In silico approach for the prospecting of molecules from Cereus jamacaru (Cereeae) in the treatment of obesity and cardiovascular diseases

In silico approach for the prospecting of molecules from Cereus jamacaru (Cereeae) in the treatment of obesity and cardiovascular diseases | 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 In silico approach for the prospecting of molecules from Cereus jamacaru (Cereeae) in the treatment of obesity and cardiovascular diseases Maria Izadora Oliveira Cardoso, Júlia Oliveira, V. Bassaneze, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5961003/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 The Caatinga biome in Brazil is home to unique plant species with significant bioactive potential, such as Cereus jamacaru (Mandacaru). Historically used in traditional medicine for treating kidney issues, diabetes, and cardiovascular conditions, this plant contains macromolecules, which may play a role in therapeutic applications. Here, we integrate transcriptomics, molecular docking, and molecular dynamics approaches to explore the potential of C. jamacaru proteomics to binding to triacylglycerol molecules, particularly targeting triacylglycerol formed by lauric, myristic, and palmitic acids. Transcriptome analysis identified 128,942 transcripts, with 14,739 homologous proteins screened for binding affinities. Molecular docking highlighted an isoform of Banyan Peroxidase as a versatile candidate, exhibiting strong binding energies across all triacylglycerols, particularly palmitic acid (-7.632 kcal/mol). Xyloglucan Endotransglycosylase demonstrated specificity for myristic acid (-7.752 kcal/mol), while Non-specific Lipid-Transfer Protein showed exceptional structural stability in dynamic simulations. The molecular dynamics simulations, performed over 100 ns, revealed key insights into protein stability and ligand interactions. Banyan Peroxidase displayed moderate flexibility, enhancing its adaptability to diverse triacylglycerol substrates. Conversely, Xyloglucan Endotransglycosylase exhibited compact stability, making it a strong candidate for targeted new applications. These findings highlight the untapped potential of C. jamacaru as a source of bioactive proteins, bridging traditional knowledge with advanced computational methodologies. Bioactive proteins bioinformatics cardiovascular diseases mandacaru triacylglycerol Figures Figure 1 Figure 2 1. Introduction Brazil has a rich floral diversity, with the Caatinga biome being particularly notable for its variety of plant species adapted to water scarcity, such as cacti. This region provides valuable resources for local communities, especially in the food and herbal medicine sectors, due to the nutritional and bioactive potential of regional plants [ 1 ]. Among these species, the genus Cereus Mill. (Cereeae; Cactaceae), particularly Cereus jamacaru D.C., known as Mandacaru, stands out. Native to Brazil's semi-arid region, this plant is rich in macromolecules, with its spines and stems containing unsaturated fatty acids, such as oleic and linoleic acids, as well as saturated fatty acids like palmitic and stearic acids [ 2 ]. For decades, the root and stem of Mandacaru have been used in infusions and teas by the populations of northeastern Brazil to treat a broader range of conditions, including diuresis, kidney issues, diabetes, and worm infections. It has also been employed as a laxative, antihypertensive, and anti-rheumatic agent [ 3 , 4 ]. With advancements in omics sciences, research on the medicinal properties and macromolecular composition of natural sources like Mandacaru has increased [ 5 ]. The bioactive compounds found in plants form the basis for discovering pharmaceuticals and industrial enzymes through innovative techniques. This aligns with traditional practices of plant prospecting, which have long been integral to cultures worldwide, observing their medicinal, cosmetic, and culinary properties. It is estimated that 80% of the global population relies on traditional medicine, with 85% of this dependence rooted in the use of plants and their bioactive compounds [ 6 , 7 ]. Mandacaru, with its diverse applications and bioactive potential, exemplifies the intersection of traditional knowledge and modern scientific exploration, reinforcing the importance of its study for both local and global health solutions. Recently, approaches to facilitate the bioprospecting of biomolecules have relied on computer-assisted ( in silico ) strategies, such as molecular docking, due to their advantages of quick access and low cost, allowing for large-scale application [ 8 , 9 ]. Inverse virtual screening (IVS), which is based on molecular docking methods, involves predicting the binding mode and affinity of a ligand with a receptor. During the IVS process, molecular docking is used as a search step within a protein database. In this context, computational methods for protein modeling and molecular docking are highlighted. Both methods aim to predict protein structures and energetically assess the affinity between the protein and its substrate [ 10 ]. Fatty acids play a critical role in cardiovascular health, particularly long-chain saturated fatty acids such as myristic and palmitic acids, which are associated with elevated Low-Density Lipoprotein cholesterol (LDL-c) levels and increased cardiovascular risk. This risk arises through mechanisms like cholesterol esterification [ 11 ]. A significant association exists between reducing LDL-c levels and lower rates of cardiovascular events, including cardiovascular death, acute myocardial infarction (MI), coronary revascularization, and stroke [ 12 ]. LDL-c is generated via the chylomicron pathway [ 13 ]. This pathway begins with the digestion of triacylglycerols by gastric lipase in the stomach and pancreatic lipase in the intestine, followed by the absorption of fatty acids and the subsequent production of chylomicrons by enterocytes [ 13 ]. Lipase, an enzyme central to this process, is targeted by pharmacological agents such as orlistat, which binds to the enzyme's active site [ 14 ]. This strategy promotes the elimination of triacylglycerols through the intestinal tract before digestion, thereby reducing cardiovascular risk. However, this approach indiscriminately eliminates all types of triacylglycerols, including beneficial unsaturated ones. To date, no strategy directly and specifically targets triacylglycerols for human health. Recent epidemiological and genetic studies have established that triacylglycerols are among the primary contributors to atherosclerotic cardiovascular disease (ASCVD; [ 15 ]). Obesity is a key underlying risk factor for ASCVD, as it facilitates other risk factors such as hypertriglyceridemia and insulin resistance, both of which can trigger ASCVD. This constellation of factors constitutes metabolic syndrome, also referred to as metabolic risk factors [ 16 ]. The bioactive composition of C. jamacaru , positions this plant as a promising source for therapeutic proteins capable of modulating triacylglycerol digestion. This unique biochemical profile, coupled with its historical use in traditional medicine, underscores its potential for addressing contemporary health challenges. Thus, this study integrates transcriptomics and in silico methodologies to investigate the therapeutic the hypothesis of triacylglycerol binding capability to C. jamacaru proteins, which might be used to affected triacylglycerol availability and metabolism and, consequently, impact cardiovascular health. Here, we explored molecular docking and molecular dynamics to uncover the therapeutic potential of C. jamacaru proteins in mitigating triacylglycerol-related health risks. By identifying candidate proteins with high binding affinities to triacylglycerol, it bridges traditional knowledge with modern computational techniques. The findings lay the groundwork for cost-effective therapeutic strategies aimed at addressing metabolic disorders and cardiovascular diseases. Moreover, this research highlights the untapped potential of C. jamacaru as a source of bioactive proteins, leveraging advanced in silico methods to target triglyceride metabolism and cardiovascular health. 2. Materials and Methods 2.1. Data collection and transcriptome assembly The transcriptome data from the roots and epidermis of C. jamacaru were previously generated [ 17 ]. Sequencing was performed using the DNB-Seq platform (BGI Americas) in paired-end mode. De novo transcriptome assembly was carried out using the Trinity software (version 2.5.11; [ 18 ]) and its associated packages. After assembly, transcripts were translated into amino acid sequences using the TransDecoder program. 2.2. Inverse Virtual Screening The inverse virtual screening (IVS) analysis was performed using an automated workflow, BioProtIS pipeline ([ 19 ]; available at https://github.com/BBMDO/BioProtIS ) , developed in our laboratory. This pipeline employs a set of Python programs to integrates "omics" data and stands out in the analysis of plant transcriptomics, detailed above: 2.2.1. Three-dimensional protein structure modeling The modeling process of the three-dimensional protein structures was carried out using the Modeller v.10.4 [ 20 ]. Subsequently, the identification of known three-dimensional structures in the PDB to be used as templates was performed. For this purpose, the predicted protein sequences from the transcriptome were compared to the protein sequences available in the PDB using the locally Blastp program. Proteins from Cereus with less than 50% homology to the template were promptly excluded, as low identity can also result in low-quality modeled protein structures. After identifying the model proteins in the PDB, the batch_download.sh script (available at: https://www.rcsb.org/scripts/batch_download.sh ), developed by the PDB itself, was applied for the automated download of tertiary structures, resulting in 3,847 proteins. Using the protein sequences of C. jamacaru and the tertiary structure of the model sequence, the Modeller program and its Python scripts were employed to perform sequence alignment between the target protein and the selected homologous protein. Based on this alignment, the initial model of the target protein was generated using the known structure of the homologous protein as a reference. 2.2.2. Preparation of inputs for molecular docking After obtaining the models we used the Openbabel 3.1.1 software [ 21 ] to prepare the protein structure. This included removing hydrogens, adding atomic types, establishing interactions and bonds, and centralizing the molecule to define the search grid (grid box). These operations were performed using the pdb2gmx and editconf functionalities, as proposed by Senra & Fonseca [ 9 ]. Additionally, the Openbabel 3.1.1 software [ 21 ] was applied to prepare the structures of triacylglycerol formed by lauric acid, myristic acid, and palmitic acid (CIDs: 10851, 11148, and 11147, respectively). 2.2.3. Molecular docking With the protein structures and their ligands prepared, we proceeded to the docking analysis, conducted using the blind docking strategy. In blind docking the three triacylglycerol target substrates were virtually positioned within the docking grid without prior information about their binding sites, utilizing the AutoDock Vina program [ 22 ] with default parameters. This method enabled the exploration of different conformations and orientations of the compounds within potential binding cavities of the target protein. This approach is particularly relevant as it allows the investigation of flexible conformations, the identification of allosteric binding sites, and functional implications in a more comprehensive manner [ 23 ]. As a result, proteins were selected based on their lowest binding energy (kcal/mol), indicating strong interactions between the protein and the substrate. This process aimed to identify potential targets for the functional and metabolic annotation of the pathways of interest. 2.3. Functional annotation To obtain information about the function and metabolic pathways of the target proteins of C. jamacaru , functional annotation was performed using a combination of tools and public databases, including the blastp program and the KEGG, NCBI-nr, and UniProt databases. Protein sequences were submitted to Prosite and InterProScan online tools to identify functional domains, protein families, and catalytic or binding residues. The data were analyzed to correlate structural properties with potential biological functions. 2.4. Molecular dynamics The molecular dynamics simulation aimed to understand the binding potential and explore the structural and dynamic properties of the complex. This analysis was conducted using GROMACS v.2023 [ 24 ] in a multi-parallel setup to leverage computational efficiency. Initially, the protein and ligand files, commonly outputted from docking software in PDBQT format, were converted to PDB format using Open Babel. The ligand topology was then prepared using ACPYPE v.2023.10.27 [ 25 ] with the GAFF2 force field, which assigns parameters tailored for small molecules, ensuring an accurate representation of the ligand’s geometry and electrostatic properties for reliable simulation results. For the protein topology, the OPLS-AA force field and TIP3P water model were employed to describe system interactions, both selected for their robust performance in simulating biomolecular systems. A dodecahedron simulation box was generated around the protein with a buffer of 1.0 nm from the protein to the box edges, providing adequate space for solvation and interactions [ 24 ]. The system was solvated using the SPC216 water model, which balances computational efficiency and accuracy, and ions were added to neutralize the system and maintain a physiological ionic concentration of 0.1 M. Following solvation and neutralization, the system underwent energy minimization to resolve steric clashes and bad atomic contacts. Equilibration was then performed in two phases. First, the system was equilibrated under constant volume and temperature (NVT ensemble) at 300K, using a V-rescale thermostat. Next, equilibration was conducted under constant pressure and temperature (NPT ensemble) using the Parrinello-Rahman barostat to maintain thermal and pressure equilibrium at 1 bar. These steps were critical to achieve thermodynamic stability and prepare the system for production dynamics. The production molecular dynamics simulation was run for 100 ns (50,000,000 steps) with a 2 fs time step, recording energy, coordinates, and velocities every 50,000 steps. Long-range electrostatic interactions were calculated using the Particle Mesh Ewald (PME) method. Periodic boundary conditions (PBC) were applied in all directions to simulate an infinite system and prevent edge effects. Bond lengths involving hydrogen atoms were constrained using the LINCS algorithm, and the Verlet scheme was utilized for neighbor searching with a 1.0 nm cutoff for Coulomb and Van der Waals interactions. In the final stages of analysis, the Root Mean Square Deviation (RMSD) was calculated to assess the structural deviation of the protein over time. 3. Results 3.1. Transcriptome assembly and modelling The transcriptome assembly of C. jamacaru yielded a total of 128,942 transcripts, which were translated into protein sequences. The IVS analysis of the C. jamacaru proteomic data began with ~ 120,000 translated aminoacids sequences. Using the BioProtIS pipeline, the protein primary structures were screened for homologous sequences in the PDB, resulting in 14,739 proteins with identified similarities. This extensive dataset served as a foundational step for subsequent structural and functional analyses, enabling a robust exploration of the C. jamacaru proteome. Next, the identified homologous sequences were subjected to three-dimensional structure modeling, generating 3,847 protein structures, which were used for molecular docking analysis. The execution time for each script was estimated based on the volume of information generated and processed. On average, the processing pipeline completed all phases, producing results for each protein in approximately 280 hours. Considering the amount of data used and the methodologies employed, the cost-benefit ratio is favorable, potentially reducing experimental time in the laboratory. 3.2. Molecular Docking The binding affinities (kcal/mol) for lauric acid, myristic acid, and palmitic acid substrates indicate the interaction between the ligands and their respective protein targets. More negative binding energy values correspond to stronger ligand-protein interactions, while less negative values suggest weaker binding. The results consistently showed strong binding affinities across all ligands, reflecting robust data quality and validation according to standard binding score metrics. A total of 3,847 proteins derived from C. jamacaru were subjected to molecular docking against triacylglycerol substrates composed of lauric acid, myristic acid, and palmitic acid (Table S1 ). Table 1 highlights the top twenty candidate proteins based on their binding energy scores (kcal/mol). These proteins represent the most promising targets for the identified ligands (Fig. 1 ). For lauric acid, notable candidates include Cytokinin Dehydrogenase ( T89423 ), which exhibited the strongest binding energy of -7.782 kcal/mol, followed by Glycolate Oxidase ( T57692 ) and Cinnamic Acid 4-Hydroxylase ( T113801 ). The presence of Banyan Peroxidase ( T97361 ) among the top candidates underscores its potential as a versatile protein for triacylglycerol interactions, a feature also observed for other substrates. Table 1 C. jamacaru transcripts, their respective binding energies, and annotations for the top twenty candidate protein targets for triacylglycerol formed by lauric acid, myristic acid, and palmitic acid (triglycerides), based on blind docking. Transcripts PDB ID Binding energy (Kcal/mol) Target protein Lauric acid triglyceride T89423 2q4w -7,782 Cytokinin dehydrogenase T57692 1gyl -7,721 Glycolate Oxidase T113801 6vby -7,673 Cinnamic acid 4-hydroxylase T97361 4cuo -7,603 Banyan Peroxidase T64835 5nzv -7,579 Coatomer subunit beta' Myristic acid triglyceride T91586 1un1 -7,752 Xyloglucan Endotransglycosylase T32032 4i94 -7,563 Probable serine/threonine-protein kinase T38514 4c44 -7,524 2-on-2 Hemoglobin T97366 4cuo -7,436 Banyan Peroxidase T6778 7ksc -7,394 Non-specific lipid-transfer protein Palmitic acid triglyceride T97361 4cuo -7,632 Banyan Peroxidase T19750 7vka -7,587 Indole-3-acetic acid-amido synthetase T62011 7aaq -7,57 Sugar transport protein 10 T97368 4cuo -7,561 Banyan Peroxidase T97353 4cuo -7,484 Banyan Peroxidase * RMSD values for all analysis were 0.0 For myristic acid, the protein Xyloglucan Endotransglycosylase ( T91586 ) displayed the strongest binding energy (-7.752 kcal/mol), followed closely by Non-specific Lipid-Transfer Protein ( T6778 ). These results suggest their suitability for interactions with triacylglycerols containing this fatty acid. Palmitic acid, on the other hand, revealed significant involvement of Banyan Peroxidase , represented by three distinct transcripts ( T97361, T97368, and T97353 ), with binding energies ranging from − 7.632 to -7.484 kcal/mol. This consistent representation of Banyan Peroxidase across all three triacylglycerol types reinforces its potential as a key player in ligand binding, making it a prime target for further functional annotation and experimental validation. These findings provide a strong foundation for subsequent project phases, which will focus on elucidating the functional roles and therapeutic applications of these proteins. To refine the analyses of the binding site potential (allosteric or not) of C. jamacaru proteins, transcripts that appeared repeatedly in the blind docking results for the three different substrates (lauric acid, myristic acid, and palmitic acid) were selected. For this purpose, variations in binding energies and the proteins most frequently observed among the three ligands simultaneously were analyzed (see Table 2 for the top five ligands; for an overview of the table, refer to Table S1 ). The results revealed a total of 59 proteins that appeared across all three substrates, representing a significant sample of proteins with potential for exploring therapeutic applications. Table 2 C. jamacaru transcripts; binding energies for triacylglycerol formed by lauric acid, myristic acid, and palmitic acid; and annotation of the twenty best candidate protein targets for the three substrates mentioned according to blind docking. Transcripts PDB ID Binding energy Lauric acid (Kcal/mol) Binding energy Myristic acid (Kcal/mol) Binding energy Palmitic acid (Kcal/mol) Target protein T97361 4cuo -7,603 -7,178 -7,632 Banyan Peroxidase T6778 7ksc -7,475 -7,394 -7,479 Non-specific lipid-transfer protein T91586 1un1 -7,102 -7,752 -7,304 Xyloglucan Endotransglycosylase T97353 4cuo -7,425 -7,063 -7,484 Banyan Peroxidase T97368 4cuo -7,386 -6,933 -7,561 Banyan Peroxidase 3.3. Functional annotation The analyses performed with Prosite and InterProScan, along with the molecular docking results, reveal that the three identified and studied proteins possess structural and functional characteristics that make them promising candidates for interacting with triacylglycerols. The nsLTP belongs to a family of proteins recognized for their ability to bind and transport lipids such as triacylglycerols. The presence of the nsLTP1 domain and a signal peptide suggests that this protein is exported to extracellular environments, where it may play a role in lipid transport and modulation, as corroborated by its affinity for triacylglycerols form by lauric, myristic, and palmitic acids indicated by docking analyses. The Banyan Peroxidase , on the other hand, is a plant peroxidase that features conserved domains associated with calcium and heme binding, essential for its role in redox processes. Interestingly, its ability to interact with triacylglycerols suggests a possible additional role, potentially modulating or oxidizing triacylglycerols, which could expand its applicability in metabolic contexts. The Xyloglucan Endotransglycosylase Protein , known for its role in carbohydrate metabolism and cell wall modification in plants, exhibited an unexpected capacity to interact with triacylglycerols, which may be related to the flexibility of its catalytic domain and its predisposition for interactions with nonpolar molecules. These findings indicate that all three proteins possess functional affinity for triacylglycerols, broadening their biological roles. 3.4. Molecular dynamics The molecular dynamics results of the seven analyzed proteins revealed significant differences in structural and energetic stability, which may influence their potential as therapies for triacylglycerol binding and removal (Fig. 2 ). The RMSD analysis demonstrated that T91586 ( Xyloglucan Endotransglycosylase ) exhibited the highest structural stability, with an average value of 6.69 nm and low variation (0.0656 nm), indicating good consistency over time. In contrast, T89423 ( Cytokinin Dehydrogenase ) showed higher variability in its RMSD, suggesting structural instability under dynamic conditions. The RMSF analysis highlighted that T6778 ( Non-specific Lipid-Transfer Protein ) exhibited the lowest residue fluctuations, indicating a compact and stable structure. Meanwhile, T97361 ( Banyan Peroxidase ) showed greater flexibility in specific regions, such as loops and termini, which may facilitate dynamic interactions with ligands. The radius of gyration (Rg) for T6778 was also the most consistent, reinforcing its global stability. In contrast, T32032 ( Probable Serine/Threonine-Protein Kinase ) displayed more pronounced variations, possibly linked to conformational transitions. Regarding total energy, T6778 ( Non-specific Lipid-Transfer Protein ) stood out with the lowest average energy (-2382.10 kJ/mol) and reduced variation, indicating an energetically stable conformation. On the other hand, T97368 ( Banyan Peroxidase ) showed the highest total energy (-7359.22 kJ/mol), reflecting a greater energetic cost to maintain its equilibrium under dynamic conditions. Overall, the molecular dynamics data indicate that T91586 , T6778 , and T97361 possess favorable characteristics for therapeutic applications, either due to global stability or the moderate flexibility needed for specific interactions. 4. Discussion The therapeutic potential of natural products has been widely recognized, but transitioning traditional knowledge into modern applications poses challenges [ 26 ]. While current therapies for hypertriglyceridemia and cardiovascular diseases are often limited by accessibility and affordability, computational approaches like molecular docking present a cost-effective avenue for exploring plant-derived proteins as potential therapeutic agents. Despite extensive studies on well-known medicinal plants, there is a notable gap in the proteomic exploration of native species from underrepresented ecosystems such as the Caatinga. The unique adaptations of C. jamacaru to its environment likely yield specialized bioactive proteins, which remain largely untapped. The molecular docking was employed in this study to evaluate, for the first time, the binding interactions between triacylglycerols composed of myristic, lauric, and palmitic acids and proteins from Cereus jamacaru . Notably, significant binding energies were observed, suggesting strong interactions between these triacylglycerols and the identified proteins. Functional annotation through the Prosite and InterProScan databases further supported the molecular docking findings, indicating a potential interaction of these proteins with lipids. This integrated approach demonstrated that the three main identified proteins exhibit structural and functional properties consistent with interactions with triacylglycerols—a feature not previously attributed to these candidates. Banyan Peroxidase was first purified from the latex of Ficus benghalensis and underwent crystallographic analysis in 2012 [ 27 ]. While its crystallographic structure has been resolved, its specific biological functions remain largely unexplored, including any potential lipid-binding capabilities. This protein is generally recognized for its role in oxidative stress responses and the detoxification of reactive oxygen species in plants. Xyloglucan Endotransglycosylase has been intensely studied since its crystallization in 1992 [ 28 ] across several plant species. Its primary known function is the cutting and rejoining of interfibrillar xyloglucan chains, facilitating wall loosening required for plant cell expansion [ 28 ]. Despite its extensive study, no lipid-binding capacity has been demonstrated for this enzyme. This protein is primarily associated with plant growth and structural dynamics of the cell wall. The Non-specific Lipid-Transfer Protein was crystallized in 1995 from maize seedlings [ 29 ], and homologs have been identified in numerous plant species. In tobacco, this protein has been proposed to transfer phospholipids, contributing to plant defense against bacterial and fungal pathogens, and promoting cell wall extension [ 30 ]. It is widely recognized for its involvement in lipid transport and signaling within plant tissues, as well as its role in stress responses. The strategy allowed for the evaluation of the affinity of the modeled proteins for these substrates, providing a deeper understanding of their molecular interaction and functions. The results from this automated approach demonstrated effectiveness in identifying and modeling the proteins with the best binding energy models for the substrates. The discovered proteins may serve as potential targets for the development of future biotechnological production of pharmacological drugs for the treatment of obesity and cardiovascular diseases. Fatty acids are classified according to the length of their carbon chain and the number and configuration of their bonds. These chemical characteristics are associated with the plasma concentration of cholesterol and its distribution in lipoproteins. Fatty acids can be divided into saturated and unsaturated [ 11 ]. Long-chain fatty acids are found in solid form at room temperature, with the main ones being lauric (12:0), myristic (14:0), palmitic (16:0), and stearic (18:0). Different saturated fatty acids may have diversified effects on the lipid profile and cardiovascular risk factors. Nonetheless, the definitive effect of a diet enriched for a specific fatty acid is still under investigation and debate. The primary sources of dietary fat include butter fat and animal fat, which are rich in palmitic acid, as well as vegetable oils such as soybean oil and palm oil. The latter contains a low concentration of myristic acid but is high in palmitic acid [ 31 , 32 ]. Due to the stronger cholesterol-raising effect of myristic acid, attributed to the length of its carbon chain, earlier conclusions suggested that palmitic acid could serve as a favorable dietary substitute in some cases [ 33 ]. However, more recent studies indicate that palmitic acid derived from palm oil may not be entirely beneficial, as it has been associated with an elevation in LDL-c levels [ 34 ]. A proposed mechanism for this effect involves a decrease in hepatic LDL receptor activity combined with an increased production rate of LDL cholesterol [ 35 ]. Nevertheless, a systematic review reported that the overall effect of palm oil on lipid profiles remains inconclusive [ 36 ], potentially due to its additional effect of raising HDL-c level. This HDL-c-raising effect may counterbalance the LDL-c-raising effect, resulting in a neutral overall impact on cardiovascular health [ 37 ]. 4.1. Integration of molecular docking and molecular dynamics The integration of molecular docking and molecular dynamics results provides a comprehensive perspective on the structural and functional behavior of the proteins analyzed. Docking studies revealed the binding affinities of lauric acid, myristic acid, and palmitic acid to key proteins derived from C. jamacaru . Among these, the Banyan Peroxidase ( T97361 ) consistently demonstrated strong binding energies across all ligands, with particularly high affinities for palmitic acid (-7.632 kcal/mol) and lauric acid (-7.603 kcal/mol). This versatility suggests its potential as a primary candidate for interacting with diverse triacylglycerol substrates (Fig. 1 ). Complementing these findings, the molecular dynamics analysis showed that the Banyan Peroxidase maintains moderate structural stability during simulations, indicating that it can retain functional integrity under physiological conditions. The Xyloglucan Endotransglycosylase ( T91586 ) also emerged as a significant candidate (Fig. 1 ), particularly for myristic acid binding, with a binding energy of -7.752 kcal/mol. Molecular dynamics further supported its candidacy by highlighting its compact and stable structure, as evidenced by low RMSD values and consistent radius of gyration. This dual evidence of strong ligand affinity and structural robustness underscores its potential role in therapeutic applications targeting specific triacylglycerol types. Meanwhile, the Non-specific Lipid-Transfer Protein (T6778), while showing slightly weaker docking results (Fig. 1 ), stood out in dynamics analyses due to its superior energetic stability and minimal structural fluctuations, suggesting its suitability for scenarios requiring high stability under diverse conditions. The integration of these datasets reveals nuanced roles for each protein in triacylglycerol interaction. Banyan Peroxidase emerges as the most versatile and robust candidate, suitable for general applications across multiple triacylglycerol types. Xyloglucan Endotransglycosylase excels in specific interactions with myristic acid, while Non-specific Lipid-Transfer Protein offers remarkable stability for experimental or therapeutic conditions that demand structural resilience. Together, these results provide a strong framework for prioritizing proteins for further experimental validation and functional annotation, bridging in silico findings with practical applications in the reduction of triacylglycerols and cholesterol. 5. Conclusion The Banyan Peroxidase ( T97361 ) stands out as a versatile candidate for therapeutic applications, showing consistent binding to all three triacylglycerols analyzed (lauric, myristic, and palmitic acids) and moderate structural stability, making it a promising option for addressing triacylglycerol-related metabolic disorders. The Xyloglucan Endotransglycosylase ( T91586 ) exhibited strong specificity for myristic acid, while the Non-specific Lipid-Transfer Protein ( T6778 ) demonstrated superior energetic stability, highlighting their potential in targeted therapeutic applications. Possibly, these candidates might act as scavengers of triacylglycerols if appropriate conditions are offered. From a global perspective, the increasing prevalence of cardiovascular diseases and obesity underscores the urgent need for innovative and cost-effective therapeutic solutions. The findings presented here could contribute to developing affordable treatments that are accessible to broader populations, particularly in regions with limited resources. Furthermore, leveraging bioactive compounds from native plants such as C. jamacaru aligns with sustainable and ethical practices, promoting biodiversity conservation while addressing pressing public health challenges.Experimental validation remains essential to confirm these findings and advance the therapeutic potential of these proteins for clinical applications. Declarations Acknowledgments We acknowledge the support of São Paulo Research Foundation (FAPESP 2022/09910-9 to D.T.A.and 2023/06831-3 to M.I.O.C) and Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq 132515/2024-5 to M.I.O.C). Competing Interests The authors have no relevant financial or non-financial interests to disclose. Author contributions The idea for this study was conceived by MIOC and DTA. Data collection and analyses were performed by MIOC, JO, and DTA. MIOC led the writing of the paper, while JO, VB, and DTA contributed with numerous conceptions and writing. All authors contributed to the intellectual development of the paper, made multiple revisions, and approved the final draft. Data Availability The datasets reused in the present study are publicly available on NCBI under the project accession PRJNA1192998. Declaration of generative AI and AI-assisted technologies in the writing process During the preparation of this work the author(s) used chatGPT 4.0 in order to improve English grammar. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the publication. References Cartaxo SL, de Almeida Souza MM, de Albuquerque UP (2010) Medicinal plants with bioprospecting potential used in semi-arid northeastern Brazil. Journal of Ethnopharmacology, 131(2), 326–342. https://doi.org/10.1016/j.jep.2010.07.003 Meiado MV, Albuquerque LSC, Rocha EA, Rojas-Aréchiga M, Leal IR. (2010). Seed germination responses of Cereus jamacaru D.C. ssp. Jamacaru (Cactaceae) to environmental factors. Plant Species Biology 25: 120-128. Sales M. de S. L., Martins LV, Souza I, Deus MSM de, Peron AP (2014) Cereus jamacaru DE CANDOLLE (CACTACEAE), O MANDACARU DO NORDESTE BRASILEIRO. Publicatio UEPG: Ciencias Biologicas e Da Saude, 20(2), 135–142. https://doi.org/10.5212/publ.biologicas.v.20i2.0006 Silva LFCR, Valle L de S, Nascimento ARC, Medeiros MFT (2018) Cereus jamacaru DC. (Cactaceae): From 17th century naturalists to modern day scientific and technological prospecting. Acta Botanica Brasilica, 33(2), 191–197. https://doi.org/10.1590/0102-33062018abb0352 Jorquera MA, Graether SP, Maruyama F (2019) Editorial: Bioprospecting and biotechnology of xtremophiles In: Frontiers in Bioengineering and Biotechnology (Vol. 7, Issue AUG). Frontiers Media S.A. https://doi.org/10.3389/fbioe.2019.00204 Arora J, Goyal S, Ramawat KG (2010) Biodiversity, biology and conservation of medicinal plants of the Thar Desert In: Ramawat KG, editor. Desert Plants Biology and Biotechnology. Berlin: Springer-Verlag, p. 3-36. Yadav G, Meena M (2021) Bioprospecting of endophytes in medicinal plants of Thar Desert: An attractive resource for biopharmaceuticals. Biotechnology Reports, 30, e00629. Xu X, Huang M, Zou X (2018) Docking-based inverse virtual screening: methods, applications, and challenges. Biophysics Reports, 4(1), 1–16. https://doi.org/10.1007/s41048-017-0045-8. Senra MV, Fonseca AL (2023) Toxicological impacts and likely protein targets of bisphenol A in Paramecium caudatum. European Journal of Protistology, 88, 125958. Xu M, Lill MA (2013) Induced fit docking, and the use of QM/MM methods in docking. Drug Discov Today Technol 10:e411– e418 Santos RD, Gagliardi ACM, Xavier HT, Magnoni CD, Cassani R, Lottenberg AMP, Casella Filho A, Araújo DB, Cesena FY, Alves RJ, Fenelon G, Nishioka SAD, Faludi AA, Geloneze B, Scherr C, Kovacs C, Tomazzela C, Carla C, Barrera-Arellano D, Ramos S (2013) I Diretriz sobre o consumo de gorduras e saúde cardiovascular. Arquivos Brasileiros De Cardiologia, 100(1), 1–40. https://doi.org/10.1590/S0066-782X2013000900001 Silverman MG, Ference BA, Im K, Wiviott SD, Giugliano RP, Grundy SM, Sabatine MS (2016) Association between lowering LDL-C and cardiovascular risk reduction among different therapeutic interventions: a systematic review and meta-analysis. Jama, 316(12), 1289-1297. Hussain MM (2014) Intestinal lipid absorption and lipoprotein formation. Current opinion in lipidology, 25(3), 200-206. Carrière F, Renou C, Ransac S, Lopez V, De Caro J, Ferrato F, Laugier R. (2001) Inhibition of gastrointestinal lipolysis by Orlistat during digestion of test meals in healthy volunteers. American Journal of Physiology-Gastrointestinal and Liver Physiology, 281(1), G16-G28. Laufs U, Parhofer KG, Ginsberg HN, Hegele RA (2020) Clinical review on triglycerides. European Heart Journal, 41(1), 99–109. https://doi.org/10.1093/eurheartj/ehz785 Grundy SM (2004) Obesity, metabolic syndrome, and cardiovascular disease. Journal of Clinical Endocrinology and Metabolism, 89(6), 2595–2600. https://doi.org/10.1210/jc.2004-0372 Amaral DT, Bonatelli IA, Romeiro-Brito M, Telhe MC, Moraes EM, Zappi DC, Franco FF (2024) Comparative transcriptome analysis reveals lineage-and environment-specific adaptations in cacti from the Brazilian Atlantic Forest. Planta, 260(1), 4. Grabherr, M., Haas, B., Yassour, M. et al. (2011) Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat Biotechnol 29, 644–652. https://doi.org/10.1038/nbt.1883 Virgens GS, Oliveira J, Cardoso MIO, Teodoro JA, Amaral DT. BioProtIS: Streamlining protein-ligand interaction pipeline for analysis in genomic and transcriptomic exploration. J Mol Graph Model. 2024 May;128:108721. doi: 10.1016/j.jmgm.2024.108721. Epub 2024 Jan 30. PMID: 38308972. Webb B, Sali A (2016) Comparative Protein Structure Modeling Using Modeller. Current Protocols in Bioinformatics 54, John Wiley & Sons, Inc., 5.6.1-5.6.37. O'Boyle, N.M., Banck, M., James, C.A. et al. (2011) Open Babel: An open chemical toolbox. J Cheminform 3, 33. https://doi.org/10.1186/1758-2946-3-33. Trott O, Olson AJ (2009) Autodock Vina: Improving the Speed and Accuracy of Docking with a New Scoring Function, Efficient Optimization, and Multithreading. Journal of Computational Chemistry, 31, 455-461. https://doi.org/10.1002/jcc.21334. Iorga B, Herlem D, Barré E, Guillou C (2006) Acetylcholine nicotinic receptors: finding the putative binding site of allosteric modulators using the “blind docking” approach. Journal of Molecular Modeling, 12 (3), pp.366-372. ff10.1007/s00894-005-0057-zff. ffhal-03161516f Abraham MJ, Murtola T, Schulz R, Páll S, Smith JC, Hess B, Lindahl E (2015) “GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers,” SoftwareX, 1–2 19–25. Sousa da Silva AW, Vranken WF (2012) ACPYPE-Antechamber python parser interface. BMC research notes , 5 , 1-8. Porras G, Chassagne F, Lyles JT, Marquez L, Dettweiler M, Salam AM, Quave CL (2020) Ethnobotany and the role of plant natural products in antibiotic drug discovery. Chemical reviews, 121(6), 3495-3560. Sharma A, Palm GJ, Kumari M, Panjikar S, Jagannadham MV, Hinrichs W (2012) Purification, crystallization and preliminary crystallographic analysis of banyan peroxidase. Acta Crystallographica Section F: Structural Biology and Crystallization Communications, 68(8), 931-934. Fry SC, Smith RC, Renwick KF, Martin DJ, Hodge SK, Matthews KJ (1992) Xyloglucan endotransglycosylase, a new wall-loosening enzyme activity from plants. Biochemical Journal, 282(3), 821-828. Shin DH, Lee JY, Hwang KY, Kim KK, Suh SW (1995) High-resolution crystal structure of the non-specific lipid-transfer protein from maize seedlings. Structure, 3(2), 189-199. Wang NJ, Lee CC, Cheng CS, Lo WC, Yang YF, Chen MN, Lyu PC (2012) Construction and analysis of a plant non-specific lipid transfer protein database (nsLTPDB). In BMC genomics (Vol. 13, pp. 1-9). BioMed Central. Fattore E, Bosetti C, Brighenti F, Agostoni C, Fattore G (2014) Palm oil and blood lipid–related markers of cardiovascular disease: a systematic review and meta-analysis of dietary intervention trials. The American journal of clinical nutrition, 99(6), 1331-1350. Mancini A, Imperlini E, Nigro E, Montagnese C, Daniele A, Orrù S, Buono P (2015) Biological and nutritional properties of palm oil and palmitic acid: effects on health. Molecules, 20(9), 17339-17361. Zock PL, de Vries JHM, Katan MB (1994) Impact of myristic acid versus palmitic acid on serum lipid and lipoprotein levels in healthy women and men. Arterioscler Thromb;14:567–75. Sun Y, Neelakantan N, Wu Y, Lote-Oke R, Pan A, van Dam RM (2015) Palm oil consumption increases LDL cholesterol compared with vegetable oils low in saturated fat in a meta-analysis of clinical trials. The Journal of nutrition, 145(7), 1549-1558. Nicolosi RJ (1997) Dietary fat saturation effects on low-density-lipoprotein concentrations and metabolism in various animal models. The American journal of clinical nutrition, 65(5), 1617S-1627S. Ismail SR, Maarof SK, Siedar Ali S, Ali A (2018) Systematic review of palm oil consumption and the risk of cardiovascular disease. PLoS One, 13(2), e0193533. Unhapipatpong C, Shantavasinkul PC, Kasemsup V, Siriyotha S, Warodomwichit D, Maneesuwannarat S, Thakkinstian A (2021) Tropical oil consumption and cardiovascular disease: An umbrella review of systematic reviews and meta analyses. Nutrients, 13(5), 1549. Additional Declarations No competing interests reported. Supplementary Files TableS1.xlsx 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-5961003","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":416288538,"identity":"57ca479e-3ed3-4625-bc8e-c005eeca914a","order_by":0,"name":"Maria Izadora Oliveira Cardoso","email":"","orcid":"","institution":"Universidade Federal do ABC (UFABC)","correspondingAuthor":false,"prefix":"","firstName":"Maria","middleName":"Izadora Oliveira","lastName":"Cardoso","suffix":""},{"id":416288539,"identity":"9a3135c5-c4aa-480e-9457-a6ed6ca89658","order_by":1,"name":"Júlia Oliveira","email":"","orcid":"","institution":"Universidade Federal do ABC (UFABC)","correspondingAuthor":false,"prefix":"","firstName":"Júlia","middleName":"","lastName":"Oliveira","suffix":""},{"id":416288540,"identity":"f76be391-8238-454c-9db4-a9cdd58b6bf4","order_by":2,"name":"V. Bassaneze","email":"","orcid":"","institution":"Universidade Federal do ABC (UFABC)","correspondingAuthor":false,"prefix":"","firstName":"V.","middleName":"","lastName":"Bassaneze","suffix":""},{"id":416288541,"identity":"79374267-9e7b-488b-99db-9af68ddbd2d0","order_by":3,"name":"D. T. Amaral","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCElEQVRIie2RsWrDMBRFnyk0i6nXZ+z8g8HQUBL6LRIGZ3E9ayhFk7sodO3fVCbgLHK9GrLkA0LRmKlUNimUorodO+ggiQvS4QoJwOH4j0gzDmPyh2UFMz6GCwCcUMi4PZ7MwZd/UOCLsv1dudq1tSQMHhaPbYMn1lExa2sNbEl5tDnYlFCVRBIFGKsyD4XaU+GXGYJaUx7vEpuSyCKRtAJEKK4jr9rTFxPAq7aUY269WNIdz0pwHJRXKkwA731C6T9bcGyRVODQwn9Wwt60EIXhM75lN0JlqTABSbNOq7ixv1hXpFqzVYDBXd2f2O1cmKD1/XL+FFVW5cz3LyBmXk4JDofD4ZjkA7e9YY7NmsU9AAAAAElFTkSuQmCC","orcid":"","institution":"Universidade Federal do ABC (UFABC)","correspondingAuthor":true,"prefix":"","firstName":"D.","middleName":"T.","lastName":"Amaral","suffix":""}],"badges":[],"createdAt":"2025-02-04 21:53:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5961003/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5961003/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":76643243,"identity":"5941daa7-92f3-4434-a9b6-fe3a17a51e90","added_by":"auto","created_at":"2025-02-19 08:35:06","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1133554,"visible":true,"origin":"","legend":"\u003cp\u003eThree-dimensional structure of the three main proteins with the best binding energy for the three substrates: T97361 - b\u003cem\u003eanyan peroxidase \u003c/em\u003e(BP), T6778 - \u003cem\u003enonspecific lipid transfer protein\u003c/em\u003e (NSLT), and T91586 - \u003cem\u003exyloglucan endotransglycosylase\u003c/em\u003e (XE). A) Docking for the anchoring of proteins with the lauric acid ligand. B) Docking for the anchoring of proteins with the palmitic acid ligand. C) Docking for the anchoring of proteins with the myristic acid ligand.\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-5961003/v1/3e314bae110b12be160dfb96.png"},{"id":76645074,"identity":"d06e431e-8986-4765-8361-2db93920741a","added_by":"auto","created_at":"2025-02-19 08:51:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":555271,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of molecular dynamics parameters for candidate proteins, presenting the results for seven proteins derived from \u003cem\u003eC. jamacaru\u003c/em\u003e. The panel include, (A) RMSD over time, showing the structural stability of each protein; (B) RMSF per residue; (C) radius of gyration (Rg); and (D) total energy, reflecting the stability and energetic requirements of the protein conformations during the simulation. Each line corresponds to a protein identified by its transcript code (T), with colors matching the legend provided.\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-5961003/v1/3cacfbbe1e100f466d50b87a.png"},{"id":76646637,"identity":"d8d97730-9fe7-4f78-a77c-946803a05e6c","added_by":"auto","created_at":"2025-02-19 09:07:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2538802,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5961003/v1/5c883d79-793a-490c-b3dc-40f456e7761f.pdf"},{"id":76644611,"identity":"c1c88d5d-918c-4f91-9a05-028d7d87c026","added_by":"auto","created_at":"2025-02-19 08:43:06","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":30379,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5961003/v1/8e2ca56e15806ea4edf82e7c.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"In silico approach for the prospecting of molecules from Cereus jamacaru (Cereeae) in the treatment of obesity and cardiovascular diseases","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eBrazil has a rich floral diversity, with the Caatinga biome being particularly notable for its variety of plant species adapted to water scarcity, such as cacti. This region provides valuable resources for local communities, especially in the food and herbal medicine sectors, due to the nutritional and bioactive potential of regional plants [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Among these species, the genus \u003cem\u003eCereus\u003c/em\u003e Mill. (Cereeae; Cactaceae), particularly \u003cem\u003eCereus jamacaru\u003c/em\u003e D.C., known as Mandacaru, stands out. Native to Brazil's semi-arid region, this plant is rich in macromolecules, with its spines and stems containing unsaturated fatty acids, such as oleic and linoleic acids, as well as saturated fatty acids like palmitic and stearic acids [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. For decades, the root and stem of Mandacaru have been used in infusions and teas by the populations of northeastern Brazil to treat a broader range of conditions, including diuresis, kidney issues, diabetes, and worm infections. It has also been employed as a laxative, antihypertensive, and anti-rheumatic agent [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWith advancements in omics sciences, research on the medicinal properties and macromolecular composition of natural sources like Mandacaru has increased [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The bioactive compounds found in plants form the basis for discovering pharmaceuticals and industrial enzymes through innovative techniques. This aligns with traditional practices of plant prospecting, which have long been integral to cultures worldwide, observing their medicinal, cosmetic, and culinary properties. It is estimated that 80% of the global population relies on traditional medicine, with 85% of this dependence rooted in the use of plants and their bioactive compounds [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Mandacaru, with its diverse applications and bioactive potential, exemplifies the intersection of traditional knowledge and modern scientific exploration, reinforcing the importance of its study for both local and global health solutions.\u003c/p\u003e \u003cp\u003eRecently, approaches to facilitate the bioprospecting of biomolecules have relied on computer-assisted (\u003cem\u003ein silico\u003c/em\u003e) strategies, such as molecular docking, due to their advantages of quick access and low cost, allowing for large-scale application [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Inverse virtual screening (IVS), which is based on molecular docking methods, involves predicting the binding mode and affinity of a ligand with a receptor. During the IVS process, molecular docking is used as a search step within a protein database. In this context, computational methods for protein modeling and molecular docking are highlighted. Both methods aim to predict protein structures and energetically assess the affinity between the protein and its substrate [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFatty acids play a critical role in cardiovascular health, particularly long-chain saturated fatty acids such as myristic and palmitic acids, which are associated with elevated Low-Density Lipoprotein cholesterol (LDL-c) levels and increased cardiovascular risk. This risk arises through mechanisms like cholesterol esterification [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. A significant association exists between reducing LDL-c levels and lower rates of cardiovascular events, including cardiovascular death, acute myocardial infarction (MI), coronary revascularization, and stroke [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. LDL-c is generated via the chylomicron pathway [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. This pathway begins with the digestion of triacylglycerols by gastric lipase in the stomach and pancreatic lipase in the intestine, followed by the absorption of fatty acids and the subsequent production of chylomicrons by enterocytes [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Lipase, an enzyme central to this process, is targeted by pharmacological agents such as orlistat, which binds to the enzyme's active site [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. This strategy promotes the elimination of triacylglycerols through the intestinal tract before digestion, thereby reducing cardiovascular risk. However, this approach indiscriminately eliminates all types of triacylglycerols, including beneficial unsaturated ones. To date, no strategy directly and specifically targets triacylglycerols for human health.\u003c/p\u003e \u003cp\u003eRecent epidemiological and genetic studies have established that triacylglycerols are among the primary contributors to atherosclerotic cardiovascular disease (ASCVD; [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]). Obesity is a key underlying risk factor for ASCVD, as it facilitates other risk factors such as hypertriglyceridemia and insulin resistance, both of which can trigger ASCVD. This constellation of factors constitutes metabolic syndrome, also referred to as metabolic risk factors [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The bioactive composition of \u003cem\u003eC. jamacaru\u003c/em\u003e, positions this plant as a promising source for therapeutic proteins capable of modulating triacylglycerol digestion. This unique biochemical profile, coupled with its historical use in traditional medicine, underscores its potential for addressing contemporary health challenges.\u003c/p\u003e \u003cp\u003eThus, this study integrates transcriptomics and in \u003cem\u003esilico\u003c/em\u003e methodologies to investigate the therapeutic the hypothesis of triacylglycerol binding capability to \u003cem\u003eC. jamacaru\u003c/em\u003e proteins, which might be used to affected triacylglycerol availability and metabolism and, consequently, impact cardiovascular health. Here, we explored molecular docking and molecular dynamics to uncover the therapeutic potential of \u003cem\u003eC. jamacaru\u003c/em\u003e proteins in mitigating triacylglycerol-related health risks. By identifying candidate proteins with high binding affinities to triacylglycerol, it bridges traditional knowledge with modern computational techniques. The findings lay the groundwork for cost-effective therapeutic strategies aimed at addressing metabolic disorders and cardiovascular diseases. Moreover, this research highlights the untapped potential of \u003cem\u003eC. jamacaru\u003c/em\u003e as a source of bioactive proteins, leveraging advanced in \u003cem\u003esilico\u003c/em\u003e methods to target triglyceride metabolism and cardiovascular health.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Data collection and transcriptome assembly\u003c/h2\u003e \u003cp\u003eThe transcriptome data from the roots and epidermis of \u003cem\u003eC. jamacaru\u003c/em\u003e were previously generated [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Sequencing was performed using the DNB-Seq platform (BGI Americas) in paired-end mode. \u003cem\u003eDe novo\u003c/em\u003e transcriptome assembly was carried out using the Trinity software (version 2.5.11; [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]) and its associated packages. After assembly, transcripts were translated into amino acid sequences using the TransDecoder program.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Inverse Virtual Screening\u003c/h2\u003e \u003cp\u003eThe inverse virtual screening (IVS) analysis was performed using an automated workflow, BioProtIS pipeline ([\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]; available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/BBMDO/BioProtIS\u003c/span\u003e\u003cspan address=\"https://github.com/BBMDO/BioProtIS\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e, developed in our laboratory. This pipeline employs a set of Python programs to integrates \"omics\" data and stands out in the analysis of plant transcriptomics, detailed above:\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1. Three-dimensional protein structure modeling\u003c/h2\u003e \u003cp\u003eThe modeling process of the three-dimensional protein structures was carried out using the Modeller v.10.4 [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Subsequently, the identification of known three-dimensional structures in the PDB to be used as templates was performed. For this purpose, the predicted protein sequences from the transcriptome were compared to the protein sequences available in the PDB using the locally Blastp program. Proteins from \u003cem\u003eCereus\u003c/em\u003e with less than 50% homology to the template were promptly excluded, as low identity can also result in low-quality modeled protein structures. After identifying the model proteins in the PDB, the \u003cem\u003ebatch_download.sh\u003c/em\u003e script (available at: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.rcsb.org/scripts/batch_download.sh\u003c/span\u003e\u003cspan address=\"https://www.rcsb.org/scripts/batch_download.sh\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), developed by the PDB itself, was applied for the automated download of tertiary structures, resulting in 3,847 proteins. Using the protein sequences of \u003cem\u003eC. jamacaru\u003c/em\u003e and the tertiary structure of the model sequence, the Modeller program and its Python scripts were employed to perform sequence alignment between the target protein and the selected homologous protein. Based on this alignment, the initial model of the target protein was generated using the known structure of the homologous protein as a reference.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2. Preparation of inputs for molecular docking\u003c/h2\u003e \u003cp\u003eAfter obtaining the models we used the Openbabel 3.1.1 software [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] to prepare the protein structure. This included removing hydrogens, adding atomic types, establishing interactions and bonds, and centralizing the molecule to define the search grid (grid box). These operations were performed using the \u003cem\u003epdb2gmx\u003c/em\u003e and \u003cem\u003eeditconf\u003c/em\u003e functionalities, as proposed by Senra \u0026amp; Fonseca [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Additionally, the Openbabel 3.1.1 software [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] was applied to prepare the structures of triacylglycerol formed by lauric acid, myristic acid, and palmitic acid (CIDs: 10851, 11148, and 11147, respectively).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3. Molecular docking\u003c/h2\u003e \u003cp\u003eWith the protein structures and their ligands prepared, we proceeded to the docking analysis, conducted using the blind docking strategy. In blind docking the three triacylglycerol target substrates were virtually positioned within the docking grid without prior information about their binding sites, utilizing the AutoDock Vina program [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] with default parameters. This method enabled the exploration of different conformations and orientations of the compounds within potential binding cavities of the target protein. This approach is particularly relevant as it allows the investigation of flexible conformations, the identification of allosteric binding sites, and functional implications in a more comprehensive manner [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. As a result, proteins were selected based on their lowest binding energy (kcal/mol), indicating strong interactions between the protein and the substrate. This process aimed to identify potential targets for the functional and metabolic annotation of the pathways of interest.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Functional annotation\u003c/h2\u003e \u003cp\u003eTo obtain information about the function and metabolic pathways of the target proteins of \u003cem\u003eC. jamacaru\u003c/em\u003e, functional annotation was performed using a combination of tools and public databases, including the \u003cem\u003eblastp\u003c/em\u003e program and the KEGG, NCBI-nr, and UniProt databases. Protein sequences were submitted to Prosite and InterProScan online tools to identify functional domains, protein families, and catalytic or binding residues. The data were analyzed to correlate structural properties with potential biological functions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Molecular dynamics\u003c/h2\u003e \u003cp\u003eThe molecular dynamics simulation aimed to understand the binding potential and explore the structural and dynamic properties of the complex. This analysis was conducted using GROMACS v.2023 [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] in a multi-parallel setup to leverage computational efficiency. Initially, the protein and ligand files, commonly outputted from docking software in PDBQT format, were converted to PDB format using Open Babel. The ligand topology was then prepared using ACPYPE v.2023.10.27 [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] with the GAFF2 force field, which assigns parameters tailored for small molecules, ensuring an accurate representation of the ligand\u0026rsquo;s geometry and electrostatic properties for reliable simulation results.\u003c/p\u003e \u003cp\u003eFor the protein topology, the OPLS-AA force field and TIP3P water model were employed to describe system interactions, both selected for their robust performance in simulating biomolecular systems. A dodecahedron simulation box was generated around the protein with a buffer of 1.0 nm from the protein to the box edges, providing adequate space for solvation and interactions [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The system was solvated using the SPC216 water model, which balances computational efficiency and accuracy, and ions were added to neutralize the system and maintain a physiological ionic concentration of 0.1 M.\u003c/p\u003e \u003cp\u003eFollowing solvation and neutralization, the system underwent energy minimization to resolve steric clashes and bad atomic contacts. Equilibration was then performed in two phases. First, the system was equilibrated under constant volume and temperature (NVT ensemble) at 300K, using a V-rescale thermostat. Next, equilibration was conducted under constant pressure and temperature (NPT ensemble) using the Parrinello-Rahman barostat to maintain thermal and pressure equilibrium at 1 bar. These steps were critical to achieve thermodynamic stability and prepare the system for production dynamics.\u003c/p\u003e \u003cp\u003eThe production molecular dynamics simulation was run for 100 ns (50,000,000 steps) with a 2 fs time step, recording energy, coordinates, and velocities every 50,000 steps. Long-range electrostatic interactions were calculated using the Particle Mesh Ewald (PME) method. Periodic boundary conditions (PBC) were applied in all directions to simulate an infinite system and prevent edge effects. Bond lengths involving hydrogen atoms were constrained using the LINCS algorithm, and the Verlet scheme was utilized for neighbor searching with a 1.0 nm cutoff for Coulomb and Van der Waals interactions. In the final stages of analysis, the Root Mean Square Deviation (RMSD) was calculated to assess the structural deviation of the protein over time.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Transcriptome assembly and modelling\u003c/h2\u003e \u003cp\u003eThe transcriptome assembly of \u003cem\u003eC. jamacaru\u003c/em\u003e yielded a total of 128,942 transcripts, which were translated into protein sequences. The IVS analysis of the \u003cem\u003eC. jamacaru\u003c/em\u003e proteomic data began with ~\u0026thinsp;120,000 translated aminoacids sequences. Using the BioProtIS pipeline, the protein primary structures were screened for homologous sequences in the PDB, resulting in 14,739 proteins with identified similarities. This extensive dataset served as a foundational step for subsequent structural and functional analyses, enabling a robust exploration of the \u003cem\u003eC. jamacaru\u003c/em\u003e proteome.\u003c/p\u003e \u003cp\u003eNext, the identified homologous sequences were subjected to three-dimensional structure modeling, generating 3,847 protein structures, which were used for molecular docking analysis. The execution time for each script was estimated based on the volume of information generated and processed. On average, the processing pipeline completed all phases, producing results for each protein in approximately 280 hours. Considering the amount of data used and the methodologies employed, the cost-benefit ratio is favorable, potentially reducing experimental time in the laboratory.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Molecular Docking\u003c/h2\u003e \u003cp\u003eThe binding affinities (kcal/mol) for lauric acid, myristic acid, and palmitic acid substrates indicate the interaction between the ligands and their respective protein targets. More negative binding energy values correspond to stronger ligand-protein interactions, while less negative values suggest weaker binding. The results consistently showed strong binding affinities across all ligands, reflecting robust data quality and validation according to standard binding score metrics.\u003c/p\u003e \u003cp\u003eA total of 3,847 proteins derived from \u003cem\u003eC. jamacaru\u003c/em\u003e were subjected to molecular docking against triacylglycerol substrates composed of lauric acid, myristic acid, and palmitic acid (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e highlights the top twenty candidate proteins based on their binding energy scores (kcal/mol). These proteins represent the most promising targets for the identified ligands (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). For lauric acid, notable candidates include \u003cem\u003eCytokinin Dehydrogenase\u003c/em\u003e (\u003cem\u003eT89423\u003c/em\u003e), which exhibited the strongest binding energy of -7.782 kcal/mol, followed by \u003cem\u003eGlycolate Oxidase\u003c/em\u003e (\u003cem\u003eT57692\u003c/em\u003e) and \u003cem\u003eCinnamic Acid 4-Hydroxylase\u003c/em\u003e (\u003cem\u003eT113801\u003c/em\u003e). The presence of \u003cem\u003eBanyan Peroxidase\u003c/em\u003e (\u003cem\u003eT97361\u003c/em\u003e) among the top candidates underscores its potential as a versatile protein for triacylglycerol interactions, a feature also observed for other substrates.\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\u003e\u003cem\u003eC. jamacaru\u003c/em\u003e transcripts, their respective binding energies, and annotations for the top twenty candidate protein targets for triacylglycerol formed by lauric acid, myristic acid, and palmitic acid (triglycerides), based on blind docking.\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTranscripts\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePDB ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBinding energy (Kcal/mol)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTarget protein\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eLauric acid triglyceride\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT89423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2q4w\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-7,782\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eCytokinin dehydrogenase\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT57692\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1gyl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-7,721\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eGlycolate Oxidase\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT113801\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6vby\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-7,673\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eCinnamic acid 4-hydroxylase\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT97361\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4cuo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-7,603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eBanyan Peroxidase\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT64835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5nzv\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-7,579\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eCoatomer subunit beta'\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eMyristic acid triglyceride\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT91586\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1un1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-7,752\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eXyloglucan Endotransglycosylase\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT32032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4i94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-7,563\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eProbable serine/threonine-protein kinase\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT38514\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4c44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-7,524\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e2-on-2 Hemoglobin\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT97366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4cuo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-7,436\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eBanyan Peroxidase\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT6778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7ksc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-7,394\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eNon-specific lipid-transfer protein\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003ePalmitic acid triglyceride\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT97361\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4cuo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-7,632\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eBanyan Peroxidase\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT19750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7vka\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-7,587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eIndole-3-acetic acid-amido synthetase\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT62011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7aaq\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-7,57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eSugar transport protein 10\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT97368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4cuo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-7,561\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eBanyan Peroxidase\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT97353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4cuo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-7,484\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eBanyan Peroxidase\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e* RMSD values for all analysis were 0.0\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor myristic acid, the protein \u003cem\u003eXyloglucan Endotransglycosylase\u003c/em\u003e (\u003cem\u003eT91586\u003c/em\u003e) displayed the strongest binding energy (-7.752 kcal/mol), followed closely by \u003cem\u003eNon-specific Lipid-Transfer Protein\u003c/em\u003e (\u003cem\u003eT6778\u003c/em\u003e). These results suggest their suitability for interactions with triacylglycerols containing this fatty acid. Palmitic acid, on the other hand, revealed significant involvement of \u003cem\u003eBanyan Peroxidase\u003c/em\u003e, represented by three distinct transcripts (\u003cem\u003eT97361, T97368, and T97353\u003c/em\u003e), with binding energies ranging from \u0026minus;\u0026thinsp;7.632 to -7.484 kcal/mol. This consistent representation of \u003cem\u003eBanyan Peroxidase\u003c/em\u003e across all three triacylglycerol types reinforces its potential as a key player in ligand binding, making it a prime target for further functional annotation and experimental validation. These findings provide a strong foundation for subsequent project phases, which will focus on elucidating the functional roles and therapeutic applications of these proteins.\u003c/p\u003e \u003cp\u003eTo refine the analyses of the binding site potential (allosteric or not) of \u003cem\u003eC. jamacaru\u003c/em\u003e proteins, transcripts that appeared repeatedly in the blind docking results for the three different substrates (lauric acid, myristic acid, and palmitic acid) were selected. For this purpose, variations in binding energies and the proteins most frequently observed among the three ligands simultaneously were analyzed (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e for the top five ligands; for an overview of the table, refer to Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The results revealed a total of 59 proteins that appeared across all three substrates, representing a significant sample of proteins with potential for exploring therapeutic applications.\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\u003e\u003cem\u003eC. jamacaru\u003c/em\u003e transcripts; binding energies for triacylglycerol formed by lauric acid, myristic acid, and palmitic acid; and annotation of the twenty best candidate protein targets for the three substrates mentioned according to blind docking.\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\u003eTranscripts\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePDB ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBinding energy Lauric acid (Kcal/mol)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBinding energy\u003c/p\u003e \u003cp\u003eMyristic acid\u003c/p\u003e \u003cp\u003e(Kcal/mol)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBinding energy Palmitic acid\u003c/p\u003e \u003cp\u003e(Kcal/mol)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTarget protein\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT97361\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4cuo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-7,603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-7,178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-7,632\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eBanyan Peroxidase\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT6778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7ksc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-7,475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-7,394\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-7,479\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eNon-specific lipid-transfer protein\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT91586\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1un1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-7,102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-7,752\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-7,304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eXyloglucan Endotransglycosylase\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT97353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4cuo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-7,425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-7,063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-7,484\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eBanyan Peroxidase\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT97368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4cuo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-7,386\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6,933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-7,561\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eBanyan Peroxidase\u003c/em\u003e\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\u003e3.3. Functional annotation\u003c/h2\u003e \u003cp\u003eThe analyses performed with Prosite and InterProScan, along with the molecular docking results, reveal that the three identified and studied proteins possess structural and functional characteristics that make them promising candidates for interacting with triacylglycerols. The \u003cem\u003ensLTP\u003c/em\u003e belongs to a family of proteins recognized for their ability to bind and transport lipids such as triacylglycerols. The presence of the \u003cem\u003ensLTP1\u003c/em\u003e domain and a signal peptide suggests that this protein is exported to extracellular environments, where it may play a role in lipid transport and modulation, as corroborated by its affinity for triacylglycerols form by lauric, myristic, and palmitic acids indicated by docking analyses. The \u003cem\u003eBanyan Peroxidase\u003c/em\u003e, on the other hand, is a plant peroxidase that features conserved domains associated with calcium and heme binding, essential for its role in redox processes. Interestingly, its ability to interact with triacylglycerols suggests a possible additional role, potentially modulating or oxidizing triacylglycerols, which could expand its applicability in metabolic contexts. The \u003cem\u003eXyloglucan Endotransglycosylase Protein\u003c/em\u003e, known for its role in carbohydrate metabolism and cell wall modification in plants, exhibited an unexpected capacity to interact with triacylglycerols, which may be related to the flexibility of its catalytic domain and its predisposition for interactions with nonpolar molecules. These findings indicate that all three proteins possess functional affinity for triacylglycerols, broadening their biological roles.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Molecular dynamics\u003c/h2\u003e \u003cp\u003eThe molecular dynamics results of the seven analyzed proteins revealed significant differences in structural and energetic stability, which may influence their potential as therapies for triacylglycerol binding and removal (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The RMSD analysis demonstrated that T91586 (\u003cem\u003eXyloglucan Endotransglycosylase\u003c/em\u003e) exhibited the highest structural stability, with an average value of 6.69 nm and low variation (0.0656 nm), indicating good consistency over time. In contrast, T89423 (\u003cem\u003eCytokinin Dehydrogenase\u003c/em\u003e) showed higher variability in its RMSD, suggesting structural instability under dynamic conditions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe RMSF analysis highlighted that \u003cem\u003eT6778\u003c/em\u003e (\u003cem\u003eNon-specific Lipid-Transfer Protein\u003c/em\u003e) exhibited the lowest residue fluctuations, indicating a compact and stable structure. Meanwhile, \u003cem\u003eT97361\u003c/em\u003e (\u003cem\u003eBanyan Peroxidase\u003c/em\u003e) showed greater flexibility in specific regions, such as loops and termini, which may facilitate dynamic interactions with ligands. The radius of gyration (Rg) for \u003cem\u003eT6778\u003c/em\u003e was also the most consistent, reinforcing its global stability. In contrast, \u003cem\u003eT32032\u003c/em\u003e (\u003cem\u003eProbable Serine/Threonine-Protein Kinase\u003c/em\u003e) displayed more pronounced variations, possibly linked to conformational transitions.\u003c/p\u003e \u003cp\u003eRegarding total energy, \u003cem\u003eT6778\u003c/em\u003e (\u003cem\u003eNon-specific Lipid-Transfer Protein\u003c/em\u003e) stood out with the lowest average energy (-2382.10 kJ/mol) and reduced variation, indicating an energetically stable conformation. On the other hand, \u003cem\u003eT97368\u003c/em\u003e (\u003cem\u003eBanyan Peroxidase\u003c/em\u003e) showed the highest total energy (-7359.22 kJ/mol), reflecting a greater energetic cost to maintain its equilibrium under dynamic conditions. Overall, the molecular dynamics data indicate that \u003cem\u003eT91586\u003c/em\u003e, \u003cem\u003eT6778\u003c/em\u003e, and \u003cem\u003eT97361\u003c/em\u003e possess favorable characteristics for therapeutic applications, either due to global stability or the moderate flexibility needed for specific interactions.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe therapeutic potential of natural products has been widely recognized, but transitioning traditional knowledge into modern applications poses challenges [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. While current therapies for hypertriglyceridemia and cardiovascular diseases are often limited by accessibility and affordability, computational approaches like molecular docking present a cost-effective avenue for exploring plant-derived proteins as potential therapeutic agents. Despite extensive studies on well-known medicinal plants, there is a notable gap in the proteomic exploration of native species from underrepresented ecosystems such as the Caatinga. The unique adaptations of \u003cem\u003eC. jamacaru\u003c/em\u003e to its environment likely yield specialized bioactive proteins, which remain largely untapped.\u003c/p\u003e \u003cp\u003eThe molecular docking was employed in this study to evaluate, for the first time, the binding interactions between triacylglycerols composed of myristic, lauric, and palmitic acids and proteins from \u003cem\u003eCereus jamacaru\u003c/em\u003e. Notably, significant binding energies were observed, suggesting strong interactions between these triacylglycerols and the identified proteins. Functional annotation through the Prosite and InterProScan databases further supported the molecular docking findings, indicating a potential interaction of these proteins with lipids. This integrated approach demonstrated that the three main identified proteins exhibit structural and functional properties consistent with interactions with triacylglycerols\u0026mdash;a feature not previously attributed to these candidates.\u003c/p\u003e \u003cp\u003e \u003cem\u003eBanyan Peroxidase\u003c/em\u003e was first purified from the latex of \u003cem\u003eFicus benghalensis\u003c/em\u003e and underwent crystallographic analysis in 2012 [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. While its crystallographic structure has been resolved, its specific biological functions remain largely unexplored, including any potential lipid-binding capabilities. This protein is generally recognized for its role in oxidative stress responses and the detoxification of reactive oxygen species in plants. \u003cem\u003eXyloglucan Endotransglycosylase\u003c/em\u003e has been intensely studied since its crystallization in 1992 [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] across several plant species. Its primary known function is the cutting and rejoining of interfibrillar xyloglucan chains, facilitating wall loosening required for plant cell expansion [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Despite its extensive study, no lipid-binding capacity has been demonstrated for this enzyme. This protein is primarily associated with plant growth and structural dynamics of the cell wall. The \u003cem\u003eNon-specific Lipid-Transfer Protein\u003c/em\u003e was crystallized in 1995 from maize seedlings [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], and homologs have been identified in numerous plant species. In tobacco, this protein has been proposed to transfer phospholipids, contributing to plant defense against bacterial and fungal pathogens, and promoting cell wall extension [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. It is widely recognized for its involvement in lipid transport and signaling within plant tissues, as well as its role in stress responses.\u003c/p\u003e \u003cp\u003eThe strategy allowed for the evaluation of the affinity of the modeled proteins for these substrates, providing a deeper understanding of their molecular interaction and functions. The results from this automated approach demonstrated effectiveness in identifying and modeling the proteins with the best binding energy models for the substrates. The discovered proteins may serve as potential targets for the development of future biotechnological production of pharmacological drugs for the treatment of obesity and cardiovascular diseases.\u003c/p\u003e \u003cp\u003eFatty acids are classified according to the length of their carbon chain and the number and configuration of their bonds. These chemical characteristics are associated with the plasma concentration of cholesterol and its distribution in lipoproteins. Fatty acids can be divided into saturated and unsaturated [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Long-chain fatty acids are found in solid form at room temperature, with the main ones being lauric (12:0), myristic (14:0), palmitic (16:0), and stearic (18:0). Different saturated fatty acids may have diversified effects on the lipid profile and cardiovascular risk factors. Nonetheless, the definitive effect of a diet enriched for a specific fatty acid is still under investigation and debate.\u003c/p\u003e \u003cp\u003eThe primary sources of dietary fat include butter fat and animal fat, which are rich in palmitic acid, as well as vegetable oils such as soybean oil and palm oil. The latter contains a low concentration of myristic acid but is high in palmitic acid [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Due to the stronger cholesterol-raising effect of myristic acid, attributed to the length of its carbon chain, earlier conclusions suggested that palmitic acid could serve as a favorable dietary substitute in some cases [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. However, more recent studies indicate that palmitic acid derived from palm oil may not be entirely beneficial, as it has been associated with an elevation in LDL-c levels [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. A proposed mechanism for this effect involves a decrease in hepatic LDL receptor activity combined with an increased production rate of LDL cholesterol [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Nevertheless, a systematic review reported that the overall effect of palm oil on lipid profiles remains inconclusive [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], potentially due to its additional effect of raising HDL-c level. This HDL-c-raising effect may counterbalance the LDL-c-raising effect, resulting in a neutral overall impact on cardiovascular health [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Integration of molecular docking and molecular dynamics\u003c/h2\u003e \u003cp\u003eThe integration of molecular docking and molecular dynamics results provides a comprehensive perspective on the structural and functional behavior of the proteins analyzed. Docking studies revealed the binding affinities of lauric acid, myristic acid, and palmitic acid to key proteins derived from \u003cem\u003eC. jamacaru\u003c/em\u003e. Among these, the \u003cem\u003eBanyan Peroxidase\u003c/em\u003e (\u003cem\u003eT97361\u003c/em\u003e) consistently demonstrated strong binding energies across all ligands, with particularly high affinities for palmitic acid (-7.632 kcal/mol) and lauric acid (-7.603 kcal/mol). This versatility suggests its potential as a primary candidate for interacting with diverse triacylglycerol substrates (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Complementing these findings, the molecular dynamics analysis showed that the \u003cem\u003eBanyan Peroxidase\u003c/em\u003e maintains moderate structural stability during simulations, indicating that it can retain functional integrity under physiological conditions.\u003c/p\u003e \u003cp\u003eThe \u003cem\u003eXyloglucan Endotransglycosylase\u003c/em\u003e (\u003cem\u003eT91586\u003c/em\u003e) also emerged as a significant candidate (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), particularly for myristic acid binding, with a binding energy of -7.752 kcal/mol. Molecular dynamics further supported its candidacy by highlighting its compact and stable structure, as evidenced by low RMSD values and consistent radius of gyration. This dual evidence of strong ligand affinity and structural robustness underscores its potential role in therapeutic applications targeting specific triacylglycerol types. Meanwhile, the \u003cem\u003eNon-specific Lipid-Transfer Protein\u003c/em\u003e (T6778), while showing slightly weaker docking results (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), stood out in dynamics analyses due to its superior energetic stability and minimal structural fluctuations, suggesting its suitability for scenarios requiring high stability under diverse conditions.\u003c/p\u003e \u003cp\u003eThe integration of these datasets reveals nuanced roles for each protein in triacylglycerol interaction. \u003cem\u003eBanyan Peroxidase\u003c/em\u003e emerges as the most versatile and robust candidate, suitable for general applications across multiple triacylglycerol types. \u003cem\u003eXyloglucan Endotransglycosylase\u003c/em\u003e excels in specific interactions with myristic acid, while \u003cem\u003eNon-specific Lipid-Transfer Protein\u003c/em\u003e offers remarkable stability for experimental or therapeutic conditions that demand structural resilience. Together, these results provide a strong framework for prioritizing proteins for further experimental validation and functional annotation, bridging in \u003cem\u003esilico\u003c/em\u003e findings with practical applications in the reduction of triacylglycerols and cholesterol.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThe \u003cem\u003eBanyan Peroxidase\u003c/em\u003e (\u003cem\u003eT97361\u003c/em\u003e) stands out as a versatile candidate for therapeutic applications, showing consistent binding to all three triacylglycerols analyzed (lauric, myristic, and palmitic acids) and moderate structural stability, making it a promising option for addressing triacylglycerol-related metabolic disorders. The \u003cem\u003eXyloglucan Endotransglycosylase\u003c/em\u003e (\u003cem\u003eT91586\u003c/em\u003e) exhibited strong specificity for myristic acid, while the \u003cem\u003eNon-specific Lipid-Transfer Protein\u003c/em\u003e (\u003cem\u003eT6778\u003c/em\u003e) demonstrated superior energetic stability, highlighting their potential in targeted therapeutic applications. Possibly, these candidates might act as scavengers of triacylglycerols if appropriate conditions are offered.\u003c/p\u003e \u003cp\u003eFrom a global perspective, the increasing prevalence of cardiovascular diseases and obesity underscores the urgent need for innovative and cost-effective therapeutic solutions. The findings presented here could contribute to developing affordable treatments that are accessible to broader populations, particularly in regions with limited resources. Furthermore, leveraging bioactive compounds from native plants such as \u003cem\u003eC. jamacaru\u003c/em\u003e aligns with sustainable and ethical practices, promoting biodiversity conservation while addressing pressing public health challenges.Experimental validation remains essential to confirm these findings and advance the therapeutic potential of these proteins for clinical applications.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe acknowledge the support of S\u0026atilde;o Paulo Research Foundation (FAPESP 2022/09910-9 to D.T.A.and 2023/06831-3 to M.I.O.C) and Conselho Nacional de Desenvolvimento Cient\u0026iacute;fico e Tecnol\u0026oacute;gico (CNPq 132515/2024-5 to M.I.O.C).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe idea for this study was conceived by MIOC and DTA. Data collection and analyses were performed by MIOC, JO, and DTA. MIOC led the writing of the paper, while JO, VB, and DTA contributed with numerous conceptions and writing. All authors contributed to the intellectual development of the paper, made multiple revisions, and approved the final draft.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets reused in the present study are publicly available on NCBI under the project accession PRJNA1192998.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of generative AI and AI-assisted technologies in the writing process\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring the preparation of this work the author(s) used chatGPT 4.0 in order to improve English grammar. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the publication.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCartaxo SL, de Almeida Souza MM, de Albuquerque UP (2010) Medicinal plants with bioprospecting potential used in semi-arid northeastern Brazil. Journal of Ethnopharmacology, 131(2), 326\u0026ndash;342. https://doi.org/10.1016/j.jep.2010.07.003\u003c/li\u003e\n\u003cli\u003eMeiado MV, Albuquerque LSC, Rocha EA, Rojas-Ar\u0026eacute;chiga M, Leal IR. (2010). Seed germination responses of \u003cem\u003eCereus jamacaru\u003c/em\u003e D.C. ssp. \u003cem\u003eJamacaru\u003c/em\u003e (Cactaceae) to environmental factors. Plant Species Biology 25: 120-128.\u003c/li\u003e\n\u003cli\u003eSales M. de S. L., Martins LV, Souza I, Deus MSM de, Peron AP (2014) Cereus jamacaru DE CANDOLLE (CACTACEAE), O MANDACARU DO NORDESTE BRASILEIRO. Publicatio UEPG: Ciencias Biologicas e Da Saude, 20(2), 135\u0026ndash;142. https://doi.org/10.5212/publ.biologicas.v.20i2.0006\u003c/li\u003e\n\u003cli\u003eSilva LFCR, Valle L de S, Nascimento ARC, Medeiros MFT (2018) Cereus jamacaru DC. (Cactaceae): From 17th century naturalists to modern day scientific and technological prospecting. Acta Botanica Brasilica, 33(2), 191\u0026ndash;197. https://doi.org/10.1590/0102-33062018abb0352\u003c/li\u003e\n\u003cli\u003eJorquera MA, Graether SP, Maruyama F (2019) Editorial: Bioprospecting and biotechnology of xtremophiles In: Frontiers in Bioengineering and Biotechnology (Vol. 7, Issue AUG). Frontiers Media S.A. https://doi.org/10.3389/fbioe.2019.00204\u003c/li\u003e\n\u003cli\u003eArora J, Goyal S, Ramawat KG (2010) Biodiversity, biology and conservation of medicinal plants of the Thar Desert In: Ramawat KG, editor. Desert Plants Biology and Biotechnology. Berlin: Springer-Verlag, p. 3-36.\u003c/li\u003e\n\u003cli\u003eYadav G, Meena M (2021) Bioprospecting of endophytes in medicinal plants of Thar Desert: An attractive resource for biopharmaceuticals. Biotechnology Reports, 30, e00629.\u003c/li\u003e\n\u003cli\u003eXu X, Huang M, Zou X (2018) Docking-based inverse virtual screening: methods, applications, and challenges. Biophysics Reports, 4(1), 1\u0026ndash;16. https://doi.org/10.1007/s41048-017-0045-8.\u003c/li\u003e\n\u003cli\u003eSenra MV, Fonseca AL (2023) Toxicological impacts and likely protein targets of bisphenol A in Paramecium caudatum. European Journal of Protistology, 88, 125958.\u003c/li\u003e\n\u003cli\u003eXu M, Lill MA (2013) Induced fit docking, and the use of QM/MM methods in docking. Drug Discov Today Technol 10:e411\u0026ndash; e418\u003c/li\u003e\n\u003cli\u003eSantos RD, Gagliardi ACM, Xavier HT, Magnoni CD, Cassani R, Lottenberg AMP, Casella Filho A, Ara\u0026uacute;jo DB, Cesena FY, Alves RJ, Fenelon G, Nishioka SAD, Faludi AA, Geloneze B, Scherr C, Kovacs C, Tomazzela C, Carla C, Barrera-Arellano D, Ramos S (2013) I Diretriz sobre o consumo de gorduras e sa\u0026uacute;de cardiovascular. Arquivos Brasileiros De Cardiologia, 100(1), 1\u0026ndash;40. https://doi.org/10.1590/S0066-782X2013000900001\u003c/li\u003e\n\u003cli\u003eSilverman MG, Ference BA, Im K, Wiviott SD, Giugliano RP, Grundy SM, Sabatine MS (2016) Association between lowering LDL-C and cardiovascular risk reduction among different therapeutic interventions: a systematic review and meta-analysis. Jama, 316(12), 1289-1297.\u003c/li\u003e\n\u003cli\u003eHussain MM (2014) Intestinal lipid absorption and lipoprotein formation. Current opinion in lipidology, 25(3), 200-206.\u003c/li\u003e\n\u003cli\u003eCarri\u0026egrave;re F, Renou C, Ransac S, Lopez V, De Caro J, Ferrato F, Laugier R. (2001) Inhibition of gastrointestinal lipolysis by Orlistat during digestion of test meals in healthy volunteers. American Journal of Physiology-Gastrointestinal and Liver Physiology, 281(1), G16-G28.\u003c/li\u003e\n\u003cli\u003eLaufs U, Parhofer KG, Ginsberg HN, Hegele RA (2020) Clinical review on triglycerides. European Heart Journal, 41(1), 99\u0026ndash;109. https://doi.org/10.1093/eurheartj/ehz785\u003c/li\u003e\n\u003cli\u003eGrundy SM (2004) Obesity, metabolic syndrome, and cardiovascular disease. Journal of Clinical Endocrinology and Metabolism, 89(6), 2595\u0026ndash;2600. https://doi.org/10.1210/jc.2004-0372\u003c/li\u003e\n\u003cli\u003eAmaral DT, Bonatelli IA, Romeiro-Brito M, Telhe MC, Moraes EM, Zappi DC, Franco FF (2024) Comparative transcriptome analysis reveals lineage-and environment-specific adaptations in cacti from the Brazilian Atlantic Forest. Planta, 260(1), 4.\u003c/li\u003e\n\u003cli\u003eGrabherr, M., Haas, B., Yassour, M. et al. (2011) Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat Biotechnol 29, 644\u0026ndash;652. https://doi.org/10.1038/nbt.1883\u003c/li\u003e\n\u003cli\u003eVirgens GS, Oliveira J, Cardoso MIO, Teodoro JA, Amaral DT. BioProtIS: Streamlining protein-ligand interaction pipeline for analysis in genomic and transcriptomic exploration. J Mol Graph Model. 2024 May;128:108721. doi: 10.1016/j.jmgm.2024.108721. Epub 2024 Jan 30. PMID: 38308972.\u003c/li\u003e\n\u003cli\u003eWebb B, Sali A (2016) Comparative Protein Structure Modeling Using Modeller. Current Protocols in Bioinformatics 54, John Wiley \u0026amp; Sons, Inc., 5.6.1-5.6.37.\u003c/li\u003e\n\u003cli\u003eO\u0026apos;Boyle, N.M., Banck, M., James, C.A. et al. (2011) Open Babel: An open chemical toolbox. J Cheminform 3, 33. https://doi.org/10.1186/1758-2946-3-33.\u003c/li\u003e\n\u003cli\u003eTrott O, Olson AJ (2009) Autodock Vina: Improving the Speed and Accuracy of Docking with a New Scoring Function, Efficient Optimization, and Multithreading. Journal of Computational Chemistry, 31, 455-461. https://doi.org/10.1002/jcc.21334.\u003c/li\u003e\n\u003cli\u003eIorga B, Herlem D, Barr\u0026eacute; E, Guillou C (2006) Acetylcholine nicotinic receptors: finding the putative binding site of allosteric modulators using the \u0026ldquo;blind docking\u0026rdquo; approach. Journal of Molecular Modeling, 12 (3), pp.366-372. ff10.1007/s00894-005-0057-zff. ffhal-03161516f\u003c/li\u003e\n\u003cli\u003eAbraham MJ, Murtola T, Schulz R, P\u0026aacute;ll S, Smith JC, Hess B, Lindahl E (2015) \u0026ldquo;GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers,\u0026rdquo; SoftwareX, 1\u0026ndash;2 19\u0026ndash;25.\u003c/li\u003e\n\u003cli\u003eSousa da Silva AW, Vranken WF (2012) ACPYPE-Antechamber python parser interface. \u003cem\u003eBMC research notes\u003c/em\u003e, \u003cem\u003e5\u003c/em\u003e, 1-8.\u003c/li\u003e\n\u003cli\u003ePorras G, Chassagne F, Lyles JT, Marquez L, Dettweiler M, Salam AM, Quave CL (2020) Ethnobotany and the role of plant natural products in antibiotic drug discovery. Chemical reviews, 121(6), 3495-3560.\u003c/li\u003e\n\u003cli\u003eSharma A, Palm GJ, Kumari M, Panjikar S, Jagannadham MV, Hinrichs W (2012) Purification, crystallization and preliminary crystallographic analysis of banyan peroxidase. Acta Crystallographica Section F: Structural Biology and Crystallization Communications, 68(8), 931-934.\u003c/li\u003e\n\u003cli\u003eFry SC, Smith RC, Renwick KF, Martin DJ, Hodge SK, Matthews KJ (1992) Xyloglucan endotransglycosylase, a new wall-loosening enzyme activity from plants. Biochemical Journal, 282(3), 821-828.\u003c/li\u003e\n\u003cli\u003eShin DH, Lee JY, Hwang KY, Kim KK, Suh SW (1995) High-resolution crystal structure of the non-specific lipid-transfer protein from maize seedlings. Structure, 3(2), 189-199.\u003c/li\u003e\n\u003cli\u003eWang NJ, Lee CC, Cheng CS, Lo WC, Yang YF, Chen MN, Lyu PC (2012) Construction and analysis of a plant non-specific lipid transfer protein database (nsLTPDB). In BMC genomics (Vol. 13, pp. 1-9). BioMed Central.\u003c/li\u003e\n\u003cli\u003eFattore E, Bosetti C, Brighenti F, Agostoni C, Fattore G (2014) Palm oil and blood lipid\u0026ndash;related markers of cardiovascular disease: a systematic review and meta-analysis of dietary intervention trials. The American journal of clinical nutrition, 99(6), 1331-1350.\u003c/li\u003e\n\u003cli\u003eMancini A, Imperlini E, Nigro E, Montagnese C, Daniele A, Orr\u0026ugrave; S, Buono P (2015) Biological and nutritional properties of palm oil and palmitic acid: effects on health. Molecules, 20(9), 17339-17361.\u003c/li\u003e\n\u003cli\u003eZock PL, de Vries JHM, Katan MB (1994) Impact of myristic acid versus palmitic acid on serum lipid and lipoprotein levels in healthy women and men. Arterioscler Thromb;14:567\u0026ndash;75.\u003c/li\u003e\n\u003cli\u003eSun Y, Neelakantan N, Wu Y, Lote-Oke R, Pan A, van Dam RM (2015) Palm oil consumption increases LDL cholesterol compared with vegetable oils low in saturated fat in a meta-analysis of clinical trials. The Journal of nutrition, 145(7), 1549-1558.\u003c/li\u003e\n\u003cli\u003eNicolosi RJ (1997) Dietary fat saturation effects on low-density-lipoprotein concentrations and metabolism in various animal models. The American journal of clinical nutrition, 65(5), 1617S-1627S.\u003c/li\u003e\n\u003cli\u003eIsmail SR, Maarof SK, Siedar Ali S, Ali A (2018) Systematic review of palm oil consumption and the risk of cardiovascular disease. PLoS One, 13(2), e0193533.\u003c/li\u003e\n\u003cli\u003eUnhapipatpong C, Shantavasinkul PC, Kasemsup V, Siriyotha S, Warodomwichit D, Maneesuwannarat S, Thakkinstian A (2021) Tropical oil consumption and cardiovascular disease: An umbrella review of systematic reviews and meta analyses. Nutrients, 13(5), 1549.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Bioactive proteins, bioinformatics, cardiovascular diseases, mandacaru, triacylglycerol","lastPublishedDoi":"10.21203/rs.3.rs-5961003/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5961003/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe Caatinga biome in Brazil is home to unique plant species with significant bioactive potential, such as \u003cem\u003eCereus jamacaru\u003c/em\u003e (Mandacaru). Historically used in traditional medicine for treating kidney issues, diabetes, and cardiovascular conditions, this plant contains macromolecules, which may play a role in therapeutic applications. Here, we integrate transcriptomics, molecular docking, and molecular dynamics approaches to explore the potential of \u003cem\u003eC. jamacaru\u003c/em\u003e proteomics to binding to triacylglycerol molecules, particularly targeting triacylglycerol formed by lauric, myristic, and palmitic acids. Transcriptome analysis identified 128,942 transcripts, with 14,739 homologous proteins screened for binding affinities. Molecular docking highlighted an isoform of \u003cem\u003eBanyan Peroxidase\u003c/em\u003e as a versatile candidate, exhibiting strong binding energies across all triacylglycerols, particularly palmitic acid (-7.632 kcal/mol). \u003cem\u003eXyloglucan Endotransglycosylase\u003c/em\u003e demonstrated specificity for myristic acid (-7.752 kcal/mol), while \u003cem\u003eNon-specific Lipid-Transfer Protein\u003c/em\u003e showed exceptional structural stability in dynamic simulations. The molecular dynamics simulations, performed over 100 ns, revealed key insights into protein stability and ligand interactions. \u003cem\u003eBanyan Peroxidase\u003c/em\u003e displayed moderate flexibility, enhancing its adaptability to diverse triacylglycerol substrates. Conversely, \u003cem\u003eXyloglucan Endotransglycosylase\u003c/em\u003e exhibited compact stability, making it a strong candidate for targeted new applications. These findings highlight the untapped potential of \u003cem\u003eC. jamacaru\u003c/em\u003e as a source of bioactive proteins, bridging traditional knowledge with advanced computational methodologies.\u003c/p\u003e","manuscriptTitle":"In silico approach for the prospecting of molecules from Cereus jamacaru (Cereeae) in the treatment of obesity and cardiovascular diseases","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-19 08:35:02","doi":"10.21203/rs.3.rs-5961003/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":"1e3cf0a4-7166-4ebb-8f62-53f907d6fb42","owner":[],"postedDate":"February 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-02-19T08:35:02+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-19 08:35:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5961003","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5961003","identity":"rs-5961003","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}