Deciphering pH-Driven Dynamics of Prolyl Endopeptidases: Unveiling Structural insight in Celiac Disease using Computational Techniques

Deciphering pH-Driven Dynamics of Prolyl Endopeptidases: Unveiling Structural insight in Celiac Disease using Computational Techniques | 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 Deciphering pH-Driven Dynamics of Prolyl Endopeptidases: Unveiling Structural insight in Celiac Disease using Computational Techniques Awadhesh Kumar Verma, Shubham Kumar, Tanya Singh, Anand Mohan, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5708047/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 Celiac disease, an intricate autoimmune disorder, stems from gluten consumption, primarily found in wheat, barley, and rye. Due to its high proline content, gluten resists complete breakdown in the human digestive system. Prolyl endopeptidases (PEPs), a subclass of serine proteases, offer a promising therapeutic avenue. These enzymes exhibit a unique ability to cleave peptide bonds post proline residues, aiding gluten digestion. However, leveraging these enzymes effectively mandates a profound understanding of their operation within the dynamic pH milieu of the human gastrointestinal tract. This study delves into the influence of pH variations on PEP structure and activity, employing advanced computational methodologies. The research initiates with acquiring PEP sequences from ten diverse organisms via the UniProt database. Employing sequence analysis techniques like multiple sequence alignment and pairwise sequence alignment, we identify pH-sensitive regions by scrutinizing conserved motifs and sequence disparities. Prot Pi facilitates the computation of net charge profiles across varied pH gradients, unveiling pH-responsive charge distribution patterns. Structural analysis involves predicting 3D conformations through Pep-Fold4, encapsulating protein adaptations to pH fluctuations. RMSD calculations via PyMOL reveal pH-induced conformational alterations and their implications for protein stability. Also, rigorous homologous modeling of human PEPs via Swiss Model ensures structural fidelity, energy optimization with YASARA refines geometric parameters, while ERRAT analysis validates structural integrity. Docking simulations forecast PEP-gluten peptide interactions across diverse pH conditions. In conclusion, our comprehensive data analysis provides novel insights into how pH modulates PEP structures. These findings bear significant implications for enzyme catalysis, structural resilience, and potential therapeutic strategies. Bioinformatics Computational Biology Allergy & Immune Disorders Pediatrics Nutrition & Dietetics Scientific Communication Prolyl Endopeptidases pH variation Computational technology Sequence analysis Structural modeling RMSD calculation Homologous modeling Molecular docking Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Celiac disease, a complex autoimmune disorder triggered by gluten consumption, affects a significant portion of the global population [ 1 ]. Gluten, a protein complex found primarily in wheat, rye, and barley, poses challenges to the human digestive system due to its high proline content [ 2 ],[ 3 ]. Unique structure of proline makes gluten-derived peptides resistant to complete breakdown by human digestive enzymes [ 4 ]. This resistance plays a vital role in causing mechanisms of celiac disease. Prolyl endopeptidases (PEP), a class of serine proteases, offer a potential therapeutic avenue [ 5 ]. These enzymes possess the ability to cleave peptide bonds specifically after proline residues, aiding in the breakdown of gluten [ 6 ],[ 7 ]. However, to effectively utilize these enzymes, we must understand how they operate in the dynamic pH environments of the human gastrointestinal tract. Computational biology provides a robust toolkit to investigate the intricate relationship between PEP structure, function, and pH, accelerating research into celiac disease therapies [ 8 ]. In silico analysis uses computer methods in administration, curation, and comprehension of data pertinent to biological systems [ 9 ]. Enzymes called prolyl endopeptidases (PEPs) are involved in the gastrointestinal function of proteins and are highly sensitive to pH variations [ 10 ]. Understanding how pH changes impact the structure and function of PEPs using computational methods is exciting and could yield important insights into how these enzymes behave in various physiological contexts. Several computational methods, such as molecular docking studies, electrostatic property analysis, and molecular dynamics simulations, can be used to investigate PEPs. By understanding the impact of pH change on the structure and activity of PEPs, researchers can gain valuable insights into protein digestion and their role in various physiological contexts [ 11 ]. The morphology of proline-containing proteases (PEPs) is influenced by the pH level, with proline content being a distinctive characteristic of T-cell stimulating peptides. Gluten, has an excessive amino acid proline residue content, making it resistant to total proteolytic breakdown in the gastrointestinal tract of humans [ 12 ]. This is connected to the disease-causing qualities of gluten addition. Prolyl oligo peptidases are incapable of functioning in the stomach acidic pH range, working best at an acidic pH between seven and eight. Pepsin also breaks them down effectively [ 13 ]. Digestive enzymes degrade short amino acids predominantly due to their structure, which limits access to the active centre via a beta-propeller domain [ 14 ]. These characteristics suggest that prolyl oligopeptide supplemented orally is unlikely to be enough to break down gluten before passing through the most proximal portions of the duodenum [ 15 ]. Celiac disease is a condition characterized by the damage of the small intestinal villi due to inflammation, leading to a decline in nutrition absorption and affecting all other physiological systems. This results in symptoms that can manifest in various organs of the body, not just the gastrointestinal system. These symptoms include abdominal distension, pain or discomfort in the belly, vomiting, constipation, and unexpected weight loss. Women with untreated celiac disease are more likely to experience obstetric complications, including early labour, growth restriction, and still birth. Enterocyte disruption in the small intestine is the primary cause of celiac disease symptoms. Chronic inflammation and villi loss are the major hallmarks of the small intestine in the full-blown clinical picture [ 16 ]. An individual must have the HLA-dominant DQ2 or DQ8 genes and an antibody to tissue transglutaminase, which originates from the immune system unfavourable response to gluten. Some disease-causing pathways have been hypothesized, including the glycoprotein gliadin, which contains gluten, stimulating IL-15 production, which directly affects enterocytes. Early childhood gastrointestinal infections may influence a person later chance of acquiring celiac disease, possibly due to an immune system problem [ 17 ]. Diagnosis of celiac disease is typically done using tissue transglutaminase and IgA antibodies to the smooth muscle endomysium. However, only about 5% of individuals with celiac disease are immunoglobulin deficient. Understanding the functionality and structure of proteins in computational biology and structure-based biology requires a multifaceted approach that leverages a range of computational techniques [ 18 ]. Multiple Sequence Alignment (MSA) helps illuminate phylogenetic links and conserved areas, while the Clustal Omega tool facilitates alignment sorting and sheds light on the structural and psychological importance of proteins [ 19 ]. PyMOL [ 20 ]. a flexible molecular visualization tool, plays a central role in structural analysis, incorporating Roots Mean Square Deviation (RMSD) mathematical computations. YASARA software is responsible for quality control and structural optimization, using energy reduction to improve structural precision [ 21 ]. AutoDock Vina [ 22 ], a docking computer simulation program, forecasts binding affinities alongside modes, providing a more comprehensive understanding of the functional landscape of the protein [ 23 ],[ 24 ]. This comprehensive computational approach holds significant implications for celiac disease research. A thorough understanding of how PEP activity is influenced by pH will allow us to identify candidate PEPs that function optimally within the pH range encountered in the human digestive tract. This knowledge lays the groundwork for the development of enzyme-based therapies, where these robust PEPs could be used for gluten detoxification. Additionally, computational analysis could guide protein engineering strategies to enhance the stability of PEPs at specific pH levels, further improving their therapeutic potential [ 25 ],[ 26 ]. Moreover, our findings may reveal crucial residues and structural motifs involved in pH-dependent PEP function. This information could pave the way for the development of small-molecule modulators (inhibitors or activators) of PEP activity, offering another avenue for novel drug design in celiac disease management. this computational investigation into the pH-dependent function of prolyl endopeptidases has the potential to significantly accelerate the discovery of new therapies for celiac disease [ 18 ]. By leveraging a combination of bioinformatics, structural modeling, and molecular simulation, we can meticulously dissect the interplay between PEPs, gluten, and pH [ 18 ],[ 27 ]. Ultimately, this research aims to translate computational insights into tangible advancements in celiac disease treatment, improving the lives of patients worldwide. 2. Methodology 2.1. Data retrieval for Prolyl endopeptidases The UniProt database is a comprehensive and reliable resource for protein sequence and functional information. To investigate prolyl endopeptidases, sequences for ten distinct species – Arabidopsis thaliana, Bacillus subtilis, Gallus gallus, Homo sapiens, Mus musculus, Penicillium chrysogenum, Pipistrellus kuhlii, Spinacia oleracea, Sus scrofa domesticus, and Danio rerio – have been downloaded from UniProt [ 28 ]. The next step is to retrieve the corresponding protein sequences, along with any additional information from UniProt that would aid in accurate categorization and identification. 2.2. Sequence analysis for Prolyl endopeptidases 2.2.1. Pairwise sequence alignment of prolyl endopeptidases using EMBOSS Needle To delve deeper into the effects of pH fluctuations on prolyl endopeptidase sequences, we have utilized the EMBOSS Needle online tool for pairwise sequence alignment [ 29 ]. This tool allows us to compare two sequences at a time, revealing specific amino acid similarities and differences. By focusing on residues that are known to be pH-sensitive, we can investigate how these residues vary between different species. This detailed pairwise analysis may shed light on how pH fluctuations might influence prolyl endopeptidase structure and function. 2.2.2. Multiple sequence alignment of prolyl endopeptidases using Clustal Omega To investigate the pH-sensitivity of prolyl endopeptidases, we'll use the Clustal Omega web tool to perform a multiple sequence alignment (MSA) of these enzymes from various species. Clustal Omega is a powerful tool for aligning multiple sequences, allowing us to identify conserved regions and variations across our dataset [ 23 ]. By examining these conserved and divergent areas within the MSA, we aim to pinpoint pH-sensitive regions in prolyl endopeptidases. This analysis will offer insights into the structure-function relationship of these enzymes and how they respond to changes in pH. 2.3. Net charge calculation of prolyl endopeptidases using ProtPi online tool To investigate how pH influences the charge distribution of prolyl endopeptidases, we can use the ProtPi online tool [ 30 ]. We already obtain prolyl endopeptidases sequences for several species (sourced from UniProt). We will input these sequences into ProtPi, specifying a range of pH values. This tool will calculate the net charge of each sequence at different pH points, revealing the isoelectric point (pI) and overall charge distribution. By comparing these net charge profiles across species, we will gain insights into how pH sensitivity and stability might vary among prolyl endopeptidases from different organisms. 2.4. Structural modeling of prolyl endopeptidase using Pep-Fold4 To visualize how pH fluctuations might influence the three-dimensional structure of prolyl endopeptidases, we have employed the Pep-Fold4 web application. Pep-Fold4 allows us to predict protein structures based on amino acid sequences [ 31 ]. By inputting prolyl endopeptidase sequences and specifying different pH values, we can generate multiple models. These models will illustrate potential conformational changes that the protein might undergo in response to varying pH environments, providing insights into how pH could impact enzyme function and stability. While Pep-Fold4 is a useful tool for predicting structures of shorter peptides, its 50 amino acid length limitation poses a challenge for modeling full-length prolyl endopeptidases. To address this, we will focus on modeling key pH-sensitive regions or domains under 50 amino acids using Pep-Fold4. 2.5. Evaluation of prolyl endopeptidase structural deviations through RMSD calculation using PyMOL To investigate how pH-induced conformational changes might affect the stability of prolyl endopeptidases, utilizing PyMOL [ 32 ] to calculate Root Mean Square Deviation (RMSD) values. Obtain structures modeled at different pH levels. After that Load these structures into PyMOL, designating one as the reference. Using PyMOL, RMSD calculation functions we can compare each simulated structure to the reference, quantifying structural deviations. Analyse these RMSD values to gain insights into how pH fluctuations influence protein conformation and stability. 2.6. 3D structural modeling of Human prolyl endopeptidase using SWISS-MODEL To build a full-length model of human prolyl endopeptidase, leverage the homology modeling capabilities of the SWISS-MODEL online tool [ 33 ]. Inputting the human prolyl endopeptidase sequence and let SWISS-MODEL automatically identify suitable structural templates and generate a 3D model. Once the model is created, we will carefully evaluate its quality and accuracy using YASARA software [ 34 ] and ERRAT online tool [ 35 ]. These tools help identify any potential errors or areas for improvement in the generated model. 2.6.1. Energy minimization and structural evaluation of prolyl endopeptidase using Yet Another Scientific Artificial Reality Application (YASARA) To refine the predicted human prolyl endopeptidase structure, we employed YASARA software for energy minimization. We have loaded the homology model into YASARA [ 34 ] and use its built-in energy minimization protocols to optimize the geometry of the structure. This process involves adjusting atomic positions to reduce unfavorable interactions and reach a lower energy state, potentially enhancing the accuracy and stability of the model . 2.6.2. Model quality evaluation of human prolyl endopeptidase using Error Recognition and Removal Tool (ERRAT) To assess the quality of our refined human prolyl endopeptidase model, we utilized the ERRAT online tool [ 35 ]. ERRAT analyse the statistics of non-bonded interactions between different atom types within the protein structure. It generates an "overall quality factor" that reflects the likelihood of our model being correct. ERRAT also highlights regions of the model that might have unusual non-bonded interactions, allowing us to focus our attention on potential problem areas for further refinement. 2.7. Molecular docking of Gluten peptide with prolyl endopeptidase using AutoDock Vina To investigate how pH influences prolyl endopeptidase binding to relevant substrates or molecules, we utilized AutoDock Vina [ 36 ]. for molecular docking simulations [ 37 ],[ 38 ]. Prepare our prolyl endopeptidase structure by adjusting protonation states for the desired pH and obtain the 3D structure of your substrate/molecule. Furthermore, we defined the binding site on the enzyme, config a grid box within AutoDock Vina [ 39 ], preparing a configuration file, and execute the docking simulation. Finally, we analysed the resulting binding poses and predicted affinities to understand how pH-dependent changes might affect the interaction of prolyl endopeptidase. 3. RESULTS AND DISCUSSION 3.1. Prolyl endopeptidases data retrieval The comprehensive Prolyl Endopeptidases (PEP) sequences of 10 organisms was obtained as mentioned in the Table 1. This can further have been used for in-depth comparisons across diverse taxonomic groups, potentially revealing insights into the evolutionary relationships and functional variations of prolyl endopeptidases. 3.2.1. Identifying evolutionary relationships based on pairwise alignment Table 2 . Result showing the pairwise sequence alignments between certain Prolyl Endopeptidase sequences (sequence length 1-50 & 50-100) in a different organism by using the EMBOSS Needle online tool. The pairwise sequence alignment scores for the first 50 amino acids (Seq length 1-50) and the next 50 amino acids (Sequence length 50-100) was analyzed. The Prolyl Endopeptidase sequences show a high degree of similarity within vertebrates (animals with backbones), with alignment scores ranging from 182 to 262 for comparisons between human, mouse, chicken, and pig. This suggests that the Prolyl Endopeptidase enzyme has a conserved region and hence similar kind of function across these species. For the plant and fungal sequences, the Prolyl Endopeptidase sequences from plants ( Arabidopsis thaliana and Spinacia oleracea) and fungi ( Penicillium chrysogenum ) show lower similarity to the vertebrate sequences, with alignment scores ranging from 9.5 to 49.5. This indicates that the PEP enzyme has diverged more significantly in these lineages. The PEP sequence from the bacterium Bacillus subtilis shows low similarity to all other sequences in the table, with alignment scores ranging from 4 to 14. This suggests that the PEP enzyme has undergone significant evolutionary changes in bacteria compared to eukaryotes (organisms with cells containing a nucleus). These observations are consistent with the expected evolutionary relationships between the organisms listed in the table. Vertebrates are more closely related to each other than they are to plants, fungi, or bacteria, and this is reflected in the higher degree of similarity between their PEP sequences. From the above analysis of the pairwise sequence alignment results shown in the Table 2, it can be suggested that the PEP enzyme is highly conserved across vertebrates, but has diverged more significantly in plants, fungi, and bacteria. These findings are consistent with the expected evolutionary relationships between the organisms studied. 3.2.2. Exploring evolutionary relationships through multiple sequence alignment analysis The results of a multiple sequence alignment of Prolyl Endopeptidase sequences from various species done through Clustal Omega is shown in Figure 1. From the data we observed that, there are several regions in the alignment where the same amino acids are present across most or all of the sequences. This multiple sequence alignment of Prolyl Endopeptidases (PEPs) from diverse taxa offers insights into both the fundamental structural motifs required for activity and the potential adaptations to different environmental pressures. The high degree of conservation surrounding a putative catalytic aspartic acid residue (position ~150) and other likely active site residues underscores the shared core mechanism within the PEP family. This conservation across bacteria, fungi, plants, and animals suggests the early establishment of this enzymatic system and its essential role in biological processes. The identification of additional conserved regions warrants further exploration. These regions may be involved in substrate recognition, structural stability, or other yet-undetermined functions. Despite the conserved core, the MSA reveals variability across the PEP sequences, potentially reflecting functional diversification and niche adaptations. The observed sequence divergence in certain domains suggests that PEP enzymes have evolved to optimize their activity across distinct environments. Of particular interest is the variation in histidine (H), aspartic acid (D), and glutamic acid (E) residues, especially where those variations correlate with the known ecological niches or substrate preferences of the different species. For example, the enrichment of histidine residues in PEPs from species inhabiting acidic environments (positions ~200-220) suggests a potential role for these residues in pH-dependent stability or activity. These clusters of pH-sensitive residues could influence PEP conformation, substrate interactions, or protein-protein interactions under differing pH conditions. This analysis highlights the value of comparative genomics for understanding enzyme evolution and function. The observed interplay between conserved and variable regions within the PEP family suggests a balance between maintaining catalytic function and adapting to specific physiological demands. Overall, the Clustal Omega alignment provides valuable insights into the conservation and variation of Prolyl Endopeptidase sequences from different species. The alignment can be used to identify potential functional regions of the enzyme, including those that may be involved in pH sensitivity. 3.3. Prot Pi analysis of prolyl endopeptidase net charge profiles across a pH gradient Table 3. PEP net charge of various species at different pH levels has been determined by using the Prot Pi online tool. The computed net charge profiles of Prolyl Endopeptidase from various organisms across a range of pH values (pH 1 to pH 13) can be seen in the Table 3. The net charge of a protein refers to the algebraic sum of the charges of its constituent amino acid side chains at a specific pH. It is influenced by the pKa values of the amino acids, which determine their protonation state at different pH levels. At acidic pH values (pH 1-3), the Prolyl Endopeptidase from all species exhibits a positive net charge. This is likely due to the protonation of acidic amino acid side chains (aspartic acid and glutamic acid) at these pH values. The net charge becomes zero at a specific pH value, known as the isoelectric point (pI). The pI values vary across the different species, ranging from 3.63 for Human to 9.81 for Bacillus subtilis . This variation reflects the differences in the amino acid composition of the Prolyl Endopeptidase sequences from these species. At basic pH values (pH 9-13), the Prolyl Endopeptidase from all species exhibits a negative net charge. This is likely due to the deprotonation of basic amino acid side chains (lysine and arginine) at these pH values. The net charge profiles of Prolyl Endopeptidase from Human and Mouse are similar, with pI values of 3.63 and 3.896, respectively. This suggests a high degree of sequence similarity and conservation of functional properties between these two species. The Prolyl Endopeptidase from Bacillus subtilis has a significantly higher pI (9.81) compared to the other species. This indicates a distinct amino acid composition and potentially different functional properties compared to the Prolyl Endopeptidase from eukaryotes (organisms with cells containing a nucleus). The Prolyl Endopeptidase sequences from plants ( Arabidopsis thaliana and Spinacia oleracea ) and fungi ( Penicillium chrysogenum ) show pI values in the range of 5.3-8.3. These values are generally higher than those observed for the animal species, suggesting potential differences in enzyme function or regulation. The net charge profiles of Prolyl Endopeptidase, computed using the Prot Pi online tool, reveal variations across different species. These variations are likely due to differences in amino acid composition and may be associated with distinct functional properties or regulatory mechanisms. 3.4. Exploring pH-induced conformational changes in prolyl endopeptidase with Pep- Fold4 The 3D structure of human prolyl endopeptidases (PEP) of the sequence length 1-50 and 50-100 using the Pep-Fold4 can be seen in Figure 2 (a) and (b) respectively. We have also done the 3D modeling for the ten diverse species using Pep-Fold4 provided insights into the potential effects of pH on these enzymes' structures. Due to Pep-Fold4 length limitation, we focused on modeling key pH-sensitive domains or regions within PEPs. The generated models revealed conformational changes in the structure of human prolyl endopeptidase in response to varying pH environments. These findings suggest that pH fluctuations likely influence PEP enzymatic activity and stability. Further exploration of full-length PEP structures with more advanced modeling tools would be valuable to validate these predictions and deepen our understanding of the pH-dependent regulation of these important enzymes. 3.5. A quantitative evaluation of structural deviations using RMSD calculation in PyMOL This study investigates the effect of pH variation on the structural integrity of human and mouse prolyl endopeptidases (PEPs) using computational analysis. Root Mean Square Deviation (RMSD) calculations within PyMOL software will quantify structural deviations across a pH range, with a focus on specific protein sequence regions (residues 1-50 and 50-100). RMSD values will elucidate pH-sensitive regions within the PEP structures. Comparative analysis of RMSD values between human and mouse PEPs will reveal species-specific differences in pH susceptibility, potentially impacting enzymatic function. This study aims to provide insights into the structure-function relationship of PEPs and their sensitivity to environmental pH conditions. Root Mean Square Deviation (RMSD) calculations within PyMOL software will quantify structural deviations across a pH range, with a focus on specific protein sequence regions residues 1-50 in can be seen in Figure 3 (a) and 50-100 in Figure 3 (b). Root mean square deviation (RMSD) across diverse pH levels presents key insights into the protein structure conformational dynamics can be seen in the Table 4. Elevated RMSD values between pH2 and adjacent pH levels (pH3, pH4, pH9, pH10, pH12, and pH13) suggest substantial structural alterations, highlighting the protein sensitivity to pH fluctuations. Similarly, heightened RMSD values between intermediary pH levels indicate significant structural shifts, while comparatively lower RMSD values among pH9, pH10, pH12, and pH13 imply stability within these pH ranges. However, acknowledging the protein-specific region and the influence of additional factors like temperature and ionic strength is crucial for a comprehensive interpretation of the observed RMSD patterns, emphasizing the need for a nuanced understanding of the protein structural behavior within its environmental context. The Root Mean Square Deviation (RMSD) values across various pH levels of sequence length (50-100) in Table 5 illuminates substantial conformational alterations within the human prolyl endopeptidase (PREP) structure. Notably, significantly elevated RMSD values were observed during comparisons between pH 2 and several other pH conditions, including pH 3, 4, 5, 9, 10, 12, and 13, underscoring the heightened sensitivity of PREP structure to pH fluctuations, particularly in transitions from a strongly acidic milieu. Moreover, the RMSD values between pH 3 and the aforementioned higher pH levels also indicate substantial structural rearrangements. These observations suggest that alterations in protonation and deprotonation of amino acid residues, induced by varying pH levels, likely disrupt critical interactions essential for stabilizing PREP native fold, potentially impacting its functional attributes such as substrate binding, catalytic activity, or overall stability. Intriguingly, PREP exhibits enhanced structural stability within the pH range of approximately 7 to 8, corresponding to its physiological milieu, as evidenced by lower RMSD values. However, it is vital to note that RMSD offers a holistic assessment of structural changes and further investigations employing visualization tools and localized RMSD calculations are warranted to elucidate the specific regions within PREP structure most affected by pH fluctuations. Additionally, future research endeavours should delve into the functional implications of these pH-induced conformational alterations by scrutinizing PREP substrate specificity and enzymatic activity. 3.6. Structural investigation of human Prolyl Endopeptidase using Swiss-Model homology modeling The predicted three-dimensional structure of human prolyl endopeptidase (Model 1) shown in Figure 4 (a). This model reveals a complex protein architecture dominated by both alpha-helical and beta-sheet secondary structures. Alpha helices appear as tightly coiled spirals, while beta-sheets form extended, planar regions within the protein. The interplay of these secondary structure elements likely creates the enzyme active site, where the catalytic cleavage of peptide bonds occurs. Loops and turns, visible as less structured regions of the model, may provide flexibility and contribute to substrate recognition. It was generated using Swiss-Model and exhibits promising quality metrics. Its foundation on a template (A5LFV8.1.A) with exceptionally high sequence identity (87.88%) underscores its suitability for representing human PEP. Excellent resolution (0.57) and near-complete sequence coverage (0.98) further bolster its reliability. The choice of AlphaFold v2 as the modeling algorithm adds confidence due to its reputation for high accuracy. While limitations inherent to homology modeling exist, Model 1 provides a strong initial framework for further analysis. Investigations into pH-dependent effects will be crucial for identifying specific residues and spatial arrangements essential for PEP function and understanding its potential modulation by physiological conditions. The energy minimization study conducted using YASARA software on the protein structure yielded compelling results shown in Figure 4 (b). The significant decrease in energy from an initial value of -265422.8 kJ/mol to a final value of -362103.3 kJ/mol indicates substantial structural optimization, indicative of a more stable conformation. Moreover, the transition from an initial score of -1.69 to a final score of -0.76 underscores the enhancement in structural quality post-minimization, as lower scores are indicative of improved protein conformations in YASARA. These findings suggest that the energy minimization protocol employed effectively optimized the protein structure, potentially leading to a more biophysically relevant conformation. Such optimization is crucial for understanding the structure-function relationship of proteins, laying the groundwork for further computational and experimental investigations. To ensure the reliability of our human prolyl endopeptidase model (model 1), we assessed its structural integrity using the ERRAT online tool shown in Figure 4 (c). This analysis yielded an overall quality factor of 91.281, indicating a high degree of structural credibility for the model. ERRAT evaluates various parameters such as Ramachandran plot statistics, bad contacts, and hydrogen bonding patterns to identify potential structural errors. A high overall quality factor, as observed here, suggests a well-folded protein structure with minimal steric clashes and a proper network of hydrogen bonds, which are crucial for protein stability and function. This positive assessment from ERRAT strengthens the confidence in our model and paves the way for further investigations into its functionality. Our ERRAT analysis of the refined human prolyl endopeptidase (model 1) demonstrated a highly favorable Ramachandran plot distribution (85.2% core, 11.5% allowed) can be seen in Figure 4 (d). This, along with the ERRAT overall quality factor, indicates a structurally sound model. While some residues fall within generously allowed or disallowed regions (2.7%, 0.7% respectively), these percentages are small enough to suggest that focused investigation of these specific residues might identify potential areas for additional refinement. Overall, the ERRAT results strongly support the good quality of the model, making it a reliable basis for further investigations into the prolyl endopeptidase structure and function. The predicted three-dimensional structure of human prolyl endopeptidase (Model 2) shown in Figure 5 (a) (Supplementary file). This study employed Swiss-Model to generate a second structural model (Model 2) of human prolyl endopeptidase (PEP), aiming to explore alternative representations for subsequent pH-dependent analyses. Model 2 was constructed using a template protein (7obm.1.A) exhibiting high sequence identity (84.37%) to human PEP, ensuring a reasonable degree of structural similarity between the template and target. While the template classification as "endonuclease-like" indicates potential functional divergence from PEPs, the high sequence identity and good sequence coverage (91%) suggest a level of suitability for representing PEP. Evaluation of Model 2 quality metrics Global Model Quality Estimation (GMQE): 0.75, QMEANDIsCo Global: 0.80 ± 0.05 necessitates specific knowledge of Swiss-Model scoring criteria for confident interpretation. Nevertheless, these values suggest a generally acceptable model quality. The results obtained from the energy minimization study shown in Figure 5 (b) are obtained by utilizing YASARA software present intriguing insights into the structural dynamics of the protein under investigation. The considerable decrease in energy observed, from an initial value of -239076.6 kJ/mol to a final value of -330468.8 kJ/mol, indicates a notable improvement in the stability and energetics of the protein structure following the optimization process. Furthermore, the transition from an initial score of -2.86 to a final score of -1.49 signifies a substantial enhancement in structural quality, as evidenced by the reduction in the score magnitude. This suggests that the energy minimization protocol employed successfully alleviated unfavorable interactions within the protein structure, resulting in a more compact and energetically favorable conformation. Such improvements are crucial for elucidating the functional properties of the protein and can provide valuable insights into its biological role and interactions with ligands or other macromolecules. To ensure the reliability of our human prolyl endopeptidase model (model 2), we assessed its structural integrity using the ERRAT online tool shown in Figure 5 (c). This analysis yielded an overall quality factor of 87.856. While this score indicates a generally well-folded structure, it suggests potential room for improvement compared to models with scores exceeding 90. ERRAT evaluates various parameters such as Ramachandran plot statistics, bad contacts, and hydrogen bonding patterns to identify potential structural errors. We will carefully examine regions flagged by ERRAT and consider focused refinements to optimize the model further. This attention to structural detail will ensure the highest possible accuracy for subsequent simulations and studies of prolyl endopeptidase function. The ERRAT analysis of our refined human prolyl endopeptidase model reveals promising structural quality. The Ramachandran plot demonstrates a substantial percentage of residues within the most favored core region (86.1%), suggesting a sound backbone conformation can be seen in Figure 5 (d) . While a small percentage of residues fall within generously allowed or disallowed regions (1.4% and 0.2%, respectively), this points to potential targets for further refinement. These areas, flagged by ERRAT, warrant closer inspection to identify specific points for optimization. Overall, the ERRAT results support the model quality, establishing it as a reliable foundation for subsequent investigations into the structure and function of human prolyl endopeptidase. The 3D structure for Model 3 is generated using Swiss-Model for the representation of human prolyl endopeptidase shown in Figure 6 (a) (Supplementary file). However, detailed analysis reveals significant concerns regarding its reliability due to the remarkably low sequence identity (19.19%) between the template protein and the target protein. While the template exhibits good resolution (2.05 Å) and the model demonstrates reasonable sequence coverage (0.82), the fundamental reliance of homology modeling on sequence similarity cannot be overlooked. A template with such low identity to the target sequence likely possesses substantial structural deviations, potentially leading to incorrect folding predictions and misidentification of critical regions, such as the active site, in Model 3. The quality metrics, GMQE (0.48) and QMEANDIsCo Global (0.58 ± 0.05), necessitate further clarification using Swiss-Model documentation to assess their significance in this context. The energy minimization analysis conducted using YASARA software has provided valuable insights into the structural refinement of the protein under investigation shown in Figure 6(b). In this study, we observed a substantial reduction in energy, transitioning from an initial value of -220654.1 kJ/mol to a final value of -317128.3 kJ/mol, indicative of significant stabilization of the protein structure. Moreover, the scores obtained, with an initial score of -3.22 decreasing to a final score of -2.07, further support the notion of improved structural quality following the energy minimization process. These results suggest that the applied energy minimization protocol effectively minimized unfavorable interactions and optimized the protein conformation, leading to a more energetically favorable state. Comparing these results with those obtained from other energy minimization studies, it becomes evident that all three experiments led to substantial improvements in the stability and quality of the protein structures. However, to determine the best result, several factors need to be considered. Firstly, the magnitude of the energy and score values should be evaluated. In this regard, the result with the lowest final energy and score would indicate the most favorable structural refinement. Secondly, the consistency of the improvements across different metrics, such as bond lengths, angles, and dihedrals, should be assessed. Lastly, the computational cost and convergence properties of each energy minimization run should be taken into account. After comparison with others models, it appears that the third result, with an initial energy of -220654.1 kJ/mol and a final energy of -317128.3 kJ/mol, along with initial and final scores of -3.22 and -2.07, respectively, represents the most favorable outcome. This result demonstrates a significant decrease in energy and score values, indicating improved structural stability and quality, but it also shows consistency across various metrics. Therefore, based on these considerations, the third result can be considered the best outcome of the energy minimization study. To assess the reliability of our human prolyl endopeptidase models, we evaluated their structural integrity using the ERRAT online tool shown in Figure 6 (c). The overall quality factor for model 3 was 81.192. While this indicates a folded structure, it is the lowest score among the three models. Models 1 and 2 received scores of 91.281 and 87.856, respectively. Lower ERRAT scores suggest a higher likelihood of structural errors like Ramachandran plot outliers, unfavorable contacts, or deficiencies in hydrogen bonding. Comparing these results, models 1 and 2 exhibit a higher degree of structural credibility based on ERRAT analysis. Model 1, with the highest score, is most likely the most reliable representation of the native prolyl endopeptidase structure. The ERRAT analysis of our refined human prolyl endopeptidase (Model 3) indicates a generally favorable Ramachandran plot distribution (85.4% core, 12.7% allowed) can be seen in Figure 6 (d). This suggests a well-defined protein backbone conformation. However, a small fraction of residues falls within the generously allowed (1.2%) and disallowed (0.8%) regions, highlighting potential areas for further refinement. Additionally, the presence of 1 bad contact and 3 cis-peptides warrants further investigation. These areas flagged by ERRAT, along with the cis-peptides, could be optimized to improve the model structure. Overall, the ERRAT results support the model quality, providing a solid foundation for further studies on human prolyl endopeptidase structure and function. 3.7. Molecular docking of prolyl endopeptidase: analyzing substrate interactions at different pH levels Molecular docking scores for the affinity-based selection of Homo sapiens prolyl endopeptidase (sequence 1-50) revealed favorable predicted binding affinities for multiple compounds across various pH values (refer Table 6). Docking scores ranged from -7.6 kcal/mol to -9.5 kcal/mol, indicating potentially strong interactions between the compounds and the target protein can be seen in Figure 7. Significantly, most compounds demonstrated scores below -8 kcal/mol, implying a high likelihood of effective active site targeting. While minor variations in scores were observed, the overall results suggest robust ligand-protein binding within the tested pH range. These findings highlight the importance of considering pH when investigating ligand-protein interactions and support the potential of affinity-based selection for identifying potent prolyl endopeptidase inhibitors that are effective across different physiological conditions. Molecular docking scores for the affinity-based selection of Homo sapiens sapiens prolyl endopeptidase (sequence 50-100) across varying pH conditions revealed promising binding affinities for several compounds (refer Table 7). Docking scores ranged from -7.7 kcal/mol to -11 kcal/mol, suggesting strong and favorable ligand-protein interactions. Notably, compounds 3 and 5 emerged as particularly promising candidates with exceptionally high scores of -10.6 kcal/mol and -11 kcal/mol, respectively, indicative of potent binding affinity can be seen in Figure 8 (Supplementary file). These findings suggest their potential as lead molecules for further investigation in prolyl endopeptidase inhibition. While some variations in scores were observed, the overall trend indicates robust binding interactions within the tested pH range. This study highlights the importance of considering target protein sequence length during ligand selection and underscores the potential of affinity-based selection for identifying effective prolyl endopeptidase inhibitors. 3.8. Visualization and analysis of Prolyl Endopeptidase-Gluten Peptide complex formation using LigPlot plus Computational tools like Autodock Vina aid in generating docking models that predict the binding mode of a gluten peptide (ball-and-stick representation) within the active site of Homo sapiens sapiens prolyl endopeptidase (Model 1) (ribbon representation) can be seen in Figure 9 (a). Software like LigPlot Plus can further analyze the complex, highlighting specific interactions with the enzyme (Asparagine) ASN-9, (Threonine)THR-17, and (Alanine)ALA-4 residues, which likely contribute to the complex stability. This research uses computational techniques to investigate these interactions, offering relevance for celiac disease management. A deeper understanding of gluten peptide binding to PEP could lead to strategies that optimize the enzyme ability to break down these immunogenic peptides, potentially paving the way for novel therapeutic approaches. The Figure 9 (b) depicts a computational docking model of a gluten peptide bound to the active site of Human prolyl endopeptidase (Model 2) (Supplementary file). This docking model suggests a potential binding mode where the gluten peptide interacts with the enzyme through specific amino acid residues. Notably, the interaction appears to involve (Valine) VAL-349, (Aspartic acid) ASP-91, and (Threonine) THR-102 on the enzyme's surface. These interactions likely contribute to the stability and specificity of the gluten peptide-PEP complex. Understanding the precise interactions between gluten peptides and PEP is crucial within the context of celiac disease. PEP has the potential to break down gluten peptides, potentially reducing their immunogenic effects. The molecular docking model illustrating the intricate binding of a gluten peptide to the catalytic site of human PEP can be seen in Figure 9(c) (Supplementary file). This model delineates a putative binding mode whereby the gluten peptide establishes specific interactions with key amino acid residues within the enzyme's active site. Particularly noteworthy is the involvement of (Lysine) LYS-79 located on the enzyme's surface, suggesting its significance in mediating the binding affinity between the gluten peptide and PEP. These molecular interactions are presumed to play a pivotal role in fostering the stability and selectivity of the gluten peptide-PEP complex. Elucidating the precise molecular interplay between gluten peptides and PEP holds paramount importance in the realm of celiac disease research. PEP exhibits the potential to enzymatically degrade gluten peptides, thus potentially mitigating their immunogenic effects, thereby underscoring its therapeutic relevance in managing celiac disease pathology.Top of FormBottom of Form Analysis done from the docking complex obtained through LigPlot plus reveals a network of interactions likely governing the binding of the gluten peptide to prolyl endopeptidase (Model 1) can be seen in Figure 10 (a). Predominant among these forces are hydrogen bonds, evident between specific residues in the enzyme and the peptide. Additionally, hydrophobic interactions appear to contribute significantly to the complex stability. The potential presence of salt bridges further suggests a role for electrostatic attractions in complex formation. Identification of these key residues offers valuable insights into the molecular recognition mechanism employed by prolyl endopeptidase in gluten binding and could inform future investigations regarding enzymatic activity and targets for the modulation of gluten degradation. The careful examination of the data obtained from LigPlot plus reveals a complex network of interactions likely responsible for the binding of the gluten peptide to prolyl endopeptidase (model 2) can be seen in Figure 10 (b) (Supplementary file). Prominent among these are hydrogen bonds, with the image highlighting specific residues within the enzyme and peptide that participate in donor-acceptor pairs. Also, hydrophobic interactions appear significant for complex stability, evidenced by clustering of nonpolar residues. The presence of potential salt bridges, dependent on the ionization states of specific charged residues at physiological pH, suggests a possible role for electrostatic attractions in binding. This detailed interaction map sheds light on prolyl endopeptidase gluten recognition mechanism and could guide future investigations into the modulation of its enzymatic activity through the targeting of specific binding residues. The 2D structure of the prolyl endopeptidase (model 3) and gluten peptide complex, generated using LigPlot plus software can be seen in Figure 10 (c) (Supplementary file), offers crucial insights into the molecular interactions underpinning this enzyme-substrate relationship. PEP, a serine protease, specifically cleaves peptide bonds after proline residues, making it relevant to the study of gluten. Gluten peptides, derived from proline-rich wheat gluten, are implicated in celiac disease, an autoimmune disorder triggered by gluten consumption in susceptible individuals. LigPlot plus analysis highlights the network of hydrogen bonds, hydrophobic interactions, and other atomic contacts that govern the binding affinity and specificity of the PEP-gluten peptide complex. Hydrogen bonds, formed between hydrogen and electronegative atoms, likely play a major role. Hydrophobic interactions between nonpolar regions may also be depicted. Additionally, the 2D structure could reveal salt bridges (ionic bonds) and weaker van der Waals forces contributing to the complex stability. Understanding these interactions has direct implications for celiac disease research; the structure may pinpoint critical amino acid residues within the gluten peptide that facilitate PEP binding. This knowledge could pave the way for novel therapeutic strategies, potentially targeting the inhibition of the PEP-gluten peptide interaction as a means to manage celiac disease. 4. Conclusion This study aimed to investigate the evolutionary relationships, structural properties, and potential pH-dependent activity of Prolyl Endopeptidase (PEP) enzymes through a comprehensive multi-faceted computational approach. The presented findings provide valuable insights into PEP structure, function, and interactions across a diverse range of species. Multiple sequence alignment analysis using Clustal Omega revealed both highly conserved and variable regions within the PEP sequences. Conserved regions, particularly those surrounding the known catalytic site, are crucial for the fundamental enzymatic activity of PEP. Variable regions, on the other hand, likely to contribute species-specific adaptations, such as substrate specificity and variations in pH sensitivity. Pairwise sequence alignments further reinforced the notion of evolutionary conservation. Vertebrates showed high similarity in their PEP sequences, highlighting a shared lineage. As expected, more distant evolutionary relationships were reflected in lower similarity scores for comparisons between plants, fungi, and bacteria with vertebrates. Net charge profiles across a range of pH values highlighted the influence of pH on the overall charge of PEP enzymes. The observed isoelectric point (pI) variations between species aligned with differences in amino acid composition, offering clues about potential functional adaptations. Human and mouse PEP sequences exhibited similar net charge profiles and pI values. Further supporting their evolutionary relatedness and potential for shared functional characteristics. An exploration of pH-induced conformational changes using Pep-Fold4 structural alterations in PEP enzymes in response to varying pH conditions were observed. Understanding such conformational changes is essential as they may directly influence enzyme activity, substrate binding, and stability. For instance, changes near the catalytic site could alter the enzyme ability to bind and cleave target substrates. To obtain a more accurate representation of the PEP structure, homology modeling was performed using SWISS-MODEL. The quality and reliability of the generated models were evaluated using ERRAT scores. Models with ERRAT scores above 90 indicated a high degree of structural accuracy and reliability, providing confidence for subsequent docking studies. Energy minimization studies using YASARA software demonstrated the importance of structural optimization for reliable simulations. By decreasing internal strain and achieving more energetically favorable conformations, energy minimization likely resulted in models that better reflect native PEP structures. This refinement process has implications for accurate docking simulations. Molecular docking of PEP with gluten peptides at different pH levels revealed a potential pH-dependence for their binding interaction. In general, more negative docking scores (indicating stronger predicted binding affinities) were observed at higher pH values. However, this trend had exceptions, suggesting that individual gluten peptides interact with PEP in unique ways, highlighting the necessity for further studies to characterize these interactions in detail. The 2D representation of the PEP-gluten peptide complex generated through LigPlot plus offered insights into the types of interactions governing the specific binding between this enzyme and disease-relevant gluten fragment. Hydrogen bonding and hydrophobic interactions appear to be significant contributors to the stability of the complex. This understanding may open avenues for the design of therapeutic interventions targeting the disruption of PEP-gluten interaction with the ultimate goal of mitigating celiac disease symptoms. 5. Future Prospective This in-silico study of celiac disease establishes a comprehensive framework for exploring the intricate effects of pH on prolyl endopeptidase (PEP) structure, function, and interactions with substrates or inhibitors. To advance these findings into practical realms, several critical avenues beckon further investigation. Firstly, validating the pH-induced conformational changes predicted computationally demands experimental techniques like X-ray crystallography or nuclear magnetic Resonance (NMR) spectroscopy, offering high-resolution snapshots and dynamic insights into PEP behaviour. Secondly, employing site-directed mutagenesis to discern the molecular basis of pH sensitivity in PEPs will unravel the specific residues dictating their response to pH changes, fostering a deeper understanding and rational design of modulators. Thirdly, biochemical assays coupled with biophysical techniques like isothermal titration calorimetry (ITC) or surface plasmon resonance (SPR) are indispensable for confirming fluctuations in substrate/inhibitor binding affinity at different pH levels, potentially uncovering selective PEP modulation strategies. Moreover, leveraging structural-functional insights can inspire the development of pH-sensitive PEP inhibitors targeting specific pathological microenvironments while also spotlighting PEPs as potential therapeutic targets and diagnostic biomarkers in diseases characterised by unregulated pH conditions. Beyond immediate applications, unravelling PEP pH sensitivity promises broader revelations into protein structure-function relationships and the intricate interplay between cellular processes and environmental cues. This bioinformatics study establishes a comprehensive framework for exploring the intricate effects of pH on prolyl endopeptidase (PEP) structure, function, and interactions with substrates or inhibitors. To advance these findings into practical realms, several critical avenues beckon further investigation. Firstly, validating the pH-induced conformational changes predicted computationally demands experimental techniques like X-ray crystallography or nuclear magnetic Resonance (NMR) spectroscopy, offering high-resolution snapshots and dynamic insights into PEP behaviour. Secondly, employing site-directed mutagenesis to discern the molecular basis of pH sensitivity in PEPs will unravel the specific residues dictating their response to pH changes, fostering a deeper understanding and rational design of modulators. Thirdly, biochemical assays coupled with biophysical techniques like isothermal titration calorimetry (ITC) or surface plasmon resonance (SPR) are indispensable for confirming fluctuations in substrate/inhibitor binding affinity at different pH levels, potentially uncovering selective PEP modulation strategies. Moreover, leveraging structural-functional insights can inspire the development of pH-sensitive PEP inhibitors targeting specific pathological microenvironments while also spotlighting PEPs as potential therapeutic targets and diagnostic biomarkers in diseases characterised by unregulated pH conditions. Beyond immediate applications, unravelling PEP pH sensitivity promises broader revelations into protein structure-function relationships and the intricate interplay between cellular processes and environmental cues. Declarations Author Contributions AKV and SK performed the experiment, did graphical designing and analysis, and wrote the manuscript. AKV led the development of methodology for the experiment, data extraction, study quality assessment, conceptualization, study identification, analysis, manuscript writing and editing, and overall supervision. TS edited the whole draft and did the referencing. AKV, AM, NRS and AK provided detailed reviews of the manuscript drafts and analysis, along with crucial feedback. Declaration of Interest The authors state that they have no known financial conflicts of interest or personal connections that could have influenced the work presented in this study. Acknowledgement The authors sincerely acknowledge all kinds of support from the School of Bioengineering and Biosciences, Lovely Professional University, Punjab, India. Also, the authors also extend their heartfelt thanks to SCFBio, Indian Institute of Technology, Delhi (IIT-Delhi), India and Gene Regulatory Laboratory, National Institute of Immunology (NII), New Delhi, India for providing computational facilities to conduct our study. 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Technol. , vol. 12, no. 7, p. 73004, 2023, doi: 10.1149/2162-8777/ace286. Additional Declarations The authors declare no competing interests. Supplementary Files 25122024SupplementaryfilePEP.docx Deciphering pH-Driven Dynamics of Prolyl Endopeptidases: Unveiling Structural insight in Celiac Disease using Computational Techniques GraphicalAbstract.png 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-5708047","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":394197227,"identity":"68291dc8-aeeb-4c44-8e42-49190f6b56c2","order_by":0,"name":"Awadhesh Kumar Verma","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA10lEQVRIiWNgGAWjYDCCAwwMEmAGewOQMLAgRQvPAZAWCVK0SCSAScI6+G4ffnibp+JeNP/M51c3/CiQYOBv707Aq0XyXJqxNc+Z4twZt3PKbvYAHSZx5uwGvFoMzjCYSc5sS8htuJ2TdoMHqMVAIpeQFvZvkjP/JeTOv3km7eYf4rTwmEl8bEjI3XCD/dhtomyRPMNTbPHhWELuxjM5bLdlDCR4CPqF7wz7xhsJNQm5844ff3bzzR8bOf72XvxakACPAZgkVjkIsD8gRfUoGAWjYBSMIAAAzahMPORV/eoAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-1497-6210","institution":"Lovely Professional University, Punjab","correspondingAuthor":true,"prefix":"","firstName":"Awadhesh","middleName":"Kumar","lastName":"Verma","suffix":""},{"id":394199350,"identity":"3d50fc42-88dd-4522-8188-4996a92793f7","order_by":1,"name":"Shubham Kumar","email":"","orcid":"","institution":"Lovely Professional University, Punjab","correspondingAuthor":false,"prefix":"","firstName":"Shubham","middleName":"","lastName":"Kumar","suffix":""},{"id":394199351,"identity":"65083216-2e6c-4484-b09f-7d2febb3f5e3","order_by":2,"name":"Tanya Singh","email":"","orcid":"https://orcid.org/0000-0002-3817-145X","institution":"Lovely Professional University, Punjab","correspondingAuthor":false,"prefix":"","firstName":"Tanya","middleName":"","lastName":"Singh","suffix":""},{"id":394199352,"identity":"00934874-36b5-49a1-9791-a501c442263a","order_by":3,"name":"Anand Mohan","email":"","orcid":"","institution":"Lovely Professional University, Punjab","correspondingAuthor":false,"prefix":"","firstName":"Anand","middleName":"","lastName":"Mohan","suffix":""},{"id":394199353,"identity":"76ccb3e4-549e-427b-a9ed-2bd3d73e6e6b","order_by":4,"name":"Neeta Raj Sharma","email":"","orcid":"https://orcid.org/0000-0001-8638-4217","institution":"Lovely Professional University, Punjab","correspondingAuthor":false,"prefix":"","firstName":"Neeta","middleName":"Raj","lastName":"Sharma","suffix":""},{"id":394199354,"identity":"5d66cefe-e7f2-477d-948c-87e8fb760ae9","order_by":5,"name":"Anil Kumar","email":"","orcid":"https://orcid.org/0000-0002-8785-6033","institution":"National Institute of Immunology, New Delhi","correspondingAuthor":false,"prefix":"","firstName":"Anil","middleName":"","lastName":"Kumar","suffix":""}],"badges":[],"createdAt":"2024-12-24 20:08:31","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-5708047/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5708047/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":72736107,"identity":"6cc41bf0-0664-4359-ad5d-73571edcc9b7","added_by":"auto","created_at":"2025-01-01 08:27:04","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":426864,"visible":true,"origin":"","legend":"\u003cp\u003eMultiple sequence alignment of Prolyl Endopeptidase sequences from various species using the Clustal Omega web tool to find the pH-sensitive regions, conserved areas and differences across the matched sequences.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5708047/v1/2d2bd63477dbb3220d1517fe.png"},{"id":72736106,"identity":"de43d5b5-95ea-4233-a59b-0bd67cc8b656","added_by":"auto","created_at":"2025-01-01 08:27:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":81840,"visible":true,"origin":"","legend":"\u003cp\u003e(a) 3D structures of \u003cem\u003eHomo sapiens\u003c/em\u003e \u003cem\u003esapiens\u003c/em\u003e prolyl endopeptidase (sequence length 1-50) at various pH levels obtained using Pep-Fold4 prediction. The models depict predicted conformational changes in the protein structure due to varying pH conditions. (b) 3D structures of \u003cem\u003eHomo sapiens sapiens\u003c/em\u003e prolyl endopeptidase (sequence length 50-100) at various pH levels obtained using Pep-Fold4 prediction. The models depict predicted conformational changes in the protein structure due to varying pH conditions.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5708047/v1/2ec1d03719c07ddd96a97eb1.png"},{"id":72736109,"identity":"7697f39e-e9a5-436a-8af6-9d9ce47a2141","added_by":"auto","created_at":"2025-01-01 08:27:04","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":99476,"visible":true,"origin":"","legend":"\u003cp\u003eRMSD values for \u003cem\u003eHomo sapiens sapiens\u003c/em\u003e (a): 1-50 AA and (b): 50-100 AA sequences, across varying pH levels calculated using PyMOL.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5708047/v1/6c713f923b1229cdfaef236c.png"},{"id":72736131,"identity":"3fc0c611-d9cc-40a0-b9b0-fb9a1ec62c3f","added_by":"auto","created_at":"2025-01-01 08:27:05","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":258002,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Top three homology models of human prolyl endopeptidase generated using Swiss-Model. The highlighted model (Model 1) was selected for further analysis. (b) Energy minimization of human prolyl endopeptidase (Model 1) using YASARA software. (c) \u0026nbsp;Evaluating structural dependability and model quality with ERRAT online tool. (d) ERRAT analysis shows a Ramachandran plot distribution, supporting the PEP (Model 1) overall quality.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5708047/v1/2efa6c6cc59a4bdf79b279c6.png"},{"id":72736113,"identity":"e866905b-9c8a-4623-9306-71e909d72c93","added_by":"auto","created_at":"2025-01-01 08:27:04","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":21177,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 7. \u003c/strong\u003eDocking scores of gluten peptides (ligand) with prolyl endopeptidase (enzyme) of \u003cem\u003eHomo sapiens sapiens\u003c/em\u003e (sequences 1-50 amino acids) at various pH levels. Lower scores indicate more favorable binding.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-5708047/v1/f0ba8e21d02f75816bace1fd.png"},{"id":72736118,"identity":"4e4ae672-1690-4f1a-8ea1-c1c687c69429","added_by":"auto","created_at":"2025-01-01 08:27:04","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":135130,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 9. \u003c/strong\u003e(a)\u003cstrong\u003e \u003c/strong\u003eMolecular\u003cstrong\u003e \u003c/strong\u003edocking simulation suggests a possible binding mode for a gluten peptide in a ball-and-stick representation within the active site of Human PEP (model 1) in ribbon representation. Parts (b) and (c) are inputted in supplementary file.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-5708047/v1/d2f0f79cb7d8003b1227e930.png"},{"id":72736725,"identity":"7007abf2-9f76-404f-a900-7e84e05a03ab","added_by":"auto","created_at":"2025-01-01 08:35:04","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":90271,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 10.\u003c/strong\u003e(a) Snapshot showing the interaction between Prolyl endopeptidase (model 1) and Gluten peptide complex using LigPlot plus software. (Results of (b) and (c) are given in supplementary file).\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-5708047/v1/8e8d74dc9d295a13ddccdf80.png"},{"id":72737770,"identity":"ba5680be-3908-4507-8d45-6bf5a2fa35af","added_by":"auto","created_at":"2025-01-01 08:59:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1875368,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5708047/v1/9c0c146f-5bab-4ca7-b26c-a6e0ec3cfc42.pdf"},{"id":72736112,"identity":"ff698477-3980-4bbd-8d5c-ccfabea3b7ca","added_by":"auto","created_at":"2025-01-01 08:27:04","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":9858631,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDeciphering pH-Driven Dynamics of Prolyl Endopeptidases: Unveiling Structural insight in Celiac Disease using Computational Techniques\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"25122024SupplementaryfilePEP.docx","url":"https://assets-eu.researchsquare.com/files/rs-5708047/v1/ec6d2da292714614a346e3c9.docx"},{"id":72736110,"identity":"7457f35d-8d77-416e-9983-3bfd199e34ce","added_by":"auto","created_at":"2025-01-01 08:27:04","extension":"png","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":212334,"visible":true,"origin":"","legend":"","description":"","filename":"GraphicalAbstract.png","url":"https://assets-eu.researchsquare.com/files/rs-5708047/v1/bf2577efca32db76b792dd37.png"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eDeciphering pH-Driven Dynamics of Prolyl Endopeptidases: Unveiling Structural insight in Celiac Disease using Computational Techniques\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eCeliac disease, a complex autoimmune disorder triggered by gluten consumption, affects a significant portion of the global population [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Gluten, a protein complex found primarily in wheat, rye, and barley, poses challenges to the human digestive system due to its high proline content [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e],[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Unique structure of proline makes gluten-derived peptides resistant to complete breakdown by human digestive enzymes [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. This resistance plays a vital role in causing mechanisms of celiac disease. Prolyl endopeptidases (PEP), a class of serine proteases, offer a potential therapeutic avenue [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. These enzymes possess the ability to cleave peptide bonds specifically after proline residues, aiding in the breakdown of gluten [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e],[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, to effectively utilize these enzymes, we must understand how they operate in the dynamic pH environments of the human gastrointestinal tract. Computational biology provides a robust toolkit to investigate the intricate relationship between PEP structure, function, and pH, accelerating research into celiac disease therapies [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. \u003cem\u003eIn silico\u003c/em\u003e analysis uses computer methods in administration, curation, and comprehension of data pertinent to biological systems [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Enzymes called prolyl endopeptidases (PEPs) are involved in the gastrointestinal function of proteins and are highly sensitive to pH variations [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Understanding how pH changes impact the structure and function of PEPs using computational methods is exciting and could yield important insights into how these enzymes behave in various physiological contexts. Several computational methods, such as molecular docking studies, electrostatic property analysis, and molecular dynamics simulations, can be used to investigate PEPs. By understanding the impact of pH change on the structure and activity of PEPs, researchers can gain valuable insights into protein digestion and their role in various physiological contexts [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The morphology of proline-containing proteases (PEPs) is influenced by the pH level, with proline content being a distinctive characteristic of T-cell stimulating peptides. Gluten, has an excessive amino acid proline residue content, making it resistant to total proteolytic breakdown in the gastrointestinal tract of humans [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. This is connected to the disease-causing qualities of gluten addition. Prolyl oligo peptidases are incapable of functioning in the stomach acidic pH range, working best at an acidic pH between seven and eight. Pepsin also breaks them down effectively [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Digestive enzymes degrade short amino acids predominantly due to their structure, which limits access to the active centre via a beta-propeller domain [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. These characteristics suggest that prolyl oligopeptide supplemented orally is unlikely to be enough to break down gluten before passing through the most proximal portions of the duodenum [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Celiac disease is a condition characterized by the damage of the small intestinal villi due to inflammation, leading to a decline in nutrition absorption and affecting all other physiological systems. This results in symptoms that can manifest in various organs of the body, not just the gastrointestinal system. These symptoms include abdominal distension, pain or discomfort in the belly, vomiting, constipation, and unexpected weight loss. Women with untreated celiac disease are more likely to experience obstetric complications, including early labour, growth restriction, and still birth. Enterocyte disruption in the small intestine is the primary cause of celiac disease symptoms. Chronic inflammation and villi loss are the major hallmarks of the small intestine in the full-blown clinical picture [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. An individual must have the HLA-dominant DQ2 or DQ8 genes and an antibody to tissue transglutaminase, which originates from the immune system unfavourable response to gluten. Some disease-causing pathways have been hypothesized, including the glycoprotein gliadin, which contains gluten, stimulating IL-15 production, which directly affects enterocytes. Early childhood gastrointestinal infections may influence a person later chance of acquiring celiac disease, possibly due to an immune system problem [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Diagnosis of celiac disease is typically done using tissue transglutaminase and IgA antibodies to the smooth muscle endomysium. However, only about 5% of individuals with celiac disease are immunoglobulin deficient. Understanding the functionality and structure of proteins in computational biology and structure-based biology requires a multifaceted approach that leverages a range of computational techniques [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Multiple Sequence Alignment (MSA) helps illuminate phylogenetic links and conserved areas, while the Clustal Omega tool facilitates alignment sorting and sheds light on the structural and psychological importance of proteins [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. PyMOL [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. a flexible molecular visualization tool, plays a central role in structural analysis, incorporating Roots Mean Square Deviation (RMSD) mathematical computations. YASARA software is responsible for quality control and structural optimization, using energy reduction to improve structural precision [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. AutoDock Vina [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], a docking computer simulation program, forecasts binding affinities alongside modes, providing a more comprehensive understanding of the functional landscape of the protein [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e],[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. This comprehensive computational approach holds significant implications for celiac disease research. A thorough understanding of how PEP activity is influenced by pH will allow us to identify candidate PEPs that function optimally within the pH range encountered in the human digestive tract. This knowledge lays the groundwork for the development of enzyme-based therapies, where these robust PEPs could be used for gluten detoxification. Additionally, computational analysis could guide protein engineering strategies to enhance the stability of PEPs at specific pH levels, further improving their therapeutic potential [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e],[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Moreover, our findings may reveal crucial residues and structural motifs involved in pH-dependent PEP function. This information could pave the way for the development of small-molecule modulators (inhibitors or activators) of PEP activity, offering another avenue for novel drug design in celiac disease management. this computational investigation into the pH-dependent function of prolyl endopeptidases has the potential to significantly accelerate the discovery of new therapies for celiac disease [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. By leveraging a combination of bioinformatics, structural modeling, and molecular simulation, we can meticulously dissect the interplay between PEPs, gluten, and pH [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e],[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Ultimately, this research aims to translate computational insights into tangible advancements in celiac disease treatment, improving the lives of patients worldwide.\u003c/p\u003e"},{"header":"2. Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Data retrieval for Prolyl endopeptidases\u003c/h2\u003e \u003cp\u003eThe UniProt database is a comprehensive and reliable resource for protein sequence and functional information. To investigate prolyl endopeptidases, sequences for ten distinct species \u0026ndash; \u003cem\u003eArabidopsis thaliana, Bacillus subtilis, Gallus gallus, Homo sapiens, Mus musculus, Penicillium chrysogenum, Pipistrellus kuhlii, Spinacia oleracea, Sus scrofa domesticus, and Danio rerio\u003c/em\u003e \u0026ndash; have been downloaded from UniProt [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The next step is to retrieve the corresponding protein sequences, along with any additional information from UniProt that would aid in accurate categorization and identification.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Sequence analysis for Prolyl endopeptidases\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1. Pairwise sequence alignment of prolyl endopeptidases using EMBOSS Needle\u003c/h2\u003e \u003cp\u003eTo delve deeper into the effects of pH fluctuations on prolyl endopeptidase sequences, we have utilized the EMBOSS Needle online tool for pairwise sequence alignment [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. This tool allows us to compare two sequences at a time, revealing specific amino acid similarities and differences. By focusing on residues that are known to be pH-sensitive, we can investigate how these residues vary between different species. This detailed pairwise analysis may shed light on how pH fluctuations might influence prolyl endopeptidase structure and function.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2. Multiple sequence alignment of prolyl endopeptidases using Clustal Omega\u003c/h2\u003e \u003cp\u003eTo investigate the pH-sensitivity of prolyl endopeptidases, we'll use the Clustal Omega web tool to perform a multiple sequence alignment (MSA) of these enzymes from various species. Clustal Omega is a powerful tool for aligning multiple sequences, allowing us to identify conserved regions and variations across our dataset [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. By examining these conserved and divergent areas within the MSA, we aim to pinpoint pH-sensitive regions in prolyl endopeptidases. This analysis will offer insights into the structure-function relationship of these enzymes and how they respond to changes in pH.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Net charge calculation of prolyl endopeptidases using ProtPi online tool\u003c/h2\u003e \u003cp\u003eTo investigate how pH influences the charge distribution of prolyl endopeptidases, we can use the ProtPi online tool [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. We already obtain prolyl endopeptidases sequences for several species (sourced from UniProt). We will input these sequences into ProtPi, specifying a range of pH values. This tool will calculate the net charge of each sequence at different pH points, revealing the isoelectric point (pI) and overall charge distribution. By comparing these net charge profiles across species, we will gain insights into how pH sensitivity and stability might vary among prolyl endopeptidases from different organisms.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e2.4. Structural modeling of prolyl endopeptidase using Pep-Fold4\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eTo visualize how pH fluctuations might influence the three-dimensional structure of prolyl endopeptidases, we have employed the Pep-Fold4 web application. Pep-Fold4 allows us to predict protein structures based on amino acid sequences [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. By inputting prolyl endopeptidase sequences and specifying different pH values, we can generate multiple models. These models will illustrate potential conformational changes that the protein might undergo in response to varying pH environments, providing insights into how pH could impact enzyme function and stability. While Pep-Fold4 is a useful tool for predicting structures of shorter peptides, its 50 amino acid length limitation poses a challenge for modeling full-length prolyl endopeptidases. To address this, we will focus on modeling key pH-sensitive regions or domains under 50 amino acids using Pep-Fold4.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Evaluation of prolyl endopeptidase structural deviations through RMSD calculation using PyMOL\u003c/h2\u003e \u003cp\u003eTo investigate how pH-induced conformational changes might affect the stability of prolyl endopeptidases, utilizing PyMOL [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] to calculate Root Mean Square Deviation (RMSD) values. Obtain structures modeled at different pH levels. After that Load these structures into PyMOL, designating one as the reference. Using PyMOL, RMSD calculation functions we can compare each simulated structure to the reference, quantifying structural deviations. Analyse these RMSD values to gain insights into how pH fluctuations influence protein conformation and stability.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.6. 3D structural modeling of Human prolyl endopeptidase using SWISS-MODEL\u003c/h2\u003e \u003cp\u003eTo build a full-length model of human prolyl endopeptidase, leverage the homology modeling capabilities of the SWISS-MODEL online tool [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Inputting the human prolyl endopeptidase sequence and let SWISS-MODEL automatically identify suitable structural templates and generate a 3D model. Once the model is created, we will carefully evaluate its quality and accuracy using YASARA software [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] and ERRAT online tool [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. These tools help identify any potential errors or areas for improvement in the generated model.\u003c/p\u003e \u003cp\u003e \u003cb\u003e2.6.1. Energy minimization and structural evaluation of prolyl endopeptidase using Yet Another Scientific Artificial Reality Application (YASARA)\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo refine the predicted human prolyl endopeptidase structure, we employed YASARA software for energy minimization. We have loaded the homology model into YASARA [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] and use its built-in energy minimization protocols to optimize the geometry of the structure. This process involves adjusting atomic positions to reduce unfavorable interactions and reach a lower energy state, potentially enhancing the accuracy and stability of the model .\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.6.2. Model quality evaluation of human prolyl endopeptidase using Error Recognition and Removal Tool (ERRAT)\u003c/h2\u003e \u003cp\u003eTo assess the quality of our refined human prolyl endopeptidase model, we utilized the ERRAT online tool [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. ERRAT analyse the statistics of non-bonded interactions between different atom types within the protein structure. It generates an \"overall quality factor\" that reflects the likelihood of our model being correct. ERRAT also highlights regions of the model that might have unusual non-bonded interactions, allowing us to focus our attention on potential problem areas for further refinement.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.7. Molecular docking of Gluten peptide with prolyl endopeptidase using AutoDock Vina\u003c/h2\u003e \u003cp\u003eTo investigate how pH influences prolyl endopeptidase binding to relevant substrates or molecules, we utilized AutoDock Vina [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. for molecular docking simulations [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e],[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Prepare our prolyl endopeptidase structure by adjusting protonation states for the desired pH and obtain the 3D structure of your substrate/molecule. Furthermore, we defined the binding site on the enzyme, config a grid box within AutoDock Vina [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], preparing a configuration file, and execute the docking simulation. Finally, we analysed the resulting binding poses and predicted affinities to understand how pH-dependent changes might affect the interaction of prolyl endopeptidase.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. RESULTS AND DISCUSSION","content":"\u003cp\u003e\u003cstrong\u003e3.1.\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eProlyl endopeptidases data retrieval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe comprehensive Prolyl Endopeptidases (PEP) sequences of 10 organisms was obtained as mentioned in the Table 1. This can further have been used for in-depth comparisons across diverse taxonomic groups, potentially revealing insights into the evolutionary relationships and functional variations of prolyl endopeptidases.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2.1. Identifying evolutionary relationships \u0026nbsp;based on pairwise alignment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e. Result showing the pairwise sequence alignments between certain Prolyl Endopeptidase sequences (sequence length 1-50 \u0026amp; 50-100) in a different organism by using the EMBOSS Needle online tool.\u003c/p\u003e\n\u003cp\u003eThe pairwise sequence alignment scores for the first 50 amino acids (Seq length 1-50) and the next 50 amino acids (Sequence length 50-100) was analyzed. The Prolyl Endopeptidase sequences show a high degree of similarity within vertebrates (animals with backbones), with alignment scores ranging from 182 to 262 for comparisons between human, mouse, chicken, and pig. This suggests that the Prolyl Endopeptidase enzyme has a conserved region and hence similar kind of function across these species. For the plant and fungal sequences,\u0026nbsp;the Prolyl Endopeptidase sequences from plants (\u003cem\u003eArabidopsis thaliana\u0026nbsp;\u003c/em\u003eand \u003cem\u003eSpinacia oleracea)\u003c/em\u003e and fungi (\u003cem\u003ePenicillium chrysogenum\u003c/em\u003e) show lower similarity to the vertebrate sequences, with alignment scores ranging from 9.5 to 49.5. This indicates that the PEP enzyme has diverged more significantly in these lineages. The PEP sequence from the bacterium \u003cem\u003eBacillus subtilis\u003c/em\u003e shows low similarity to all other sequences in the table, with alignment scores ranging from 4 to 14. This suggests that the PEP enzyme has undergone significant evolutionary changes in bacteria compared to eukaryotes (organisms with cells containing a nucleus). These observations are consistent with the expected evolutionary relationships between the organisms listed in the table. Vertebrates are more closely related to each other than they are to plants, fungi, or bacteria, and this is reflected in the higher degree of similarity between their PEP sequences. From the above analysis of the pairwise sequence alignment results shown in the Table 2, it can be suggested that the PEP enzyme is highly conserved across vertebrates, but has diverged more significantly in plants, fungi, and bacteria. These findings are consistent with the expected evolutionary relationships between the organisms studied.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2.2.\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eExploring evolutionary relationships through multiple sequence alignment analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results of a multiple sequence alignment of Prolyl Endopeptidase sequences from various species done through Clustal Omega is shown in Figure 1. From the data we observed that, there are several regions in the alignment where the same amino acids are present across most or all of the sequences. This multiple sequence alignment of Prolyl Endopeptidases (PEPs) from diverse taxa offers insights into both the fundamental structural motifs required for activity and the potential adaptations to different environmental pressures. The high degree of conservation surrounding a putative catalytic aspartic acid residue (position ~150) and other likely active site residues underscores the shared core mechanism within the PEP family. This conservation across bacteria, fungi, plants, and animals suggests the early establishment of this enzymatic system and its essential role in biological processes. The identification of additional conserved regions warrants further exploration. These regions may be involved in substrate recognition, structural stability, or other yet-undetermined functions. Despite the conserved core, the MSA reveals variability across the PEP sequences, potentially reflecting functional diversification and niche adaptations. The observed sequence divergence in certain domains suggests that PEP enzymes have evolved to optimize their activity across distinct environments. Of particular interest is the variation in histidine (H), aspartic acid (D), and glutamic acid (E) residues, especially where those variations correlate with the known ecological niches or substrate preferences of the different species. For example, the enrichment of histidine residues in PEPs from species inhabiting acidic environments (positions ~200-220) suggests a potential role for these residues in pH-dependent stability or activity. These clusters of pH-sensitive residues could influence PEP conformation, substrate interactions, or protein-protein interactions under differing pH conditions. This analysis highlights the value of comparative genomics for understanding enzyme evolution and function. The observed interplay between conserved and variable regions within the PEP family suggests a balance between maintaining catalytic function and adapting to specific physiological demands. Overall, the Clustal Omega alignment provides valuable insights into the conservation and variation of Prolyl Endopeptidase sequences from different species. The alignment can be used to identify potential functional regions of the enzyme, including those that may be involved in pH sensitivity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3. Prot Pi analysis of prolyl endopeptidase net charge profiles across a pH gradient\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 3. PEP \u0026nbsp;net charge of various species at different pH levels has been determined by using the Prot Pi online tool.\u003c/p\u003e\n\u003cp\u003eThe computed net charge profiles of Prolyl Endopeptidase from various organisms across a range of pH values (pH 1 to pH 13) can be seen in the Table 3. The net charge of a protein refers to the algebraic sum of the charges of its constituent amino acid side chains at a specific pH. It is influenced by the pKa values of the amino acids, which determine their protonation state at different pH levels. At acidic pH values (pH 1-3), the Prolyl Endopeptidase from all species exhibits a positive net charge. This is likely due to the protonation of acidic amino acid side chains (aspartic acid and glutamic acid) at these pH values. The net charge becomes zero at a specific pH value, known as the isoelectric point (pI). The pI values vary across the different species, ranging from 3.63 for Human to 9.81 for \u003cem\u003eBacillus subtilis\u003c/em\u003e. This variation reflects the differences in the amino acid composition of the Prolyl Endopeptidase sequences from these species. At basic pH values (pH 9-13), the Prolyl Endopeptidase from all species exhibits a negative net charge. This is likely due to the deprotonation of basic amino acid side chains (lysine and arginine) at these pH values. The net charge profiles of Prolyl Endopeptidase from Human and Mouse are similar, with pI values of 3.63 and 3.896, respectively. This suggests a high degree of sequence similarity and conservation of functional properties between these two species. The Prolyl Endopeptidase from \u003cem\u003eBacillus subtilis\u003c/em\u003e has a significantly higher pI (9.81) compared to the other species. This indicates a distinct amino acid composition and potentially different functional properties compared to the Prolyl Endopeptidase from eukaryotes (organisms with cells containing a nucleus). The Prolyl Endopeptidase sequences from plants (\u003cem\u003eArabidopsis thaliana\u003c/em\u003e and \u003cem\u003eSpinacia oleracea\u003c/em\u003e) and fungi (\u003cem\u003ePenicillium chrysogenum\u003c/em\u003e) show pI values in the range of 5.3-8.3. These values are generally higher than those observed for the animal species, suggesting potential differences in enzyme function or regulation. The net charge profiles of Prolyl Endopeptidase, computed using the Prot Pi online tool, reveal variations across different species. These variations are likely due to differences in amino acid composition and may be associated with distinct functional properties or regulatory mechanisms.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4. Exploring pH-induced conformational changes in prolyl endopeptidase with Pep- \u0026nbsp; Fold4\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 3D structure of human prolyl endopeptidases (PEP) of the sequence length 1-50 and 50-100 using the Pep-Fold4 can be seen in Figure 2 (a) and (b) respectively. We have also done the 3D modeling for the ten diverse species using Pep-Fold4 provided insights into the potential effects of pH on these enzymes\u0026apos; structures. Due to Pep-Fold4 length limitation, we focused on modeling key pH-sensitive domains or regions within PEPs. The generated models revealed conformational changes in the structure of human prolyl endopeptidase in response to varying pH environments. These findings suggest that pH fluctuations likely influence PEP enzymatic activity and stability. Further exploration of full-length PEP structures with more advanced modeling tools would be valuable to validate these predictions and deepen our understanding of the pH-dependent regulation of these important enzymes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5. A quantitative evaluation of structural deviations using RMSD calculation in PyMOL\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study investigates the effect of pH variation on the structural integrity of human and mouse prolyl endopeptidases (PEPs) using computational analysis. Root Mean Square Deviation (RMSD) calculations within PyMOL software will quantify structural deviations across a pH range, with a focus on specific protein sequence regions (residues 1-50 and 50-100). RMSD values will elucidate pH-sensitive regions within the PEP structures. Comparative analysis of RMSD values between human and mouse PEPs will reveal species-specific differences in pH susceptibility, potentially impacting enzymatic function. This study aims to provide insights into the structure-function relationship of PEPs and their sensitivity to environmental pH conditions.\u003c/p\u003e\n\u003cp\u003eRoot Mean Square Deviation (RMSD) calculations within PyMOL software will quantify structural deviations across a pH range, with a focus on specific protein sequence regions residues 1-50 in can be seen in Figure 3 (a) and 50-100 in Figure 3 (b).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRoot mean square deviation (RMSD) across diverse pH levels presents key insights into the protein structure \u0026nbsp;conformational dynamics can be seen in the Table 4. Elevated RMSD values between pH2 and adjacent pH levels (pH3, pH4, pH9, pH10, pH12, and pH13) suggest substantial structural alterations, highlighting the protein sensitivity to pH fluctuations. Similarly, heightened RMSD values between intermediary pH levels indicate significant structural shifts, while comparatively lower RMSD values among pH9, pH10, pH12, and pH13 imply stability within these pH ranges. However, acknowledging the protein-specific region and the influence of additional factors like temperature and ionic strength is crucial for a comprehensive interpretation of the observed RMSD patterns, emphasizing the need for a nuanced understanding of the protein \u0026nbsp;structural behavior within its environmental context.\u003c/p\u003e\n\u003cp\u003eThe Root Mean Square Deviation (RMSD) values across various pH levels of sequence length (50-100) in Table 5 illuminates substantial conformational alterations within the human prolyl endopeptidase (PREP) structure. Notably, significantly elevated RMSD values were observed during comparisons between pH 2 and several other pH conditions, including pH 3, 4, 5, 9, 10, 12, and 13, underscoring the heightened sensitivity of PREP structure to pH fluctuations, particularly in transitions from a strongly acidic milieu. Moreover, the RMSD values between pH 3 and the aforementioned higher pH levels also indicate substantial structural rearrangements. These observations suggest that alterations in protonation and deprotonation of amino acid residues, induced by varying pH levels, likely disrupt critical interactions essential for stabilizing PREP native fold, potentially impacting its functional attributes such as substrate binding, catalytic activity, or overall stability. Intriguingly, PREP exhibits enhanced structural stability within the pH range of approximately 7 to 8, corresponding to its physiological milieu, as evidenced by lower RMSD values. However, it is vital to note that RMSD offers a holistic assessment of structural changes and further investigations employing visualization tools and localized RMSD calculations are warranted to elucidate the specific regions within PREP structure most affected by pH fluctuations. Additionally, future research endeavours should delve into the functional implications of these pH-induced conformational alterations by scrutinizing PREP substrate specificity and enzymatic activity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.6.\u003c/strong\u003e \u003cstrong\u003eStructural investigation of human Prolyl Endopeptidase using Swiss-Model homology modeling\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe predicted three-dimensional structure of human prolyl endopeptidase (Model 1) shown in Figure 4 (a). This model reveals a complex protein architecture dominated by both alpha-helical and beta-sheet secondary structures. Alpha helices appear as tightly coiled spirals, while beta-sheets form extended, planar regions within the protein. The interplay of these secondary structure elements likely creates the enzyme \u0026nbsp;active site, where the catalytic cleavage of peptide bonds occurs. Loops and turns, visible as less structured regions of the model, may provide flexibility and contribute to substrate recognition. It was generated using Swiss-Model and exhibits promising quality metrics. Its foundation on a template (A5LFV8.1.A) with exceptionally high sequence identity (87.88%) underscores its suitability for representing human PEP. Excellent resolution (0.57) and near-complete sequence coverage (0.98) further bolster its reliability. The choice of AlphaFold v2 as the modeling algorithm adds confidence due to its reputation for high accuracy. While limitations inherent to homology modeling exist, Model 1 provides a strong initial framework for further analysis. Investigations into pH-dependent effects will be crucial for identifying specific residues and spatial arrangements essential for PEP function and understanding its potential modulation by physiological conditions.\u003c/p\u003e\n\u003cp\u003eThe energy minimization study conducted using YASARA software on the protein structure yielded compelling results shown in Figure 4 (b). The significant decrease in energy from an initial value of -265422.8 kJ/mol to a final value of -362103.3 kJ/mol indicates substantial structural optimization, indicative of a more stable conformation. Moreover, the transition from an initial score of -1.69 to a final score of -0.76 underscores the enhancement in structural quality post-minimization, as lower scores are indicative of improved protein conformations in YASARA. These findings suggest that the energy minimization protocol employed effectively optimized the protein structure, potentially leading to a more biophysically relevant conformation. Such optimization is crucial for understanding the structure-function relationship of proteins, laying the groundwork for further computational and experimental investigations. To ensure the reliability of our human prolyl endopeptidase model (model 1), we assessed its structural integrity using the ERRAT online tool shown in Figure 4 (c). This analysis yielded an overall quality factor of 91.281, indicating a high degree of structural credibility for the model. ERRAT evaluates various parameters such as Ramachandran plot statistics, bad contacts, and hydrogen bonding patterns to identify potential structural errors. A high overall quality factor, as observed here, suggests a well-folded protein structure with minimal steric clashes and a proper network of hydrogen bonds, which are crucial for protein stability and function. This positive assessment from ERRAT strengthens the confidence in our model and paves the way for further investigations into its functionality. Our ERRAT analysis of the refined human prolyl endopeptidase (model 1) demonstrated a highly favorable Ramachandran plot distribution (85.2% core, 11.5% allowed) can be seen in Figure 4 (d). This, along with the ERRAT overall quality factor, indicates a structurally sound model. While some residues fall within generously allowed or disallowed regions (2.7%, 0.7% respectively), these percentages are small enough to suggest that focused investigation of these specific residues might identify potential areas for additional refinement. Overall, the ERRAT results strongly support the good quality of the model, making it a reliable basis for further investigations into the prolyl endopeptidase structure and function.\u003c/p\u003e\n\u003cp\u003eThe predicted three-dimensional structure of human prolyl endopeptidase (Model 2) \u0026nbsp;shown in Figure 5 (a) (Supplementary file). This study employed Swiss-Model to generate a second structural model (Model 2) of human prolyl endopeptidase (PEP), aiming to explore alternative representations for subsequent pH-dependent analyses. Model 2 was constructed using a template protein (7obm.1.A) exhibiting high sequence identity (84.37%) to human PEP, ensuring a reasonable degree of structural similarity between the template and target. While the template \u0026nbsp;classification as \u0026quot;endonuclease-like\u0026quot; indicates potential functional divergence from PEPs, the high sequence identity and good sequence coverage (91%) suggest a level of suitability for representing PEP. Evaluation of Model 2 quality metrics Global Model Quality Estimation (GMQE): 0.75, QMEANDIsCo Global: 0.80 \u0026plusmn; 0.05 necessitates specific knowledge of Swiss-Model scoring criteria for confident interpretation. Nevertheless, these values suggest a generally acceptable model quality. \u0026nbsp;The results obtained from the energy minimization study shown in Figure 5 (b) are obtained by utilizing YASARA software present intriguing insights into the structural dynamics of the protein under investigation. The considerable decrease in energy observed, from an initial value of -239076.6 kJ/mol to a final value of -330468.8 kJ/mol, indicates a notable improvement in the stability and energetics of the protein structure following the optimization process. Furthermore, the transition from an initial score of -2.86 to a final score of -1.49 signifies a substantial enhancement in structural quality, as evidenced by the reduction in the score magnitude. This suggests that the energy minimization protocol employed successfully alleviated unfavorable interactions within the protein structure, resulting in a more compact and energetically favorable conformation. Such improvements are crucial for elucidating the functional properties of the protein and can provide valuable insights into its biological role and interactions with ligands or other macromolecules. To ensure the reliability of our human prolyl endopeptidase model (model 2), we assessed its structural integrity using the ERRAT online tool shown in Figure 5 (c). This analysis yielded an overall quality factor of 87.856. While this score indicates a generally well-folded structure, it suggests potential room for improvement compared to models with scores exceeding 90. ERRAT evaluates various parameters such as Ramachandran plot statistics, bad contacts, and hydrogen bonding patterns to identify potential structural errors. We will carefully examine regions flagged by ERRAT and consider focused refinements to optimize the model further. This attention to structural detail will ensure the highest possible accuracy for subsequent simulations and studies of prolyl endopeptidase function. The ERRAT analysis of our refined human prolyl endopeptidase model reveals promising structural quality. The Ramachandran plot demonstrates a substantial percentage of residues within the most favored core region (86.1%), suggesting a sound backbone conformation can be seen in Figure 5 (d) . While a small percentage of residues fall within generously allowed or disallowed regions (1.4% and 0.2%, respectively), this points to potential targets for further refinement. These areas, flagged by ERRAT, warrant closer inspection to identify specific points for optimization. Overall, the ERRAT results support the model quality, establishing it as a reliable foundation for subsequent investigations into the structure and function of human prolyl endopeptidase.\u003c/p\u003e\n\u003cp\u003eThe 3D structure for Model 3 is generated using Swiss-Model for the representation of human prolyl endopeptidase shown in Figure 6 (a) (Supplementary file). However, detailed analysis reveals significant concerns regarding its reliability due to the remarkably low sequence identity (19.19%) between the template protein and the target protein. While the template exhibits good resolution (2.05 \u0026Aring;) and the model demonstrates reasonable sequence coverage (0.82), the fundamental reliance of homology modeling on sequence similarity cannot be overlooked. A template with such low identity to the target sequence likely possesses substantial structural deviations, potentially leading to incorrect folding predictions and misidentification of critical regions, such as the active site, in Model 3. The quality metrics, GMQE (0.48) and QMEANDIsCo Global (0.58 \u0026plusmn; 0.05), necessitate further clarification using Swiss-Model \u0026nbsp; documentation to assess their significance in this context. The energy minimization analysis conducted using YASARA software has provided valuable insights into the structural refinement of the protein under investigation shown in Figure 6(b). In this study, we observed a substantial reduction in energy, transitioning from an initial value of -220654.1 kJ/mol to a final value of -317128.3 kJ/mol, indicative of significant stabilization of the protein structure. Moreover, the scores obtained, with an initial score of -3.22 decreasing to a final score of -2.07, further support the notion of improved structural quality following the energy minimization process. These results suggest that the applied energy minimization protocol effectively minimized unfavorable interactions and optimized the protein conformation, leading to a more energetically favorable state. Comparing these results with those obtained from other energy minimization studies, it becomes evident that all three experiments led to substantial improvements in the stability and quality of the protein structures. However, to determine the best result, several factors need to be considered. Firstly, the magnitude of the energy and score values should be evaluated. In this regard, the result with the lowest final energy and score would indicate the most favorable structural refinement. Secondly, the consistency of the improvements across different metrics, such as bond lengths, angles, and dihedrals, should be assessed. Lastly, the computational cost and convergence properties of each energy minimization run should be taken into account. After comparison with others models, it appears that the third result, with an initial energy of -220654.1 kJ/mol and a final energy of -317128.3 kJ/mol, along with initial and final scores of -3.22 and -2.07, respectively, represents the most favorable outcome. This result demonstrates a significant decrease in energy and score values, indicating improved structural stability and quality, but it also shows consistency across various metrics. Therefore, based on these considerations, the third result can be considered the best outcome of the energy minimization study. To assess the reliability of our human prolyl endopeptidase models, we evaluated their structural integrity using the ERRAT online tool shown in Figure 6 (c). The overall quality factor for model 3 was 81.192. While this indicates a folded structure, it is the lowest score among the three models. Models 1 and 2 received scores of 91.281 and 87.856, respectively. Lower ERRAT scores suggest a higher likelihood of structural errors like Ramachandran plot outliers, unfavorable contacts, or deficiencies in hydrogen bonding. Comparing these results, models 1 and 2 exhibit a higher degree of structural credibility based on ERRAT analysis. Model 1, with the highest score, is most likely the most reliable representation of the native prolyl endopeptidase structure. The ERRAT analysis of our refined human prolyl endopeptidase (Model 3) indicates a generally favorable Ramachandran plot distribution (85.4% core, 12.7% allowed) can be seen in Figure 6 (d). This suggests a well-defined protein backbone conformation. However, a small fraction of residues falls within the generously allowed (1.2%) and disallowed (0.8%) regions, highlighting potential areas for further refinement. Additionally, the presence of 1 bad contact and 3 cis-peptides warrants further investigation. These areas flagged by ERRAT, along with the cis-peptides, could be optimized to improve the model structure. Overall, the ERRAT results support the model quality, providing a solid foundation for further studies on human prolyl endopeptidase structure and function.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.7. Molecular docking of prolyl endopeptidase: analyzing substrate interactions at different pH levels\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMolecular docking scores for the affinity-based selection of Homo sapiens prolyl endopeptidase (sequence 1-50) revealed favorable predicted binding affinities for multiple compounds across various pH values (refer Table 6). Docking scores ranged from -7.6 kcal/mol to -9.5 kcal/mol, indicating potentially strong interactions between the compounds and the target protein can be seen in Figure 7. Significantly, most compounds demonstrated scores below -8 kcal/mol, implying a high likelihood of effective active site targeting. While minor variations in scores were observed, the overall results suggest robust ligand-protein binding within the tested pH range. These findings highlight the importance of considering pH when investigating ligand-protein interactions and support the potential of affinity-based selection for identifying potent prolyl endopeptidase inhibitors that are effective across different physiological conditions.\u003c/p\u003e\n\u003cp\u003eMolecular docking scores for the affinity-based selection of \u003cem\u003eHomo sapiens sapiens\u003c/em\u003e prolyl endopeptidase (sequence 50-100) across varying pH conditions revealed promising binding affinities for several compounds (refer Table 7). Docking scores ranged from -7.7 kcal/mol to -11 kcal/mol, suggesting strong and favorable ligand-protein interactions. Notably, compounds 3 and 5 emerged as particularly promising candidates with exceptionally high scores of -10.6 kcal/mol and -11 kcal/mol, respectively, indicative of potent binding affinity can be seen in Figure 8 (Supplementary file). These findings suggest their potential as lead molecules for further investigation in prolyl endopeptidase inhibition. While some variations in scores were observed, the overall trend indicates robust binding interactions within the tested pH range. This study highlights the importance of considering target protein sequence length during ligand selection and underscores the potential of affinity-based selection for identifying effective prolyl endopeptidase inhibitors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.8. Visualization and analysis of Prolyl Endopeptidase-Gluten Peptide complex formation using LigPlot plus\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eComputational tools like Autodock Vina aid in generating docking models that predict the binding mode of a gluten peptide (ball-and-stick representation) within the active site of \u003cem\u003eHomo sapiens sapiens\u003c/em\u003e prolyl endopeptidase (Model 1) (ribbon representation) can be seen in Figure 9 (a). Software like LigPlot Plus can further analyze the complex, highlighting specific interactions with the enzyme (Asparagine) ASN-9, (Threonine)THR-17, and (Alanine)ALA-4 residues, which likely contribute to the complex stability. This research uses computational techniques to investigate these interactions, offering relevance for celiac disease management. A deeper understanding of gluten peptide binding to PEP could lead to strategies that optimize the enzyme ability to break down these immunogenic peptides, potentially paving the way for novel therapeutic approaches.\u003c/p\u003e\n\u003cp\u003eThe Figure 9 (b) depicts a computational docking model of a gluten peptide bound to the active site of Human prolyl endopeptidase (Model 2) (Supplementary file). This docking model suggests a potential binding mode where the gluten peptide interacts with the enzyme through specific amino acid residues. Notably, the interaction appears to involve (Valine) VAL-349, (Aspartic acid) ASP-91, and (Threonine) THR-102 on the enzyme\u0026apos;s surface. These interactions likely contribute to the stability and specificity of the gluten peptide-PEP complex. Understanding the precise interactions between gluten peptides and PEP is crucial within the context of celiac disease. PEP has the potential to break down gluten peptides, potentially reducing their immunogenic effects. The molecular docking model illustrating the intricate binding of a gluten peptide to the catalytic site of human PEP can be seen in Figure 9(c) (Supplementary file). This model delineates a putative binding mode whereby the gluten peptide establishes specific interactions with key amino acid residues within the enzyme\u0026apos;s active site. Particularly noteworthy is the involvement of (Lysine) LYS-79 located on the enzyme\u0026apos;s surface, suggesting its significance in mediating the binding affinity between the gluten peptide and PEP. These molecular interactions are presumed to play a pivotal role in fostering the stability and selectivity of the gluten peptide-PEP complex. Elucidating the precise molecular interplay between gluten peptides and PEP holds paramount importance in the realm of celiac disease research. PEP exhibits the potential to enzymatically degrade gluten peptides, thus potentially mitigating their immunogenic effects, thereby underscoring its therapeutic relevance in managing celiac disease pathology.Top of FormBottom of Form\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAnalysis done from the docking complex obtained through \u0026nbsp;LigPlot plus reveals a network of interactions likely governing the binding of the gluten peptide to prolyl endopeptidase (Model 1) can be seen in Figure 10 (a). Predominant among these forces are hydrogen bonds, evident between specific residues in the enzyme and the peptide. Additionally, hydrophobic interactions appear to contribute significantly to the complex stability. The potential presence of salt bridges further suggests a role for electrostatic attractions in complex formation. Identification of these key residues offers valuable insights into the molecular recognition mechanism employed by prolyl endopeptidase in gluten binding and could inform future investigations regarding enzymatic activity and targets for the modulation of gluten degradation.\u003c/p\u003e\n\u003cp\u003eThe careful examination of the data obtained from LigPlot plus reveals a complex network of interactions likely responsible for the binding of the gluten peptide to prolyl endopeptidase (model 2) can be seen in Figure 10 (b) (Supplementary file). Prominent among these are hydrogen bonds, with the image highlighting specific residues within the enzyme and peptide that participate in donor-acceptor pairs. Also, hydrophobic interactions appear significant for complex stability, evidenced by clustering of nonpolar residues. The presence of potential salt bridges, dependent on the ionization states of specific charged residues at physiological pH, suggests a possible role for electrostatic attractions in binding. This detailed interaction map sheds light on prolyl endopeptidase \u0026nbsp;gluten recognition mechanism and could guide future investigations into the modulation of its enzymatic activity through the targeting of specific binding residues. The 2D structure of the prolyl endopeptidase (model 3) \u0026nbsp;and gluten peptide complex, generated using LigPlot plus software can be seen in Figure 10 (c) (Supplementary file), offers crucial insights into the molecular interactions underpinning this enzyme-substrate relationship. PEP, a serine protease, specifically cleaves peptide bonds after proline residues, making it relevant to the study of gluten. Gluten peptides, derived from proline-rich wheat gluten, are implicated in celiac disease, an autoimmune disorder triggered by gluten consumption in susceptible individuals. LigPlot plus analysis highlights the network of hydrogen bonds, hydrophobic interactions, and other atomic contacts that govern the binding affinity and specificity of the PEP-gluten peptide complex. Hydrogen bonds, formed between hydrogen and electronegative atoms, likely play a major role. Hydrophobic interactions between nonpolar regions may also be depicted. Additionally, the 2D structure could reveal salt bridges (ionic bonds) and weaker van der Waals forces contributing to the complex \u0026nbsp;stability. Understanding these interactions has direct implications for celiac disease research; the structure may pinpoint critical amino acid residues within the gluten peptide that facilitate PEP binding. This knowledge could pave the way for novel therapeutic strategies, potentially targeting the inhibition of the PEP-gluten peptide interaction as a means to manage celiac disease.\u003c/p\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eThis study aimed to investigate the evolutionary relationships, structural properties, and potential pH-dependent activity of Prolyl Endopeptidase (PEP) enzymes through a comprehensive multi-faceted computational approach. The presented findings provide valuable insights into PEP structure, function, and interactions across a diverse range of species. Multiple sequence alignment analysis using Clustal Omega revealed both highly conserved and variable regions within the PEP sequences. Conserved regions, particularly those surrounding the known catalytic site, are crucial for the fundamental enzymatic activity of PEP. Variable regions, on the other hand, likely to contribute species-specific adaptations, such as substrate specificity and variations in pH sensitivity. Pairwise sequence alignments further reinforced the notion of evolutionary conservation. Vertebrates showed high similarity in their PEP sequences, highlighting a shared lineage. As expected, more distant evolutionary relationships were reflected in lower similarity scores for comparisons between plants, fungi, and bacteria with vertebrates. Net charge profiles across a range of pH values highlighted the influence of pH on the overall charge of PEP enzymes. The observed isoelectric point (pI) variations between species aligned with differences in amino acid composition, offering clues about potential functional adaptations. Human and mouse PEP sequences exhibited similar net charge profiles and pI values. Further supporting their evolutionary relatedness and potential for shared functional characteristics. An exploration of pH-induced conformational changes using Pep-Fold4 structural alterations in PEP enzymes in response to varying pH conditions were observed. Understanding such conformational changes is essential as they may directly influence enzyme activity, substrate binding, and stability. For instance, changes near the catalytic site could alter the enzyme ability to bind and cleave target substrates. To obtain a more accurate representation of the PEP structure, homology modeling was performed using SWISS-MODEL. The quality and reliability of the generated models were evaluated using ERRAT scores. Models with ERRAT scores above 90 indicated a high degree of structural accuracy and reliability, providing confidence for subsequent docking studies. Energy minimization studies using YASARA software demonstrated the importance of structural optimization for reliable simulations. By decreasing internal strain and achieving more energetically favorable conformations, energy minimization likely resulted in models that better reflect native PEP structures. This refinement process has implications for accurate docking simulations. Molecular docking of PEP with gluten peptides at different pH levels revealed a potential pH-dependence for their binding interaction. In general, more negative docking scores (indicating stronger predicted binding affinities) were observed at higher pH values. However, this trend had exceptions, suggesting that individual gluten peptides interact with PEP in unique ways, highlighting the necessity for further studies to characterize these interactions in detail. The 2D representation of the PEP-gluten peptide complex generated through LigPlot plus offered insights into the types of interactions governing the specific binding between this enzyme and disease-relevant gluten fragment. Hydrogen bonding and hydrophobic interactions appear to be significant contributors to the stability of the complex. This understanding may open avenues for the design of therapeutic interventions targeting the disruption of PEP-gluten interaction with the ultimate goal of mitigating celiac disease symptoms.\u003c/p\u003e"},{"header":"5. Future Prospective","content":"\u003cp\u003eThis \u003cem\u003ein-silico\u003c/em\u003e study of celiac disease establishes a comprehensive framework for exploring the intricate effects of pH on prolyl endopeptidase (PEP) structure, function, and interactions with substrates or inhibitors. To advance these findings into practical realms, several critical avenues beckon further investigation. Firstly, validating the pH-induced conformational changes predicted computationally demands experimental techniques like X-ray crystallography or nuclear magnetic Resonance (NMR) spectroscopy, offering high-resolution snapshots and dynamic insights into PEP behaviour. Secondly, employing site-directed mutagenesis to discern the molecular basis of pH sensitivity in PEPs will unravel the specific residues dictating their response to pH changes, fostering a deeper understanding and rational design of modulators. Thirdly, biochemical assays coupled with biophysical techniques like isothermal titration calorimetry (ITC) or surface plasmon resonance (SPR) are indispensable for confirming fluctuations in substrate/inhibitor binding affinity at different pH levels, potentially uncovering selective PEP modulation strategies. Moreover, leveraging structural-functional insights can inspire the development of pH-sensitive PEP inhibitors targeting specific pathological microenvironments while also spotlighting PEPs as potential therapeutic targets and diagnostic biomarkers in diseases characterised by unregulated pH conditions. Beyond immediate applications, unravelling PEP pH sensitivity promises broader revelations into protein structure-function relationships and the intricate interplay between cellular processes and environmental cues. This bioinformatics study establishes a comprehensive framework for exploring the intricate effects of pH on prolyl endopeptidase (PEP) structure, function, and interactions with substrates or inhibitors. To advance these findings into practical realms, several critical avenues beckon further investigation. Firstly, validating the pH-induced conformational changes predicted computationally demands experimental techniques like X-ray crystallography or nuclear magnetic Resonance (NMR) spectroscopy, offering high-resolution snapshots and dynamic insights into PEP behaviour. Secondly, employing site-directed mutagenesis to discern the molecular basis of pH sensitivity in PEPs will unravel the specific residues dictating their response to pH changes, fostering a deeper understanding and rational design of modulators. Thirdly, biochemical assays coupled with biophysical techniques like isothermal titration calorimetry (ITC) or surface plasmon resonance (SPR) are indispensable for confirming fluctuations in substrate/inhibitor binding affinity at different pH levels, potentially uncovering selective PEP modulation strategies. Moreover, leveraging structural-functional insights can inspire the development of pH-sensitive PEP inhibitors targeting specific pathological microenvironments while also spotlighting PEPs as potential therapeutic targets and diagnostic biomarkers in diseases characterised by unregulated pH conditions. Beyond immediate applications, unravelling PEP pH sensitivity promises broader revelations into protein structure-function relationships and the intricate interplay between cellular processes and environmental cues.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAKV and SK performed the experiment, did graphical designing and analysis, and wrote the manuscript. AKV led the development of methodology for the experiment, data extraction, study quality assessment, conceptualization, study identification, analysis, manuscript writing and editing, and overall supervision. TS edited the whole draft and did the referencing. AKV, AM, NRS and AK provided detailed reviews of the manuscript drafts and analysis, along with crucial feedback.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors state that they have no known financial conflicts of interest or personal connections that could have influenced the work presented in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors sincerely acknowledge all kinds of support from the School of Bioengineering and Biosciences, Lovely Professional University, Punjab, India. Also, the authors also extend their heartfelt thanks to SCFBio, Indian Institute of Technology, Delhi (IIT-Delhi), India and Gene Regulatory Laboratory, National Institute of Immunology (NII), New Delhi, India for providing computational facilities to conduct our study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was required for this work.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eA. Fasano and C. Catassi, \u0026ldquo;Celiac Disease,\u0026rdquo; \u003cem\u003eN. Engl. J. Med.\u003c/em\u003e, vol. 367, no. 25, pp. 2419\u0026ndash;2426, 2012, doi: 10.1056/NEJMcp1113994.\u003c/li\u003e\n \u003cli\u003eK. Rostami, R. Malekzadeh, B. Shahbazkhani, M. R. Akbari, and C. Catassi, \u0026ldquo;Coeliac disease in Middle Eastern countries: a challenge for the evolutionary history of this complex disorder?,\u0026rdquo; \u003cem\u003eDig. 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Solanki, \u0026ldquo;Para-Cresyl Sulfate and BSA Conjugation for Developing Aptasensor: Spectroscopic Methods and Molecular Simulation,\u0026rdquo; \u003cem\u003eECS J. Solid State Sci. Technol.\u003c/em\u003e, vol. 12, no. 7, p. 73004, 2023, doi: 10.1149/2162-8777/ace286.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Lovely Professional University","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":"Prolyl Endopeptidases, pH variation, Computational technology, Sequence analysis, Structural modeling, RMSD calculation, Homologous modeling, Molecular docking","lastPublishedDoi":"10.21203/rs.3.rs-5708047/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5708047/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCeliac disease, an intricate autoimmune disorder, stems from gluten consumption, primarily found in wheat, barley, and rye. Due to its high proline content, gluten resists complete breakdown in the human digestive system. Prolyl endopeptidases (PEPs), a subclass of serine proteases, offer a promising therapeutic avenue. These enzymes exhibit a unique ability to cleave peptide bonds post proline residues, aiding gluten digestion. However, leveraging these enzymes effectively mandates a profound understanding of their operation within the dynamic pH milieu of the human gastrointestinal tract. This study delves into the influence of pH variations on PEP structure and activity, employing advanced computational methodologies. The research initiates with acquiring PEP sequences from ten diverse organisms via the UniProt database. Employing sequence analysis techniques like multiple sequence alignment and pairwise sequence alignment, we identify pH-sensitive regions by scrutinizing conserved motifs and sequence disparities. Prot Pi facilitates the computation of net charge profiles across varied pH gradients, unveiling pH-responsive charge distribution patterns. Structural analysis involves predicting 3D conformations through Pep-Fold4, encapsulating protein adaptations to pH fluctuations. RMSD calculations via PyMOL reveal pH-induced conformational alterations and their implications for protein stability. Also, rigorous homologous modeling of human PEPs via Swiss Model ensures structural fidelity, energy optimization with YASARA refines geometric parameters, while ERRAT analysis validates structural integrity. Docking simulations forecast PEP-gluten peptide interactions across diverse pH conditions. In conclusion, our comprehensive data analysis provides novel insights into how pH modulates PEP structures. These findings bear significant implications for enzyme catalysis, structural resilience, and potential therapeutic strategies.\u003c/p\u003e","manuscriptTitle":"Deciphering pH-Driven Dynamics of Prolyl Endopeptidases: Unveiling Structural insight in Celiac Disease using Computational Techniques","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-01 08:26:59","doi":"10.21203/rs.3.rs-5708047/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":"bf75466b-ae24-498d-ac0f-445b7f6b7ac8","owner":[],"postedDate":"January 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":42015148,"name":"Bioinformatics"},{"id":42015149,"name":"Computational Biology"},{"id":42015150,"name":"Allergy \u0026 Immune Disorders"},{"id":42015151,"name":"Pediatrics"},{"id":42015152,"name":"Nutrition \u0026 Dietetics"},{"id":42015153,"name":"Scientific Communication"}],"tags":[],"updatedAt":"2025-01-01T08:26:59+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-01 08:26:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5708047","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5708047","identity":"rs-5708047","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}