cold temperatures will slow down the enzyme activities and their kinetics, thus reducing their biochemical reactions. If any protein associated with the Calvin cycle gets impacted by chilling stress, there will be a hindrance in photosynthesis, leading to poor growth and development of the plants. Similarly, chilling stress can precipitate the membrane-associated lipid thus disrupting membrane fluidity and signaling pathways. Protein-protein interactions were also greatly impacted due to the denaturation of proteins, leading to alternations in charges and protein conformation. Further, post-translational modification gets impacted by such chilling or heat stress, which regulates protein activity and stability. A lot of studies are conducted to find different genes and proteins associated with stress tolerance in plants [ 63 , 64 ]. Starting from 2D protein gel electrophoresis to single-cell transcriptome and from small nucleotide polymorphism to marker-assisted identification of novel traits, several such approaches are being implemented to identify novel genetic traits in plants [ 65 – 67 ]. These approaches are still in force to identify novel traits in plants. However, so far, no study is being conducted to identify the novel genetic and genomic traits using the physiological parameters of their translated product, i.e., protein. Proteins are quite sensitive to pH change, which affect their structure, function, and physiochemical properties. Further, physiological parameters like the melting temperature (Tm) of proteins can be one of the most important factors that can play a crucial role in protein function. Tm is a temperature at which protein undergoes denaturation and loses its three-dimensional structure and function. At Tm, half of the protein remains in a folded (native) state, and half of the protein remains in an unfolded state. It is one of the important parameters to understand the protein stability. A higher Tm indicates higher stability of the protein, where the protein can maintain and sustain its three-dimensional shape and function. The amino acid composition of the protein greatly influences the thermal stability and overall structure of the protein [ 68 , 69 ]. Further, larger and more complex proteins might have more intricate folding and may have a higher melting temperature. The presence of hydrophobic amino acids in the protein tends to increase the Tm of the protein and thus protein stability [ 70 – 72 ]. Similarly, a properly folded protein has a higher Tm compared to partially or misfolded proteins. Further, post-translational modifications like glycosylation and phosphorylation affect protein stability [ 73 , 74 ] through its Tm. The presence of other factors like pH, ionic strength of the protein, and stabilizing agents can impact the Tm of the protein. The presence of acidic (aspartic acid and glutamic acid) and basic (lysine and arginine) ionization groups affects the electrostatic interactions of the proteins and stabilizes the protein structure [ 75 , 76 ]. This shows that Tm is a fundamental property of the protein that influences the protein’s stability, function, and interactions. Therefore, it becomes crucial to understand this parameter to understand the protein's behavior under various environmental conditions. By analyzing Tm of proteins, we can understand the basic insight of the mechanism of the protein by which the protein adapts to the harsh environmental condition and mitigates stress. This physiochemical parameter of the protein can be quite crucial to identify the stress-tolerant proteins and subsequently stress-tolerant crops for biotechnological application. Therefore, we identified the Tm values of all the protein sequences of Arabidopsis thaliana proteome and reported them Methods Retrieval of Arabidopsis proteome sequences The protein sequences of Arabidopsis thaliana were downloaded from the TAIR (The Arabidopsis Information Resources) database. The proteome file contained the translated protein sequences of all the coding DNA sequences (CDS) and their splice variants. It included a total of 48359 protein sequences. The downloaded protein sequences were subjected to analysis of melting temperature (Tm). Prediction of Melting Temperature The melting temperature prediction of the entire Arabidopsis thaliana proteome was made using the “Melting temperature prediction” ( http://tm.life.nthu.edu.tw/ ) pipeline [ 77 ]. The online platform calculates the melting temperature and melting temperature index (TI) of the protein. Only one sequence can be submitted at once to find the melting temperature and melting index. The resulted Tm and TI were recorded in a Microsoft Excel file for further analysis. Prediction of molecular mass and isoelectric point of Arabidopsis thaliana proteome The molecular mass and isoelectric point of all the chloroplast protein sequences were calculated using the IPC-isoelectric point calculator ( http://isoelectric.org/ ), version 2.0 [ 78 ]. The IPC Python software was downloaded and run on a Linux-based command-line platform. Command lines were used as per the instructions of the software. The IPC software resulted in the molecular mass and isoelectric point of individual proteins. We saved the data in text file format for further analysis. Statistical analysis of Arabidopsis thaliana Proteome Melting temperature The melting temperatures of the Arabidopsis thaliana proteome were divided into three groups. They are as follows: (I) 65°C. Later, the protein sequences of the A. thaliana proteome were segregated according to their melting temperature, and subsequent analysis was performed. Correlation analysis of proteins with Tm 65°C with respect to molecular mass and isoelectric point was conducted using JASP 0.19.0.0 version software. Pearson’s correlation with a 95% confidence interval was used for the study. One sample student t-test of the Tm index of the A. thaliana proteome was conducted using JASP 0.19.0.0 version software to confirm that the mean is different from zero ( p < 0.01). A frequency distribution of molecular mass and isoelectric point of all three Tm groups was conducted with p < 0.05 using Past4 software. The amino acid composition of the A. thaliana proteome was calculated based on their melting temperature groups by running a Linux-based command. All other basic statistical details were calculated using Microsoft Excel 2016. Gene expression analysis of A. thaliana Genes It was important to understand the function expression profiles of the important genes of various Tm groups. Therefore, we considered the top 10 genes of the Tm group at 55°C-65°C, and > 65°C having the highest Tm index and the bottom 10 genes of the Tm group at < 55°C having the lowest Tm index. Gene expression profiles of the genes were retrieved from the Expression Atlas of the European Molecular Biology Laboratory ( https://www.ebi.ac.uk/gxa/home ). Expression profiles of individual genes were searched and recorded in Log 2 fold change. Tm Based Evolutionary Analysis of A. thaliana proteome To understand the evolutionary details of the A. thaliana proteomes, we selected the top 50 protein sequences from three TI groups. Group I contained the bottom 50 TI protein sequences ( 65°C. The CDS sequences of the A. thaliana proteins were retrieved from the TAIR database, and a multiple sequence alignment was conducted within the individual TI group using MAFT software [ 79 ]. The multiple sequence alignment of the protein sequences was saved in CLUSTALW file format that was subsequently converted to .AL file format using MEGA7 software [ 80 ]. The resulting .aln file was subjected to model selection to conduct the phylogenetic analysis in MEGA7 software [ 80 ]. Later, we subjected the .aln file to phylogenetic analysis. Based on the resulting models, the best model was used to construct the phylogenetic tree. Various statistical parameters used to construct the phylogenetic tree were as follows: statistical method, maximum likelihood, test of phylogeny; bootstrap method, model/method, Tamura-Nei model. Gamma distribution with discrete gamma categories (5) was used to construct the phylogenetic tree. The nearest-neighbor-interchange (NII) ML heuristic method was used with a strong branch swap filter. There were no gaps or missing data treatment, and all sites were used to construct the phylogenetic tree. The resulting phylogenetic tree was saved in Newick file format and referred to as a gene tree. To understand the gene loss, duplication, and divergence, it was important to compare the respective gene tree with their species tree. The species tree was constructed using the following link: https://www.ncbi.nlm.nih.gov/Taxonomy/CommonTree/wwwcmt.cgi . The gene tree and species tree were loaded and later reconciled in Notung software [ 81 ] version 2.9.1.5 to get the gene loss, duplication, and divergence. Relative synonymous codon usage and nucleotide position of codons were calculated using MEGA software version 7 [ 80 ]. Results The Melting Temperature Index (TI) of Arabidopsis thaliana proteome ranged from 9.605 to -15.6008 The study was conducted to deduce the melting temperature and melting temperature index of the Arabidopsis thaliana proteome. All the protein sequences of the A. thaliana proteome were downloaded from the TAIR database. The proteome file included all the protein sequences, including the splice variants. Individual protein sequences were subjected to Tm analysis using the Tm predictor. The resulting Tm and TI were documented in an Excel file. It was found that the TI of the A. thaliana proteome ranged from − 15.6008 (Tm 65°C) (Table 1 ). The mediator of RNA polymerase II transcription subunit-like protein (At1g55080.2) was found to encode the highest TI (9.605), and the protein NADH dehydrogenase 5B subunit (AtMG00665.1) encoded the lowest TI (-15.6008) (Table 1 ). The melting temperature index was grouped into three categories ( 65°C) as mentioned above. It was found that the average TI of the A. thaliana proteome in the group of 65°C it was 1.508. In the entire proteome of A. thaliana , there were 48359 protein sequences. From them, 20640 were found to contain Tm > 65°C, 22826 were found to contain Tm 55–65°C, and 4893 were found to contain Tm 65 o C, 55-65 o C, and 65 o C AT1G55080.2 mediator of RNA polymerase II transcription subunit-like protein 9.605825 AT3G57160.1 cysteine-rich TM module stress tolerance protein 8.847668 AT1G55080.1 mediator of RNA polymerase II transcription subunit-like protein 8.763272 AT2G41420.1 proline-rich family protein 8.684553 AT3G49845.1 cysteine-rich TM module stress tolerance protein 7.537937 AT5G59170.1 Proline-rich extensin-like family protein 7.47778 AT3G57160.2 cysteine-rich TM module stress tolerance protein 7.292566 AT3G22930.2 calmodulin-like 11 6.713252 AT4G04601.1 hypothetical protein 5.91422 AT1G12810.1 proline-rich family protein 5.505747 AT5G67600.1 cysteine-rich TM module stress tolerance protein 5.426804 AT5G28640.2 SSXT family protein 5.308339 AT5G28640.3 SSXT family protein 5.308339 AT3G43583.1 hypothetical protein 5.279883 AT1G33355.1 hypothetical protein 5.237808 AT3G23450.1 transmembrane protein 5.168681 AT1G53260.1 hypothetical protein 5.118861 AT3G23450.2 transmembrane protein 5.116701 AT3G23450.3 transmembrane protein 5.062314 AT3G23450.4 transmembrane protein 5.062314 AT5G45350.3 proline-rich family protein 5.003393 AT5G45350.5 proline-rich family protein 5.003393 AT5G45350.7 proline-rich family protein 5.003393 AT1G51915.1 cryptdin protein-like protein 4.954335 AT2G35343.1 hypothetical protein 4.894497 AT1G12810.2 proline-rich family protein 4.845825 AT3G17626.1 structural constituent of ribosome 4.833655 AT1G62333.1 hypothetical protein 4.824776 AT5G17510.1 mediator of RNA polymerase II transcription subunit-like protein 4.779996 AT5G17510.2 mediator of RNA polymerase II transcription subunit-like protein 4.779996 AT1G43825.1 hypothetical protein 4.700155 AT2G04870.1 hypothetical protein 4.654217 AT3G46616.3 hypothetical protein 4.639269 AT4G16983.1 hypothetical protein 4.628392 AT5G36920.1 transmembrane protein 4.593216 AT1G52855.1 hypothetical protein 4.583521 AT3G46616.1 hypothetical protein 4.565323 AT3G46616.2 hypothetical protein 4.565323 AT4G12050.1 Putative AT-hook DNA-binding family protein 4.556438 AT2G20562.1 taximin 4.534934 AT5G66780.1 late embryogenesis abundant protein 4.485143 AT3G20470.1 glycine-rich protein 5 4.465337 AT5G35660.1 Glycine-rich protein family 4.372616 AT3G16080.2 Zinc-binding ribosomal protein family protein 4.370846 AT1G28630.5 transcriptional regulator EFH1-like protein 4.33809 AT2G30590.1 WRKY DNA-binding protein 21 4.337233 AT4G11430.1 hydroxyproline-rich glycoprotein family protein 4.309887 AT4G25225.1 transmembrane protein 4.309825 AT3G05870.7 anaphase-promoting complex/cyclosome 11 4.233014 AT2G05520.1 glycine-rich protein 3 4.220797 55-65 o C AT1G27260.1 Paired amphipathic helix (PAH2) superfamily protein 0.999993 AT5G09720.1 Magnesium transporter CorA-like family protein 0.999965 AT5G09720.2 Magnesium transporter CorA-like family protein 0.999965 AT4G00630.1 K + efflux antiporter 2 0.999852 AT1G33290.1 P-loop containing nucleoside triphosphate hydrolases superfamily protein 0.999807 AT1G35350.1 EXS (ERD1/XPR1/SYG1) family protein 0.99976 AT4G39950.2 cytochrome P450%2C family 79%2C subfamily B%2C polypeptide 2 0.999757 AT1G19520.1 pentatricopeptide (PPR) repeat-containing protein 0.999754 AT4G35630.1 phosphoserine aminotransferase 0.999682 AT4G38760.1 nucleoporin (DUF3414) 0.999622 AT2G30650.1 ATP-dependent caseinolytic (Clp) protease/crotonase family protein 0.999545 AT2G44480.5 beta glucosidase 17 0.999541 AT1G48280.1 hydroxyproline-rich glycoprotein family protein 0.999532 AT5G62830.1 F-box associated ubiquitination effector family protein 0.999498 AT3G19510.1 Homeodomain-like protein with RING/FYVE/PHD-type zinc finger domain-containing protein 0.999471 AT3G19510.2 Homeodomain-like protein with RING/FYVE/PHD-type zinc finger domain-containing protein 0.999471 AT1G74550.1 cytochrome P450%2C family 98%2C subfamily A%2C polypeptide 9 0.999441 AT2G45700.1 sterile alpha motif (SAM) domain-containing protein 0.999376 AT3G55320.1 P-glycoprotein 20 0.999333 AT3G56810.1 hypothetical protein 0.999328 AT5G23540.2 Mov34/MPN/PAD-1 family protein 0.999305 AT3G11960.4 Cleavage and polyadenylation specificity factor (CPSF) A subunit protein 0.999299 AT3G62180.2 Plant invertase/pectin methylesterase inhibitor superfamily protein 0.999287 AT2G15360.1 fucosyltransferase 0.999274 AT2G41930.1 Protein kinase superfamily protein 0.999257 AT5G43870.1 auxin canalization protein (DUF828) 0.999231 AT2G46660.1 cytochrome P450%2C family 78%2C subfamily A%2C polypeptide 6 0.999198 AT1G43970.1 hypothetical protein 0.999195 AT5G36120.1 cofactor assembly%2C complex C (B6F) 0.999189 AT1G23180.1 ARM repeat superfamily protein 0.999184 AT4G09990.1 glucuronoxylan 4-O-methyltransferase-like protein (DUF579) 0.999059 AT5G64670.1 Ribosomal protein L18e/L15 superfamily protein 0.998885 AT4G25770.2 alpha/beta-Hydrolases superfamily protein 0.99881 AT1G77810.1 Galactosyltransferase family protein 0.998705 AT3G14990.2 Class I glutamine amidotransferase-like superfamily protein 0.998685 AT3G14990.3 Class I glutamine amidotransferase-like superfamily protein 0.998685 AT2G39810.1 ubiquitin-protein ligase 0.998677 AT1G15720.1 TRF-like 5 0.998636 AT4G28890.1 RING/U-box superfamily protein 0.998635 AT1G45233.2 THO complex%2C subunit 5 0.998623 AT1G56120.2 Leucine-rich repeat transmembrane protein kinase 0.998561 AT1G68940.1 Armadillo/beta-catenin-like repeat family protein 0.998193 AT3G52320.1 F-box and associated interaction domains-containing protein 0.998148 AT5G21040.1 F-box protein 2 0.998107 AT5G21040.2 F-box protein 2 0.998107 AT5G21040.3 F-box protein 2 0.998107 AT1G36730.1 Translation initiation factor IF2/IF5 0.998034 AT5G59550.2 zinc finger (C3HC4-type RING finger) family protein 0.998023 AT2G20940.2 transmembrane protein%2C putative (DUF1279) 0.997973 AT1G77360.1 Tetratricopeptide repeat (TPR)-like superfamily protein 0.997932 < 55 o C AT1G10715.1 EMBRYO SURROUNDING FACTOR-like protein -3.36815 AT4G38960.1 B-box type zinc finger family protein -3.38287 AT4G38960.6 B-box type zinc finger family protein -3.38287 AT4G38960.2 B-box type zinc finger family protein -3.39699 AT4G38960.5 B-box type zinc finger family protein -3.39699 AT5G38330.1 low-molecular-weight cysteine-rich 80 -3.44673 AT2G04621.1 transmembrane protein -3.51447 AT4G15735.1 SCR-like 10 -3.52163 AT5G42280.1 Cysteine/Histidine-rich C1 domain family protein -3.53152 AT4G30070.1 low-molecular-weight cysteine-rich 59 -3.56614 AT2G02026.1 hypothetical protein -3.6482 AT2G21320.1 B-box zinc finger family protein -3.66898 AT3G15548.1 transmembrane protein -3.72664 AT1G14755.1 S locus-related glycoprotein 1 (SLR1) binding pollen coat protein family -3.7273 AT4G35430.1 hypothetical protein -3.79968 AT3G47965.1 hypothetical protein -3.86685 AT5G46874.1 Putative membrane lipoprotein -3.89865 AT4G33735.1 hypothetical protein -3.95442 AT5G42280.2 Cysteine/Histidine-rich C1 domain family protein -4.0398 AT1G07600.1 metallothionein 1A -4.06107 AT5G20447.1 hypothetical protein -4.15162 AT2G16535.1 maternally expressed family protein -4.15854 AT2G22807.2 Defensin-like (DEFL) family protein -4.21377 AT5G46825.1 hypothetical protein -4.23425 AT2G16505.1 maternally expressed family protein -4.2409 AT3G28216.1 hypothetical protein -4.25618 ATCG00690.1 photosystem II reaction center protein T -4.26142 AT2G41355.1 hypothetical protein -4.31003 AT5G03545.1 expressed in response to phosphate starvation protein -4.35042 AT3G07522.1 hypothetical protein -4.53537 AT1G53970.1 GDSL esterase/lipase-like protein -4.55369 AT1G53970.2 GDSL esterase/lipase-like protein -4.55369 AT1G54773.1 hypothetical protein -4.60278 ATCG00510.1 photsystem I subunit I -4.6412 AT5G24575.1 hypothetical protein -4.70326 ATCG00080.1 photosystem II reaction center protein I -4.81682 AT1G03325.1 hypothetical protein -4.86125 AT4G12850.3 Far-red impaired responsive (FAR1) family protein -4.92786 ATCG00760.1 ribosomal protein L36 -5.03606 AT5G04045.1 Putative membrane lipoprotein -5.06174 AT5G07545.1 hypothetical protein -5.12925 AT5G56555.1 hypothetical protein -5.39349 AT1G02490.1 hypothetical protein -5.87035 AT3G26395.1 hypothetical protein -6.33018 AT3G61172.1 low-molecular-weight cysteine-rich 8 -6.76256 AT2G26515.1 hypothetical protein -6.78223 AT1G13805.1 hypothetical protein -7.21064 AT5G40315.1 hypothetical protein -7.37162 AT3G23122.1 hypothetical protein -9.97686 ATMG00665.1 NADH dehydrogenase 5B -15.6008 Trp was the lowest encoding amino acid in A. thaliana proteome with Tm > 65 o C In total, 48359 protein sequences of A. thaliana were found to encode 20855719 amino acids. From them, 1115151 amino acids were from the group with Tm 65 o C. It was found that the percentage of amino acid composition of Trp (1.135%) was lowest in the protein sequences that encode proteins with Tm > 65 o C. The rate of Trp composition was highest (1.377%) in the proteins with Tm < 55 o C. However, the percentage composition of Leu amino acid was highest (9.682%) in Tm 55-65 o C and lowest (8.961%) in Tm < 55 o C (Table 2 ). The percentage of amino acid composition of Ala, Asp, Glu, Gly, Lys, Gln, and Val was found to increase with the increase in the Tm of the proteins (Table 2 , Fig. 1 ). While the percentage of amino acid composition of Cys, Phe, His, Ile, Met, Asn, Arg, Ser, Thr, Trp, and Tyr was found to decrease with the increase in Tm of the proteins (Table 2 , Fig. 1 ). However, the composition of Leu amino acid was increased from Tm 65 o C (Table 2 ). Table 2 Amino acid composition of Arabidopsis thaliana proteome in different melting temperature (Tm) groups. The composition of red shaded amino acids Ala, Asp, Glu, Gly, Lys, Gln, and Val increased while the composition of green shaded amino acids Cys, Phe, His, Ile, Met, Asn, Pro, Arg, Ser, Thr, Trp, and Tyr decreased with the increase in the Tm of the proteins. Amino Acids Tm 65 o C Ala 5.767 6.194 6.460 Cys 2.554 1.879 1.684 Asp 5.161 5.399 5.500 Glu 6.019 6.720 7.043 Phe 4.732 4.300 4.041 Gly 6.269 6.312 6.430 His 2.397 2.289 2.255 Ile 5.493 5.402 5.131 Lys 6.205 6.259 6.564 Leu 8.961 9.682 9.478 Met 2.765 2.452 2.404 Asn 4.494 4.403 4.371 Pro 4.964 4.667 4.869, Met, Gln 3.301 3.498 3.728 Arg 5.477 5.429 5.379 Ser 9.333 9.278 9.118 Thr 5.174 5.080 5.023 Val 6.447 6.606 6.680 Trp 1.377 1.282 1.135 Tyr 3.099 2.858 2.695 The molecular mass of A. thaliana proteome ranged from 0.149 kDa to 611.888 kDa It was pertinent to understand whether molecular mass plays any role in deciding the Tm of the proteins. Therefore, we calculated the molecular mass of individual proteins of the A. thaliana . It was found that the A. thaliana proteome encodes proteins from 0.149 kDa to 611.888 kDa. The average molecular mass of proteins of the Tm group > 55°C, 55–65°C, and > 65°C was 25.617, 52.482, and 48.924 kDa, respectively (Fig. 2 ). The molecular mass of proteins with Tm group 55–65°C was the highest, whereas the proteins with Tm group < 55°C was the lowest (Fig. 2 ). The protein that encoded the lowest molecular mass (0.149 kDa) protein in A. thaliana proteome was a hypothetical protein (AT1G64633.1) with a Tm of 55–65°C and a TI index of zero. Similarly, the highest molecular mass protein of the A. thaliana (611.888 kDa) proteome was a midasin-like protein (AT1G67120.2). It has a Tm of > 65°C with a TI index of 1.223. A correlation analysis of the molecular mass of A. thaliana proteome with three different Tm groups ( 65°C) was conducted to understand if the Tm groups with respect to molecular mass are correlated (Fig. 3 ). The correlation plot was < 55°C vs. 55–65°C, 65°C, and 55°C-65°C vs. > 65°C with an r value of 0.044, 0.002, and 0.021 (Fig. 3 ). The frequency distribution of the molecular mass of A. thaliana proteome is depicted in Fig. 2 . Isoelectric point (pI) of A. thaliana proteome ranged from 2.753 to 12.749 The isoelectric point of the A. thaliana proteome was calculated to understand whether the isoelectric point of the protein has any role towards the Tm of proteins. It was found that glycine-rich protein (AT3G44950.1) encoded the lowest pI (2.753) and ribosomal protein L41 family (AT3G56020.1) encoded the highest pI (12.749). The average pI of the A. thaliana proteome was 6.78. The average pI of the A. thaliana proteome with Tm 65°C was 6.74 (Fig. 4 ). A correlation analysis was conducted to infer the relationship between Tm groups. They were Tm < 55°C vs 55–65°C, 65°C, and 55–65°C vs > 65°C (Fig. 5 ). The correlation coefficient ( r ) for the group < 55°C vs 55–65°C, 65°C, and 55–65°C vs > 65°C was − 0.006, 0.011, and − 0.003, respectively (Fig. 5 ). The frequency distribution of A. thaliana proteomes with different Tm was depicted in Fig. 4 . When a correlation plot was drawn to understand the role of Tm, pI , and molecular mass (kDa), it was found that molecular mass has a role towards the Tm of Arabidopsis proteins with a correlation coefficient r = 0.259. However, the pI of the Arabidopsis proteome is negatively correlated with coefficient r = -0.091 (Fig. 6 ). High and low TI index genes Shows Significant Expression in Plant Development and Drought Understanding the roles of A. thaliana genes for drought and heat tolerance mechanisms is important to uncover their importance. So that they can be useful for generating stress-tolerant crop varieties to withstand harsh environmental conditions. A search of gene expression data in Expression Atlas revealed that the maximum of the genes searched for in the gene expression data had undergone up-regulation. The mediator of the RNA polymerase II transcription subunit-like gene has undergone a 2.5-fold upregulation due to drought stress (Table 3 ). The cysteine-rich TM module stress tolerance gene in tcx2 (Tesmin-like CXC2) mutant has undergone a 2.8-fold up-regulation compared to the wild type. The cysteine-rich TM module for stress tolerance gene in the CT101 mutant underwent a 6.6-fold up-regulation upon 350 ppb ozone exposure. The proline-rich extensin-like family gene has undergone 7.9-fold up-regulation due to drought stress (Table 3 ). Calmodulin-like gene 11has undergone 4.4-fold up-regulation in the vtc2.5 mutant in drought environment. In the Tm group at 55–65°C, the magnesium transporter CorA-like family gene underwent a 3.6-fold up-regulation when treated with 350 nanoliters of ozone. However, the EXS gene and CYTP79B2 undergone 4.4-and 7.1-fold down regulation, respectively, upon drought environment (Table 3 ). In the Tm group < 55°C, from the selected ten genes, the expression profile of three genes was not found. However, the gene NADH dehydrogenase 5B, which has the lowest Tm index, underwent 7.7-fold up-regulation in tcx2; WOX5:GFP mutant. One hypothetical protein also underwent 7.4-fold up-regulation in pao-1 mutant when compared with the wild type (Table 3 ). A low-molecular-weight cysteine-rich 8 gene has undergone 4.3-fold up-regulation in dark-induced senescence (Table 3 ). Table 3 Gene expression data for the ten genes with the highest Tm Index in the 55–65°C and 65°C groups, as well as the ten genes with the lowest Tm Index in the group below 55°C. Accession Number Gene Log 2 Fold Change Tm Index (TI) Environmental Conditions/Experiment > 65 o C AT1G55080.2 Mediator of RNA polymerase II transcription subunit-like protein 2.5 9.605825 Drought environment vs normal watering AT3G57160.1 Cysteine-rich TM module stress tolerance protein 2.8 8.847668 tcx2; TMO5:3xGFP vs wild type genotype AT2G41420.1 Proline-rich family protein 1.7 8.684553 Developmental stages, 35 days vs 29 days AT3G49845.1 Cysteine-rich TM module stress tolerance protein 6.6 7.537937 CT101; 350 ppb ozone exposure for 2hr vs CT101; control AT5G59170.1 Proline-rich extensin-like family protein 7.9 7.47778 drought environment vs normal watering in vtc2.5 mutant AT3G22930.1 Calmodulin-like 11 4.4 6.713252 35S:HSFA1b-RFP vs wild type genotype in warm/hot temperature regimen AT4G04601.1 Hypothetical protein 1.4 5.91422 ref8-1 mutant vs wild type AT1G12810.1 Proline-rich family protein 1.7 5.505747 Drought environment vs normal watering in wild type genotype AT5G67600.1 Cysteine-rich TM module stress tolerance protein 2.4 5.426804 Drought environment vs normal watering in vtc2 mutant AT5G28640.2 SSXT family protein 3.4 5.308339 10 days vs 0 day. Light exposure to study chloroplast development 55 o C-65 o C AT1G27260.1 Paired amphipathic helix (PAH2) superfamily protein NA 0.999993 NA AT5G09720.1 Magnesium transporter CorA-like family protein 3.6 0.999965 Ozone; 350 nanoliter vs none in Cvi-0 AT4G00630.1 K + efflux antiporter 2 2.3 0.999852 Seed after 48 h of stratification (48 h S) vs seed after 12 h of stratification (12 h S) AT1G33290.1 P-loop containing nucleoside triphosphate hydrolases superfamily protein 2.3 0.999807 tcx2; TMO5:3xGFP vs wild type genotype AT1G35350.1 EXS (ERD1/XPR1/SYG1) family protein -4.4 0.99976 Drought environment vs normal watering in vtc2 mutant AT4G39950.2 CYP79B2 (Cytochrome p450, family 79) -7.1 0.999757 Drought environment vs normal watering in wild type genotype AT1G19520.1 Pentatricopeptide (PPR) repeat-containing protein 2.6 0.999754 Dark-induced senescence AT4G35630.1 Phosphoserine aminotransferase 5.4 0.999682 S-nitrosocysteine; 1 millimolar vs buffer AT4G38760.1 Nucleoporin (DUF3414) 2.8 0.999622 Ler/Kas-2 hybrid vs Kas-2 in wild type genotype AT2G30650.1 ATP-dependent caseinolytic (Clp) protease/crotonase family protein 4 0.999545 10 days vs 7 day in wild type AT2G44480.5 Beta glucosidase 17 -1.3 0.999541 Heat stress vs none < 55 o C AT5G07545.1 Hypothetical protein --- -5.12925 NA AT5G56555.1 Hypothetical protein 3.1 -5.39349 5 days vs 0 day AT1G02490.1 Hypothetical protein -4.5 -5.87035 Drought environment vs normal watering in vtc2.5 mutant AT3G26395.1 Hypothetical protein 2.4 -6.33018 Drought environment vs normal watering in vtc2 mutant AT3G61172.1 Low-molecular-weight cysteine-rich 8 4.3 -6.76256 Dark Induced senescence AT2G26515.1 Hypothetical protein 3.2 -6.78223 Drought environment vs normal watering in vtc2.5 mutant AT1G13805.1 Hypothetical protein --- -7.21064 NA AT5G40315.1 Hypothetical protein --- -7.37162 NA AT3G23122.1 Hypothetical protein 7.4 -9.97686 pao-1 mutant vs wild type genotype in continuous dark (no light) regimen at 2 day ATMG00665.1 NADH dehydrogenase 5B 7.7 -15.6008 tcx2; WOX5:GFP vs wild type genotype Machine Learning Approach Showed Molecular Mass has Influence on Protein Tm To understand the role of molecular mass, isoelectric point, and Tm index in deciding the Tm of A. thaliana protein, a machine learning approach was adopted to find their role. We conducted a boosting regression to understand the influence of different variables on Tm. It was found that molecular mass has a relative influence on Tm than the isoelectric point (Fig. 7 ). The study contained 30950 training, 7738 validation, and 9671 test sets. Again, a decision tree regression study was conducted, and it was found that molecular mass (kDa, n = 30950) plays an important role in the Tm of the Arabidopsis proteins (Fig. 8 ). Further, a network plot analysis of molecular mass (kDa), Tm, and pI was conducted. It was found that kDa and Tm are positively correlated (blue) while kDa and TI are negatively correlated (red) (Fig. 8 ). However, pI was only linked to kDa, and it did not show any network with TI and Tm of proteins (Fig. 8 D). High and low TI Encoding Arabidopsis Gene undergone duplication We picked the top 50 highest TI (Tm > 65°C), middle TI (55–65°C), and lowest TI (< 55°C) encoding CDS sequences of A. thaliana proteins and conducted a phylogenetic analysis separately. The phylogenetic tree of A. thaliana genes with protein TI < 55°C showed two major clusters (Fig. 9 A). Although the proteins were from diverse groups, their clustering in the phylogenetic tree was quite smooth. The phylogenetic tree of proteins with Tm < 55°C showed all of the genes were duplicated (Table 4 ). None of the genes was found to undergo loss, transfer, or codivergence (Fig. 9 A, Table 4 ). The phylogenetic tree of genes with TI group 55–65°C also resulted in two major clusters (Fig. 9 B), while the phylogenetic tree of genes with TI > 65°C showed three distinct clusters (Fig. 9 C). However, all the genes of TI 65°C underwent duplication, and none of them were found to undergo loss, transfer, or codivergence (Supplementary Fig. 1, Supplementary Fig. 2, Supplementary Fig. 3, Table 4 ) during evolution. Table 4 Gene duplication, loss, transfer, and divergence of Arabidopsis thaliana with different Tm groups. Tm Group Duplicated Losses Transferred Codiverged Tm > 65 o C 34 0 0 0 Tm 55-65 o C 43 0 0 0 Tm < 55 o C 38 0 0 0 Further, we tried to understand the relative synonymous codon usage (RSCU) of the genes those used for the phylogenetic analysis. We found that the AGA codon coding for the Arg amino acid has the highest RSCU for genes for the Tm group 65°C was found in codon CCA (1.8), which codes for the Pro amino acid. Similarly, the lowest RSCU for the Tm group 65°C, the lowest RSCU was found in codon CCC (0.36), which encodes for the Pro amino acid (Supplementary File 1). In the compositional analysis of nucleotides, the nucleotide composition for the Tm group 65°C, T = 22.3%, C = 23.3%, A = 27.9%, and G = 26.4%. Nucleotide Position in Codon Explain genetic variation and adaptive role for stress tolerance We analyzed the nucleotide position in the codon of the studied genes to understand and evaluate their roles towards shaping the protein structure, function, and evolution. We analyzed the nucleotide position of the codons of different Tm groups. In the Tm group < 55°C, at the first position, the abundance of T (31) was followed by A (29.5), G (22), and C (18.2) (Fig. 10 ); at the 2nd position the abundance of T (31) was followed by A (27.2), G (24.6), and C (17.7) (Fig. 10 ); at the 3rd position the abundance of T (32.04) was followed by A (27.6), C (20.4), and G (19.9) (Fig. 10 ). In the Tm group 55–65°C, at the 1st position, the abundance of nucleotide A (29.2) was followed by T (28), G (23.3), and C (19.8) (Fig. 10 ); at the 2nd position, the abundance of nucleotide A (28.1) was followed by T (27), G (24.7), and C (20.4) (Fig. 10 ); at the 3rd position, the abundance of nucleotide A (28.3) was followed by T (27), G (24.5), and C (20.2) (Fig. 10 ). In the Tm group > 65°C, at the 1st position, the abundance of nucleotide A (26.7) followed by C (25.1), G (24.1), and T (23) (Fig. 10 ); at the 2nd position, the abundance of nucleotide G (28.4) followed by A (26.9), C (22.5), and T (22) (Fig. 10 ); at the 3rd position, the abundance of nucleotide A (26.9) followed by T (25), C (24.3), and G (24.2) (Fig. 10 ). When we analyzed the nucleotide position by grouping them with the Tm group (e.g., A nucleotide at the 1st position for the Tm group 65°C). We found the frequency of A nucleotide (28.46) at the 1st position was highest for all three Tm groups, followed by T at the 3rd position (27.87), A at the 3rd position (27.62), and A at the 2nd position (27.38) (Fig. 11 ). The frequency of C at the 2nd position was the lowest (20.18), followed by C at the 1st (21.03) and 3rd positions (21.63) (Fig. 11 ). When we analyzed the frequency of nucleotide variations for all three Tm groups at the 1st, 2nd, and 3rd positions, we found the frequency of nucleotide variance was highest in T at the 2nd position (17.34), followed by T at the 3rd (14.59), T at the 1st (13.83), and C at the 1st (13.06) positions (Fig. 11 ). The lowest nucleotide variance was recorded for A at the 2nd position (0.37), followed by A at the 3rd position (0.49) and G at the 1st position (1.81). Further, a cluster analysis revealed the frequency of nucleotide positions for the Tm group 65°C grouped separately. Relative Synonymous Codon Usage (RSCU) of A. thaliana Genes We conducted a study to understand the relative synonymous codon usage of genes of the top 50 A. thaliana proteins from three Tm groups. We found the RSCU of codon AGA (R) was highest (2.33) in the Tm group 65°C, the RSCU of CCA (P) was highest (1.8) (Supplementary file). The lowest RSCU was found in CCC (P) (0.36) in the Tm group > 65°C, followed by CGC (R) (0.38) in the Tm group < 55°C and 0.44 in the Tm group 55–65°C (Supplementary File). The second highest RSCU was found in the stop codon UGA in the Tm group 1 in Tm group < 55 C while 34 codons contained RSCU of < 1. Only three codons (AUG (M), CCU (P), and UGG (W)) of the Tm group 1, while 32 codons contained RSCU 65°C, 31 codons contained RSCU > 1, and a similar number of codons were found to contain RSCU 65°C. Tm Based Codon Context of Arabidopsis CDS Codon usage preferred the use of codons; the “codon context” explains the sequential presence of codon pairs in a gene. To understand the codon pairs in Arabidopsis CDS with protein Tm groups of 65°C, we conducted codon pair analysis. For CDS of protein Tm group < 55°C, ATG ATG was the preferred codon pair and found in the highest number (37), followed by TGT GAT (13), ATG GAA (10), CTT TGC (9), and AAA TGT (8). For the Tm group 55–65°C, the GAA GAT and GAG AAG (30) codon pairs were found in the highest number, followed by GAT GAA (25), GAT GAT (25), AAA GAG (24), AAA GAT (23), GAA GAA (23), AAA GAA (21), and others. For the Tm group > 65°C, the CAA CAA (58) codon pair was found in the highest number, followed by GGA GGA (49), CCA CCA (44), GGT GGA (38), GGT GGT (32), and TAT CCT (31). Discussion The proteins are developed to perform their function within a specific temperature range. However, the stability of a plant proteome is directly proportional to the survival rate of the plants under the heat stress. Heat stress can disrupt the cellular and extracellular proteins, leading to the disruption in the cellular homeostasis, physiological process, and metabolism. The inactivation of important and functional proteins can lead to compromised enzyme activities that will impact the enzymatic pathways in the plant. Therefore, understanding the protein stability for temperature resistance is an important parameter to study the heat stress in plants. Thermal stability of protein, referred to as melting temperature (Tm), provides the strength to the protein to cope with extreme temperature stress. The melting temperature of the protein is defined as the temperature at which the protein undergoes a transition from native, folded form to unfolded form under equilibrium conditions. The proteins having high Tm will be less prone to denaturation and structural instability, leading to high cellular integrity and function under adverse heat conditions. It can be well speculated that proteins with high Tm can better adapt to perform their function under high-temperature conditions, thus making Tm a critical thermodynamic parameter for understanding heat resistance in plants. Due to the denaturation by high temperature, protein will lose its functional conformation, leading to its inactivation [ 82 ]. Therefore, it is important to understand the thermodynamic properties of proteins, and hence we have conducted a study to deduce the melting temperature (Tm) of the entire Arabidopsis thaliana proteome. For a better understanding, protein Tm was grouped into three groups with temperature ranges 65°C. However, the Tm of the protein relies on several factors, including amino acid composition, structure, and environmental factors, including pH , ionic strength, and the presence of stabilizing and destabilizing factors. From these factors, amino acid composition is one of the major parameters that might be controlling the Tm and stability of the protein. Therefore, we conducted an in-depth analysis of the amino acid composition of the Arabidopsis thaliana proteome. We found 20640 proteins (including splice variants) of A. thaliana fall in Tm range > 65°C, 22826 proteins fall in the Tm range 55°C– 65°C, and 4893 proteins fall in the Tm range 65°C. This shows the plant has developed proteins to withstand high temperatures. Considering the evolutionary consequences, the earth has passed two ice ages and subsequently gained temperature, and now we are facing global warming. During this evolutionary process, plants have developed their protein machinery systems and encoded more proteins that can withstand high-temperature stress. This evolving process of rising temperatures might continue in the future, and hence we need to find ways to identify important heat stress-resistant proteins to tackle the global warming problem. Therefore, finding the heat-resistant proteins in plants is of paramount interest to understand their heat-resistant mechanism. To have a better understanding of high and low Tm proteins, we studied the amino acid composition of the A. thaliana proteome. It was found that the amino acid composition of Ala, Asp, Glu, Gly, Lys, Leu, Gln, and Val increased with an increase in the Tm of the proteins, while the composition of Cys, Phe, His, Ile, Asn, Pro, Arg, Ser, Thr, Trp, and Tyr decreased with an increase in the Tm of the protein (Fig. 1 , Table 2 ). This linearity of increase and decrease in amino acid composition reflects their role in determining the Tm of the proteins. Further, we calculated the molecular mass and isoelectric point of individual proteins of A. thaliana and studied their role towards the Tm. The average molecular mass of A. thaliana for protein Tm group 65°C it was 48.92 kDa (Fig. 2 ). The molecular mass of proteins with Tm 65°C (Fig. 2 ). This gives a hint that proteins with low molecular mass possess low Tm. A correlation analysis within the molecular mass group showed proteins with Tm < 55°C marginally correlate with the proteins with Tm 55–65°C (Fig. 3 ). Analysis of the isoelectric point of A. thaliana protein revealed that proteins with Tm 65°C was 6.74 (Fig. 5 ). This shows that high pI protein tends towards low Tm in A. thaliana . The trend of pI of the proteins for three different groups was in the decreasing order from Tm 65°C. However, the pI difference was not significant enough. Similarly, there were no such detectable correlations in the isoelectric point of proteins found in three different Tm groups (Fig. 6 ). Further, a correlation study was conducted using molecular weight, isoelectric point, and Tm as input parameters, and the result showed a positive correlation between molecular weight and Tm (Fig. 8 ). The relative influence of molecular weight (kDa) on the Tm of A. thaliana protein was further confirmed using a machine learning approach (Fig. 9 ). Boosting regression analysis revealed the relative influence of molecular weight (kDa) on the melting temperature of the proteins. Further, the validation role of molecular weight (kDa) towards the melting temperature of protein was observed in the decision tree regression analysis and network plot study (Fig. 10 ). A study regarding the melting point analysis was reported by Jarzab et al. (2020), where they studied 48000 protein sequences from humans and archaea and covered 13 species [ 82 ]. They reported that protein sequence, composition, and size affect the thermal stability of proteins in prokaryotes and eukaryotes [ 83 ]. They used the species that lived in the 40°C to 70°C temperature range. It is well known that the enzyme DNA polymerase from the bacteria Thermus aquaticus is highly resistant to heat, having a half-life period of 40 minutes at 95°C. The study reported that RNA polymerase of Thermus aquaticus remains unaltered at 80°C and helps in synthesizing tRNA transcripts [ 84 ]. In our study, we found the mediator protein of the RNA polymerase II transcription subunit-like protein that falls in the Tm > 65°C temperature range has the highest Tm index (TI = 9.60). This shows the mediator protein of RNA polymerase II might be contributing towards the thermal stability of the RNA polymerase. Further, proline-rich, cysteine-rich, calmodulin-like proteins, and transmembrane proteins were found to contain higher Tm index (Table 1 ). This shows that proteins involved in transmembrane activity, calcium signaling, and proline metabolism are associated with heat resistance in A. thaliana . Tang et al. reported the melting temperature of 95°C in legume protein edestin and found that the melting temperature was unaffected in the presence of 20–40 µM sodium dodecyl sulphate [ 85 ]. Gliadin protein functions in a temperature range of 70–115°C, and as the temperature rises to 135°C, gliadin protein softens much better [ 86 ]. The thermal stability of plant H2-ferritin protein (soybean) was reported to be quite high. When H2-ferritin protein is caged with extra peptides, it denatures at 106°C [ 87 ]. Miller et al. (2013) reported the thermal stability of ribulose-1,5-bisphosphate (Rubisco) and measured the Tm of native, ancestral, and variant proteins from Synechococcus . They found that OH28 purified Rubisco exhibited greater stability at 79.5°C than the less thermotolerant strain (72.3–73.6°C) [ 88 ]. This led them to conclude that thermostable Rubisco enzyme diverged from the ancestor of Synechococcus [ 88 ]. The gene expression analysis revealed the up regulation and down regulation of important genes due to drought stress and at different stages of plant development. For example, cysteine-rich TM module stress tolerance protein undergone 2.4 folds upregulation due to drought stress (Table 3 ). Venacio and Aravind (2010) reported the role of CYSTM in stress tolerance across eukaryotic lineage [ 89 ]. Calmodulin-like protein was reported to undergone 4.4 folds up-regulation. The role of calmodulin-like protein in stress mitigation is also well documented in various studies [ 90 ]. Cytochrome p450 CYP79B2 was reported to undergone 7.1 folds down regulation due to drought environmental stress. This gene is involved in Trp-dependent auxin biosynthesis in association with CYP79B3 [ 91 ]. This shows that during drought stress CYP79B2 undergo down regulation and hence plant fail to produce sufficient auxin hormones to withstand the harsh environment. Further, codon evolution revealed the substitution of the Ala amino acid by isoleucine in the OH28 strain, suggesting a positive selection in evolution [ 88 ]. To understand the evolutionary aspects of the high Tm, low, and medium Tm proteins, we conducted gene duplication and loss analysis. We found that all the studied genes of three Tm groups had undergone duplication, and none of them were found to undergo transfer, codivergence, or losses (Table 4 ). Gene duplication is an important event as it accumulates mutations over time without affecting the function of the original gene, thus bringing the new function and characteristics. The duplication of the genes with high, low, and medium Tm genes reflects their tendency towards the innovation of new functions. In gene duplication, one gene can continue to exhibit its original function while allowing others to evolve new functions, leading to the adaptability of the species to adverse temperature conditions. The RSCU value of 1 indicates a codon is used in expected frequency, and 1 shows, codons are used more frequently than expected. For the Tm group 1, and 32 codons contained RSCU 1, and 32 codons contained RSCU 65°C, 31 codons contained RSCU > 1, and 31 codons contained RSCU < 1. From them, the Tm group 1, while 32 codons contained an RSCU of 1 increased with the increase in the Tm of the proteins, while the RSCU of value < 1 decreased with the increase in the Tm of the proteins. In the Tm group at 65°C, two codons were found to contain an RSCU of 1. The RSCU of codons having value 1 for all the Tm groups were AUG (M) and UGG (W) (Supplementary File). For the Tm group < 55°C, unique codon with RSCU value 1 was CCU (P) while for Tm group 55–65°C unique codons with RSCU 1 were CUC (L) and AGC (S). The second highest RSCU was found in codon UGA for the Tm group < 55°C and 55–65°C. The UGA codon also plays an important role in encoding selenocysteine amino acid in the protein [ 93 , 94 ]. This analysis revealed that A. thaliana encodes differential RSCU according to the Tm groups, reflecting the role of codons in encoding proteins with different melting temperatures. The codon usage bias in the different Tm groups was quite evident. The RSCU keeps the protein sequence intact while altering their influences on gene expression, protein translation, and stress responses [ 95 , 96 ]. The RSCU bias is influenced by several factors, including natural selection, mutation pressure, translation efficiency, and gene expression levels [ 97 , 98 ]. The presence of higher RSCU in codon AGA that encode for Arg amino acid in the Tm group 65°C shows CCA codon is optimized for translational accuracy under stress condition. The RSCU study is a vital tool that helps to understand plant adaptation to environmental stress conditions by influencing protein translation efficiency and protein folding. However, the codon usage bias may vary in tissue-specific expression and developmental stages of plants. The position of nucleotide at the 1st place for all the Tm groups showed the frequency of nucleotide A was the highest. The highest frequency of nucleotide A across the category suggests its vital roles in codons for encoding amino acids critical for protein function. Its higher frequency at Tm > 65°C shows its importance in protein function towards high-stress adaptations. The G nucleotide position has quite high variability, suggesting its role in genetic diversity and potential for adaptive evolution. Nucleotide C is more prone to mutation without severely impacting the protein function, thus providing a buffer for evolutionary changes [ 99 ]. Similarly, nucleotide T showed higher consistency, indicating its role in encoding essential amino acids. Overall, it suggests that nucleotides for Tm 65°C was highly variable. The high variability of nucleotides indicates genomic regions with higher potential for evolutionary innovation, contributing to the genomic diversity and adaptation. The presence of higher nucleotide variability in the Tm group > 65°C indicates A. thaliana plants are inclining more towards enhancing the Tm of their protein for stress adaptation while keeping the conserved genomic architecture in the Tm group < 55°C. A comparative less variable nucleotides in different position of codons reflect their conserved structure and their association with protein of the Tm group < 55°C reflects the plants have evolved from cold climatic conditions and now diverged its genomic pool towards stress adaptation. The nucleotide positions in the groups A1, A2, A3, C1, C2, C3, G1, G2, G3, T1, T2, and T3 with their variation reflect a delicate balance between the conservation and stress adaptation in the evolution of genes. Conclusion The present study provides a comprehensive analysis of thermal properties of Arabidopsis thaliana proteome and highlighted the relationship between protein thermal stability and molecular mass of proteins, amino acid composition, gene expression, and codon usage. The study revealed, maximum of the A. thaliana proteins resides in Tm range of 55–65°C with a smaller fraction of proteins found below 55°C. The study reported the that the genes associated with high Tm index are undergone up-regulation due to drought stress. A correlation analysis revealed a higher Tm of protein associated with increase in amino acid composition of Ala, Asp, Glu, Gly, Lys, Gln, and Val while Cys, Phe, and Trp were less prominent in proteins with high Tm. Molecular mass was found to positively correlate with the Tm. We believe, the study will contribute to the field of plant stress biology and more specifically in understanding the molecular mechanism behind the thermal stress resistance in Arabidopsis . The study will enhance our understanding of structural stability of A. thaliana proteins under different temperature stress. The study possesses enormous implication in agriculture science, particularly towards the development of crop varieties with improved tolerance to extreme heat stress. Abbreviations Tm: melting temperature, TI: Tm index, kDa: kilo Dalton, CDS: coding DNA sequence Declarations Ethics approval and consent to participate: Not applicable Consent for publication: Not applicable Funding: No funding received Availability of data and materials All data generated or analysed during this study are included in this published article and provided as supplementary File 1 Competing of interest The authors declare that they have no competing interests. Funding : Not available Author’s contributions KMM: identified the melting temperature of the proteins, revised the manuscript; TKM: conceived the idea, analyzed the data, drafted and revised the manuscript Acknowledgement The authors would like to express their sincere thanks to Indian School Nizwa, Oman, and Medi-Caps International School, Indore, Madhya Pradesh, India for their support and encouragement to conduct this research. The authors would also like to extend their sincere thanks to Mrs. Asma Khan, Indian School Nizwa, Oman for her extensive encouragement to author Karan Martens Mohanta to conduct the research. References Zhang H, Zhao Y, Zhu J-K. Thriving under Stress: How Plants Balance Growth and the Stress Response. Dev Cell. 2020;55:529–43. Mishra S, Spaccarotella K, Gido J, Samanta I, Chowdhary G. Effects of Heat Stress on Plant-Nutrient Relations: An Update on Nutrient Uptake, Transport, and Assimilation. Int J Mol Sci. 2023;24. 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Supplementary Files SupplementaryFigure1.docx SupplementaryFigure2.docx SupplementaryFigure3.docx SupplementaryFile1.xlsx Cite Share Download PDF Status: Published Journal Publication published 28 Nov, 2025 Read the published version in BMC Genomics → Version 1 posted Editorial decision: Revision requested 24 Jul, 2025 Reviews received at journal 23 Jul, 2025 Reviews received at journal 22 Jul, 2025 Reviewers agreed at journal 02 Jul, 2025 Reviewers agreed at journal 01 Jul, 2025 Reviewers invited by journal 18 Jun, 2025 Editor invited by journal 14 May, 2025 Editor assigned by journal 12 May, 2025 Submission checks completed at journal 12 May, 2025 First submitted to journal 09 May, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6629178","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":473172307,"identity":"bd3e1341-d4f3-4a75-905d-adbfcfd5ec4d","order_by":0,"name":"Karan Martens Mohanta","email":"","orcid":"","institution":"Indian School Nizwa","correspondingAuthor":false,"prefix":"","firstName":"Karan","middleName":"Martens","lastName":"Mohanta","suffix":""},{"id":473172308,"identity":"4317cea4-d5c5-470b-8437-7c7cca4083aa","order_by":1,"name":"Tapan Kumar Mohanta","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIiWNgGAWjYNCDD0DMxk5YHWMDkJAAs2aAtDCTooWZB0wSUG/O3v78wYcahjr+2c3PHtv82ibPx8zA+OFjDm4tlj1nDBtnHGOQkLhzzNw4t++2YRszA7PkzG24tRjcyGFs5m0AOuxGgpl0bs9tRqAWNmZevFrSH4K1yN9I/yZt2XPbnggtCYZgLUDrzKQZftxOJKzlzBnDmTOOSUhuvHOmTLK34XZyGzNjM36/HG9/8OFDjQ2/3O32bRI//ty2nd/efPDDRzxaoEACEjOMbSAOOKKIAeDI/EOk4lEwCkbBKBhRAACjYU6BwdHpsQAAAABJRU5ErkJggg==","orcid":"","institution":"Medi-Caps University","correspondingAuthor":true,"prefix":"","firstName":"Tapan","middleName":"Kumar","lastName":"Mohanta","suffix":""}],"badges":[],"createdAt":"2025-05-09 13:38:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6629178/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6629178/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12864-025-12131-6","type":"published","date":"2025-11-28T15:58:44+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":85044491,"identity":"6bb551f2-d70d-46f8-821a-01e9fdb2b4dc","added_by":"auto","created_at":"2025-06-20 10:06:16","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2257753,"visible":true,"origin":"","legend":"\u003cp\u003eAmino acid composition of \u003cem\u003eArabidopsis thaliana\u003c/em\u003e proteome based on three different melting temperature range (\u0026lt; 55°C, 55-65°C, and \u0026gt; 65°C) of proteins. Cys and Trp were the lowest abundant amino acids found in Tm group \u0026gt; 65\u003csup\u003eo\u003c/sup\u003e C.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6629178/v1/de22433b16c9137369883da8.png"},{"id":85044492,"identity":"212c8a74-198b-4b4a-8770-8babc93401c5","added_by":"auto","created_at":"2025-06-20 10:06:16","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":148175,"visible":true,"origin":"","legend":"\u003cp\u003eFigure depicting the (A) molecular mass (kDa) and (B) frequency of occurrence of the molecular mass of \u003cem\u003eA. thaliana\u003c/em\u003e proteome in different Tm groups. The molecular mass of the proteins with the lowest Tm group \u0026lt; 55°C was found to be the lowest while the molecular mass of proteins with Tm \u0026gt; 65°C was moderate, and the molecular mass of proteins in Tm group 55-65°C was highest.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6629178/v1/3e62124ae8bf5a41327fc68c.png"},{"id":85044497,"identity":"4e402dc1-f66d-4bf0-9f36-76ff8a9903ee","added_by":"auto","created_at":"2025-06-20 10:06:16","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":560308,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation analysis between molecular mass (kDa) of different Tm groups. The correlation was studied between the molecular masses of Tm group of 55-65°C vs \u0026lt; 55°C, \u0026gt; 65°C vs \u0026lt; 55°C, and \u0026gt; 65°C vs 55-65°C. Correlation coefficient of molecular masses between the Tm groups 55-65°C vs \u0026lt; 55°C was found highest ( \u003cem\u003er\u003c/em\u003e = 0.044). however, the correlation coefficient of molecular masses between the Tm groups was not significant to draw any conclusion. Pearson’s correlation was used to conduct the analysis with p \u0026lt; 0.05. The statistical analysis was conducted using the software JASP version 0.19.0.0.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-6629178/v1/200e5c2b5b07be7b968cddf4.png"},{"id":85044496,"identity":"0df1e331-4c05-4c50-94ed-bef405a63955","added_by":"auto","created_at":"2025-06-20 10:06:16","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":150381,"visible":true,"origin":"","legend":"\u003cp\u003eFigure depicting the (A) isoelectric point and (B) frequency of occurrence of isoelectric point of \u003cem\u003eA. thaliana\u003c/em\u003e proteome in three different Tm groups. The isoelectric point of proteins with Tm \u0026lt; 55°C was found to be the highest in \u003cem\u003eA. thaliana\u003c/em\u003e proteome.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6629178/v1/e69070a559fb51ee7a52effa.png"},{"id":85044499,"identity":"bfa9bf54-8fd7-4241-8526-5adbe3f3b458","added_by":"auto","created_at":"2025-06-20 10:06:16","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":696762,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation analysis of isoelectric points of \u003cem\u003eA. thaliana\u003c/em\u003e proteome within three different Tm groups. The Tm groups were \u0026lt; 55°C, 55-65°C, and \u0026gt; 65°C. The correlation study was carried out with groups \u0026lt; 55°C vs 55-65°C, \u0026lt; 55°C vs \u0026gt; 65°C, and 55-65°C vs \u0026gt; 65°C. Analysis shows, correlation coefficient between the Tm group of \u0026lt; 55\u003csup\u003eo\u003c/sup\u003e C vs \u0026gt; 65\u003csup\u003eo\u003c/sup\u003e C was highest ( r = 0.011). Pearson’s correlation was used for the correlation study with p \u0026lt; 0.05. The analysis was conducted using JASP software, version 0.19.0.0.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-6629178/v1/c4fd3b829633b8d753b84f4a.png"},{"id":85044504,"identity":"a5e5fb19-0a4d-47bb-ab8e-3c9d5bcba725","added_by":"auto","created_at":"2025-06-20 10:06:16","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":586676,"visible":true,"origin":"","legend":"\u003cp\u003eFigure depicting the (A) correlation analysis between the molecular weight, isoelectric point, and Tm of the \u003cem\u003eA. thaliana\u003c/em\u003e proteome. Figure (B) shows the heat map of the correlation and (C) presents the statistical details of the correlation study. The figure shows positive correlation between Tm and molecular weight (kDa) (\u003cem\u003er\u003c/em\u003e= 0.259) while Tm and \u003cem\u003epI\u003c/em\u003e (\u003cem\u003er\u003c/em\u003e = -0.091) and molecular weight and \u003cem\u003epI\u003c/em\u003e(\u003cem\u003er\u003c/em\u003e = -0.198) had negative correlation. Pearson’s correlation was used in this analysis with \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01. The analysis was conducted using JASP software version 0.19.0.0.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-6629178/v1/2c58d119c9f3ed588c883a64.png"},{"id":85046648,"identity":"974b32f5-c71f-4ba4-83dc-f7b7802b347d","added_by":"auto","created_at":"2025-06-20 10:30:16","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":583913,"visible":true,"origin":"","legend":"\u003cp\u003eMachine learning analysis of molecular weight (kDa) and isoelectric point to identify their role in deciding the Tm of the \u003cem\u003eA. thaliana\u003c/em\u003e proteome. Figure depicts (A) boosting regression, (B) data splits, (C) model performance matrics, and (D) relative influence of the data. In the study, there was 30950 training, 7738 validation, and 9671 test set data. The boosting regression model was optimized with respect to out-of-bag mean square error. The analysis revealed the influence of molecular weight (kDa) towards deciding the Tm of \u003cem\u003eA. thaliana\u003c/em\u003e proteome. The analysis was conducted using JASP software version 0.19.0.0.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-6629178/v1/981ef3d5d874076c8157b876.png"},{"id":85047081,"identity":"1690c4ac-7baf-49c8-8b5e-f7218537cc02","added_by":"auto","created_at":"2025-06-20 10:38:16","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":458930,"visible":true,"origin":"","legend":"\u003cp\u003eDecision tree regression analysis of molecular weight and isoelectric point of \u003cem\u003eA. thaliana\u003c/em\u003e proteome. The figure depicts (A) decision tree regression with data split (B) Decision tree (C) network-plot between molecular weight (kDa), Tm, and \u003cem\u003epI\u003c/em\u003e, and (D) network-plot between Tm, kDa, \u003cem\u003epI\u003c/em\u003e, and TI (Tm index). The analysis contained 30950 training, 7738 validation, and 9671 test set data. The analysis resulted kDa (n = 30950) in the decision tree towards deciding its role in Tm of \u003cem\u003eA. thaliana\u003c/em\u003e proteome. Network-plot shows, Tm and kDa (Fig. 8C) positively related (blue) while kDa and \u003cem\u003epI\u003c/em\u003e negatively related (red). Similarly, there is a positive relationship between Tm and TI and Tm and kDa. While TI and kDa and kDa and \u003cem\u003epI\u003c/em\u003e were negatively related.\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-6629178/v1/293b26955a9c6a9c90113750.png"},{"id":85044508,"identity":"e66da7ad-4d1a-4ebe-932e-e42b628cdfbd","added_by":"auto","created_at":"2025-06-20 10:06:16","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":560148,"visible":true,"origin":"","legend":"\u003cp\u003ePhylogenetic trees of \u003cem\u003eA. thaliana\u003c/em\u003e CDS sequences with different Tm groups. The phylogenetic tree (A) belongs to the CDS of proteins with the Tm \u0026lt; 55°C (B) belongs to the CDS of proteins with the Tm group 55-65°C, and (C) belongs to the CDS of proteins with the Tm \u0026gt; 65°C. Top 50 highest TI containing protein sequences from each Tm group was considered to construct the phylogenetic tree. The CDS sequences of the protein Tm group \u0026gt; 65°C resulted only three major clusters while the CDS sequences of other Tm group resulted multiple clusters. This reflects, the protein sequences of the Tm group with \u0026gt; 65°C were evolutionarily more closer with each other than the other Tm groups. The phylogenetic tree was constructed using MEGA software version 7.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-6629178/v1/c104b8dd470606a4ff868e15.png"},{"id":85044532,"identity":"14bc916b-7b04-4e04-b423-93faf836f5f5","added_by":"auto","created_at":"2025-06-20 10:06:17","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":198596,"visible":true,"origin":"","legend":"\u003cp\u003eThe figure illustrates the nucleotides position and variation in the codons in different position. The positions of the nucleotides adenine (A), cytosine (C), guanine (G), and Thiamine (T) at 1\u003csup\u003est\u003c/sup\u003e, 2\u003csup\u003end\u003c/sup\u003e, and 3\u003csup\u003erd\u003c/sup\u003e was studied to understand their preferences in the codon. The nucleotide positions in the codon was studied with three different melting temperature groups (A) \u0026lt; 55°C, (B) 55-65°C, and (C) \u0026gt; 65°C.\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-6629178/v1/451a8f84165b2278be14d9b3.png"},{"id":85044515,"identity":"554ffb1d-058c-432a-bddf-7381fecaf543","added_by":"auto","created_at":"2025-06-20 10:06:16","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":544327,"visible":true,"origin":"","legend":"\u003cp\u003eMelting temperature specific nucleotide position of codons in \u003cem\u003eA. thaliana\u003c/em\u003e CDS sequences. Nucleotide position of three different Tm groups were clubbed together to understand their frequency. There is a significant variation of Cytosine nucleotide at 2\u003csup\u003end\u003c/sup\u003e position in the Tm \u0026gt; 65°C. Similarly, the frequency of Guanine nucleotide was quite higher in Tm \u0026gt; 65°C compared to other Tm groups. Cluster analysis revealed, the nucleotide positions with Tm groups \u0026lt; 55°C, \u0026amp; 55-65°C grouped together while nucleotides position of Tm group \u0026gt; 65°C fall separately. This signifies there is a significant variation in nucleotide position in the codon of the protein having Tm \u0026gt; 65°C.\u003c/p\u003e","description":"","filename":"Figure11.png","url":"https://assets-eu.researchsquare.com/files/rs-6629178/v1/f2dcb370a59c014a0871472b.png"},{"id":97179647,"identity":"7d450454-e1c6-4f29-b6f3-a459cec910db","added_by":"auto","created_at":"2025-12-01 16:16:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8703293,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6629178/v1/24cc7ce8-7e60-416d-889b-65a2e486f19a.pdf"},{"id":85045857,"identity":"7260e2c3-8d7a-443f-9415-3bb29f73950e","added_by":"auto","created_at":"2025-06-20 10:22:16","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":641403,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6629178/v1/e06aa9c4a94cd8cc49d23676.docx"},{"id":85044507,"identity":"75b12f39-02c1-4439-b9e8-f9f7cada4008","added_by":"auto","created_at":"2025-06-20 10:06:16","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":262727,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure2.docx","url":"https://assets-eu.researchsquare.com/files/rs-6629178/v1/6d8a2941f3abde97935fc8bf.docx"},{"id":85045451,"identity":"f40f5e60-85c8-49a4-8325-ae11425db3bd","added_by":"auto","created_at":"2025-06-20 10:14:16","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":552688,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure3.docx","url":"https://assets-eu.researchsquare.com/files/rs-6629178/v1/9d3396d9602974b9c04cfcd6.docx"},{"id":85045447,"identity":"6ab61adc-5e51-483f-b3aa-e93be4e4ce73","added_by":"auto","created_at":"2025-06-20 10:14:16","extension":"xlsx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":13413,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFile1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6629178/v1/a83a3c84ff34420f5f0b1d1e.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Meltome Atlas of Arabidopsis thaliana Proteome: A Melting Temperature-Based Identification of Heat \u0026 Cold Resistant Proteins","fulltext":[{"header":"Background","content":"\u003cp\u003ePlants are ubiquitous sessile organisms, and hence they face a plethora of stresses and environmental challenges. These challenges adversely affect the growth, development, and productivity of the plants [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. However, they have established a tremendous ability to respond and adapt to these stresses for their survival [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The translation product, proteins, is crucial to these events, and they play a fundamental role in these stress responses [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. They act as key signaling molecules and key regulators of plant defence responses [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. They perceive, respond, regulate, and adapt to various stress conditions. The roles of receptor-like kinase (RLK) and receptor-like proteins (RLP) are tremendous in perceiving external stress signals [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Some proteins act as second messengers like calcium-binding proteins (calcium-dependent protein kinase, calmodulin, calmodulin-like protein, calcineurin B-like proteins) [\u003cspan additionalcitationids=\"CR15 CR16 CR17\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], and phospholipases [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Transcription factors like NAC, WRKY, MYB, AP2/ERF, and bZIP regulate the expression of several stress-responsive genes in plants [\u003cspan additionalcitationids=\"CR22 CR23\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. These TFs bind to the specific promoter region and regulate the gene expression, thus adapting to the stress responses [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Kinases (MAPKs) and phosphatases (PP2C) play phosphorylation and dephosphorylation events to alter the protein function [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Ubiquitin ligase conducts ubiquitination through the ubiquitin-proteasome complex and removes the damaged proteins and thus regulates the function of key stress proteins [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. During the stress event, the generation of reactive oxygen species occurs, and the oxidative stress gets mitigated by superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT) [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Similarly, osmotic adjustment in cells is regulated by proline dehydrogenase, trehalose 6-phosphate, and other proteins and balances the osmotic stress condition [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. During heat stress, heat shock protein (HSP) helps in protein folding and prevents aggregation of denatured proteins resulting from the heat stress [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The role of HSP protein is extraordinary in maintaining protein homeostasis [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Like HSP, aquaporin protein regulates drought stress in plants [\u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFrom several environmental stresses, heat and cold stress are the most common types of stresses. Cold stress includes chilling stress (0\u0026ndash;15\u0026deg;C) and freezing stress (below 0\u0026deg;C) [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. The chilling and freezing stress arrest the proteomic and enzymatic activities and thus slow down the metabolic process [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. The cold stress leads to the production of Osmo protectants like proline through the enzyme pyrroline-5-carboxylase synthetase (P5CS) [\u003cspan additionalcitationids=\"CR42\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Similarly, sucrose-phosphate synthetase (SPS) and fructan fructosyltransferase (FFT) help in sugar metabolism [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Unlike cold stress, when temperature rises above 35\u0026deg;C, plants encounter heat stress. The heat stress greatly impacts the protein stability and membrane permeability [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. The heat stress is sensed by protein denaturation and changes in membrane permeability [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. The HSPs help in protein folding and prevent the aggregation of denatured and damaged proteins [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Sometimes plants encounter a combined stress response of heat and drought stress [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. During this event, proteins associated with crosstalk get involved, and their signaling mechanism helps to mitigate the stresses. Thus, proteins are the pivotal elements involved in heat and cold stress mitigation by orchestrating complex networks of signaling pathways and metabolic adjustment [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. A lot of studies are being conducted to identify and implement different genes and proteins involved in such stress tolerance for the development of stress-tolerant crops. Indeed, this is also pivotal for the development of sustainable agriculture in the face of global warming. So that plants can thrive in challenging environmental conditions.\u003c/p\u003e \u003cp\u003eMore specifically, when a plant undergoes extreme heat or chilling stress, the protein either undergoes denaturation or strong folding/misfolding [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e] thus impacting the protein function. Proteins with a higher melting temperature tend to be more stable and can uphold their structure and function at the higher temperature [\u003cspan additionalcitationids=\"CR56\" citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. This can be quite crucial to withstand the heat stress. Similarly, proteins with cold stress can also undergo denaturation (less common) and can lead to disruption in cellular and metabolic activities [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. Cold stress can alter the secondary and tertiary structures of the protein, making them quite rigid and nonfunctional [\u003cspan additionalcitationids=\"CR61\" citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. The proteins associated with the membrane and structural components of cells will be greatly impacted due to such chilling stress. Prolonged cold stress can lead to protein aggregation, leading to the formation of insoluble protein complexes and ultimately cellular death. Cold temperatures will slow down the enzyme activities and their kinetics, thus reducing their biochemical reactions. If any protein associated with the Calvin cycle gets impacted by chilling stress, there will be a hindrance in photosynthesis, leading to poor growth and development of the plants. Similarly, chilling stress can precipitate the membrane-associated lipid thus disrupting membrane fluidity and signaling pathways. Protein-protein interactions were also greatly impacted due to the denaturation of proteins, leading to alternations in charges and protein conformation. Further, post-translational modification gets impacted by such chilling or heat stress, which regulates protein activity and stability.\u003c/p\u003e \u003cp\u003eA lot of studies are conducted to find different genes and proteins associated with stress tolerance in plants [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. Starting from 2D protein gel electrophoresis to single-cell transcriptome and from small nucleotide polymorphism to marker-assisted identification of novel traits, several such approaches are being implemented to identify novel genetic traits in plants [\u003cspan additionalcitationids=\"CR66\" citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. These approaches are still in force to identify novel traits in plants. However, so far, no study is being conducted to identify the novel genetic and genomic traits using the physiological parameters of their translated product, i.e., protein. Proteins are quite sensitive to pH change, which affect their structure, function, and physiochemical properties. Further, physiological parameters like the melting temperature (Tm) of proteins can be one of the most important factors that can play a crucial role in protein function. Tm is a temperature at which protein undergoes denaturation and loses its three-dimensional structure and function. At Tm, half of the protein remains in a folded (native) state, and half of the protein remains in an unfolded state. It is one of the important parameters to understand the protein stability. A higher Tm indicates higher stability of the protein, where the protein can maintain and sustain its three-dimensional shape and function. The amino acid composition of the protein greatly influences the thermal stability and overall structure of the protein [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. Further, larger and more complex proteins might have more intricate folding and may have a higher melting temperature. The presence of hydrophobic amino acids in the protein tends to increase the Tm of the protein and thus protein stability [\u003cspan additionalcitationids=\"CR71\" citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e]. Similarly, a properly folded protein has a higher Tm compared to partially or misfolded proteins. Further, post-translational modifications like glycosylation and phosphorylation affect protein stability [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e] through its Tm. The presence of other factors like pH, ionic strength of the protein, and stabilizing agents can impact the Tm of the protein. The presence of acidic (aspartic acid and glutamic acid) and basic (lysine and arginine) ionization groups affects the electrostatic interactions of the proteins and stabilizes the protein structure [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]. This shows that Tm is a fundamental property of the protein that influences the protein\u0026rsquo;s stability, function, and interactions. Therefore, it becomes crucial to understand this parameter to understand the protein's behavior under various environmental conditions. By analyzing Tm of proteins, we can understand the basic insight of the mechanism of the protein by which the protein adapts to the harsh environmental condition and mitigates stress. This physiochemical parameter of the protein can be quite crucial to identify the stress-tolerant proteins and subsequently stress-tolerant crops for biotechnological application. Therefore, we identified the Tm values of all the protein sequences of \u003cem\u003eArabidopsis thaliana\u003c/em\u003e proteome and reported them\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eRetrieval of Arabidopsis proteome sequences\u003c/h2\u003e \u003cp\u003eThe protein sequences of \u003cem\u003eArabidopsis thaliana\u003c/em\u003e were downloaded from the TAIR (The \u003cem\u003eArabidopsis\u003c/em\u003e Information Resources) database. The proteome file contained the translated protein sequences of all the coding DNA sequences (CDS) and their splice variants. It included a total of 48359 protein sequences. The downloaded protein sequences were subjected to analysis of melting temperature (Tm).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePrediction of Melting Temperature\u003c/h3\u003e\n\u003cp\u003eThe melting temperature prediction of the entire \u003cem\u003eArabidopsis thaliana\u003c/em\u003e proteome was made using the \u0026ldquo;Melting temperature prediction\u0026rdquo; (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://tm.life.nthu.edu.tw/\u003c/span\u003e\u003cspan address=\"http://tm.life.nthu.edu.tw/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) pipeline [\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e]. The online platform calculates the melting temperature and melting temperature index (TI) of the protein. Only one sequence can be submitted at once to find the melting temperature and melting index. The resulted Tm and TI were recorded in a Microsoft Excel file for further analysis.\u003c/p\u003e\n\u003ch3\u003ePrediction of molecular mass and isoelectric point of Arabidopsis thaliana proteome\u003c/h3\u003e\n\u003cp\u003eThe molecular mass and isoelectric point of all the chloroplast protein sequences were calculated using the IPC-isoelectric point calculator (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://isoelectric.org/\u003c/span\u003e\u003cspan address=\"http://isoelectric.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), version 2.0 [\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e]. The IPC Python software was downloaded and run on a Linux-based command-line platform. Command lines were used as per the instructions of the software. The IPC software resulted in the molecular mass and isoelectric point of individual proteins. We saved the data in text file format for further analysis.\u003c/p\u003e\n\u003ch3\u003eStatistical analysis of Arabidopsis thaliana Proteome Melting temperature\u003c/h3\u003e\n\u003cp\u003eThe melting temperatures of the \u003cem\u003eArabidopsis thaliana\u003c/em\u003e proteome were divided into three groups. They are as follows: (I)\u0026thinsp;\u0026lt;\u0026thinsp;55\u0026deg;C, (II) 55\u0026ndash;65\u0026deg;C, and (III)\u0026thinsp;\u0026gt;\u0026thinsp;65\u0026deg;C. Later, the protein sequences of the \u003cem\u003eA. thaliana\u003c/em\u003e proteome were segregated according to their melting temperature, and subsequent analysis was performed. Correlation analysis of proteins with Tm\u0026thinsp;\u0026lt;\u0026thinsp;55\u0026deg;C, 55\u0026ndash;60\u0026deg;C, and \u0026gt;\u0026thinsp;65\u0026deg;C with respect to molecular mass and isoelectric point was conducted using JASP 0.19.0.0 version software. Pearson\u0026rsquo;s correlation with a 95% confidence interval was used for the study. One sample student t-test of the Tm index of \u003cem\u003ethe A. thaliana\u003c/em\u003e proteome was conducted using JASP 0.19.0.0 version software to confirm that the mean is different from zero (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). A frequency distribution of molecular mass and isoelectric point of all three Tm groups was conducted with \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 using Past4 software. The amino acid composition of \u003cem\u003ethe A. thaliana\u003c/em\u003e proteome was calculated based on their melting temperature groups by running a Linux-based command. All other basic statistical details were calculated using Microsoft Excel 2016.\u003c/p\u003e \u003cp\u003e \u003cb\u003eGene expression analysis of\u003c/b\u003e \u003cb\u003eA. thaliana\u003c/b\u003e \u003cb\u003eGenes\u003c/b\u003e\u003c/p\u003e \u003cp\u003eIt was important to understand the function expression profiles of the important genes of various Tm groups. Therefore, we considered the top 10 genes of the Tm group at 55\u0026deg;C-65\u0026deg;C, and \u0026gt;\u0026thinsp;65\u0026deg;C having the highest Tm index and the bottom 10 genes of the Tm group at \u0026lt;\u0026thinsp;55\u0026deg;C having the lowest Tm index. Gene expression profiles of the genes were retrieved from the Expression Atlas of the European Molecular Biology Laboratory (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ebi.ac.uk/gxa/home\u003c/span\u003e\u003cspan address=\"https://www.ebi.ac.uk/gxa/home\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Expression profiles of individual genes were searched and recorded in Log\u003csub\u003e2\u003c/sub\u003e fold change.\u003c/p\u003e\n\u003ch3\u003eTm Based Evolutionary Analysis of A. thaliana proteome\u003c/h3\u003e\n\u003cp\u003eTo understand the evolutionary details of the \u003cem\u003eA. thaliana\u003c/em\u003e proteomes, we selected the top 50 protein sequences from three TI groups. Group I contained the bottom 50 TI protein sequences (\u0026lt;\u0026thinsp;55\u0026deg;C), group II contained the top 50 TI protein sequences of the 55\u0026ndash;65\u0026deg;C temperature range, and group III contained the top 50 TI protein sequences with Tm\u0026thinsp;\u0026gt;\u0026thinsp;65\u0026deg;C. The CDS sequences of the \u003cem\u003eA. thaliana\u003c/em\u003e proteins were retrieved from the TAIR database, and a multiple sequence alignment was conducted within the individual TI group using MAFT software [\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e]. The multiple sequence alignment of the protein sequences was saved in CLUSTALW file format that was subsequently converted to .AL file format using MEGA7 software [\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e]. The resulting .aln file was subjected to model selection to conduct the phylogenetic analysis in MEGA7 software [\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e]. Later, we subjected the .aln file to phylogenetic analysis. Based on the resulting models, the best model was used to construct the phylogenetic tree. Various statistical parameters used to construct the phylogenetic tree were as follows: statistical method, maximum likelihood, test of phylogeny; bootstrap method, model/method, Tamura-Nei model. Gamma distribution with discrete gamma categories (5) was used to construct the phylogenetic tree. The nearest-neighbor-interchange (NII) ML heuristic method was used with a strong branch swap filter. There were no gaps or missing data treatment, and all sites were used to construct the phylogenetic tree. The resulting phylogenetic tree was saved in Newick file format and referred to as a gene tree.\u003c/p\u003e \u003cp\u003eTo understand the gene loss, duplication, and divergence, it was important to compare the respective gene tree with their species tree. The species tree was constructed using the following link: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/Taxonomy/CommonTree/wwwcmt.cgi\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/Taxonomy/CommonTree/wwwcmt.cgi\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. The gene tree and species tree were loaded and later reconciled in Notung software [\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e] version 2.9.1.5 to get the gene loss, duplication, and divergence. Relative synonymous codon usage and nucleotide position of codons were calculated using MEGA software version 7 [\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e].\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eThe Melting Temperature Index (TI) of Arabidopsis thaliana proteome ranged from 9.605 to -15.6008\u003c/h2\u003e \u003cp\u003eThe study was conducted to deduce the melting temperature and melting temperature index of the \u003cem\u003eArabidopsis thaliana\u003c/em\u003e proteome. All the protein sequences of the \u003cem\u003eA. thaliana\u003c/em\u003e proteome were downloaded from the TAIR database. The proteome file included all the protein sequences, including the splice variants. Individual protein sequences were subjected to Tm analysis using the Tm predictor. The resulting Tm and TI were documented in an Excel file. It was found that the TI of the \u003cem\u003eA. thaliana\u003c/em\u003e proteome ranged from \u0026minus;\u0026thinsp;15.6008 (Tm\u0026thinsp;\u0026lt;\u0026thinsp;55\u0026deg;C) to 9.605 (Tm\u0026thinsp;\u0026gt;\u0026thinsp;65\u0026deg;C) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The mediator of RNA polymerase II transcription subunit-like protein (At1g55080.2) was found to encode the highest TI (9.605), and the protein NADH dehydrogenase 5B subunit (AtMG00665.1) encoded the lowest TI (-15.6008) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The melting temperature index was grouped into three categories (\u0026lt;\u0026thinsp;55\u0026deg;C, 55\u0026ndash;65\u0026deg;C, \u0026amp; \u0026gt; 65\u0026deg;C) as mentioned above. It was found that the average TI of the \u003cem\u003eA. thaliana\u003c/em\u003e proteome in the group of \u0026lt;\u0026thinsp;55\u0026deg;C was \u0026minus;\u0026thinsp;0.623, for the group 55\u0026ndash;65\u0026deg;C was 0.596, and for the group\u0026thinsp;\u0026gt;\u0026thinsp;65\u0026deg;C it was 1.508. In the entire proteome of \u003cem\u003eA. thaliana\u003c/em\u003e, there were 48359 protein sequences. From them, 20640 were found to contain Tm\u0026thinsp;\u0026gt;\u0026thinsp;65\u0026deg;C, 22826 were found to contain Tm 55\u0026ndash;65\u0026deg;C, and 4893 were found to contain Tm\u0026thinsp;\u0026lt;\u0026thinsp;55\u0026deg;C. The average TI of the entire \u003cem\u003eA. thaliana\u003c/em\u003e proteome was found to be 0.493.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMelting temperature index of top 50 proteins of Tm group\u0026thinsp;\u0026gt;\u0026thinsp;65\u003csup\u003eo\u003c/sup\u003e C, 55-65\u003csup\u003eo\u003c/sup\u003e C, and \u0026lt;\u0026thinsp;55\u003csup\u003eo\u003c/sup\u003e C with their accession number.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTAIR Accession\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProtein Name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTm Index (TI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eTm\u0026thinsp;\u0026gt;\u0026thinsp;65\u003csup\u003eo\u003c/sup\u003e C\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT1G55080.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emediator of RNA polymerase II transcription subunit-like protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.605825\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT3G57160.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecysteine-rich TM module stress tolerance protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.847668\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT1G55080.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emediator of RNA polymerase II transcription subunit-like protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.763272\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT2G41420.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eproline-rich family protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.684553\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT3G49845.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecysteine-rich TM module stress tolerance protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.537937\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT5G59170.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProline-rich extensin-like family protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.47778\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT3G57160.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecysteine-rich TM module stress tolerance protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.292566\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT3G22930.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecalmodulin-like 11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.713252\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT4G04601.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehypothetical protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.91422\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT1G12810.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eproline-rich family protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.505747\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT5G67600.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecysteine-rich TM module stress tolerance protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.426804\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT5G28640.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSSXT family protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.308339\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT5G28640.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSSXT family protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.308339\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT3G43583.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehypothetical protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.279883\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT1G33355.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehypothetical protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.237808\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT3G23450.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003etransmembrane protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.168681\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT1G53260.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehypothetical protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.118861\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT3G23450.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003etransmembrane protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.116701\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT3G23450.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003etransmembrane protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.062314\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT3G23450.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003etransmembrane protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.062314\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT5G45350.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eproline-rich family protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.003393\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT5G45350.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eproline-rich family protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.003393\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT5G45350.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eproline-rich family protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.003393\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT1G51915.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecryptdin protein-like protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.954335\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT2G35343.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehypothetical protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.894497\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT1G12810.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eproline-rich family protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.845825\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT3G17626.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003estructural constituent of ribosome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.833655\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT1G62333.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehypothetical protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.824776\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT5G17510.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emediator of RNA polymerase II transcription subunit-like protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.779996\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT5G17510.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emediator of RNA polymerase II transcription subunit-like protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.779996\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT1G43825.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehypothetical protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.700155\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT2G04870.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehypothetical protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.654217\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT3G46616.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehypothetical protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.639269\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT4G16983.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehypothetical protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.628392\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT5G36920.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003etransmembrane protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.593216\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT1G52855.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehypothetical protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.583521\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT3G46616.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehypothetical protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.565323\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT3G46616.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehypothetical protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.565323\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT4G12050.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePutative AT-hook DNA-binding family protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.556438\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT2G20562.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003etaximin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.534934\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT5G66780.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003elate embryogenesis abundant protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.485143\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT3G20470.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eglycine-rich protein 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.465337\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT5G35660.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlycine-rich protein family\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.372616\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT3G16080.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZinc-binding ribosomal protein family protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.370846\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT1G28630.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003etranscriptional regulator EFH1-like protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.33809\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT2G30590.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWRKY DNA-binding protein 21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.337233\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT4G11430.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehydroxyproline-rich glycoprotein family protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.309887\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT4G25225.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003etransmembrane protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.309825\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT3G05870.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eanaphase-promoting complex/cyclosome 11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.233014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT2G05520.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eglycine-rich protein 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.220797\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003e55-65\u003c/b\u003e\u003csup\u003e\u003cb\u003eo\u003c/b\u003e\u003c/sup\u003e \u003cb\u003eC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT1G27260.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePaired amphipathic helix (PAH2) superfamily protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.999993\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT5G09720.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMagnesium transporter CorA-like family protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.999965\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT5G09720.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMagnesium transporter CorA-like family protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.999965\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT4G00630.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eK\u0026thinsp;+\u0026thinsp;efflux antiporter 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.999852\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT1G33290.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP-loop containing nucleoside triphosphate hydrolases superfamily protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.999807\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT1G35350.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEXS (ERD1/XPR1/SYG1) family protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.99976\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT4G39950.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecytochrome P450%2C family 79%2C subfamily B%2C polypeptide 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.999757\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT1G19520.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epentatricopeptide (PPR) repeat-containing protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.999754\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT4G35630.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ephosphoserine aminotransferase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.999682\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT4G38760.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003enucleoporin (DUF3414)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.999622\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT2G30650.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eATP-dependent caseinolytic (Clp) protease/crotonase family protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.999545\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT2G44480.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ebeta glucosidase 17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.999541\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT1G48280.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehydroxyproline-rich glycoprotein family protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.999532\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT5G62830.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF-box associated ubiquitination effector family protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.999498\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT3G19510.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHomeodomain-like protein with RING/FYVE/PHD-type zinc finger domain-containing protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.999471\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT3G19510.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHomeodomain-like protein with RING/FYVE/PHD-type zinc finger domain-containing protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.999471\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT1G74550.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecytochrome P450%2C family 98%2C subfamily A%2C polypeptide 9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.999441\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT2G45700.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003esterile alpha motif (SAM) domain-containing protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.999376\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT3G55320.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP-glycoprotein 20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.999333\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT3G56810.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehypothetical protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.999328\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT5G23540.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMov34/MPN/PAD-1 family protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.999305\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT3G11960.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCleavage and polyadenylation specificity factor (CPSF) A subunit protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.999299\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT3G62180.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlant invertase/pectin methylesterase inhibitor superfamily protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.999287\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT2G15360.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003efucosyltransferase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.999274\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT2G41930.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProtein kinase superfamily protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.999257\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT5G43870.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eauxin canalization protein (DUF828)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.999231\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT2G46660.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecytochrome P450%2C family 78%2C subfamily A%2C polypeptide 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.999198\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT1G43970.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehypothetical protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.999195\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT5G36120.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecofactor assembly%2C complex C (B6F)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.999189\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT1G23180.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eARM repeat superfamily protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.999184\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT4G09990.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eglucuronoxylan 4-O-methyltransferase-like protein (DUF579)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.999059\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT5G64670.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRibosomal protein L18e/L15 superfamily protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.998885\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT4G25770.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ealpha/beta-Hydrolases superfamily protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.99881\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT1G77810.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGalactosyltransferase family protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.998705\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT3G14990.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClass I glutamine amidotransferase-like superfamily protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.998685\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT3G14990.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClass I glutamine amidotransferase-like superfamily protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.998685\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT2G39810.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eubiquitin-protein ligase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.998677\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT1G15720.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTRF-like 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.998636\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT4G28890.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRING/U-box superfamily protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.998635\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT1G45233.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTHO complex%2C subunit 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.998623\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT1G56120.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLeucine-rich repeat transmembrane protein kinase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.998561\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT1G68940.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArmadillo/beta-catenin-like repeat family protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.998193\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT3G52320.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF-box and associated interaction domains-containing protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.998148\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT5G21040.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF-box protein 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.998107\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT5G21040.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF-box protein 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.998107\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT5G21040.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF-box protein 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.998107\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT1G36730.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTranslation initiation factor IF2/IF5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.998034\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT5G59550.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ezinc finger (C3HC4-type RING finger) family protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.998023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT2G20940.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003etransmembrane protein%2C putative (DUF1279)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.997973\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT1G77360.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTetratricopeptide repeat (TPR)-like superfamily protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.997932\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;55\u003c/b\u003e\u003csup\u003e\u003cb\u003eo\u003c/b\u003e\u003c/sup\u003e \u003cb\u003eC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT1G10715.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEMBRYO SURROUNDING FACTOR-like protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3.36815\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT4G38960.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB-box type zinc finger family protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3.38287\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT4G38960.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB-box type zinc finger family protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3.38287\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT4G38960.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB-box type zinc finger family protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3.39699\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT4G38960.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB-box type zinc finger family protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3.39699\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT5G38330.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003elow-molecular-weight cysteine-rich 80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3.44673\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT2G04621.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003etransmembrane protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3.51447\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT4G15735.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSCR-like 10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3.52163\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT5G42280.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCysteine/Histidine-rich C1 domain family protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3.53152\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT4G30070.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003elow-molecular-weight cysteine-rich 59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3.56614\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT2G02026.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehypothetical protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3.6482\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT2G21320.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB-box zinc finger family protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3.66898\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT3G15548.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003etransmembrane protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3.72664\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT1G14755.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS locus-related glycoprotein 1 (SLR1) binding pollen coat protein family\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3.7273\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT4G35430.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehypothetical protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3.79968\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT3G47965.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehypothetical protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3.86685\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT5G46874.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePutative membrane lipoprotein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3.89865\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT4G33735.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehypothetical protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3.95442\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT5G42280.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCysteine/Histidine-rich C1 domain family protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-4.0398\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT1G07600.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emetallothionein 1A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-4.06107\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT5G20447.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehypothetical protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-4.15162\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT2G16535.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ematernally expressed family protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-4.15854\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT2G22807.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDefensin-like (DEFL) family protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-4.21377\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT5G46825.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehypothetical protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-4.23425\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT2G16505.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ematernally expressed family protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-4.2409\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT3G28216.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehypothetical protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-4.25618\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eATCG00690.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ephotosystem II reaction center protein T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-4.26142\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT2G41355.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehypothetical protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-4.31003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT5G03545.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eexpressed in response to phosphate starvation protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-4.35042\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT3G07522.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehypothetical protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-4.53537\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT1G53970.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGDSL esterase/lipase-like protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-4.55369\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT1G53970.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGDSL esterase/lipase-like protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-4.55369\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT1G54773.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehypothetical protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-4.60278\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eATCG00510.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ephotsystem I subunit I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-4.6412\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT5G24575.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehypothetical protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-4.70326\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eATCG00080.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ephotosystem II reaction center protein I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-4.81682\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT1G03325.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehypothetical protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-4.86125\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT4G12850.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFar-red impaired responsive (FAR1) family protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-4.92786\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eATCG00760.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eribosomal protein L36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-5.03606\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT5G04045.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePutative membrane lipoprotein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-5.06174\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT5G07545.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehypothetical protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-5.12925\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT5G56555.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehypothetical protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-5.39349\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT1G02490.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehypothetical protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-5.87035\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT3G26395.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehypothetical protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-6.33018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT3G61172.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003elow-molecular-weight cysteine-rich 8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-6.76256\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT2G26515.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehypothetical protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-6.78223\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT1G13805.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehypothetical protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-7.21064\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT5G40315.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehypothetical protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-7.37162\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT3G23122.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehypothetical protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-9.97686\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eATMG00665.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNADH dehydrogenase 5B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-15.6008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eTrp was the lowest encoding amino acid in A. thaliana proteome with Tm\u0026thinsp;\u0026gt;\u0026thinsp;65\u003c/b\u003e \u003csup\u003e \u003cb\u003eo\u003c/b\u003e \u003c/sup\u003e \u003cb\u003eC\u003c/b\u003e\u003c/p\u003e \u003cp\u003eIn total, 48359 protein sequences of \u003cem\u003eA. thaliana\u003c/em\u003e were found to encode 20855719 amino acids. From them, 1115151 amino acids were from the group with Tm\u0026thinsp;\u0026lt;\u0026thinsp;55\u003csup\u003eo\u003c/sup\u003eC, 10696941 amino acids were from the group with Tm 55-65\u003csup\u003eo\u003c/sup\u003e C, and 9043627 amino acids were from the group with Tm\u0026thinsp;\u0026gt;\u0026thinsp;65\u003csup\u003eo\u003c/sup\u003eC. It was found that the percentage of amino acid composition of Trp (1.135%) was lowest in the protein sequences that encode proteins with Tm\u0026thinsp;\u0026gt;\u0026thinsp;65\u003csup\u003eo\u003c/sup\u003eC. The rate of Trp composition was highest (1.377%) in the proteins with Tm\u0026thinsp;\u0026lt;\u0026thinsp;55\u003csup\u003eo\u003c/sup\u003eC. However, the percentage composition of Leu amino acid was highest (9.682%) in Tm 55-65\u003csup\u003eo\u003c/sup\u003eC and lowest (8.961%) in Tm\u0026thinsp;\u0026lt;\u0026thinsp;55\u003csup\u003eo\u003c/sup\u003eC (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The percentage of amino acid composition of Ala, Asp, Glu, Gly, Lys, Gln, and Val was found to increase with the increase in the Tm of the proteins (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). While the percentage of amino acid composition of Cys, Phe, His, Ile, Met, Asn, Arg, Ser, Thr, Trp, and Tyr was found to decrease with the increase in Tm of the proteins (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). However, the composition of Leu amino acid was increased from Tm\u0026thinsp;\u0026lt;\u0026thinsp;55 \u003csup\u003eo\u003c/sup\u003eC to 55\u0026ndash;65 \u003csup\u003eo\u003c/sup\u003eC and decreased for Tm\u0026thinsp;\u0026gt;\u0026thinsp;65 \u003csup\u003eo\u003c/sup\u003eC (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAmino acid composition of \u003cem\u003eArabidopsis thaliana\u003c/em\u003e proteome in different melting temperature (Tm) groups. The composition of red shaded amino acids Ala, Asp, Glu, Gly, Lys, Gln, and Val increased while the composition of green shaded amino acids Cys, Phe, His, Ile, Met, Asn, Pro, Arg, Ser, Thr, Trp, and Tyr decreased with the increase in the Tm of the proteins.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmino Acids\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTm\u0026thinsp;\u0026lt;\u0026thinsp;55\u003csup\u003eo\u003c/sup\u003eC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTm 55\u0026ndash;65 \u003csup\u003eo\u003c/sup\u003eC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTm\u0026thinsp;\u0026gt;\u0026thinsp;65 \u003csup\u003eo\u003c/sup\u003eC\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAla\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.460\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCys\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.554\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.684\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.399\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.720\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.043\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.732\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.041\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.430\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.397\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.255\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.493\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.131\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLys\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.564\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.961\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.682\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.478\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.765\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.404\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.494\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.371\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePro\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.964\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.869, Met,\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGln\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.728\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.477\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.379\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.118\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.447\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.606\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.680\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.377\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.282\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.135\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.858\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.695\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eThe molecular mass of A. thaliana proteome ranged from 0.149 kDa to 611.888 kDa\u003c/h3\u003e\n\u003cp\u003eIt was pertinent to understand whether molecular mass plays any role in deciding the Tm of the proteins. Therefore, we calculated the molecular mass of individual proteins of the \u003cem\u003eA. thaliana\u003c/em\u003e. It was found that the \u003cem\u003eA. thaliana\u003c/em\u003e proteome encodes proteins from 0.149 kDa to 611.888 kDa. The average molecular mass of proteins of the Tm group\u0026thinsp;\u0026gt;\u0026thinsp;55\u0026deg;C, 55\u0026ndash;65\u0026deg;C, and \u0026gt;\u0026thinsp;65\u0026deg;C was 25.617, 52.482, and 48.924 kDa, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The molecular mass of proteins with Tm group 55\u0026ndash;65\u0026deg;C was the highest, whereas the proteins with Tm group\u0026thinsp;\u0026lt;\u0026thinsp;55\u0026deg;C was the lowest (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The protein that encoded the lowest molecular mass (0.149 kDa) protein in \u003cem\u003eA. thaliana\u003c/em\u003e proteome was a hypothetical protein (AT1G64633.1) with a Tm of 55\u0026ndash;65\u0026deg;C and a TI index of zero. Similarly, the highest molecular mass protein of the \u003cem\u003eA. thaliana\u003c/em\u003e (611.888 kDa) proteome was a midasin-like protein (AT1G67120.2). It has a Tm of \u0026gt;\u0026thinsp;65\u0026deg;C with a TI index of 1.223. A correlation analysis of the molecular mass of \u003cem\u003eA. thaliana\u003c/em\u003e proteome with three different Tm groups (\u0026lt;\u0026thinsp;55\u0026deg;C, 55\u0026ndash;65\u0026deg;C, and \u0026gt;\u0026thinsp;65\u0026deg;C) was conducted to understand if the Tm groups with respect to molecular mass are correlated (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The correlation plot was \u0026lt;\u0026thinsp;55\u0026deg;C vs. 55\u0026ndash;65\u0026deg;C, \u0026lt; 55\u0026deg;C vs. \u0026gt; 65\u0026deg;C, and 55\u0026deg;C-65\u0026deg;C vs. \u0026gt; 65\u0026deg;C with an \u003cem\u003er\u003c/em\u003e value of 0.044, 0.002, and 0.021 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The frequency distribution of the molecular mass of \u003cem\u003eA. thaliana\u003c/em\u003e proteome is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eIsoelectric point (pI) of A. thaliana proteome ranged from 2.753 to 12.749\u003c/h2\u003e \u003cp\u003eThe isoelectric point of the \u003cem\u003eA. thaliana\u003c/em\u003e proteome was calculated to understand whether the isoelectric point of the protein has any role towards the Tm of proteins. It was found that glycine-rich protein (AT3G44950.1) encoded the lowest \u003cem\u003epI\u003c/em\u003e (2.753) and ribosomal protein L41 family (AT3G56020.1) encoded the highest \u003cem\u003epI\u003c/em\u003e (12.749). The average \u003cem\u003epI\u003c/em\u003e of the \u003cem\u003eA. thaliana\u003c/em\u003e proteome was 6.78. The average \u003cem\u003epI\u003c/em\u003e of the \u003cem\u003eA. thaliana\u003c/em\u003e proteome with Tm\u0026thinsp;\u0026lt;\u0026thinsp;55\u0026deg;C was 7.11; the \u003cem\u003epI\u003c/em\u003e of proteins with Tm 55\u0026ndash;65\u0026deg;C was 6.75, and the \u003cem\u003epI\u003c/em\u003e of proteins with Tm\u0026thinsp;\u0026gt;\u0026thinsp;65\u0026deg;C was 6.74 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). A correlation analysis was conducted to infer the relationship between Tm groups. They were Tm\u0026thinsp;\u0026lt;\u0026thinsp;55\u0026deg;C vs 55\u0026ndash;65\u0026deg;C, \u0026lt; 55\u0026deg;C vs\u0026thinsp;\u0026gt;\u0026thinsp;65\u0026deg;C, and 55\u0026ndash;65\u0026deg;C vs\u0026thinsp;\u0026gt;\u0026thinsp;65\u0026deg;C (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The correlation coefficient (\u003cem\u003er\u003c/em\u003e) for the group\u0026thinsp;\u0026lt;\u0026thinsp;55\u0026deg;C vs 55\u0026ndash;65\u0026deg;C, \u0026lt; 55\u0026deg;C vs\u0026thinsp;\u0026gt;\u0026thinsp;65\u0026deg;C, and 55\u0026ndash;65\u0026deg;C vs\u0026thinsp;\u0026gt;\u0026thinsp;65\u0026deg;C was \u0026minus;\u0026thinsp;0.006, 0.011, and \u0026minus;\u0026thinsp;0.003, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The frequency distribution of \u003cem\u003eA. thaliana\u003c/em\u003e proteomes with different Tm was depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. When a correlation plot was drawn to understand the role of Tm, \u003cem\u003epI\u003c/em\u003e, and molecular mass (kDa), it was found that molecular mass has a role towards the Tm of \u003cem\u003eArabidopsis\u003c/em\u003e proteins with a correlation coefficient \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.259. However, the \u003cem\u003epI\u003c/em\u003e of the \u003cem\u003eArabidopsis\u003c/em\u003e proteome is negatively correlated with coefficient \u003cem\u003er\u003c/em\u003e = -0.091 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eHigh and low TI index genes Shows Significant Expression in Plant Development and Drought\u003c/h2\u003e \u003cp\u003eUnderstanding the roles of \u003cem\u003eA. thaliana\u003c/em\u003e genes for drought and heat tolerance mechanisms is important to uncover their importance. So that they can be useful for generating stress-tolerant crop varieties to withstand harsh environmental conditions. A search of gene expression data in Expression Atlas revealed that the maximum of the genes searched for in the gene expression data had undergone up-regulation. The mediator of the RNA polymerase II transcription subunit-like gene has undergone a 2.5-fold upregulation due to drought stress (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The cysteine-rich TM module stress tolerance gene in tcx2 (Tesmin-like CXC2) mutant has undergone a 2.8-fold up-regulation compared to the wild type. The cysteine-rich TM module for stress tolerance gene in the CT101 mutant underwent a 6.6-fold up-regulation upon 350 ppb ozone exposure. The proline-rich extensin-like family gene has undergone 7.9-fold up-regulation due to drought stress (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Calmodulin-like gene 11has undergone 4.4-fold up-regulation in the vtc2.5 mutant in drought environment. In the Tm group at 55\u0026ndash;65\u0026deg;C, the magnesium transporter CorA-like family gene underwent a 3.6-fold up-regulation when treated with 350 nanoliters of ozone. However, the EXS gene and CYTP79B2 undergone 4.4-and 7.1-fold down regulation, respectively, upon drought environment (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In the Tm group\u0026thinsp;\u0026lt;\u0026thinsp;55\u0026deg;C, from the selected ten genes, the expression profile of three genes was not found. However, the gene NADH dehydrogenase 5B, which has the lowest Tm index, underwent 7.7-fold up-regulation in tcx2; WOX5:GFP mutant. One hypothetical protein also underwent 7.4-fold up-regulation in pao-1 mutant when compared with the wild type (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). A low-molecular-weight cysteine-rich 8 gene has undergone 4.3-fold up-regulation in dark-induced senescence (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGene expression data for the ten genes with the highest Tm Index in the 55\u0026ndash;65\u0026deg;C and 65\u0026deg;C groups, as well as the ten genes with the lowest Tm Index in the group below 55\u0026deg;C.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAccession Number\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eLog\u003csub\u003e2\u003c/sub\u003e Fold Change\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTm Index (TI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEnvironmental Conditions/Experiment\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;65\u003csup\u003eo\u003c/sup\u003eC\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT1G55080.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMediator of RNA polymerase II transcription subunit-like protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.605825\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDrought environment vs normal watering\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT3G57160.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCysteine-rich TM module stress tolerance protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.847668\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003etcx2; TMO5:3xGFP vs wild type genotype\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT2G41420.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProline-rich family protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.684553\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDevelopmental stages, 35 days vs 29 days\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT3G49845.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCysteine-rich TM module stress tolerance protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e6.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.537937\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCT101; 350 ppb ozone exposure for 2hr vs CT101; control\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT5G59170.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProline-rich extensin-like family protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e7.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.47778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003edrought environment vs normal watering in vtc2.5 mutant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT3G22930.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCalmodulin-like 11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e4.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.713252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e35S:HSFA1b-RFP vs wild type genotype in warm/hot temperature regimen\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT4G04601.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHypothetical protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.91422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eref8-1 mutant vs wild type\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT1G12810.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProline-rich family protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.505747\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDrought environment vs normal watering in wild type genotype\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT5G67600.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCysteine-rich TM module stress tolerance protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.426804\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDrought environment vs normal watering in vtc2 mutant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT5G28640.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSSXT family protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.308339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10 days vs 0 day. Light exposure to study chloroplast development\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e\u003cb\u003e55\u003c/b\u003e\u003csup\u003e\u003cb\u003eo\u003c/b\u003e\u003c/sup\u003e\u003cb\u003eC-65\u003c/b\u003e\u003csup\u003e\u003cb\u003eo\u003c/b\u003e\u003c/sup\u003e\u003cb\u003eC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT1G27260.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePaired amphipathic helix (PAH2) superfamily protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.999993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT5G09720.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMagnesium transporter CorA-like family protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.999965\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOzone; 350 nanoliter vs none in Cvi-0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT4G00630.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eK\u0026thinsp;+\u0026thinsp;efflux antiporter 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.999852\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSeed after 48 h of stratification (48 h S) vs seed after 12 h of stratification (12 h S)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT1G33290.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP-loop containing nucleoside triphosphate hydrolases superfamily protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.999807\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003etcx2; TMO5:3xGFP vs wild type genotype\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT1G35350.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEXS (ERD1/XPR1/SYG1) family protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e-4.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.99976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDrought environment vs normal watering in vtc2 mutant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT4G39950.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCYP79B2 (Cytochrome p450, family 79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e-7.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.999757\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDrought environment vs normal watering in wild type genotype\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT1G19520.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePentatricopeptide (PPR) repeat-containing protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.999754\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDark-induced senescence\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT4G35630.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhosphoserine aminotransferase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e5.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.999682\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eS-nitrosocysteine; 1 millimolar vs buffer\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT4G38760.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNucleoporin (DUF3414)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.999622\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLer/Kas-2 hybrid vs Kas-2 in wild type genotype\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT2G30650.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eATP-dependent caseinolytic (Clp) protease/crotonase family protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.999545\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10 days vs 7 day in wild type\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT2G44480.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBeta glucosidase 17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e-1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.999541\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHeat stress vs none\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;55\u003c/b\u003e\u003csup\u003e\u003cb\u003eo\u003c/b\u003e\u003c/sup\u003e\u003cb\u003eC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT5G07545.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHypothetical protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-5.12925\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT5G56555.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHypothetical protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-5.39349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5 days vs 0 day\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT1G02490.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHypothetical protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e-4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-5.87035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDrought environment vs normal watering in vtc2.5 mutant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT3G26395.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHypothetical protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-6.33018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDrought environment vs normal watering in vtc2 mutant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT3G61172.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow-molecular-weight cysteine-rich 8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e4.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-6.76256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDark Induced senescence\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT2G26515.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHypothetical protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-6.78223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDrought environment vs normal watering in vtc2.5 mutant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT1G13805.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHypothetical protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-7.21064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT5G40315.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHypothetical protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-7.37162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT3G23122.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHypothetical protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e7.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-9.97686\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003epao-1 mutant vs wild type genotype in continuous dark (no light) regimen at 2 day\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eATMG00665.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNADH dehydrogenase 5B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e7.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-15.6008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003etcx2; WOX5:GFP vs wild type genotype\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eMachine Learning Approach Showed Molecular Mass has Influence on Protein Tm\u003c/h2\u003e \u003cp\u003eTo understand the role of molecular mass, isoelectric point, and Tm index in deciding the Tm of \u003cem\u003eA. thaliana\u003c/em\u003e protein, a machine learning approach was adopted to find their role. We conducted a boosting regression to understand the influence of different variables on Tm. It was found that molecular mass has a relative influence on Tm than the isoelectric point (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). The study contained 30950 training, 7738 validation, and 9671 test sets. Again, a decision tree regression study was conducted, and it was found that molecular mass (kDa, n\u0026thinsp;=\u0026thinsp;30950) plays an important role in the Tm of the \u003cem\u003eArabidopsis\u003c/em\u003e proteins (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). Further, a network plot analysis of molecular mass (kDa), Tm, and \u003cem\u003epI\u003c/em\u003e was conducted. It was found that kDa and Tm are positively correlated (blue) while kDa and TI are negatively correlated (red) (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). However, \u003cem\u003epI\u003c/em\u003e was only linked to kDa, and it did not show any network with TI and Tm of proteins (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eHigh and low TI Encoding Arabidopsis Gene undergone duplication\u003c/h2\u003e \u003cp\u003eWe picked the top 50 highest TI (Tm\u0026thinsp;\u0026gt;\u0026thinsp;65\u0026deg;C), middle TI (55\u0026ndash;65\u0026deg;C), and lowest TI (\u0026lt;\u0026thinsp;55\u0026deg;C) encoding CDS sequences of \u003cem\u003eA. thaliana\u003c/em\u003e proteins and conducted a phylogenetic analysis separately. The phylogenetic tree of \u003cem\u003eA. thaliana\u003c/em\u003e genes with protein TI\u0026thinsp;\u0026lt;\u0026thinsp;55\u0026deg;C showed two major clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA). Although the proteins were from diverse groups, their clustering in the phylogenetic tree was quite smooth. The phylogenetic tree of proteins with Tm\u0026thinsp;\u0026lt;\u0026thinsp;55\u0026deg;C showed all of the genes were duplicated (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). None of the genes was found to undergo loss, transfer, or codivergence (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA, Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The phylogenetic tree of genes with TI group 55\u0026ndash;65\u0026deg;C also resulted in two major clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eB), while the phylogenetic tree of genes with TI\u0026thinsp;\u0026gt;\u0026thinsp;65\u0026deg;C showed three distinct clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eC). However, all the genes of TI\u0026thinsp;\u0026lt;\u0026thinsp;55\u0026deg;C, 55\u0026ndash;65\u0026deg;C, and \u0026gt;\u0026thinsp;65\u0026deg;C underwent duplication, and none of them were found to undergo loss, transfer, or codivergence (Supplementary Fig.\u0026nbsp;1, Supplementary Fig.\u0026nbsp;2, Supplementary Fig.\u0026nbsp;3, Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) during evolution.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGene duplication, loss, transfer, and divergence of \u003cem\u003eArabidopsis thaliana\u003c/em\u003e with different Tm groups.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTm Group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDuplicated\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLosses\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTransferred\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCodiverged\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTm\u0026thinsp;\u0026gt;\u0026thinsp;65\u003csup\u003eo\u003c/sup\u003e C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTm 55-65\u003csup\u003eo\u003c/sup\u003e C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTm\u0026thinsp;\u0026lt;\u0026thinsp;55\u003csup\u003eo\u003c/sup\u003e C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFurther, we tried to understand the relative synonymous codon usage (RSCU) of the genes those used for the phylogenetic analysis. We found that the AGA codon coding for the Arg amino acid has the highest RSCU for genes for the Tm group\u0026thinsp;\u0026lt;\u0026thinsp;55\u0026deg;C (2.33) and 55\u0026ndash;65\u0026deg;C (2.26) (Supplementary File 1). However, the highest RSCU for genes for the Tm group\u0026thinsp;\u0026gt;\u0026thinsp;65\u0026deg;C was found in codon CCA (1.8), which codes for the Pro amino acid. Similarly, the lowest RSCU for the Tm group\u0026thinsp;\u0026lt;\u0026thinsp;55\u0026deg;C (0.38) and 55\u0026ndash;65\u0026deg;C (0.44) group was found in the codon GCG that encodes for the Arg amino acid (Supplementary File 1). However, for Tm group\u0026thinsp;\u0026gt;\u0026thinsp;65\u0026deg;C, the lowest RSCU was found in codon CCC (0.36), which encodes for the Pro amino acid (Supplementary File 1). In the compositional analysis of nucleotides, the nucleotide composition for the Tm group\u0026thinsp;\u0026lt;\u0026thinsp;55\u0026deg;C was T\u0026thinsp;=\u0026thinsp;30.2%, C\u0026thinsp;=\u0026thinsp;19.5%, A\u0026thinsp;=\u0026thinsp;28.3%, and G\u0026thinsp;=\u0026thinsp;21.9%; for Tm group 55\u0026ndash;65\u0026deg;C, T\u0026thinsp;=\u0026thinsp;27.6%, C\u0026thinsp;=\u0026thinsp;19.8%, A\u0026thinsp;=\u0026thinsp;28.5% and G\u0026thinsp;=\u0026thinsp;24.1%; and for Tm group\u0026thinsp;\u0026gt;\u0026thinsp;65\u0026deg;C, T\u0026thinsp;=\u0026thinsp;22.3%, C\u0026thinsp;=\u0026thinsp;23.3%, A\u0026thinsp;=\u0026thinsp;27.9%, and G\u0026thinsp;=\u0026thinsp;26.4%.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eNucleotide Position in Codon Explain genetic variation and adaptive role for stress tolerance\u003c/h2\u003e \u003cp\u003eWe analyzed the nucleotide position in the codon of the studied genes to understand and evaluate their roles towards shaping the protein structure, function, and evolution. We analyzed the nucleotide position of the codons of different Tm groups. In the Tm group\u0026thinsp;\u0026lt;\u0026thinsp;55\u0026deg;C, at the first position, the abundance of T (31) was followed by A (29.5), G (22), and C (18.2) (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e); at the 2nd position the abundance of T (31) was followed by A (27.2), G (24.6), and C (17.7) (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e); at the 3rd position the abundance of T (32.04) was followed by A (27.6), C (20.4), and G (19.9) (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). In the Tm group 55\u0026ndash;65\u0026deg;C, at the 1st position, the abundance of nucleotide A (29.2) was followed by T (28), G (23.3), and C (19.8) (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e); at the 2nd position, the abundance of nucleotide A (28.1) was followed by T (27), G (24.7), and C (20.4) (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e); at the 3rd position, the abundance of nucleotide A (28.3) was followed by T (27), G (24.5), and C (20.2) (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). In the Tm group\u0026thinsp;\u0026gt;\u0026thinsp;65\u0026deg;C, at the 1st position, the abundance of nucleotide A (26.7) followed by C (25.1), G (24.1), and T (23) (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e); at the 2nd position, the abundance of nucleotide G (28.4) followed by A (26.9), C (22.5), and T (22) (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e); at the 3rd position, the abundance of nucleotide A (26.9) followed by T (25), C (24.3), and G (24.2) (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWhen we analyzed the nucleotide position by grouping them with the Tm group (e.g., A nucleotide at the 1st position for the Tm group\u0026thinsp;\u0026lt;\u0026thinsp;55\u0026deg;C, 55\u0026ndash;65\u0026deg;C, and \u0026gt;\u0026thinsp;65\u0026deg;C). We found the frequency of A nucleotide (28.46) at the 1st position was highest for all three Tm groups, followed by T at the 3rd position (27.87), A at the 3rd position (27.62), and A at the 2nd position (27.38) (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e). The frequency of C at the 2nd position was the lowest (20.18), followed by C at the 1st (21.03) and 3rd positions (21.63) (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e). When we analyzed the frequency of nucleotide variations for all three Tm groups at the 1st, 2nd, and 3rd positions, we found the frequency of nucleotide variance was highest in T at the 2nd position (17.34), followed by T at the 3rd (14.59), T at the 1st (13.83), and C at the 1st (13.06) positions (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e). The lowest nucleotide variance was recorded for A at the 2nd position (0.37), followed by A at the 3rd position (0.49) and G at the 1st position (1.81). Further, a cluster analysis revealed the frequency of nucleotide positions for the Tm group\u0026thinsp;\u0026lt;\u0026thinsp;55\u0026deg;C and the 55\u0026ndash;65\u0026deg;C group fall close to each other, while the nucleotide position of the Tm group\u0026thinsp;\u0026gt;\u0026thinsp;65\u0026deg;C grouped separately.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eRelative Synonymous Codon Usage (RSCU) of A. thaliana Genes\u003c/h2\u003e \u003cp\u003eWe conducted a study to understand the relative synonymous codon usage of genes of the top 50 \u003cem\u003eA. thaliana\u003c/em\u003e proteins from three Tm groups. We found the RSCU of codon AGA (R) was highest (2.33) in the Tm group\u0026thinsp;\u0026lt;\u0026thinsp;55\u0026deg;C and 55\u0026ndash;65\u0026deg;C (2.26). For the Tm group\u0026thinsp;\u0026gt;\u0026thinsp;65\u0026deg;C, the RSCU of CCA (P) was highest (1.8) (Supplementary file). The lowest RSCU was found in CCC (P) (0.36) in the Tm group\u0026thinsp;\u0026gt;\u0026thinsp;65\u0026deg;C, followed by CGC (R) (0.38) in the Tm group\u0026thinsp;\u0026lt;\u0026thinsp;55\u0026deg;C and 0.44 in the Tm group 55\u0026ndash;65\u0026deg;C (Supplementary File). The second highest RSCU was found in the stop codon UGA in the Tm group\u0026thinsp;\u0026lt;\u0026thinsp;55\u0026deg;C and 55\u0026ndash;65\u0026deg;C. From the 64 codons, at least 27 codons contained RSCU of \u0026gt;\u0026thinsp;1 in Tm group\u0026thinsp;\u0026lt;\u0026thinsp;55 C while 34 codons contained RSCU of \u0026lt;\u0026thinsp;1. Only three codons (AUG (M), CCU (P), and UGG (W)) of the Tm group\u0026thinsp;\u0026lt;\u0026thinsp;55\u0026deg;C contained an RSCU value of 1. For the Tm group 55\u0026ndash;65\u0026deg;C, 28 codons contained RSCU of \u0026gt;\u0026thinsp;1, while 32 codons contained RSCU\u0026thinsp;\u0026lt;\u0026thinsp;1. Four codons (CCU (P), UGG (W), CGR (R), and ACU (T)) in the Tm group 55\u0026ndash;65\u0026deg;C were found to contain an RSCU value of 1. For the Tm group\u0026thinsp;\u0026gt;\u0026thinsp;65\u0026deg;C, 31 codons contained RSCU\u0026thinsp;\u0026gt;\u0026thinsp;1, and a similar number of codons were found to contain RSCU\u0026thinsp;\u0026lt;\u0026thinsp;1. Only two codons, AUG (M) and UGG (W), contained an RSCU value of 1 in the Tm group\u0026thinsp;\u0026gt;\u0026thinsp;65\u0026deg;C.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eTm Based Codon Context of Arabidopsis CDS\u003c/h2\u003e \u003cp\u003eCodon usage preferred the use of codons; the \u0026ldquo;codon context\u0026rdquo; explains the sequential presence of codon pairs in a gene. To understand the codon pairs in \u003cem\u003eArabidopsis\u003c/em\u003e CDS with protein Tm groups of \u0026lt;\u0026thinsp;55\u0026deg;C, 55\u0026ndash;65\u0026deg;C, and \u0026gt;\u0026thinsp;65\u0026deg;C, we conducted codon pair analysis. For CDS of protein Tm group\u0026thinsp;\u0026lt;\u0026thinsp;55\u0026deg;C, ATG ATG was the preferred codon pair and found in the highest number (37), followed by TGT GAT (13), ATG GAA (10), CTT TGC (9), and AAA TGT (8). For the Tm group 55\u0026ndash;65\u0026deg;C, the GAA GAT and GAG AAG (30) codon pairs were found in the highest number, followed by GAT GAA (25), GAT GAT (25), AAA GAG (24), AAA GAT (23), GAA GAA (23), AAA GAA (21), and others. For the Tm group\u0026thinsp;\u0026gt;\u0026thinsp;65\u0026deg;C, the CAA CAA (58) codon pair was found in the highest number, followed by GGA GGA (49), CCA CCA (44), GGT GGA (38), GGT GGT (32), and TAT CCT (31).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe proteins are developed to perform their function within a specific temperature range. However, the stability of a plant proteome is directly proportional to the survival rate of the plants under the heat stress. Heat stress can disrupt the cellular and extracellular proteins, leading to the disruption in the cellular homeostasis, physiological process, and metabolism. The inactivation of important and functional proteins can lead to compromised enzyme activities that will impact the enzymatic pathways in the plant. Therefore, understanding the protein stability for temperature resistance is an important parameter to study the heat stress in plants. Thermal stability of protein, referred to as melting temperature (Tm), provides the strength to the protein to cope with extreme temperature stress. The melting temperature of the protein is defined as the temperature at which the protein undergoes a transition from native, folded form to unfolded form under equilibrium conditions. The proteins having high Tm will be less prone to denaturation and structural instability, leading to high cellular integrity and function under adverse heat conditions. It can be well speculated that proteins with high Tm can better adapt to perform their function under high-temperature conditions, thus making Tm a critical thermodynamic parameter for understanding heat resistance in plants. Due to the denaturation by high temperature, protein will lose its functional conformation, leading to its inactivation [\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e]. Therefore, it is important to understand the thermodynamic properties of proteins, and hence we have conducted a study to deduce the melting temperature (Tm) of the entire \u003cem\u003eArabidopsis thaliana\u003c/em\u003e proteome. For a better understanding, protein Tm was grouped into three groups with temperature ranges\u0026thinsp;\u0026lt;\u0026thinsp;55\u0026deg;C, 55\u0026ndash;65\u0026deg;C, and \u0026gt;\u0026thinsp;65\u0026deg;C. However, the Tm of the protein relies on several factors, including amino acid composition, structure, and environmental factors, including \u003cem\u003epH\u003c/em\u003e, ionic strength, and the presence of stabilizing and destabilizing factors. From these factors, amino acid composition is one of the major parameters that might be controlling the Tm and stability of the protein. Therefore, we conducted an in-depth analysis of the amino acid composition of the \u003cem\u003eArabidopsis thaliana\u003c/em\u003e proteome. We found 20640 proteins (including splice variants) of \u003cem\u003eA. thaliana\u003c/em\u003e fall in Tm range\u0026thinsp;\u0026gt;\u0026thinsp;65\u0026deg;C, 22826 proteins fall in the Tm range 55\u0026deg;C\u0026ndash; 65\u0026deg;C, and 4893 proteins fall in the Tm range\u0026thinsp;\u0026lt;\u0026thinsp;55\u003csup\u003eo\u003c/sup\u003eC. From this study, it is evident that \u003cem\u003eA. thaliana\u003c/em\u003e has developed more proteins with Tm\u0026thinsp;\u0026gt;\u0026thinsp;65\u0026deg;C. This shows the plant has developed proteins to withstand high temperatures. Considering the evolutionary consequences, the earth has passed two ice ages and subsequently gained temperature, and now we are facing global warming. During this evolutionary process, plants have developed their protein machinery systems and encoded more proteins that can withstand high-temperature stress. This evolving process of rising temperatures might continue in the future, and hence we need to find ways to identify important heat stress-resistant proteins to tackle the global warming problem. Therefore, finding the heat-resistant proteins in plants is of paramount interest to understand their heat-resistant mechanism.\u003c/p\u003e \u003cp\u003eTo have a better understanding of high and low Tm proteins, we studied the amino acid composition of the \u003cem\u003eA. thaliana\u003c/em\u003e proteome. It was found that the amino acid composition of Ala, Asp, Glu, Gly, Lys, Leu, Gln, and Val increased with an increase in the Tm of the proteins, while the composition of Cys, Phe, His, Ile, Asn, Pro, Arg, Ser, Thr, Trp, and Tyr decreased with an increase in the Tm of the protein (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This linearity of increase and decrease in amino acid composition reflects their role in determining the Tm of the proteins. Further, we calculated the molecular mass and isoelectric point of individual proteins of \u003cem\u003eA. thaliana\u003c/em\u003e and studied their role towards the Tm. The average molecular mass of \u003cem\u003eA. thaliana\u003c/em\u003e for protein Tm group\u0026thinsp;\u0026lt;\u0026thinsp;55\u0026deg;C was 25.61 kDa, for Tm group 55\u0026ndash;65\u0026deg;C it was 52.48 kDa, and for Tm group\u0026thinsp;\u0026gt;\u0026thinsp;65\u0026deg;C it was 48.92 kDa (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The molecular mass of proteins with Tm\u0026thinsp;\u0026lt;\u0026thinsp;55\u0026deg;C was quite low compared to the Tm group 55\u0026ndash;65\u0026deg;C and \u0026gt;\u0026thinsp;65\u0026deg;C (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This gives a hint that proteins with low molecular mass possess low Tm. A correlation analysis within the molecular mass group showed proteins with Tm\u0026thinsp;\u0026lt;\u0026thinsp;55\u0026deg;C marginally correlate with the proteins with Tm 55\u0026ndash;65\u0026deg;C (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Analysis of the isoelectric point of \u003cem\u003eA. thaliana\u003c/em\u003e protein revealed that proteins with Tm\u0026thinsp;\u0026lt;\u0026thinsp;55\u0026deg;C possess high \u003cem\u003epI\u003c/em\u003e (7.22), while the \u003cem\u003epI\u003c/em\u003e of proteins of the Tm group 55\u0026ndash;65\u0026deg;C was 6.75 and the \u003cem\u003epI\u003c/em\u003e of the Tm group\u0026thinsp;\u0026gt;\u0026thinsp;65\u0026deg;C was 6.74 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). This shows that high \u003cem\u003epI\u003c/em\u003e protein tends towards low Tm in \u003cem\u003eA. thaliana\u003c/em\u003e. The trend of \u003cem\u003epI\u003c/em\u003e of the proteins for three different groups was in the decreasing order from Tm\u0026thinsp;\u0026lt;\u0026thinsp;55\u0026deg;C to \u0026gt;\u0026thinsp;65\u0026deg;C. However, the \u003cem\u003epI\u003c/em\u003e difference was not significant enough. Similarly, there were no such detectable correlations in the isoelectric point of proteins found in three different Tm groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Further, a correlation study was conducted using molecular weight, isoelectric point, and Tm as input parameters, and the result showed a positive correlation between molecular weight and Tm (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). The relative influence of molecular weight (kDa) on the Tm of \u003cem\u003eA. thaliana\u003c/em\u003e protein was further confirmed using a machine learning approach (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). Boosting regression analysis revealed the relative influence of molecular weight (kDa) on the melting temperature of the proteins. Further, the validation role of molecular weight (kDa) towards the melting temperature of protein was observed in the decision tree regression analysis and network plot study (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA study regarding the melting point analysis was reported by Jarzab et al. (2020), where they studied 48000 protein sequences from humans and archaea and covered 13 species [\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e]. They reported that protein sequence, composition, and size affect the thermal stability of proteins in prokaryotes and eukaryotes [\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e]. They used the species that lived in the 40\u0026deg;C to 70\u0026deg;C temperature range. It is well known that the enzyme DNA polymerase from the bacteria \u003cem\u003eThermus aquaticus\u003c/em\u003e is highly resistant to heat, having a half-life period of 40 minutes at 95\u0026deg;C. The study reported that RNA polymerase of \u003cem\u003eThermus aquaticus\u003c/em\u003e remains unaltered at 80\u0026deg;C and helps in synthesizing tRNA transcripts [\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e]. In our study, we found the mediator protein of the RNA polymerase II transcription subunit-like protein that falls in the Tm\u0026thinsp;\u0026gt;\u0026thinsp;65\u0026deg;C temperature range has the highest Tm index (TI\u0026thinsp;=\u0026thinsp;9.60). This shows the mediator protein of RNA polymerase II might be contributing towards the thermal stability of the RNA polymerase. Further, proline-rich, cysteine-rich, calmodulin-like proteins, and transmembrane proteins were found to contain higher Tm index (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This shows that proteins involved in transmembrane activity, calcium signaling, and proline metabolism are associated with heat resistance in \u003cem\u003eA. thaliana\u003c/em\u003e. Tang et al. reported the melting temperature of 95\u0026deg;C in legume protein edestin and found that the melting temperature was unaffected in the presence of 20\u0026ndash;40 \u0026micro;M sodium dodecyl sulphate [\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e]. Gliadin protein functions in a temperature range of 70\u0026ndash;115\u0026deg;C, and as the temperature rises to 135\u0026deg;C, gliadin protein softens much better [\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e]. The thermal stability of plant H2-ferritin protein (soybean) was reported to be quite high. When H2-ferritin protein is caged with extra peptides, it denatures at 106\u0026deg;C [\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e]. Miller et al. (2013) reported the thermal stability of ribulose-1,5-bisphosphate (Rubisco) and measured the Tm of native, ancestral, and variant proteins from \u003cem\u003eSynechococcus\u003c/em\u003e. They found that OH28 purified Rubisco exhibited greater stability at 79.5\u0026deg;C than the less thermotolerant strain (72.3\u0026ndash;73.6\u0026deg;C) [\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e]. This led them to conclude that thermostable Rubisco enzyme diverged from the ancestor of \u003cem\u003eSynechococcus\u003c/em\u003e [\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe gene expression analysis revealed the up regulation and down regulation of important genes due to drought stress and at different stages of plant development. For example, cysteine-rich TM module stress tolerance protein undergone 2.4 folds upregulation due to drought stress (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Venacio and Aravind (2010) reported the role of CYSTM in stress tolerance across eukaryotic lineage [\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e]. Calmodulin-like protein was reported to undergone 4.4 folds up-regulation. The role of calmodulin-like protein in stress mitigation is also well documented in various studies [\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e]. Cytochrome p450 CYP79B2 was reported to undergone 7.1 folds down regulation due to drought environmental stress. This gene is involved in Trp-dependent auxin biosynthesis in association with CYP79B3 [\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e]. This shows that during drought stress CYP79B2 undergo down regulation and hence plant fail to produce sufficient auxin hormones to withstand the harsh environment.\u003c/p\u003e \u003cp\u003eFurther, codon evolution revealed the substitution of the Ala amino acid by isoleucine in the OH28 strain, suggesting a positive selection in evolution [\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e]. To understand the evolutionary aspects of the high Tm, low, and medium Tm proteins, we conducted gene duplication and loss analysis. We found that all the studied genes of three Tm groups had undergone duplication, and none of them were found to undergo transfer, codivergence, or losses (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Gene duplication is an important event as it accumulates mutations over time without affecting the function of the original gene, thus bringing the new function and characteristics. The duplication of the genes with high, low, and medium Tm genes reflects their tendency towards the innovation of new functions. In gene duplication, one gene can continue to exhibit its original function while allowing others to evolve new functions, leading to the adaptability of the species to adverse temperature conditions.\u003c/p\u003e \u003cp\u003eThe RSCU value of 1 indicates a codon is used in expected frequency, and \u0026lt;\u0026thinsp;1 indicates the codons are used quite less frequently [\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e]. However, RSCU of \u0026gt;\u0026thinsp;1 shows, codons are used more frequently than expected. For the Tm group\u0026thinsp;\u0026lt;\u0026thinsp;55\u0026deg;C, 27 codons contained RSCU\u0026thinsp;\u0026gt;\u0026thinsp;1, and 32 codons contained RSCU\u0026thinsp;\u0026lt;\u0026thinsp;1; for the Tm group 55\u0026ndash;65\u0026deg;C, 28 codons contained RSCU\u0026thinsp;\u0026gt;\u0026thinsp;1, and 32 codons contained RSCU\u0026thinsp;\u0026lt;\u0026thinsp;1; for the Tm group\u0026thinsp;\u0026gt;\u0026thinsp;65\u0026deg;C, 31 codons contained RSCU\u0026thinsp;\u0026gt;\u0026thinsp;1, and 31 codons contained RSCU\u0026thinsp;\u0026lt;\u0026thinsp;1. From them, the Tm group\u0026thinsp;\u0026lt;\u0026thinsp;55\u0026deg;C and 55\u0026ndash;65\u0026deg;C, 27 and 28 codons, respectively, contained codons with an RSCU of \u0026gt;\u0026thinsp;1, while 32 codons contained an RSCU of \u0026lt;\u0026thinsp;1. The result depicts RSCU of value\u0026thinsp;\u0026gt;\u0026thinsp;1 increased with the increase in the Tm of the proteins, while the RSCU of value\u0026thinsp;\u0026lt;\u0026thinsp;1 decreased with the increase in the Tm of the proteins. In the Tm group at \u0026lt;\u0026thinsp;55\u0026deg;C, only three codons had an RSCU of 1; in the Tm group at 55\u0026ndash;65\u0026deg;C, only four codons contained an RSCU of 1, and in the Tm group at \u0026gt;\u0026thinsp;65\u0026deg;C, two codons were found to contain an RSCU of 1. The RSCU of codons having value 1 for all the Tm groups were AUG (M) and UGG (W) (Supplementary File). For the Tm group\u0026thinsp;\u0026lt;\u0026thinsp;55\u0026deg;C, unique codon with RSCU value 1 was CCU (P) while for Tm group 55\u0026ndash;65\u0026deg;C unique codons with RSCU 1 were CUC (L) and AGC (S). The second highest RSCU was found in codon UGA for the Tm group\u0026thinsp;\u0026lt;\u0026thinsp;55\u0026deg;C and 55\u0026ndash;65\u0026deg;C. The UGA codon also plays an important role in encoding selenocysteine amino acid in the protein [\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e, \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e]. This analysis revealed that \u003cem\u003eA. thaliana\u003c/em\u003e encodes differential RSCU according to the Tm groups, reflecting the role of codons in encoding proteins with different melting temperatures. The codon usage bias in the different Tm groups was quite evident. The RSCU keeps the protein sequence intact while altering their influences on gene expression, protein translation, and stress responses [\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e, \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e]. The RSCU bias is influenced by several factors, including natural selection, mutation pressure, translation efficiency, and gene expression levels [\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e, \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e]. The presence of higher RSCU in codon AGA that encode for Arg amino acid in the Tm group\u0026thinsp;\u0026lt;\u0026thinsp;55\u0026deg;C and 55\u0026ndash;65\u0026deg;C reflects its role in lower to moderate temperature while the higher frequency of codon CCA that encodes for Pro amino acid in the Tm group\u0026thinsp;\u0026gt;\u0026thinsp;65\u0026deg;C shows CCA codon is optimized for translational accuracy under stress condition. The RSCU study is a vital tool that helps to understand plant adaptation to environmental stress conditions by influencing protein translation efficiency and protein folding. However, the codon usage bias may vary in tissue-specific expression and developmental stages of plants.\u003c/p\u003e \u003cp\u003eThe position of nucleotide at the 1st place for all the Tm groups showed the frequency of nucleotide A was the highest. The highest frequency of nucleotide A across the category suggests its vital roles in codons for encoding amino acids critical for protein function. Its higher frequency at Tm\u0026thinsp;\u0026gt;\u0026thinsp;65\u0026deg;C shows its importance in protein function towards high-stress adaptations. The G nucleotide position has quite high variability, suggesting its role in genetic diversity and potential for adaptive evolution. Nucleotide C is more prone to mutation without severely impacting the protein function, thus providing a buffer for evolutionary changes [\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e]. Similarly, nucleotide T showed higher consistency, indicating its role in encoding essential amino acids. Overall, it suggests that nucleotides for Tm\u0026thinsp;\u0026lt;\u0026thinsp;55\u0026deg;C were less variable, for Tm 55\u0026ndash;65\u0026deg;C, intermediate variable, and the Tm\u0026thinsp;\u0026gt;\u0026thinsp;65\u0026deg;C was highly variable. The high variability of nucleotides indicates genomic regions with higher potential for evolutionary innovation, contributing to the genomic diversity and adaptation. The presence of higher nucleotide variability in the Tm group\u0026thinsp;\u0026gt;\u0026thinsp;65\u0026deg;C indicates \u003cem\u003eA. thaliana\u003c/em\u003e plants are inclining more towards enhancing the Tm of their protein for stress adaptation while keeping the conserved genomic architecture in the Tm group\u0026thinsp;\u0026lt;\u0026thinsp;55\u0026deg;C. A comparative less variable nucleotides in different position of codons reflect their conserved structure and their association with protein of the Tm group\u0026thinsp;\u0026lt;\u0026thinsp;55\u0026deg;C reflects the plants have evolved from cold climatic conditions and now diverged its genomic pool towards stress adaptation. The nucleotide positions in the groups A1, A2, A3, C1, C2, C3, G1, G2, G3, T1, T2, and T3 with their variation reflect a delicate balance between the conservation and stress adaptation in the evolution of genes.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe present study provides a comprehensive analysis of thermal properties of \u003cem\u003eArabidopsis thaliana\u003c/em\u003e proteome and highlighted the relationship between protein thermal stability and molecular mass of proteins, amino acid composition, gene expression, and codon usage. The study revealed, maximum of the \u003cem\u003eA. thaliana\u003c/em\u003e proteins resides in Tm range of 55\u0026ndash;65\u0026deg;C with a smaller fraction of proteins found below 55\u0026deg;C. The study reported the that the genes associated with high Tm index are undergone up-regulation due to drought stress. A correlation analysis revealed a higher Tm of protein associated with increase in amino acid composition of Ala, Asp, Glu, Gly, Lys, Gln, and Val while Cys, Phe, and Trp were less prominent in proteins with high Tm. Molecular mass was found to positively correlate with the Tm. We believe, the study will contribute to the field of plant stress biology and more specifically in understanding the molecular mechanism behind the thermal stress resistance in \u003cem\u003eArabidopsis\u003c/em\u003e. The study will enhance our understanding of structural stability of \u003cem\u003eA. thaliana\u003c/em\u003e proteins under different temperature stress. The study possesses enormous implication in agriculture science, particularly towards the development of crop varieties with improved tolerance to extreme heat stress.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eTm: melting temperature, TI: Tm index, kDa: kilo Dalton, CDS: coding DNA sequence\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e No funding received\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analysed during this study are included in this published article and provided as supplementary File 1\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e: Not available\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor’s contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKMM: identified the melting temperature of the proteins, revised the manuscript; TKM: conceived the idea, analyzed the data, drafted and revised the manuscript\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to express their sincere thanks to Indian School Nizwa, Oman, and Medi-Caps International School, Indore, Madhya Pradesh, India for their support and encouragement to conduct this research. The authors would also like to extend their sincere thanks to Mrs. Asma Khan, Indian School Nizwa, Oman for her extensive encouragement to author Karan Martens Mohanta to conduct the research.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eZhang H, Zhao Y, Zhu J-K. Thriving under Stress: How Plants Balance Growth and the Stress Response. Dev Cell. 2020;55:529\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMishra S, Spaccarotella K, Gido J, Samanta I, Chowdhary G. Effects of Heat Stress on Plant-Nutrient Relations: An Update on Nutrient Uptake, Transport, and Assimilation. Int J Mol Sci. 2023;24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZahra N, Hafeez MB, Ghaffar A, Kausar A, Zeidi M, Al, Siddique KHM, et al. Plant photosynthesis under heat stress: Effects and management. 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EMBO J. 2024;43:2294\u0026ndash;307.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-genomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gics","sideBox":"Learn more about [BMC Genomics](http://bmcgenomics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/gics","title":"BMC Genomics","twitterHandle":"#BMCGenomics","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Protein, Proteome, Melting temperature, Heat stress, Arabidopsis","lastPublishedDoi":"10.21203/rs.3.rs-6629178/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6629178/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePlants are always exposed to a variety of stressful environment including heat and drought stress, which severely impacts the growth, development, and productivity of the plants. To overcome such challenges, plants have evolved diverse arrays of defense mechanisms. From several defense strategies, expression and evolution of heat stress-tolerant proteins are crucial. They protect the cellular structures, maintain cellular homeostasis, and overcome the stress condition. Although several studies are conducted to identify the heat-and cold-stress tolerant proteins, studies using the physiochemical properties of the proteins remain scarce. Therefore, we used melting temperature-based identification of heat-and col- tolerant proteins in \u003cem\u003eA. thaliana\u003c/em\u003e.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe study elucidated the thermal properties of the entire \u003cem\u003eArabidopsis thaliana\u003c/em\u003e proteome by considering the melting temperature (Tm) and the melting temperature index (TI). In total, 48359 protein sequences were analyzed, and the melting temperature of the proteins was recorded in three groups (Tm\u0026thinsp;\u0026lt;\u0026thinsp;55\u0026deg;C, 55\u0026ndash;65\u0026deg;C, and \u0026gt;\u0026thinsp;65\u0026deg;C). The Tm index of the \u003cem\u003eA. thaliana\u003c/em\u003e proteome ranged from \u0026minus;\u0026thinsp;15.6008 (\u0026lt;\u0026thinsp;55\u0026deg;C) to 9.605 (\u0026gt;\u0026thinsp;65\u0026deg;C). At least 22826 proteins were found in the Tm group of 55\u0026deg;C to 65\u0026deg;C, 20640 proteins were found in the Tm group of \u0026gt;\u0026thinsp;65\u0026deg;C, and only 4893 proteins were found in the Tm group of \u0026lt;\u0026thinsp;55\u0026deg;C. The mediator of RNA polymerase II transcription subunit-like protein was found to possess the highest Tm index (9.60), while the NADH dehydrogenase 5B subunit was found to contain the lowest TI (-15.60). The amino acid composition analysis of the \u003cem\u003eA. thaliana\u003c/em\u003e proteome revealed that the frequency of Ala, Asp, Glu, Gly, Lys, Gln, and Val increased with the increase in Tm, while the amino acids Cys, Phe, and Trp decreased with the increase in the Tm of the \u003cem\u003eA. thaliana\u003c/em\u003e proteome. The molecular mass of the \u003cem\u003eA. thaliana\u003c/em\u003e proteome ranged from 0.149 to 611.888 kDa, and protein in the Tm group at 55\u0026ndash;65\u0026deg;C showed the highest average molecular mass. The machine learning analysis revealed an increase in the molecular mass positively correlated with the increase in the Tm of the proteins. The codon usage pattern revealed, the codon pair prefer the Tm group specific occurrence where ATG-ATG, CAA-CAA codon pairs were predominated. Relative synonymous codon usage of the three Tm groups revealed AGA (Arg) and CCA (Pro) were the preferred codons for the low and high Tm group DNA sequences, respectively. Codon context analysis revealed the presence of preferences of the Tm group specific codon pairing. There was a variation in the nucleotide position of the codons in different Tm groups. Evolutionary study revealed, gene duplication was the predominant evolutionary feature and all of the studied genes in the three Tm group undergone duplication. Codon context analysis revealed distinct clustering pattern in high Tm protein group. The study underscores the role of amino acid composition, molecular mass, and codon usage in determining the thermal stability of the proteins in the \u003cem\u003eA. thaliana\u003c/em\u003e.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe study reflected the evolution of high Tm-adapting genes through gene duplication, highlighting the role of gene and genome evolution towards encoding high Tm proteins for stress resilience.\u003c/p\u003e","manuscriptTitle":"Meltome Atlas of Arabidopsis thaliana Proteome: A Melting Temperature-Based Identification of Heat \u0026amp; Cold Resistant Proteins","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-20 10:06:11","doi":"10.21203/rs.3.rs-6629178/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-24T09:16:07+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-23T07:30:55+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-22T15:18:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"233285556401938426920883181792523516198","date":"2025-07-02T21:39:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"188512045568932610029915106133509469694","date":"2025-07-01T10:29:05+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-18T15:03:58+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-05-14T10:59:29+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-12T04:07:01+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-12T04:04:29+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Genomics","date":"2025-05-09T13:30:43+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-genomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gics","sideBox":"Learn more about [BMC Genomics](http://bmcgenomics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/gics","title":"BMC Genomics","twitterHandle":"#BMCGenomics","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fe89f04b-a889-4c25-bdf4-f23f7b63f493","owner":[],"postedDate":"June 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-01T16:12:52+00:00","versionOfRecord":{"articleIdentity":"rs-6629178","link":"https://doi.org/10.1186/s12864-025-12131-6","journal":{"identity":"bmc-genomics","isVorOnly":false,"title":"BMC Genomics"},"publishedOn":"2025-11-28 15:58:44","publishedOnDateReadable":"November 28th, 2025"},"versionCreatedAt":"2025-06-20 10:06:11","video":"","vorDoi":"10.1186/s12864-025-12131-6","vorDoiUrl":"https://doi.org/10.1186/s12864-025-12131-6","workflowStages":[]},"version":"v1","identity":"rs-6629178","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6629178","identity":"rs-6629178","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}