Comparative Proteomic Analysis of Venom from Three Viper Taxa: Evaluating Software-Specific Protein and Peptide Profiles

Comparative Proteomic Analysis of Venom from Three Viper Taxa: Evaluating Software-Specific Protein and Peptide Profiles | 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 Comparative Proteomic Analysis of Venom from Three Viper Taxa: Evaluating Software-Specific Protein and Peptide Profiles Bruno Malheiro, Mert Karış, Bayram Göçmen, Ayse Nalbantsoy, Rui Vitorino This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5404907/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Snake venom is increasingly recognised in biomedical research as a potential source of relevant proteins that are still relatively unknown in various species. In this experiment, we performed proteomic quantification and identification of the venomic profile of three viper taxa: Montivipera blugardaghica subsp. bulgardaghica (MB), Vipera ammodytes subsp. montandoni (VA) and Vipera kaznakovi (KV); and compared the performance of three peptide identification software: PEAKS, MaxQuant and Proteome Discoverer. Overall, PEAKS identified 19 unique proteins (19 in MB, 11 in VA and 19 for KV) and 125 unique peptides (55 in MB, 35 in VA and 63 for KV); MaxQuant identified 577 unique proteins (234 in MB, 275 in VA and 297 for KV) and 1233 unique peptides (518 in MB, 647 in VA and 642 for KV); Proteome Discoverer identified 621 unique proteins (310 in MB, 248 for VA and 346 for VK) and 1657 unique peptides (894 in MB, 830 in VA and 1041 for VK). The three software shared 5 identified proteins and 67 peptides; PEAKS shared 6 proteins and 69 peptides with MaxQuant and 6 proteins and 79 peptides with Proteome Discoverer; MaxQuant shared 139 proteins and 781 peptides with Proteome Discoverer. All identified proteins were categorised into families for each taxon and then compared with the existing literature. This revealed significant discrepancies in the results between the software and the reviewed literature. Overall, PEAKS performed very poorly, while MaxQuant and Proteome Discoverer performed best for both protein and peptide identification, with the latter software being particularly noteworthy. Montivipera bulgardaghica Vipera kaznakovi Vipera ammodytes PEAKS MaxQuant Prteome Discoverer Mass Spectrometry Snake venom Proteomics Bioinformatics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 MAIN CONCLUSIONS Our proteomic analysis closely matches the characteristic profile observed in viper venom. The relative abundance of venom protein families differs from results reported in previous studies. Discrepancies were noted between software platforms as the same samples gave different results depending on the analytical tool used. The PEAKS software performed suboptimally for all three samples analyzed. Proteome Discoverer achieved the highest number of peptide and protein identifications, followed by MaxQuant in terms of efficiency. Establishing a standardized bioinformatics framework for venom research is essential to ensure consistency and accuracy in proteomic analyzes 1. INTRODUCTION Toxins of animal and plant origin represent a naturally occurring source of unique proteins and diverse biomolecules, many of which remain largely unexplored and uncategorized by scientific research [ 1 ]. These bioactive compounds have evolved as specialized mechanisms for defense and predation and represent a remarkable evolutionary adaptation [ 2 – 4 ]. Among other biotoxins, the study of snake venoms has been somewhat neglected, mainly due to the negative perception these animals enjoy among humans (either due to superstition or human-animal conflict), but also because of the low yield of venom samples, their elusive nature and limited funding [ 4 , 5 ]. The most extensively characterized venoms are from emblematic species that pose a significant and regular risk of poisoning to local populations, which has led research efforts to focus largely on the development of antidotes. Although critical in countries with high rates of envenomation, the complex diversity of unique bioactive peptides and proteins that these samples contain may be important for pharmacological and biomedical research and even critical for potential new medical breakthroughs and the development of life-saving therapies. The proteomic profile differs in each snake species. Each has a unique combination of peptides, enzymes and other molecules that becomes more apparent the further apart two species are phylogenetically. This becomes even clearer when comparing the ophidian families: the venoms of elapid snakes are predominantly neurotoxic, while the venoms of vipers and venomous colubrids have a more hemorrhagic and hemotoxic composition [ 6 – 10 ]. It has also been described that the intraspecific composition of toxins exhibits a high degree of evolvability, which is influenced by age, prey availability and prey co-adaptation to the toxins. Considering all these different combinations of protein diversity within and between species, snake venom is an immensely rich source of bioactive proteins and peptides waiting to be discovered, characterized and used in biomedical research. De Lima et al. (2005) describe that compounds extracted from snake venom are used in biomedical research in three ways: as a direct therapeutic agent [ 11 – 13 ] as a means of diagnosing various medical conditions [ 12 , 14 , 15 ] as a means of studying the basic mechanisms of metabolic and disease processes [ 15 – 18 ] also summarize several biotoxins with approved drugs and therapies for human use, including some isolated from snake venoms, such as batroxobin - purified directly from the venom of Bothrops atrox, cleaves the Aα chain of fibrinogen and is used to treat acute cerebral infarction and angina pectoris (Defibrase® (Pentapharm DSM Nutritional Products Ltd, 2024), for blood gelling (Plateltex-Act® (Plateltex, 2024)) or as a fibrin sealant in surgery (Vivostat®Fibrin (Vivostat, 2024)); Captopril – a synthetic compound derived from the venom of Bothrops jararaca, acts as an angiotensin-converting enzyme inhibitor and is used to treat high blood pressure and heart failure (Capoten® (Bristol-Myers Squibb Pharmaceutical S.A., 2024), among other examples. The author also describes other protein candidates that are in clinical trials and testing. Before the commercialization phase, it is essential to identify, isolate and purify the target proteins - a process that is particularly challenging due to the high complexity of snake venoms. Therefore, research methods must utilize state-of-the-art multidisciplinary techniques at the “omics” level, including genomics, transcriptomics, peptidomics, proteomics and metabolomics. These techniques are converging in the field of toxinology, now referred to as “venomics",” which aims at a comprehensive characterization of the entire toxin profile and bioactive compounds in venomous animals [ 19 – 21 ]. Among the most common protocols for analyzing venoms, shotgun or bottom-up proteomics is the most widely used. In this method, samples are first run through an SDS-PAGE gel to separate proteins by molecular weight and facilitate visualization of targets, followed by enzymatic digestion (proteolysis) into smaller peptides, usually using trypsin. The peptides are then fractionated and processed by LC-MS/MS (Liquid Chromatography Tandem Mass Spectrometry), which provides detailed information about their sequence and post-translational modifications. The output data is then compared with peptide and protein databases and used to reconstruct the original protein sequences, effectively identifying the venom components [ 22 ]. By combining the data thus obtained with genomic and transcriptomic elements, information on the genetic basis of the composition of venoms and their drivers of evolutionary adaptations can be generated and used to predict protein patterns in snake clades, facilitating the search for new bioactive and pharmaceutically relevant compounds [ 19 ]. More recently, efforts have been made to understand how higher structural levels of proteins can determine their bioactivity. Insights into the tertiary and quaternary structure of proteins are lost in traditional bottom-up proteomics, which primarily focuses on analyzing the peptide level [ 23 ]. Due to their complex heterogeneity, venom proteins may form covalent and non-covalent complexes both within and between toxin families, which may be important for a better understanding of the influence of protein structure on function and interactions with other biomolecules. These insights may in turn be crucial to understand the bioactivity of toxins in depth beyond the general protein composition [ 24 ]. These interactions are still largely unexplored but may be key to developing structure-based therapeutics, such as the identification of epitopes in antigenic regions of proteins that enable immune recognition [ 25 ]. The emergence of novel structural analysis techniques such as cryo-electron microscopy (cryo-EM) and X-ray crystallography has shown great promise to generate complementary data to tandem mass spectrometry. Although X-ray crystallography is the classical method for determining protein structures, it has some limitations. While it offers atomic-level resolution and the ability to process very small proteins and complexes (< 60 kDa) [ 26 ], the crystallization process is tedious and often requires the use of small detergents that can denature molecules, compromising their native lipidomic aspects and potentially inactivating them. Cryo-EM, on the other hand, allows the determination of the three-dimensional structure of larger proteins or complexes, both in the inactivated and activated state, with a much shorter sample preparation time [ 27 ]. This technique preserves the native state of the proteins by rapidly freezing the samples in glassy ice, avoiding crystallization and preserving functional integrity[ 28 – 30 ] (Bai et al., 2015; Dubochet et al., 1981, 1988). The result is near-atomic resolution and exceptional image quality. The combination of structural biology (namely cryo-EM), proteomics and advanced data analysis will revolutionize the field of venomics and bring significant advances in both basic biochemical knowledge and clinical applications such as drug development. To cope with the large amounts of data generated by these methods, peptide identification software must be reliable, accurate and constantly evolving [ 31 , 32 ](Christin et al., 2011; Xu & Ma, 2006). There are several peptide identification software, and each has unique algorithms and databases that affect their performance and accuracy, which may make them more or less suitable for certain types of biospecimens. To maximize their applicability and performance in future studies, their advantages and disadvantages need to be monitored and compared for different sample types so that the results generated are validated and reliable for medical research. Three of the most commonly used software programs are PEAKS, MaxQuant and Proteome Discoverer. PEAKS uses de novo sequencing and matches queries to predefined spectral libraries and databases using DIA data searches and an intuitive interface for post-search data visualization and processing [ 33 – 35 ]; MaxQuant uses the integrated engine “Andromeda” for peptide searching and includes models for quantification, statistical analysis (e.g. the Perseus framework) and post-processing data visualization. It also enables differential expression analysis and post-translational profiling for large-scale proteome mapping [ 36 – 38 ]. Proteome Discovery uses multiple proteomic and peptidomic workflows for molecule identification, post-translational modifications, isobaric massing, statistical tools and other functions and provides a larger number of identified peptides in the output. It includes algorithms such as INFERYS for rescoring and CHIMERYS for intelligent searching [ 21 ] With this in mind, let us process the venoms of three viper taxa: Montivipera bulgardaghica subsp. bulgardaghica (MB), Vipera ammodytes subsp. montandoni (VA) and Vipera kaznakovi (VK) and to determine their proteomic profile using three different peptide identification software (PEAKS, MaxQuant and Proteome Discoverer). By comparing the results of these three viper taxa, we hope to gain insight into the selection of the most appropriate tools for venom samples and catalogue the proteomic profile for these three viper taxa. 2. MATERIALS AND METHODS 2 .1 Sample collection The venom samples were collected in spring and summer 2016 in Turkish Thrace (VA), Mersin Province (MB) and Artvin Province (UK). The VA and MB samples were collected from two individuals each, while the UK sample was collected from nine individuals. Venom extraction was performed at the capture sites following a standardized protocol in which the crude venom was collected using a laboratory cup covered with parafilm without applying pressure to the venom glands. The samples were centrifuged at 2000 × g for 10 minutes at 4°C to remove cell debris. The supernatant was frozen in liquid nitrogen on site, transported to the laboratory, freeze-dried and stored at 4°C until further analysis. After venom extraction, all individuals were returned to their capture site. The freeze-dried venom samples were then sent to the Institute of Biomedicine (iBiMED) of the College of Aveiro, Portugal, for processing. Sampling was performed with ethical approval (Ege College, Animal Experiments Ethics Committee, 2013#50) and with a field study permit (2015#183897) from the Ministry of Forestry and Water Affairs of the Republic of Turkey. 2.2 Quantification of proteins Protein concentrations were quantified using a DC Protein Assay Kit (BioRad, RC DCTM Protein Assay, #5000122). A calibration curve was generated using bovine serum albumin (BSA) solutions, consistently achieving an R² > 0.95. Absorbance values were measured using a TECAN Nanoquant Infinite M200 Pro microplate reader at 750 nm on a transparent plate. The protocol includes 10 seconds of shaking and a 1-second data acquisition per well. 2.3 LC-MS/MS analysis Proteins of interest were excised from the SDS-PAGE bands and subjected to a series of washes with ammonium bicarbonate (25 mM) and acetonitrile (ACN, VWR Chemicals). Reduction was performed with dithiothreitol (DTT, 10 mM, 30 minutes, 60°C), followed by alkylation with iodoacetamide (IAA, 55 mM, 30 minutes, 25°C) in the dark. The gel pieces were then dried in vacuo (SpeedVac, Thermo Savant) and digested with modified trypsin (Thermo Scientific™ Pierce™ Trypsin Protease, MS Grade, #90057) in 50 mM NH₄HCO₃ at a 1:25 enzyme to protein ratio. After 30 minutes on ice, 50 µL of 50 mM NH₄HCO₃ was added and the samples were incubated overnight at 37°C. The tryptic peptides were extracted by successively adding 10% formic acid (FA), 10% FA/ACN (1:1) and 90% ACN. They were then freeze-dried (SpeedVac, Thermo Savant) and resuspended in 1% FA for HPLC injection. Peptide separation was performed using an Orbitrap Q Exactive mass spectrometer (Thermo Fisher Scientific) with an EASY-spray nano ESI source coupled to an Ultimate 3000 HPLC system (Dionex). Peptides were captured on a 5 mm × 300 µm C18 Pepmap100 column (3 µm particle size) and eluted with solvent B (0.1% FA/80% ACN) at 300 nL/min using a 92-min gradient. The mass spectrometer was operated in data-dependent acquisition (DDA) mode with an FT survey scan of 400–1600 m/z (70,000 resolution, AGC target 1E6). The 10 most intense peaks were fragmented using high collision dissociation (HCD) at 28% normalized collision energy, with a resolution of 17,500, an AGC target of 5E4, 100 ms injection time and a dynamic exclusion window of 35 seconds. 4.4 Bioinformatics The raw mass spectrometry data was analyzed using three peptide identification platforms: PEAKS Studio XPro, Proteome Discoverer (version 3.1) and MaxQuant (version 2.2.0.0), applying a specific confidence threshold for peptide identification. Contaminants and reverse sequences were excluded, and unique peptides were assigned to the leading Razor protein sequences. The protein sequences were then aligned using BLAST analysis. Data organization, graphing, and descriptive statistics were performed in MS Excel, while Venn diagrams were generated using JVenn ( https://jvenn.toulouse.inrae.fr/app/example.html ). 3. RESULTS 3.1 Proteins Identified Proteome Discoverer detected 621 unique protein accessions across the venom samples from the three taxa. Specifically, the MB sample contained 310 distinct proteins (Annex 1), VK contained 346 (Annex 2), and VA contained 248 (Annex 3). MB shared 131 unique proteins with VK and 102 with VA, while VA shared 215 proteins with VK. A total of 66 unique proteins were detected across all three venom samples (Fig. 1). MaxQuant identified 577 unique protein accessions across the three taxa. The MB sample contained 243 unique proteins (Annex 4), VK contained 297 (Annex 5), and VA contained 275 (Annex 6). MB shared 105 unique proteins with VK and 98 with VA, while VA shared 104 with VK. There were 69 unique proteins consistently present across all three venom samples (Fig. 2). PEAKS software identified 19 unique protein accessions among the three taxa. MB contained 19 unique proteins (Annex 7), VK contained 19 (Annex 8), and VA contained 11 (Annex 9). MB shared 8 unique proteins with VK and none with VA, while VA and VK had no shared proteins. A total of 11 unique proteins were common to all three samples (Fig. 3). In comparing the protein identification capabilities across the three software platforms, Proteome Discoverer and MaxQuant had 139 overlapping protein matches, while PEAKS shared only 6 proteins with the other two platforms. Across all three software platforms, 5 proteins were commonly identified (Fig. 4). Overall, Proteome Discoverer yielded the greatest diversity of proteins and peptides, followed by MaxQuant, with PEAKS exhibiting the lowest performance in terms of unique protein identification. 3.2 Venom Composition Proteins were categorized based on their families, with emphasis on major protein classes reported in previous studies: Snake Venom Serine Protease (svSP), L-Amino Acid Oxidase (LAAO), Snake Venom Metalloprotease (svMP), Phospholipase A (PLA), Snake Venom Lectin (SNACLEC), Cysteine-Rich Protein (CRISP), Vascular Endothelial Growth Factor (VEGF), and Neural Growth Factor (NGF). Proteome Discoverer Results In MB venom, 894 peptides were identified and classified, yielding the following distribution: svSP (21%, 35 proteins), LAAO (5%, 8 proteins), svMP (36%, 61 proteins), PLA (15%, 26 proteins), SNACLEC (15%, 25 proteins), CRISP (6%, 10 proteins), VEGF (2%, 3 proteins), and NGF (1%, 1 protein) (Fig. 5A). These families represent 55% of the identified proteins (169 of 310). For VK venom, 1041 peptides matched to proteins distributed as follows: svSP (22%, 35 proteins), LAAO (7%, 11 proteins), svMP (38%, 61 proteins), PLA (14%, 22 proteins), SNACLEC (8%, 13 proteins), CRISP (7%, 12 proteins), VEGF (2%, 4 proteins), and NGF (2%, 3 proteins) (Fig. 5B), comprising 83% of identified proteins (161 of 193). In VA venom, 830 peptides yielded proteins with these distributions: svSP (27%, 31 proteins), LAAO (9%, 10 proteins), svMP (22%, 25 proteins), PLA (23%, 27 proteins), SNACLEC (8%, 9 proteins), CRISP (8%, 9 proteins), VEGF (3%, 3 proteins), and NGF (2%, 2 proteins) (Fig. 5C), covering 46% of identified proteins (116 of 249). MaxQuant Results In MB venom, 518 peptides yielded proteins as follows: svSP (8%, 19 proteins), LAAO (2%, 6 proteins), svMP (15%, 37 proteins), PLA (5%, 13 proteins), SNACLEC (8%, 20 proteins), CRISP (2%, 6 proteins), VEGF (2%, 4 proteins), and NGF (0%, 1 protein) (Fig. 6A), covering 44% of identified proteins (106 of 243). VK venom yielded 642 peptides, corresponding to proteins in these proportions: svSP (6%, 17 proteins), LAAO (2%, 7 proteins), svMP (11%, 33 proteins), PLA (2%, 7 proteins), SNACLEC (4%, 11 proteins), CRISP (2%, 7 proteins), VEGF (1%, 3 proteins), and NGF (0%, 1 protein) (Fig. 6B), representing 29% of identified proteins (86 of 297). For VA venom, 647 peptides matched proteins with the following distribution: svSP (8%, 21 proteins), LAAO (3%, 7 proteins), svMP (9%, 26 proteins), PLA (5%, 15 proteins), SNACLEC (3%, 8 proteins), CRISP (3%, 8 proteins), VEGF (1%, 2 proteins), and NGF (0%, 1 protein) (Fig. 6C), encompassing 32% of identified proteins (88 of 275). PEAKS Results In MB venom, from 55 peptides, proteins were categorized as follows: svSP (19%, 3 proteins), svMP (28%, 6 proteins), SNACLEC (25%, 4 proteins), and CRISP (13%, 2 proteins), representing 94% of identified proteins (15 of 16) (Fig. 7A). VK venom yielded 63 peptides, with proteins distributed as follows: svSP (17%, 3 proteins), svMP (33%, 6 proteins), SNACLEC (28%, 5 proteins), and CRISP (11%, 2 proteins), covering 89% of identified proteins (16 of 18) (Fig. 7B). In VA venom, from 35 peptides, proteins were distributed as svSP (30%, 3 proteins), svMP (20%, 2 proteins), SNACLEC (10%, 1 protein), and CRISP (20%, 2 proteins), covering 80% of identified proteins (8 of 10) (Fig. 7C). 4. DISCUSSION 4.1 Software performance and protein identification The three software platforms, Proteome Discoverer, MaxQuant and PEAKS, showed significant differences in their ability to identify unique proteins in the venom samples of the three viper taxa. Proteome Discoverer identified the most unique proteins, totaling 621, followed by MaxQuant with 577 and PEAKS with only 19. This discrepancy underscores the remarkable differences in each software’s ability to process and interpret identical mass spectrometry data. The consistently lower number of proteins identified by PEAKS across all samples suggests possible limitations in the detection of proteins in complex venom compositions. In terms of protein overlap between species, Proteome Discoverer and MaxQuant showed considerable agreement. In the Proteome Discoverer dataset, MB shared 131 proteins with VK and 102 with VA, while MaxQuant indicated that MB shared 105 proteins with VK and 98 with VA. Conversely, PEAKS showed minimal overlap, with MB sharing only 8 proteins with VK and none with VA. In addition, Proteome Discoverer detected 66 proteins common to all three species, MaxQuant identified 69, while PEAKS identified only 11. These results demonstrate the superior performance of Proteome Discoverer and MaxQuant in capturing comprehensive venom profiles compared to PEAKS. When comparing overall performance, Proteome Discoverer and MaxQuant shared 139 common protein identifications, while PEAKS had only 6 proteins in common with the other two. Only 5 proteins were consistently identified across all three platforms, highlighting the variability and potential limitations in identifying proteins across different software tools. This inconsistency is particularly notable in PEAKS, which not only identified significantly fewer proteins, but also had minimal overlap with the other platforms. Overall, Proteome Discoverer showed the highest efficiency, both in terms of unambiguous protein identification and matching with other datasets, followed by MaxQuant. PEAKS, on the other hand, identified significantly fewer proteins, indicating possible algorithmic limitations or lower sensitivity in its settings. These discrepancies between the software tools emphasize the need for cross-validation in proteomic studies, as reliance on a single platform may lead to incomplete or biased interpretations of the venom's composition. 4.2 Venom Composition Comparison When examining the composition of the venoms by protein family, remarkable differences were found between the taxa depending on the software platform used. Comparisons with previous venomic studies on the same species (Table 1) reveal considerable discrepancies. Proteomic data relating specifically to the venoms of the three taxa studied are limited, highlighting a major gap in the scientific study of this area. Our literature review revealed only two studies on Montivipera bulgardaghica [ 39 , 40 ], two on Vipera kaznakovi [ 41 , 42 ] and three on Vipera ammodytes [ 43 – 45 ]. This lack of studies highlights the need for more comprehensive research efforts to refine the understanding of venom proteomics in these species. Each software tool has specific biases that affect the type of proteins identified, with Proteome Discoverer and MaxQuant having differences in protein abundance and type, although they are generally more reliable. For example, Proteome Discoverer identified 18% of snake venom metalloprotease (svMP) proteins in Vipera kaznakovi , compared to only 11% for MaxQuant. In addition, our results showed a lower proportion of phospholipase A (PLA) proteins than reported in previous studies. These discrepancies could be due to differences in algorithmic design, search parameters and database dependencies of each software. While within-species variability could explain the differences between our samples and those in other studies, this does not explain the inconsistencies observed between software platforms in our study. These differences between software platforms have direct implications for the biological interpretation of venom profiles. For example, the underrepresentation of certain protein families by one software may lead to inaccurate conclusions about the functional properties of the venom, such as proteolytic activity or specific toxicities (e.g. neurotoxicity, hematotoxicity or hemorrhagic effects). This problem is particularly important for applications that require precise characterization of toxins, such as the production of antivenoms or the identification of bioactive peptides for therapeutic purposes. Consequently, the choice of software can strongly influence conclusions in proteomic studies, emphasizing the need to use multiple platforms for cross-validation to obtain reliable results. However, even cross-validation across multiple software tools cannot guarantee accuracy, as demonstrated by the variability observed in this study. To address this issue, it is imperative to standardize bioinformatics methods for venom proteomics to achieve consistency of results across different software programs. Until this uniformity is achieved, there will remain an inherent uncertainty in confirming the exact composition of venom proteomes. 4.3 Software comparison in other studies Among the few studies comparing peptide analysis software, a consensus on performance remains elusive. In a comparison of Proteome Discoverer (PD) and MaxQuant (MQ), Zhao et al. (2020) [ 46 ] concluded that PD is more effective in quantifying low-abundance proteins with reasonable accuracy. In contrast, Peng et al. (2023) [ 47 ] suggested that MQ is better for identifying low-abundance proteins, while PD provides a larger total number of identifications. In both studies, these label-free quantification tools were used to analyze HeLa cell proteins. In the studies by Parker et al. (2021) [ 48 ] and Zhang & Sun (2018), PEAKS was found to be superior in peptide identification and less reliant on specific databases or peptide libraries for validation. Both studies focused on immunopeptidome data, especially HLA class I and II (human leukocyte antigen) peptides. Comparing these results with ours, PEAKS shows a contrasting performance. While PEAKS emerged as the leading software in the above studies, it performed poorly in this study. This difference may be attributed to the different nature of the samples analyzed, as immunopeptidomes differ significantly from the complex mixture of proteins and peptides in snake venoms. However, this explanation does not explain the variability between Proteome Discoverer and MaxQuant observed in other studies. Discrepancies between studies comparing PD and MQ may be due to different software settings which affect the algorithmic processes and lead to different results, even when analyzing the same type of sample (in this case HeLa cell proteins). In addition, differences in cell strains and culture protocols may contribute to these variations. Nevertheless, these factors alone do not appear to be sufficient to explain why PD performed better than MQ in detecting low-abundance proteins in one study, while MQ showed the same strength in another study. Of note, none of the aforementioned studies evaluated the performance of the software in the context of snake venom proteomics, which may explain some of the discrepancies observed here, particularly for PEAKS. In addition, only one other venom proteomics study cited in Table 1 used any of the three software platforms in our study. Kovalchuk et al. (2016)[ 41 ] used MaxQuant to profile venom proteins of Vipera kaznakovi, and their results differed markedly from ours. Their analysis showed a predominance of phospholipase A (41%), whereas in our study it accounted for only 2%. This difference probably reflects the differences in the processing protocols of the crude venom samples before, during and after trypsin digestion. 5. Conclusion and future prospects Overall, the three software tools used in this study failed to produce consistent proteomic profiles for the same venom samples. Although each software identified the characteristic composition of viperid venom, which is dominated by metalloproteases and phospholipases A, the relative proportions of these proteins varied considerably between the platforms. Furthermore, these results were not fully consistent with previously published venom profiles for these species, which is a major limitation in the reliability of label-free proteomic analysis. To obtain reliable results, different software programs should analyze the same sample and provide comparable results — however, this was clearly not the case in our study. This inconsistency poses a critical challenge for applications that require precise proteomic characterization, such as antivenom development and therapeutic research. The observed variability between software tools complicates efforts to accurately interpret both intra- and inter-specific venom variation. Consequently, studies of snake venom that rely solely on one software platform may be inaccurate and should include cross-validation. Future research should focus on refining peptide identification algorithms to improve consistency between different software platforms when analyzing identical samples. To achieve this goal, it is important to understand the underlying algorithmic differences and evaluate software performance on a range of sample types to ultimately facilitate the development of optimized bioinformatic protocols for venom analysis. Based on our results, we recommend Proteome Discoverer for proteomic profiling of snake venom, with cross-validation by MaxQuant. In addition, further research is warranted to investigate why PEAKS performed suboptimally compared to the other software tools, as lessons learned could lead to improvements in its functionality and reliability. Declarations Author Contributions: Conceptualization: R.V. and A. N.; writing—original draft preparation: B.M., and R.V.; formal analysis and investigation: R.V. and B.M.; writing—review and editing: B.M, M.K. B.G. A. N and R.V. All authors have read and agreed to the published version of the manuscript. Funding: This work was supported within the scope of national funds from FCT—Portuguese Foundation for Science and Technology, under iBiMED (UIDB/04501/2020 and PO-CI-01-0145-FEDER-007628) and the Cardiovascular R&D Center—UnIC (UIDB/00051/2020 and UIDP/00051/2020 (RV), COST Action CA19144 European Venom Network (EUVEN, https://euven-network.eu/. Conflicts of Interest: The authors declare no conflicts of interest. References J. White, D. Warrell, M. Eddleston, B.J. 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Zhang, A.R. Wheeler, Comparison of Database Searching Programs for the Analysis of Single-Cell Proteomics Data, Journal of proteome research, 22 (2023) 1298-1308. R. Parker, A. Tailor, X. Peng, A. Nicastri, J. Zerweck, U. Reimer, H. Wenschuh, K. Schnatbaum, N. Ternette, The Choice of Search Engine Affects Sequencing Depth and HLA Class I Allele-Specific Peptide Repertoires, Molecular & cellular proteomics : MCP, 20 (2021) 100124. Additional Declarations No competing interests reported. Supplementary Files GraphicalAbstract.png Annex1ProteinsidentifiedbyProteomeDiscoveryinthevenomfromMontiviperabulgardaghica.pdf Annex2ProteinsidentifiedbyProteomeDiscoveryinthevenomfromViperakaznakovi.pdf Annex3ProteinsidentifiedbyProteomeDiscoveryinthevenomfromViperaammodytesmontandoni.pdf Annex4ProteinsidentifiedbyMaxQuantinthevenomfromMontiviperabulgardaghica.pdf Annex5ProteinsidentifiedbyMaxQuantinthevenomfromViperakaznakovi.pdf Annex6ProteinsidentifiedbyMaxQuantinthevenomfromViperaammodytesmontandoni.pdf Annex7ProteinsidentifiedbyPEAKSinthevenomfromMontiviperabulgardaghica.pdf Annex8ProteinsidentifiedbyPEAKSinthevenomfromViperakaznakovi.pdf Annex9ProteinsidentifiedbyPEAKSinthevenomfromViperaammodytesmontandoni.pdf Cite Share Download PDF Status: Posted Version 1 posted 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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18:38:43","extension":"pdf","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":166263,"visible":true,"origin":"","legend":"","description":"","filename":"Annex9ProteinsidentifiedbyPEAKSinthevenomfromViperaammodytesmontandoni.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5404907/v1/265e7b88dd168f524bf6d734.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comparative Proteomic Analysis of Venom from Three Viper Taxa: Evaluating Software-Specific Protein and Peptide Profiles","fulltext":[{"header":"MAIN CONCLUSIONS","content":"\u003cul\u003e\n \u003cli\u003eOur proteomic analysis closely matches the characteristic profile observed in viper venom.\u003c/li\u003e\n \u003cli\u003eThe relative abundance of venom protein families differs from results reported in previous studies.\u003c/li\u003e\n \u003cli\u003eDiscrepancies were noted between software platforms as the same samples gave different results depending on the analytical tool used.\u003c/li\u003e\n \u003cli\u003eThe PEAKS software performed suboptimally for all three samples analyzed.\u003c/li\u003e\n \u003cli\u003eProteome Discoverer achieved the highest number of peptide and protein identifications, followed by MaxQuant in terms of efficiency.\u003c/li\u003e\n \u003cli\u003eEstablishing a standardized bioinformatics framework for venom research is essential to ensure consistency and accuracy in proteomic analyzes\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"1. INTRODUCTION","content":"\u003cp\u003eToxins of animal and plant origin represent a naturally occurring source of unique proteins and diverse biomolecules, many of which remain largely unexplored and uncategorized by scientific research [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. These bioactive compounds have evolved as specialized mechanisms for defense and predation and represent a remarkable evolutionary adaptation [\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Among other biotoxins, the study of snake venoms has been somewhat neglected, mainly due to the negative perception these animals enjoy among humans (either due to superstition or human-animal conflict), but also because of the low yield of venom samples, their elusive nature and limited funding [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The most extensively characterized venoms are from emblematic species that pose a significant and regular risk of poisoning to local populations, which has led research efforts to focus largely on the development of antidotes. Although critical in countries with high rates of envenomation, the complex diversity of unique bioactive peptides and proteins that these samples contain may be important for pharmacological and biomedical research and even critical for potential new medical breakthroughs and the development of life-saving therapies.\u003c/p\u003e \u003cp\u003eThe proteomic profile differs in each snake species. Each has a unique combination of peptides, enzymes and other molecules that becomes more apparent the further apart two species are phylogenetically. This becomes even clearer when comparing the ophidian families: the venoms of elapid snakes are predominantly neurotoxic, while the venoms of vipers and venomous colubrids have a more hemorrhagic and hemotoxic composition [\u003cspan additionalcitationids=\"CR7 CR8 CR9\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. It has also been described that the intraspecific composition of toxins exhibits a high degree of evolvability, which is influenced by age, prey availability and prey co-adaptation to the toxins. Considering all these different combinations of protein diversity within and between species, snake venom is an immensely rich source of bioactive proteins and peptides waiting to be discovered, characterized and used in biomedical research.\u003c/p\u003e \u003cp\u003eDe Lima et al. (2005) describe that compounds extracted from snake venom are used in biomedical research in three ways: as a direct therapeutic agent [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] as a means of diagnosing various medical conditions [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] as a means of studying the basic mechanisms of metabolic and disease processes [\u003cspan additionalcitationids=\"CR16 CR17\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] also summarize several biotoxins with approved drugs and therapies for human use, including some isolated from snake venoms, such as batroxobin - purified directly from the venom of Bothrops atrox, cleaves the Aα chain of fibrinogen and is used to treat acute cerebral infarction and angina pectoris (Defibrase\u0026reg; (Pentapharm DSM Nutritional Products Ltd, 2024), for blood gelling (Plateltex-Act\u0026reg; (Plateltex, 2024)) or as a fibrin sealant in surgery (Vivostat\u0026reg;Fibrin (Vivostat, 2024)); Captopril \u0026ndash; a synthetic compound derived from the venom of Bothrops jararaca, acts as an angiotensin-converting enzyme inhibitor and is used to treat high blood pressure and heart failure (Capoten\u0026reg; (Bristol-Myers Squibb Pharmaceutical S.A., 2024), among other examples. The author also describes other protein candidates that are in clinical trials and testing.\u003c/p\u003e \u003cp\u003eBefore the commercialization phase, it is essential to identify, isolate and purify the target proteins - a process that is particularly challenging due to the high complexity of snake venoms. Therefore, research methods must utilize state-of-the-art multidisciplinary techniques at the \u0026ldquo;omics\u0026rdquo; level, including genomics, transcriptomics, peptidomics, proteomics and metabolomics. These techniques are converging in the field of toxinology, now referred to as \u0026ldquo;venomics\",\u0026rdquo; which aims at a comprehensive characterization of the entire toxin profile and bioactive compounds in venomous animals [\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Among the most common protocols for analyzing venoms, shotgun or bottom-up proteomics is the most widely used. In this method, samples are first run through an SDS-PAGE gel to separate proteins by molecular weight and facilitate visualization of targets, followed by enzymatic digestion (proteolysis) into smaller peptides, usually using trypsin. The peptides are then fractionated and processed by LC-MS/MS (Liquid Chromatography Tandem Mass Spectrometry), which provides detailed information about their sequence and post-translational modifications. The output data is then compared with peptide and protein databases and used to reconstruct the original protein sequences, effectively identifying the venom components [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. By combining the data thus obtained with genomic and transcriptomic elements, information on the genetic basis of the composition of venoms and their drivers of evolutionary adaptations can be generated and used to predict protein patterns in snake clades, facilitating the search for new bioactive and pharmaceutically relevant compounds [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMore recently, efforts have been made to understand how higher structural levels of proteins can determine their bioactivity. Insights into the tertiary and quaternary structure of proteins are lost in traditional bottom-up proteomics, which primarily focuses on analyzing the peptide level [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Due to their complex heterogeneity, venom proteins may form covalent and non-covalent complexes both within and between toxin families, which may be important for a better understanding of the influence of protein structure on function and interactions with other biomolecules. These insights may in turn be crucial to understand the bioactivity of toxins in depth beyond the general protein composition [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. These interactions are still largely unexplored but may be key to developing structure-based therapeutics, such as the identification of epitopes in antigenic regions of proteins that enable immune recognition [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The emergence of novel structural analysis techniques such as cryo-electron microscopy (cryo-EM) and X-ray crystallography has shown great promise to generate complementary data to tandem mass spectrometry. Although X-ray crystallography is the classical method for determining protein structures, it has some limitations. While it offers atomic-level resolution and the ability to process very small proteins and complexes (\u0026lt;\u0026thinsp;60 kDa) [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], the crystallization process is tedious and often requires the use of small detergents that can denature molecules, compromising their native lipidomic aspects and potentially inactivating them. Cryo-EM, on the other hand, allows the determination of the three-dimensional structure of larger proteins or complexes, both in the inactivated and activated state, with a much shorter sample preparation time [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. This technique preserves the native state of the proteins by rapidly freezing the samples in glassy ice, avoiding crystallization and preserving functional integrity[\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] (Bai et al., 2015; Dubochet et al., 1981, 1988). The result is near-atomic resolution and exceptional image quality.\u003c/p\u003e \u003cp\u003eThe combination of structural biology (namely cryo-EM), proteomics and advanced data analysis will revolutionize the field of venomics and bring significant advances in both basic biochemical knowledge and clinical applications such as drug development. To cope with the large amounts of data generated by these methods, peptide identification software must be reliable, accurate and constantly evolving [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e](Christin et al., 2011; Xu \u0026amp; Ma, 2006). There are several peptide identification software, and each has unique algorithms and databases that affect their performance and accuracy, which may make them more or less suitable for certain types of biospecimens. To maximize their applicability and performance in future studies, their advantages and disadvantages need to be monitored and compared for different sample types so that the results generated are validated and reliable for medical research. Three of the most commonly used software programs are PEAKS, MaxQuant and Proteome Discoverer. PEAKS uses de novo sequencing and matches queries to predefined spectral libraries and databases using DIA data searches and an intuitive interface for post-search data visualization and processing [\u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]; MaxQuant uses the integrated engine \u0026ldquo;Andromeda\u0026rdquo; for peptide searching and includes models for quantification, statistical analysis (e.g. the Perseus framework) and post-processing data visualization. It also enables differential expression analysis and post-translational profiling for large-scale proteome mapping [\u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Proteome Discovery uses multiple proteomic and peptidomic workflows for molecule identification, post-translational modifications, isobaric massing, statistical tools and other functions and provides a larger number of identified peptides in the output. It includes algorithms such as INFERYS for rescoring and CHIMERYS for intelligent searching [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eWith this in mind, let us process the venoms of three viper taxa: \u003cem\u003eMontivipera bulgardaghica subsp. bulgardaghica\u003c/em\u003e (MB), \u003cem\u003eVipera ammodytes subsp. montandoni\u003c/em\u003e (VA) and \u003cem\u003eVipera kaznakovi\u003c/em\u003e (VK) and to determine their proteomic profile using three different peptide identification software (PEAKS, MaxQuant and Proteome Discoverer). By comparing the results of these three viper taxa, we hope to gain insight into the selection of the most appropriate tools for venom samples and catalogue the proteomic profile for these three viper taxa.\u003c/p\u003e"},{"header":"2. MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e2\u003c/b\u003e.1 Sample collection\u003c/h2\u003e \u003cp\u003eThe venom samples were collected in spring and summer 2016 in Turkish Thrace (VA), Mersin Province (MB) and Artvin Province (UK). The VA and MB samples were collected from two individuals each, while the UK sample was collected from nine individuals. Venom extraction was performed at the capture sites following a standardized protocol in which the crude venom was collected using a laboratory cup covered with parafilm without applying pressure to the venom glands. The samples were centrifuged at 2000 \u0026times; g for 10 minutes at 4\u0026deg;C to remove cell debris. The supernatant was frozen in liquid nitrogen on site, transported to the laboratory, freeze-dried and stored at 4\u0026deg;C until further analysis. After venom extraction, all individuals were returned to their capture site. The freeze-dried venom samples were then sent to the Institute of Biomedicine (iBiMED) of the College of Aveiro, Portugal, for processing. Sampling was performed with ethical approval (Ege College, Animal Experiments Ethics Committee, 2013#50) and with a field study permit (2015#183897) from the Ministry of Forestry and Water Affairs of the Republic of Turkey.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Quantification of proteins\u003c/h2\u003e \u003cp\u003eProtein concentrations were quantified using a DC Protein Assay Kit (BioRad, RC DCTM Protein Assay, #5000122). A calibration curve was generated using bovine serum albumin (BSA) solutions, consistently achieving an R\u0026sup2; \u0026gt; 0.95. Absorbance values were measured using a TECAN Nanoquant Infinite M200 Pro microplate reader at 750 nm on a transparent plate. The protocol includes 10 seconds of shaking and a 1-second data acquisition per well.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 LC-MS/MS analysis\u003c/h2\u003e \u003cp\u003eProteins of interest were excised from the SDS-PAGE bands and subjected to a series of washes with ammonium bicarbonate (25 mM) and acetonitrile (ACN, VWR Chemicals). Reduction was performed with dithiothreitol (DTT, 10 mM, 30 minutes, 60\u0026deg;C), followed by alkylation with iodoacetamide (IAA, 55 mM, 30 minutes, 25\u0026deg;C) in the dark. The gel pieces were then dried in vacuo (SpeedVac, Thermo Savant) and digested with modified trypsin (Thermo Scientific\u0026trade; Pierce\u0026trade; Trypsin Protease, MS Grade, #90057) in 50 mM NH₄HCO₃ at a 1:25 enzyme to protein ratio. After 30 minutes on ice, 50 \u0026micro;L of 50 mM NH₄HCO₃ was added and the samples were incubated overnight at 37\u0026deg;C. The tryptic peptides were extracted by successively adding 10% formic acid (FA), 10% FA/ACN (1:1) and 90% ACN. They were then freeze-dried (SpeedVac, Thermo Savant) and resuspended in 1% FA for HPLC injection. Peptide separation was performed using an Orbitrap Q Exactive mass spectrometer (Thermo Fisher Scientific) with an EASY-spray nano ESI source coupled to an Ultimate 3000 HPLC system (Dionex). Peptides were captured on a 5 mm \u0026times; 300 \u0026micro;m C18 Pepmap100 column (3 \u0026micro;m particle size) and eluted with solvent B (0.1% FA/80% ACN) at 300 nL/min using a 92-min gradient. The mass spectrometer was operated in data-dependent acquisition (DDA) mode with an FT survey scan of 400\u0026ndash;1600 m/z (70,000 resolution, AGC target 1E6). The 10 most intense peaks were fragmented using high collision dissociation (HCD) at 28% normalized collision energy, with a resolution of 17,500, an AGC target of 5E4, 100 ms injection time and a dynamic exclusion window of 35 seconds.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Bioinformatics\u003c/h2\u003e \u003cp\u003eThe raw mass spectrometry data was analyzed using three peptide identification platforms: PEAKS Studio XPro, Proteome Discoverer (version 3.1) and MaxQuant (version 2.2.0.0), applying a specific confidence threshold for peptide identification. Contaminants and reverse sequences were excluded, and unique peptides were assigned to the leading Razor protein sequences. The protein sequences were then aligned using BLAST analysis. Data organization, graphing, and descriptive statistics were performed in MS Excel, while Venn diagrams were generated using JVenn (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://jvenn.toulouse.inrae.fr/app/example.html\u003c/span\u003e\u003cspan address=\"https://jvenn.toulouse.inrae.fr/app/example.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. RESULTS","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Proteins Identified\u003c/h2\u003e \u003cp\u003eProteome Discoverer detected 621 unique protein accessions across the venom samples from the three taxa. Specifically, the MB sample contained 310 distinct proteins (Annex 1), VK contained 346 (Annex 2), and VA contained 248 (Annex 3). MB shared 131 unique proteins with VK and 102 with VA, while VA shared 215 proteins with VK. A total of 66 unique proteins were detected across all three venom samples (Fig.\u0026nbsp;1).\u003c/p\u003e \u003cp\u003eMaxQuant identified 577 unique protein accessions across the three taxa. The MB sample contained 243 unique proteins (Annex 4), VK contained 297 (Annex 5), and VA contained 275 (Annex 6). MB shared 105 unique proteins with VK and 98 with VA, while VA shared 104 with VK. There were 69 unique proteins consistently present across all three venom samples (Fig.\u0026nbsp;2).\u003c/p\u003e \u003cp\u003ePEAKS software identified 19 unique protein accessions among the three taxa. MB contained 19 unique proteins (Annex 7), VK contained 19 (Annex 8), and VA contained 11 (Annex 9). MB shared 8 unique proteins with VK and none with VA, while VA and VK had no shared proteins. A total of 11 unique proteins were common to all three samples (Fig.\u0026nbsp;3).\u003c/p\u003e \u003cp\u003eIn comparing the protein identification capabilities across the three software platforms, Proteome Discoverer and MaxQuant had 139 overlapping protein matches, while PEAKS shared only 6 proteins with the other two platforms. Across all three software platforms, 5 proteins were commonly identified (Fig.\u0026nbsp;4). Overall, Proteome Discoverer yielded the greatest diversity of proteins and peptides, followed by MaxQuant, with PEAKS exhibiting the lowest performance in terms of unique protein identification.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Venom Composition\u003c/h2\u003e \u003cp\u003eProteins were categorized based on their families, with emphasis on major protein classes reported in previous studies: Snake Venom Serine Protease (svSP), L-Amino Acid Oxidase (LAAO), Snake Venom Metalloprotease (svMP), Phospholipase A (PLA), Snake Venom Lectin (SNACLEC), Cysteine-Rich Protein (CRISP), Vascular Endothelial Growth Factor (VEGF), and Neural Growth Factor (NGF).\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eProteome Discoverer Results\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eIn MB venom, 894 peptides were identified and classified, yielding the following distribution: svSP (21%, 35 proteins), LAAO (5%, 8 proteins), svMP (36%, 61 proteins), PLA (15%, 26 proteins), SNACLEC (15%, 25 proteins), CRISP (6%, 10 proteins), VEGF (2%, 3 proteins), and NGF (1%, 1 protein) (Fig.\u0026nbsp;5A). These families represent 55% of the identified proteins (169 of 310).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eFor VK venom, 1041 peptides matched to proteins distributed as follows: svSP (22%, 35 proteins), LAAO (7%, 11 proteins), svMP (38%, 61 proteins), PLA (14%, 22 proteins), SNACLEC (8%, 13 proteins), CRISP (7%, 12 proteins), VEGF (2%, 4 proteins), and NGF (2%, 3 proteins) (Fig.\u0026nbsp;5B), comprising 83% of identified proteins (161 of 193).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eIn VA venom, 830 peptides yielded proteins with these distributions: svSP (27%, 31 proteins), LAAO (9%, 10 proteins), svMP (22%, 25 proteins), PLA (23%, 27 proteins), SNACLEC (8%, 9 proteins), CRISP (8%, 9 proteins), VEGF (3%, 3 proteins), and NGF (2%, 2 proteins) (Fig.\u0026nbsp;5C), covering 46% of identified proteins (116 of 249).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eMaxQuant Results\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eIn MB venom, 518 peptides yielded proteins as follows: svSP (8%, 19 proteins), LAAO (2%, 6 proteins), svMP (15%, 37 proteins), PLA (5%, 13 proteins), SNACLEC (8%, 20 proteins), CRISP (2%, 6 proteins), VEGF (2%, 4 proteins), and NGF (0%, 1 protein) (Fig.\u0026nbsp;6A), covering 44% of identified proteins (106 of 243).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eVK venom yielded 642 peptides, corresponding to proteins in these proportions: svSP (6%, 17 proteins), LAAO (2%, 7 proteins), svMP (11%, 33 proteins), PLA (2%, 7 proteins), SNACLEC (4%, 11 proteins), CRISP (2%, 7 proteins), VEGF (1%, 3 proteins), and NGF (0%, 1 protein) (Fig.\u0026nbsp;6B), representing 29% of identified proteins (86 of 297).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eFor VA venom, 647 peptides matched proteins with the following distribution: svSP (8%, 21 proteins), LAAO (3%, 7 proteins), svMP (9%, 26 proteins), PLA (5%, 15 proteins), SNACLEC (3%, 8 proteins), CRISP (3%, 8 proteins), VEGF (1%, 2 proteins), and NGF (0%, 1 protein) (Fig.\u0026nbsp;6C), encompassing 32% of identified proteins (88 of 275).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003ePEAKS Results\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eIn MB venom, from 55 peptides, proteins were categorized as follows: svSP (19%, 3 proteins), svMP (28%, 6 proteins), SNACLEC (25%, 4 proteins), and CRISP (13%, 2 proteins), representing 94% of identified proteins (15 of 16) (Fig.\u0026nbsp;7A).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eVK venom yielded 63 peptides, with proteins distributed as follows: svSP (17%, 3 proteins), svMP (33%, 6 proteins), SNACLEC (28%, 5 proteins), and CRISP (11%, 2 proteins), covering 89% of identified proteins (16 of 18) (Fig.\u0026nbsp;7B).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eIn VA venom, from 35 peptides, proteins were distributed as svSP (30%, 3 proteins), svMP (20%, 2 proteins), SNACLEC (10%, 1 protein), and CRISP (20%, 2 proteins), covering 80% of identified proteins (8 of 10) (Fig.\u0026nbsp;7C).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. DISCUSSION","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Software performance and protein identification\u003c/h2\u003e \u003cp\u003eThe three software platforms, Proteome Discoverer, MaxQuant and PEAKS, showed significant differences in their ability to identify unique proteins in the venom samples of the three viper taxa. Proteome Discoverer identified the most unique proteins, totaling 621, followed by MaxQuant with 577 and PEAKS with only 19. This discrepancy underscores the remarkable differences in each software\u0026rsquo;s ability to process and interpret identical mass spectrometry data. The consistently lower number of proteins identified by PEAKS across all samples suggests possible limitations in the detection of proteins in complex venom compositions.\u003c/p\u003e \u003cp\u003eIn terms of protein overlap between species, Proteome Discoverer and MaxQuant showed considerable agreement. In the Proteome Discoverer dataset, MB shared 131 proteins with VK and 102 with VA, while MaxQuant indicated that MB shared 105 proteins with VK and 98 with VA. Conversely, PEAKS showed minimal overlap, with MB sharing only 8 proteins with VK and none with VA. In addition, Proteome Discoverer detected 66 proteins common to all three species, MaxQuant identified 69, while PEAKS identified only 11. These results demonstrate the superior performance of Proteome Discoverer and MaxQuant in capturing comprehensive venom profiles compared to PEAKS.\u003c/p\u003e \u003cp\u003eWhen comparing overall performance, Proteome Discoverer and MaxQuant shared 139 common protein identifications, while PEAKS had only 6 proteins in common with the other two. Only 5 proteins were consistently identified across all three platforms, highlighting the variability and potential limitations in identifying proteins across different software tools. This inconsistency is particularly notable in PEAKS, which not only identified significantly fewer proteins, but also had minimal overlap with the other platforms.\u003c/p\u003e \u003cp\u003eOverall, Proteome Discoverer showed the highest efficiency, both in terms of unambiguous protein identification and matching with other datasets, followed by MaxQuant. PEAKS, on the other hand, identified significantly fewer proteins, indicating possible algorithmic limitations or lower sensitivity in its settings. These discrepancies between the software tools emphasize the need for cross-validation in proteomic studies, as reliance on a single platform may lead to incomplete or biased interpretations of the venom's composition.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Venom Composition Comparison\u003c/h2\u003e \u003cp\u003eWhen examining the composition of the venoms by protein family, remarkable differences were found between the taxa depending on the software platform used. Comparisons with previous venomic studies on the same species (Table\u0026nbsp;1) reveal considerable discrepancies. Proteomic data relating specifically to the venoms of the three taxa studied are limited, highlighting a major gap in the scientific study of this area.\u003c/p\u003e \u003cp\u003eOur literature review revealed only two studies on Montivipera bulgardaghica [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], two on Vipera kaznakovi [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] and three on Vipera ammodytes [\u003cspan additionalcitationids=\"CR44\" citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. This lack of studies highlights the need for more comprehensive research efforts to refine the understanding of venom proteomics in these species.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eEach software tool has specific biases that affect the type of proteins identified, with Proteome Discoverer and MaxQuant having differences in protein abundance and type, although they are generally more reliable. For example, Proteome Discoverer identified 18% of snake venom metalloprotease (svMP) proteins in \u003cem\u003eVipera kaznakovi\u003c/em\u003e, compared to only 11% for MaxQuant. In addition, our results showed a lower proportion of phospholipase A (PLA) proteins than reported in previous studies. These discrepancies could be due to differences in algorithmic design, search parameters and database dependencies of each software.\u003c/p\u003e \u003cp\u003eWhile within-species variability could explain the differences between our samples and those in other studies, this does not explain the inconsistencies observed between software platforms in our study. These differences between software platforms have direct implications for the biological interpretation of venom profiles. For example, the underrepresentation of certain protein families by one software may lead to inaccurate conclusions about the functional properties of the venom, such as proteolytic activity or specific toxicities (e.g. neurotoxicity, hematotoxicity or hemorrhagic effects). This problem is particularly important for applications that require precise characterization of toxins, such as the production of antivenoms or the identification of bioactive peptides for therapeutic purposes. Consequently, the choice of software can strongly influence conclusions in proteomic studies, emphasizing the need to use multiple platforms for cross-validation to obtain reliable results.\u003c/p\u003e \u003cp\u003eHowever, even cross-validation across multiple software tools cannot guarantee accuracy, as demonstrated by the variability observed in this study. To address this issue, it is imperative to standardize bioinformatics methods for venom proteomics to achieve consistency of results across different software programs. Until this uniformity is achieved, there will remain an inherent uncertainty in confirming the exact composition of venom proteomes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Software comparison in other studies\u003c/h2\u003e \u003cp\u003eAmong the few studies comparing peptide analysis software, a consensus on performance remains elusive. In a comparison of Proteome Discoverer (PD) and MaxQuant (MQ), Zhao et al. (2020) [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e] concluded that PD is more effective in quantifying low-abundance proteins with reasonable accuracy. In contrast, Peng et al. (2023) [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e] suggested that MQ is better for identifying low-abundance proteins, while PD provides a larger total number of identifications. In both studies, these label-free quantification tools were used to analyze HeLa cell proteins.\u003c/p\u003e \u003cp\u003eIn the studies by Parker et al. (2021) [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e] and Zhang \u0026amp; Sun (2018), PEAKS was found to be superior in peptide identification and less reliant on specific databases or peptide libraries for validation. Both studies focused on immunopeptidome data, especially HLA class I and II (human leukocyte antigen) peptides.\u003c/p\u003e \u003cp\u003eComparing these results with ours, PEAKS shows a contrasting performance. While PEAKS emerged as the leading software in the above studies, it performed poorly in this study. This difference may be attributed to the different nature of the samples analyzed, as immunopeptidomes differ significantly from the complex mixture of proteins and peptides in snake venoms. However, this explanation does not explain the variability between Proteome Discoverer and MaxQuant observed in other studies. Discrepancies between studies comparing PD and MQ may be due to different software settings which affect the algorithmic processes and lead to different results, even when analyzing the same type of sample (in this case HeLa cell proteins). In addition, differences in cell strains and culture protocols may contribute to these variations. Nevertheless, these factors alone do not appear to be sufficient to explain why PD performed better than MQ in detecting low-abundance proteins in one study, while MQ showed the same strength in another study.\u003c/p\u003e \u003cp\u003eOf note, none of the aforementioned studies evaluated the performance of the software in the context of snake venom proteomics, which may explain some of the discrepancies observed here, particularly for PEAKS. In addition, only one other venom proteomics study cited in Table\u0026nbsp;1 used any of the three software platforms in our study. Kovalchuk et al. (2016)[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] used MaxQuant to profile venom proteins of Vipera kaznakovi, and their results differed markedly from ours. Their analysis showed a predominance of phospholipase A (41%), whereas in our study it accounted for only 2%. This difference probably reflects the differences in the processing protocols of the crude venom samples before, during and after trypsin digestion.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion and future prospects","content":"\u003cp\u003eOverall, the three software tools used in this study failed to produce consistent proteomic profiles for the same venom samples. Although each software identified the characteristic composition of viperid venom, which is dominated by metalloproteases and phospholipases A, the relative proportions of these proteins varied considerably between the platforms. Furthermore, these results were not fully consistent with previously published venom profiles for these species, which is a major limitation in the reliability of label-free proteomic analysis. To obtain reliable results, different software programs should analyze the same sample and provide comparable results \u0026mdash; however, this was clearly not the case in our study.\u003c/p\u003e \u003cp\u003eThis inconsistency poses a critical challenge for applications that require precise proteomic characterization, such as antivenom development and therapeutic research. The observed variability between software tools complicates efforts to accurately interpret both intra- and inter-specific venom variation. Consequently, studies of snake venom that rely solely on one software platform may be inaccurate and should include cross-validation. Future research should focus on refining peptide identification algorithms to improve consistency between different software platforms when analyzing identical samples. To achieve this goal, it is important to understand the underlying algorithmic differences and evaluate software performance on a range of sample types to ultimately facilitate the development of optimized bioinformatic protocols for venom analysis. Based on our results, we recommend Proteome Discoverer for proteomic profiling of snake venom, with cross-validation by MaxQuant. In addition, further research is warranted to investigate why PEAKS performed suboptimally compared to the other software tools, as lessons learned could lead to improvements in its functionality and reliability.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAuthor Contributions: Conceptualization: R.V. and A. N.; writing\u0026mdash;original draft preparation: B.M., and R.V.; formal analysis and investigation: R.V. \u0026nbsp;and B.M.; writing\u0026mdash;review and editing: B.M, M.K. B.G. A. N and R.V. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003eFunding: This work was supported within the scope of national funds from FCT\u0026mdash;Portuguese Foundation for Science and Technology, under iBiMED (UIDB/04501/2020 and PO-CI-01-0145-FEDER-007628) and the Cardiovascular R\u0026amp;D Center\u0026mdash;UnIC (UIDB/00051/2020 and UIDP/00051/2020 (RV), \u0026nbsp;COST Action CA19144 European Venom Network (EUVEN, https://euven-network.eu/.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConflicts of Interest: The authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eJ. White, D. Warrell, M. Eddleston, B.J. Currie, I.M. Whyte, G.K. Isbister, Clinical toxinology--where are we now?, J Toxicol Clin Toxicol, 41 (2003) 263-276.\u003c/li\u003e\n\u003cli\u003eA. Barlow, C.E. Pook, R.A. Harrison, W. 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Ternette, The Choice of Search Engine Affects Sequencing Depth and HLA Class I Allele-Specific Peptide Repertoires, Molecular \u0026amp; cellular proteomics : MCP, 20 (2021) 100124.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Montivipera bulgardaghica, Vipera kaznakovi, Vipera ammodytes, PEAKS, MaxQuant, Prteome Discoverer, Mass Spectrometry, Snake venom, Proteomics, Bioinformatics","lastPublishedDoi":"10.21203/rs.3.rs-5404907/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5404907/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSnake venom is increasingly recognised in biomedical research as a potential source of relevant proteins that are still relatively unknown in various species. In this experiment, we performed proteomic quantification and identification of the venomic profile of three viper taxa: Montivipera blugardaghica subsp. bulgardaghica (MB), Vipera ammodytes subsp. montandoni (VA) and Vipera kaznakovi (KV); and compared the performance of three peptide identification software: PEAKS, MaxQuant and Proteome Discoverer. Overall, PEAKS identified 19 unique proteins (19 in MB, 11 in VA and 19 for KV) and 125 unique peptides (55 in MB, 35 in VA and 63 for KV); MaxQuant identified 577 unique proteins (234 in MB, 275 in VA and 297 for KV) and 1233 unique peptides (518 in MB, 647 in VA and 642 for KV); Proteome Discoverer identified 621 unique proteins (310 in MB, 248 for VA and 346 for VK) and 1657 unique peptides (894 in MB, 830 in VA and 1041 for VK). The three software shared 5 identified proteins and 67 peptides; PEAKS shared 6 proteins and 69 peptides with MaxQuant and 6 proteins and 79 peptides with Proteome Discoverer; MaxQuant shared 139 proteins and 781 peptides with Proteome Discoverer. All identified proteins were categorised into families for each taxon and then compared with the existing literature. This revealed significant discrepancies in the results between the software and the reviewed literature. Overall, PEAKS performed very poorly, while MaxQuant and Proteome Discoverer performed best for both protein and peptide identification, with the latter software being particularly noteworthy.\u003c/p\u003e","manuscriptTitle":"Comparative Proteomic Analysis of Venom from Three Viper Taxa: Evaluating Software-Specific Protein and Peptide Profiles","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-22 18:38:38","doi":"10.21203/rs.3.rs-5404907/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3ce8d7b2-c584-4f49-961c-dbe370129aa0","owner":[],"postedDate":"November 22nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-17T08:08:39+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-22 18:38:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5404907","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5404907","identity":"rs-5404907","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}