CLEC7A) gene encodes a Dectin-1 protein that recognizes beta-1,3-linked and beta-1,6-linked glucans, which form the cell walls of pathogenic bacteria and fungi. It plays a role in immunity against fungal infections through pathogen recognition and immune signaling. This study aimed to explore the impact of nsSNPs in the human CLEC7A gene through computational tools  to identify the most deleterious and damaging nsSNPs. Further, their effect on protein stability was checked along with conservation and solvent accessibility analysis by I-Mutant 2.0, ConSurf, and Project HOPE and post-translational modification analysis using MusiteDEEP. Out of the 28 nsSNPs that were found to be deleterious, 25 nsSNPs affected protein stability. Some SNPs were finalized for structural analysis with Missense 3D. Seven nsSNPs affected protein stability. Results from this study predicted that C54R, L64P, C120G, C120S, S135C, W141R, W141S, C148G, L155P, L155V, I158M, I158T, D159G, D159R, I167T, W180R, L183F, W192R, G197E, G197V, C220S, C233Y, I240T, E242G, and Y3D were the most structurally and functionally significant nsSNPs in the human CLEC7A gene. No nsSNPs were found in the predicted sites for post-translational modifications. In the 5′ untranslated region, two SNPs, rs536465890 and rs527258220, showed possible miRNA target sites and DNA binding sites. The present study identified structurally and functionally significant nsSNPs in the CLEC7A gene. These nsSNPs may potentially be used for further evaluation as diagnostic and prognostic biomarkers.The human C-type lectin domain family 7 member A ( Dectin-1 is a type II membrane protein expressed in many myeloid cells, including macrophages, monocytes, dendritic cells, neutrophils, and gamma delta T cells from the lymphoid lineage [Dectin-1 was found to increase immunological tolerance by binding to the conserved core domain of annexins  produced on apoptotic cells [Dectin-1 receptor interactions since they contribute to the binding sites and functions of the receptor [CLEC7A isoform 1 consists of 247 amino acids and serves as a pattern-recognition receptor (PRR) that recognizes β-1,3-linked and β-1,6-linked glucans, which form the cell walls of pathogenic bacteria and fungi [Dectin-1 lacks Ca2+-binding sites in its CRD as it does not require Ca2+ for ligand recognition [2+ binding sites have shown that bound calcium ions (Ca2+) are needed to stabilize the domain structure [Dectin-1 binds specifically to β-glucan receptors, requiring at least 10- or 11-mer chain length in Dectin-1’s lectin domain [Dectin-1 lectin domain [Dectin-1 only recognizes the middle part of the β-glucan chain, not the reducing/non-reducing ends [Dectin-1 accomplishes two tasks: it internalizes β-glucan-containing particles and sends signals into the nucleus [Human  lineage ,3,4. It  lineage ,6. Recenic cells . It contic cells , and tworeceptor . The CLEnd fungi . Dectin-ognition . Howevertructure . Dectin-n domain . Dectin-ing ends . In fung nucleus .Dectin-1 (Y238X) was discovered. Leading to the prematurely terminated translation of the Dectin-1 receptor’s tyrosine residue [Dectin-1 receptor. The mutations in CLEC7A  have been associated with fungal infection susceptibility, and Dectin-1 genetic variation plays a crucial role in fungal infection. Meta-analysis results suggest that CLEC7A SNPs may affect profound fungal infection susceptibility. In contrast, polymorphisms in rs16910526 are unlikely to have a significant effect.Single point mutations (SNPs), including synonymous (sSNPs) and non-synonymous (nsSNP) SNPs, have been shown to contribute to the pathophysiology of fungal infection. In a family with mucocutaneous fungal infections, a recessive mutation that changed an amino acid in  residue . Even if residue . The finDectin-1 protein on fungal disease pathogenesis was not fully understood. This study aimed to identify potential coding and non-coding SNPs that may affect Dectin-1 protein function, utilizing various computational approaches and bioinformatics tools.Further investigations are warranted to verify and extend the present results and design novel immunotherapeutic strategies to optimize or replace conventional antifungal treatments. Previous studies used nsSNPs as computational tools to understand the molecular mechanisms of various diseases . These tDectin-1 gene were retrieved from the dbSNP database (https://www.ncbi.nlm.nih.gov/snp (accessed on 7 April 2023)). Primarily, 975,620 SNPs were found, but after applying the missense, pathogenic, and frequency filters, 2068 SNPs were retrieved. The 247 nsSNPs in the coding region were selected for further analysis as the change in codon results in different amino acids. To analyze the non-coding region SNPs, the ENSEMBL database was used to collect the dataset for the Dectin-1 protein. An overview of the methodological approaches is summarized in SNPs of the Dectin-1 nsSNPs and find the highly risky and deleterious SNPs that can significantly alter the structure or function of Dectin-1 protein. The software includes MAPP, PhD-SNP, PolyPhen-1, PolyPhen-2, SIFT, and SNAP. As a result of combining the six best-performing tools into a consensus classifier, PredictSNP, prediction performance improved significantly. At the same time, all mutation results were returned, confirming that consensus prediction offers a more reliable and accurate alternative to individual tool predictions [PredictSNP, which combines six software tools, was used to precisely analyze and predict the pathogenicity of dictions .Dectin-1 [https://www.ebi.ac.uk/interpro/ (accessed on 7 April 2023)), which can identify motifs and domains of a protein. Consequently, the software was able to determine the functional characterization of a protein using the database consisting of protein families, domains, and functional sites [InterPro software was used to locate the site of nsSNPs on the conserved domains of Dectin-1  ). I-Mutant 2.0 is another SVM-based tool that measures the change in free energy (Delta Delta G) and predicts whether it is increasing or decreasing. A Delta Delta G (DDG)  value of 0 indicates a decrease in protein stability, whereas a DDG  value > 0 indicates an increase in protein stability. This program predicts the stability of a protein after mutation. A protein stability test was then run on the pathogenic SNPs. Substantially deleterious nsSNPs are those that reduce protein stability.To identify the effect of the damaging nsSNPs on the structure and stability of the was used   and the 25 SNPs individually as the input. Missense 3D [Project HOPE was used to analyze the predicted effect of the nsSNPs or point mutations on the structure of the sequence . We usedsense 3D  confirmehttps://www.musite.net/ (accessed on 7 April 2023)). MusiteDeep determines post-translational modifications such as phosphorylation, glycosylation, ubiquitination, sumoylation, acetyl-lysine, methylation, pyrrolidone carboxylic acid, palmitoylation, and hydroxylation [Dectin-1 protein sequence.MusiteDeep is an online tool for visualizing PTM sites in protein sequences (xylation . The inpThe ENSEMBL database was used to investigate non-coding regions . We filtCLEC7A protein (https://string-db.org (accessed on 7 April 2023)). The STRING algorithm predicts the interaction partners of a protein based on gene fusion, co-expression, function, and experimental data. Each interacting protein is scored from 0 to 1, where 0 indicates the lowest interaction and 1 indicates the highest interaction [CLEC7A FASTA protein sequence was submitted as input. All predicted interactions associated with confidence scores are included in the output.Protein-protein interactions are essential for regulating and executing a protein’s biological functions. STRING  predicts the top ten proteins interacting with the query gene by predicting their interactions with the eraction . The CLEThe functional analysis involved annotating and enriching proteins in the network based on their functional properties. The most common enrichment analysis is based on gene ontology (GO) terms  and pathways. In order to interpret the physical function of a network, functional analysis is necessary. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted using STRING .Dectin-1 SNPs were retrieved using the NCBI dbSNP database. It comprised 3904 SNPs, of which 216 were missense (nsSNP), 758 non-coding transcripts, 247 codings, 93 synonymous, 3858 intronic, 1 inframe insertion, and 2 inframe deletions—regarding clinically significant, 1 pathogenic, 6 benign, and 8 likely benign. For our current study, we only selected nsSNPs in the coding and non-coding regions (3UTR and 5UTR) for further analyses.Dectin-1 nsSNPs precisely and label the highly risky deleterious SNPs that can significantly alter the structure or function. Out of 247 nsSNPs, 27 were predicted to be deleterious SNPs in all computational algorithms (Results from predictSNP software were compared to predict the pathogenicity of gorithms .Dectin-1 were predicted. The domains were the non-cytoplasmic domain (1–44), TMhelix domain (43–65), transmembrane (45–70), and cytoplasmic domain (71–274)  .Dectin-1 protein using the I-Mutant 2.0 tool. The outcome revealed that 25 out of 28 deleterious nsSNPs decreased stability  C denotes the creation of a new miRNA site; and (4) O denotes the absence of a determination of an ancestral allele. The results show that all of the miRNA target sites for miRNA predicted to be disrupted by SNPs in Dectin-1 were obtained from CLASH experimental data (n). No nsSNPs were obtained when filtered, considering the functional classes of C and D and conservation scores of 10  D describes the disruption of a conserved miRNA site; (2) es of 10 .CLEC7A gene is involved in many molecular and biological processes. The CLEC7A has high-confidence interactions with predicted functional partners, including spleen-associated tyrosine kinase (SYK), which mediates signal transduction downstream of a variety of transmembrane receptors, including classical immunoreceptors like the B-cell receptor; Toll-like receptor 2 (TLR2), which cooperates with LY96 to mediate the innate immune response to bacterial lipoproteins and other microbial cell wall components; Galectin-3; Galactose-specific lectin (LGALS3), which binds IgE; and Fc gamma receptor IIb (FCGR2B), aggregated immunoglobulins gamma. It is involved in various effector and regulatory functions, such as phagocytosis of immune complexes and modulation of antibody production by B-cells. Toll-like receptor 4 (TLR4) cooperates with LY96 and CD14 to mediate the innate immune response to bacterial lipopolysaccharide (LPS). Caspase recruitment domain-containing protein 9 (CARD9) is an adapter protein that plays a crucial role in the innate immune response to several intracellular pathogens, such as C. albicans and L. monocytogenes. Proto-oncogene tyrosine-protein kinase (SRC), a non-receptor protein tyrosine kinase that is activated following the engagement of many different classes of cellular receptors, including immune response receptors; 1-phosphatidylinositol 4,5-bisphosphate phosphodiesterase gamma-2 (PLCG2), which is a crucial enzyme in transmembrane signaling; C2 domain containing phospholipases; leukocyte antigen CD37 (CD37); and Galectin 9 (LGALS9), which stimulates bactericidal activity in infected macrophages by causing macrophage activation. All interactions are shown in Dectin-1 network, including KEGG and GO, demonstrate that the Dectin-1 protein is involved in multiple immune signaling pathways [A STRING interaction analysis revealed that the pathways  , I158T 306 (rs138005591), and D159G (rs758623997) [Dectin-1 gene as a risk assessment against fungal infections.SNPs are considered among the most significant risk factors associated with many diseases. The presence of SNPs within the protein-coding and non-coding regions can adversely affect the function and conformation of the protein. This study aimed to determine which of the most damaging nsSNP variants may affect the functionality of Effectively distinguishing between harmful and tolerated nsSNP types involves successive filtering using several disease-associated bioinformatics tools. Six functional tools  and two structural servers (I-Mutant3.0 and Project Hope) were applied to determine whether the identified nsSNPs were harmful or benign. Among the 247 nsSNPs found in the SNPs from the NCBI database, 25 nsSNPs with distinct mutations  were identified as likely to be harmful, deleterious, or disease-causing, based on the outcomes of 8 software tools. Other previously reported FSNPs included Y238X, which has been implicated in increasing the risk of Candida species colonization in the gastrointestinal tract of immunosuppressed patients without increasing the risk of candidemia. In diabetic populations, FSNPs (C/G SNP at position -44) are protective in secreting antifungal peptides by epithelial b-defensins.Dectin-1. It revealed 28 nsSNPs in three protein domains, of which two were positioned in the non-cytoplasmic domain. Nine nsSNPs were located in the TMhelix/transmembrane domain, and the rest (16 nsSNPs) were present in the cytoplasmic domain containing the immunoreceptor tyrosine-based activation motif-like sequence (ITAM-like). This ITAM mediates a ligand-induced signaling response’s activation by interacting with the SKY protein and producing various immune modulators, suggesting the presence of any of the nsSNPs could disrupt the immune signaling pathways.InterPro software was used to determine the location of these nsSNPs on different domains of Dectin-1 and reducing antifungal effects. This suggests that these variants may alter the function and conformation of the protein. The conformational changes of the Dectin-1 protein during biomolecular interactions are vital for executing its function. In addition, nsSNPs can lead to aberrant conformations, which may inactivate their antifungal properties [Dectin-1 and their association with various diseases.According to the ConSurf server, the evolutionary model of macromolecules is interpreted to demonstrate the importance of highly conserved regions in the function and conformation of macromolecules. Furthermore, the functional regions of proteins are often conserved and associated with various functions, including catalysis, interaction, and binding ,28. Our operties . Hence, The stability of proteins is vital to their structural and functional activity . The I-MDectin-1 structure and consequently contribute to the loss of its function and anti-fungal or microbial properties. Furthermore, the Missense 3D tool predicted the consequences of the 28 structural nsSNPs and showed that only seven nsSNPs deleteriously affect the structural conformation of the Dectin-1 protein. There is a high risk of pathology when mutations occur in or near some special amino acids that contribute to functional and spatial conformation. A missense mutation results in amino acid substitutions, resulting in changes in amino acid size, charge, and hydrophobicity, which may disrupt the folding and interaction of proteins. The analysis from HOPE indicates that mutations lead to either loss of interactions or structural changes, particularly in transmembrane domains. In addition, introducing or losing charge or hydrophobicity would result in repulsion, misfolding, or loss of interactions. The Hope project results demonstrated that all 25 nsSNPs have a deleterious effect on the Biological processes and disease development depend on the regulation of PTMs. Among the most harmful nsSNPs are those that alter PTM sites and are associated with the disease. We found none of the PTM sites we identified were affected by our nsSNPs using MusiteDeep software.Dectin-1. In Regulome DB, only the SNPs with a ranking < 4 were considered. No nsSNPs were found for 3UTR. The rankings, along with the probability score, are given. According to Regulome DB, among the non-coding SNPs, rs536465890 and rs527258220 showed the best results for 5UTR. miRNA binds to mRNA and inhibits mRNA translation through mRNA degradation to regulate protein production. A 3UTR nsSNP in the gene can modify or disrupt mRNA target sites, altering miRNA-mRNA interactions and possibly leading to abnormal gene expression . In thisDectin-1 was predicted to have strong interactions with SYK, TLR2, LGALS3, FCGR2B, TLR4, CARD9, SRC, PLCG2, LGALS9, and CD37. The STRING interaction result was further validated by using KEGG pathways for Dectin-1. Interestingly, the same set of proteins was involved in an immune signaling pathway. STRING analysis demonstrated that SYK, TLR4, CARD9, and TLR2 are involved in vulvovaginal candidiasis and infectious disease pathways. Therefore, any of the identified nsSNPs may change the Dectin-1 protein’s function.From the STRING protein-protein interaction analysis, Dectin-1 receptor activity, possibly enhancing vulnerability to invasive aspergillosis (IA) [Dectin-1 receptor variant is a risk factor for IA in high-risk patients. Both groups had an associated degree of significance . Sainz et al. further demonstrated an increase in the proportion of these polymorphism-carrying galactomannan-positive patients. Chai et al. and Smith et al. also confirmed the relationship between variation in the gene encoding Dectin-1 and IA. Recently, it has been discovered that several polymorphisms in genes that encode innate immunity-related proteins enhance susceptibility to Aspergillus infections [Dectin-1 is one of the main phagocytic receptors in macrophages for fungal infections, other SNPs in other genes have also been reported to affect human-fungal recognition and interaction. These genes include mannose-binding lectin (MBL) 2, toll-like receptors , CARD9 caspase recruitment domain-containing protein 9, IL-4, and b-Defensin-1 [Studies have shown that a stop codon polymorphism causes inadequate sis (IA) . Cunha efections . The likfensin-1 .Dectin-1 gene that may be considered risk targets against fungal infections. The frequency of such mutations in local populations can be determined in the future through epidemiological research, as can whether these mutations are associated with fungal infections. This study could aid in identifying populations more susceptible to developing fungal infections by identifying genetic variations associated with IA or Candidiasis.In this study, we have identified several novel nsSNPs in the


Despite near-experimental accuracy on single-chain predictions, there is still scope for improvement among multimeric predictions. Methods like AlphaFold-Multimer and FoldDock can accurately model dimers. However, how well these methods fare on larger complexes is still unclear. Further, evaluation methods of the quality of multimeric complexes are not well established.We analysed the performance of AlphaFold-Multimer on a homology-reduced dataset of homo- and heteromeric protein complexes. We highlight the differences between the pairwise and multi-interface evaluation of chains within a multimer. We describe why certain complexes perform well on one metric (e.g. TM-score) but poorly on another (e.g. DockQ). We propose a new score, Predicted DockQ version 2 (pDockQ2), to estimate the quality of each interface in a multimer. Finally, we modelled protein complexes (from CORUM) and identified two highly confident structures that do not have sequence homology to any existing structures.https://gitlab.com/ElofssonLab/afm-benchmark.All scripts, models, and data used to perform the analysis in this study are freely available at  Most biological processes and cellular functions depend on protein structures and their interactions. Understanding how proteins form three-dimensional structures can give critical insights into protein function. Therefore, the determination of the 3D structure of a protein from its primary amino acid sequence has been a fundamental problem in biology , often cPredicting the structure of large molecular complexes has long been difficult unless a suitable template existed. Earlier methods used biophysical and biochemical interaction constraints [i.e. HADDOCK  released after 30 April 2018 (the last date used in the training set of AlphaFold) was downloaded from the biounits part of the Protein Data Bank (PDB). The structures were classified as homomers  or heteromers. Further, nine structures where at least one protein chain does not have contact with other proteins  were removed from the dataset, resulting in 23 222 proteins, with 13 136 dimers, 3025 trimers, 4257 tetramers, 942 pentamers, and 1862 hexamers.We removed similar structures within each oligomeric state separately by aligning all-versus-all structures with MMalign  and subsMMseqs2  removed For further evaluation, we used the CORUM Version 3.0 Core Set (released in September 2018)  between AlphaFold-Multimer  as illustrated in i and all the other chains, interface DockQ (DockQi) as illustrated in i ≠ ∑j DockQij. To ensure the order of the chains in the AlphaFold-Multimer model is the same as the native structure, we use the MMalign output alignment mapping chains between the native and modelled structures so that the chain names are consistent. For example, for a tetramer with chains A, B, C, and D, the DockQi was calculated using the following command line options:python3 DockQ.py model.pdb native.pdb -model_chain1 A -native_chain1 A -model_chain2 B C D -native_chain2 B C Dwhile DockQij was calculated for each pair of chains (A and B in the example) with the optionspython3 DockQ.py model.pdb native.pdb -model_chain1 A -native_chain1 A -model_chain2 B -native_chain2 BThe DockQ score signifies the quality of an interface of a model compared with the native structure, with the larger protein acting as the receptor and the smaller protein as the ligand. In a multioligomeric complex, there are several ways to define an interface, e.g. (i) residues in contact between any pair of chains Note that the AlphaFold-Multimer models were generated for the full-length (PDB SEQRES section) sequences. Thus, to avoid the impact of residues, possibly not resolved experimentally, we calculated the DockQ score only for those residues present in both modelled and reference (PDB ATOM section) structures.int stands for the average of pLDDT [i.e. predicted lDDT score . Therefore, we propose a novel variation of the pDockQ score, pDockQ2, using the relationAlphaFold-Multimer provides two intrinsic model accuracy estimates, pTM and ipTM. Both these scores estimate the average quality of the complex , predicting the TM-score. However, in addition to estimating the quality of the entire predicted model, it is sometimes desirable to estimate each interface’s quality within a multichain complex. For dimers, int is the PAE over all interfaces for chain i, first being scaled by an optimized parameter = 10 Å. <pLDDT>int is the average pLDDT of that combined interface residues. As in pDockQ, we fit a sigmoid curve  or for each pair of proteins (DockQij). This study investigates correlations between these metrics types and the consistency within a complex. In ij scores for a complex and in i.How to best evaluate the quality of a multimeric protein model is not well examined. Some metrics (e.g. MM-score) evaluate a complex in its entirety, while others provide evaluation per interface  possess internal symmetry, repeating similar interfaces between subunits, leading to the uniform quality of the predicted interfaces and, thus, to a smaller variation in per-interface quality scores compared with heteromers, where each interface may be structurally unique. That explains the observation that for 97.5% of the complexes, all or none of the interfaces are predicted correctly in homomers  for an entire complex, using minimum or maximum scores for all interfaces. Variations in DockQij values within a complex tend to be larger than in DockQi. The average difference between min and max DockQi is 0.11, while the average difference between min and max DockQij is 0.30 , indicating that one protein chain is not correctly docked while the rest are correct. Out of these 58 complexes, only 5 are homomers, strengthening the earlier ‘all or none’ observation for homomers. i and high MM-score for the AlphaFold-Multimer model. Here, the single protein chain contains a repeat of three domains. In the AlphaFold-Multimer model, the rotation between the chains is 120 degrees off, leading to a high MM-score as the structural alignment algorithm is performed without paying attention to the exact residue matching.Further, ij > 0.23 and min DockQi > 0.23  than the correlation between min DockQij and max DockQij (ρ = 0.78). Moreover, our benchmarking study aimed to identify the models where all the chains and interfaces are completely correct (min DockQi) instead of considering only the best possible interface (i.e. max DockQi); hence, all our subsequent analysis involves only the min DockQi for each complex.Finally, we note a high overlap among the good models identified by the different methods using the following cutoffs, MM-score > 0.75, min DockQ0.23 see . Overalli for each oligomeric state is shown in i as the metric. The success rate for AlphaFold-Multimer for each oligomeric state, using min DockQi > 0.23, is reported in Performance of AlphaFold-Multimer using MM-score and min DockQAnalysis of other multichain prediction methods, FoldDock . FoldDocP-value = .49 obtained using Mann–Whitney T-test). However, heteromeric structures show a significant difference  with newer proteins being better than those used for training, i.e. there seems to be no indication that AlphaFold-Multimer is severely overtrained.In i > 0.23 and MM-score > 0.75) and incorrect docking models (min DockQi ≤ 0.23 and MM-score < 0.75), we found that the number of effective sequences (Neff) in the paired alignment in wrong models is lower on average  but only considers the size of the interface and the predicted quality (pLDDT) of residues in the interface. Therefore, it does not work if a method generates models with large, highly confident incorrect interfaces. To correctly classify such models as wrong, it is necessary to consider the PAE. Therefore, we developed pDockQ2 (see Section 3), which considers PAEs between all chains. It is visually better correlated with DockQi for all subsets of models , we believe there is a need to quantify the quality of each interface in a multichain complex prediction. Many models have a low min DockQand ipTM . Next, wf models , except f models . A compaFoldDock .i, for some models, the difference is large, i.e. some wrong models are predicted to be good or vice versa, and we took a further step on the possible reasons to explain these outliers. Using pDockQ2 < 0.1 and DockQi > 0.6 resulted in 55 interfaces. By comparing different metrics, we found that these models have a considerable length difference between the SEQRES sequences and the PDB sequences (P < .01 obtained using Mann–Whitney T-test)  see . An exami (<0.1). By manually checking those cases, the most prominent finding is that usually individual chains are predicted quite accurately, but the docking positions are wrong compared with the native structures. However, the high pDockQ2 values indicate that AlphaFold-Multimer is quite confident regarding the interface residues. Apart from this, 79% of the pTM scores for the complexes are above 0.8. Thus, other possible explanations might be needed. For example, the number of residues in contacts at the interface in the native structure of PDB 6JBD (We found that 72 predicted interfaces are the extreme cases with high pDockQ2 (>0.8) but low DockQPDB 6JBD  and PDB PDB 6JBD  is far fPDB 6JBD .Thermothelomyces thermophilus.Now we asked if AlphaFold-Multimer can be used to predict truly novel structures by turning to the CORUM database. Out of the 29 potentially novel complexes  that weri over all interfaces is a useful way to evaluate the quality of the multimeric complex. Also, the performance of AlphaFold-Multimer (By preparing an independent homology-reduced dataset for benchmarking the performance of protein complex predictors, we have shown that taking the min DockQMultimer  and MM-sMultimer , we showbtad424_Supplementary_DataClick here for additional data file.


Carbohydrates dynamically and transiently interact with proteins for cell–cell recognition, cellular differentiation, immune response, and many other cellular processes. Despite the molecular importance of these interactions, there are currently few reliable computational tools to predict potential carbohydrate-binding sites on any given protein. Here, we present two deep learning (DL) models named CArbohydrate–Protein interaction Site IdentiFier (CAPSIF) that predicts non-covalent carbohydrate-binding sites on proteins: (1) a 3D-UNet voxel-based neural network model (CAPSIF:V) and (2) an equivariant graph neural network model (CAPSIF:G). While both models outperform previous surrogate methods used for carbohydrate-binding site prediction, CAPSIF:V performs better than CAPSIF:G, achieving test Dice scores of 0.597 and 0.543 and test set Matthews correlation coefficients (MCCs) of 0.599 and 0.538, respectively. We further tested CAPSIF:V on AlphaFold2-predicted protein structures. CAPSIF:V performed equivalently on both experimentally determined structures and AlphaFold2-predicted structures. Finally, we demonstrate how CAPSIF models can be used in conjunction with local glycan-docking protocols, such as GlycanDock, to predict bound protein–carbohydrate structures. The carbohydrate–protein handshake is the first step in many pathological and physiological processes . PathogeKnowledge of carbohydrate–protein interactions has been leveraged to develop therapeutic candidates to neutralize infections and inspire proper health function . One botRecent developments in deep learning (DL) have substantially enhanced the theoretical modeling of proteins and protein–protein interactions. For example, neural networks can design stable proteins with unique folds using graph representations . 3D struRecent computational studies have demonstrated new ways to explore protein–carbohydrate interactions. Our laboratory has also contributed to the advancement of this field by adding the following: (1) a shotgun scanning glycomutagenesis protocol to predict the stability and activity of protein glycovariants  and (2) Recently, there have been developments in small molecule-binding site predictors. Small molecule-binding site predictors typically fall into four categories: template, geometry, energy, or ML-based . TemplatAlthough there are many general small molecule-binding site predictors , few taiLarger protein–carbohydrate structural databases currently include UniLectin3D  and ProCMany drug targets, from pathogen lectins to aberrant selectins, are carbohydrate-binding proteins . Understversus experimentally determined structures. Finally, we present a proof-of-concept pipeline to predict bound protein–carbohydrate structures.In this study, we develop two DL methods for residue-level carbohydrate-binding site prediction for non-covalently bound carbohydrates. The two methods have different architectures, one using voxel convolutions and the other using graph convolutions. We also present a dataset of 808 non-covalently bound nonhomologous protein chain carbohydrate structures and use it to train and test both models. We compare the performance of the models with each other and with FTMap  and KalaTo construct a method to predict carbohydrate–protein interactions, we needed a large and reliable dataset for training and testing. The dataset should contain as many nonhomologous structures as possible to avoid biasing to specific folds. By filtering the PDB , we consWe constructed convolutional neural networks (CNNs) named CArbohydrate–Protein Site IdentiFier (CAPSIF) to predict carbohydrate-binding residues from a protein structure. CNNs were initially developed for images, exploiting the spatial relationship of nearby pixels for prediction tasks. They have been applied to predict protein structure  and smalapo to holo), we sought a protein representation that is robust to these and other binding-induced changes. We chose a residue-level representation, using only the Cβ positions of all residues (or Cα in glycine), as the Cβ position is frequently equivalent in both the apo and holo states  into a corresponding voxel. CAPSIF:V predicts a label P (carbohydrate-binding residue) for each voxel on the initial grid , is an equivariant graph neural network (EGNN) , with ead) as our loss function , FP residues (blue), and FN residues (red). CAPSIF:V captures the binding pocket visually for endoglucanase  to +1 (best). The Dice coefficient measures the overlap of correctly predicted interacting residues with all predicted interacting residues. We define success as a Dice score greater than 0.6 or, following For comparison, we evaluated how small molecule-binding site predictors FTMap  and KalaOn the CAPSIF test set, FTMap achieved an average Dice coefficient of 0.351 and an average DCC of 10.5 Å, and Kalasanty achieved an average Dice coefficient of 0.108 and an average DCC of 14.6 Å . FurtherWe then investigated whether our CAPSIF models, which are specifically tuned for carbohydrate binding, predict the carbohydrate-binding regions more accurately than Kalasanty and FTMap. On the held-out CAPSIF test set, CAPSIF:V achieves an average 0.597 Dice coefficient and 4.48 Å DCC metric, and CAPSIF:G achieves an average 0.543 Dice coefficient and 5.85 Å DCC metric . FurtherAs CAPSIF is ML-based and FTMap is energy-based, FTMap may predict more accurately in different cases compared to CAPSIF. We compared the CAPSIF:V and FTMap Dice scores for each structure . FTMap aFinally, we compared the CAPSIF:V and CAPSIF:G architectures. CAPSIF:V has an average Dice coefficient of 0.597 and CAPSIF:G has an average Dice coefficient of 0.543 across the test dataset . When coL) conformation; both are sparse in our dataset. Furthermore, CAPSIF:V performs best on transport proteins, membrane proteins, and hydrolases; however, it performs weakly on viral proteins and lyases  , predictAlthough CAPSIF:V has a lower average DCC on AF2 structures compared to experimental structures, CAPSIF:V fails to predict any sites at all on 15% of AF2 structures, whereas it fails in only 5% of PDB structures, suggesting that the signal about sugar binding is removed for some of the small backbone errors produced by AF2.The multiple outliers where CAPSIF:V fails to predict the region of carbohydrate binding in only AF2-predicted structures are sorted in CAPSIF:V predicts the carbohydrate-binding site on the majority of proteins with high accuracy, suggesting that it might be used in a pipeline to predict bound protein–carbohydrate structures. As a proof-of-concept, we developed a prospective pipeline and tested it on five proteins from the GlycanDock  test datWe constructed the following rudimentary pipeline. We predicted the binding site from each unbound protein’s experimentally determined structure with CAPSIF:V and constructed the known carbohydrate with Rosetta. The carbohydrate center of mass (CoM) was then placed in the CoM of the predicted binding region and manually rotated to align with the binding region shape. Subsequently, we used the Rosetta FastRelax  protocolP. aeruginosa lectin 1, a glycan-binding protein , two carbohydrate-binding modules (CBMs) , a glycoside hydrolase enzyme (1OLR), and an anti-HIV-1 antibody (Ab) (6N32). holo conformation (2VXJ) undergoes a conformational change at the carbohydrate-binding site. Although this Ab case example failed, CAPSIF successfully predicted the carbohydrate-binding regions of 9 of the 11 Abs tested from the GlycanDock test set, which has no overlap with the CAPSIF training set. These predictions demonstrate the potential of CAPSIF to help inform experimental hypotheses or for high-throughput predictions of bound protein–carbohydrate structures.We tested the pipeline on five experimentally solved unbound proteins: We demonstrated that both CAPSIF models predict residues of proteins that bind carbohydrates with much higher accuracy than prior approaches. CAPSIF:V uses a voxelized approach and predicts 62.7% of crystal structures with a distance from the center of the predicted region to the center of the experimentally determined region (DCC) within 4 Å. CAPSIF:G performs strongly on the dataset, predicting 55.2% of crystal structures with a DCC less than 4 Å, with CAPSIF:V performing similarly or outperforming CAPSIF:G in 88.8% of cases. CAPSIF:V is robust to most errors in the protein structure of the magnitude in AF2 structures (Ångström-level) : the algFurthermore, CAPSIF outperforms previous methods specifically tuned for carbohydrate binding. CAPSIF:V achieved a 0.599 MCC, and CAPSIF:G achieved a 0.538 MCC on the test dataset. Although CAPSIF accurately captures the protein–carbohydrate-binding interface, there are limitations. CAPSIF is carbohydrate-agnostic, so it only predicts that a protein residue will bind one of the 16 carbohydrate monomers. In other words, CAPSIF predicts the location of carbohydrate binding, but it does not predict which carbohydrate preferentially binds there. Furthermore, CAPSIF was only trained and tested on known non-covalent carbohydrate-binding proteins. Therefore, CAPSIF may not be informative on non-carbohydrate-binding proteins or proteins that bind glycoconjugates, such as ribose in nucleic acids, ATP/ADP, or GTP/GDP . CAPSIF The scope of CAPSIF makes it well-suited for a computational pipeline. We suggest the use of DeepFRI , a DL moab initio from sequence only.We tested part of this pipeline by predicting the binding region using CAPSIF:V and docking the known carbohydrate binder to the region with GlycanDock . CAPSIF:Nres × 30 to a higher dimensionality of Nres × 64. CAPSIF:V has 102 M parameters, and CAPSIF:G has 236K parameters, reflecting how graph-based methods capture the spatially equivariant information in fewer parameters. One characteristic of using the voxel representation is that the grid contains voxels with the protein and the voxels outside the protein, including binding pocket cavities, whereas the graph representation only contains the protein. Voxel network reasoning over the surface pocket volume may be the key factor for improved carbohydrate-binding residue prediction.To our knowledge, voxelized and graph-based site prediction has not been presented simultaneously before. Existing studies have used graphs to either predict binding affinity  or a docBy building on this initial screen, future studies could focus on improving the CAPSIF data representation for improved accuracy and extending these models to predict which carbohydrate monomer a residue most preferentially binds and whether the protein is a carbohydrate-binding protein. In the future, the dataset could include oligomeric structures that bind carbohydrates at the oligomeric interface. Furthermore, model performance could be improved by leveraging homologous structures with data splits across families. Although lectins are well known for carbohydrate binding, some protein families, such as G protein-coupled receptors (GPCRs) and antibodies, do not exclusively bind carbohydrates . AdditioNo dataset of nonhomologous bound protein–carbohydrate structures that leveraged the total size of the current PDB existed, so we constructed one. A simple selection of all RCSB  structurThe final dataset consists of 808 structures, with a split of 521 training structures, 125 validation structures, and 162 test structures. Each structure has one or more of the following carbohydrate monomers: glucose (Glc), glucosamine (GlcNAc), glucuronic acid (GlcA), fucose (Fuc), mannose (Man), mannosamine (ManNAc), galactose , galactosamine , galacturonic acid , neuraminic acid (Neu)/sialic acid (Sia), arabinose (Ara), xylose (Xyl), ribose, rhamnose (Rha), abequose (Abe), and fructose (Fru). We split the training, validation, and test sets pseudo-randomly to ensure an equal representation of all carbohydrate species in each split. The numbers of each monomer per structure and the Dice coefficient for each carbohydrate monomer type and each protein family in the test set from CAPSIF:V are included in CNNs are not rotation invariant, and so data augmentation by rotations improves their performance . Therefoapo and holo states , with the 28 features from CAPSIF:V contains an embedding layer and nine convolutional blocks where four blocks encode the structure, one block forms the bottleneck, and four blocks decode the structural information. The embedding layer lifts the 28-channel input into a 32-dimension space. Each block has a double convolution, performing the following methods twice: 3D convolution, with the same number of input channels as the number of output channels, (5 × 5 × 5) kernel with a stride of 1 and padding of 2, a batch normalization layer, and a rectified linear unit (ReLU) activation function. In addition, each encoding block has a MaxPooling layer to double the size of the channels  while reducing the 3D cubic voxel numbers . Each decoding block first concatenates the results of the encoding layer of the same size and then performs a double convolution and a 3D-transposed convolution operator, reducing the number of channels  while increasing the 3D cubic voxel numbers  After the nine blocks, there is a single convolutional layer condensing the input channels (32) into a single output channel, which is then followed by a sigmoid activation function to output the probability that the voxel contains a residue that binds a sugar . CAPSIF:−4 and batch size of 20 grids using the Adam  with another decoding/encoding layer in the UNet architecture. We also attempted different configurations of skip connections, such as UNet++ . These mCAPSIF:G is an equivariant graph neural network  that perCAPSIF:G first lifts the 29-feature input node into a 64-dimension space. The 64-feature vector, alongside the edge features (distances), is then input to eight consecutive equivariant graph convolutional layers (EGCLs) . Each EG−4 and batch size of one protein using the Adam optimizer  and used different dimensionalities of the latent space . The best-performing model is described previously.In optimizing CAPSIF:G, we explored changing the number of graph convolutional layers and the latent space dimensionality. We tested the number of layers (


Biomolecules Special Issue on Protein Modeling and Simulation was to encourage the authors of selected articles from the Computational Structural Bioinformatics Workshop (CSBW) 2021 to publish the extended version of their manuscripts. Eight articles were published in this Special Issue with topics relevant to protein structure, protein–ligand interactions, protein mutations, and molecular dynamics simulation.Computational structural biology has demonstrated a key role in improving human health. By harnessing big data techniques and machine learning methods, computational structural biology has shed light on molecular interactions and mechanisms. The main goal of the With the release of AlphaFold2, we can now determine a protein tertiary structure with a high accuracy. Despite protein molecules’ high structural plasticity, a single-structure view is insufficient. In computational structural biology, obtaining a multi-structure view of a protein molecule is still challenging. In , Alam anAs it was estimated that about 30% of the FDA-approved drugs in the United States target G protein-coupled receptors (GPCRs), understanding the interactions between GPCRs and their ligands is important for providing a theoretical basis for new drug design and discovery. In , DankwahInteractions between proteins and small molecules, called ligands, govern vital biological processes in the human body. It also plays a key role in molecular recognition that is central to drug design and discovery. One key factor for studying protein–ligand interactions is the strength of binding, or affinity, which could be measured as the binding free energy. Classic implicit solvent models, which have been widely used to accomplish this task, lack accuracy compared to experimental references. Emerging data-driven models, on the other hand, are often accurate, yet not fully interpretable, and are likely to be overfitted. In , Cain etThe advancement of AlphaFold2 to computational models, which can now be as accurate as experimentally resolved models, signals the emergence of several next holy grails in computational structural biology. An essential one in this direction is to move from a single-structure to a multi-structure view of protein molecules as obtaining a detailed understanding of the structure and dynamics of unbound and bound molecular systems remains a fundamental challenge. In , Kabir eIn , Jilani To characterize the structural arrangement of cytoskeletal filaments in 3D maps of subcellular components, Sazzed et al. presented a framework named Spaghetti Tracer , which uCryo-electron microscopy (cryo-EM) is a structural technique that has played a significant role in protein structure determination in recent years. Cryo-EM has some advantages over conventional methods such as X-ray crystallography and nuclear magnetic resonance (NMR). One of the advantages is the ability to visualize large protein complexes. Cryo-EM 3D images are produced at different ranges of the resolution, near-atomic, or high, medium, and low. At a medium resolution, the atomic information of protein molecules is hard to determine. Usually, computational methods are used to help in the determination of the protein structure. This set of methods is called de novo. In this technique, one intermediate step is to determine the correct topology of the protein sequence to the 3D image; in particular, determining the assignment and direction of each secondary structure element (SSE) of the sequence to some detected sticks and plates that represent the location of these elements on the cryo-EM 3D image. In , BehkamaIn , Sun inv


Background: AKT1 is a serine/threonine kinase necessary for the mediation of apoptosis, angiogenesis, metabolism, and cell proliferation in both normal and cancerous cells. The mutations in the AKT1 gene have been associated with different types of cancer. Further, the AKT1 gene mutations are also reported to be associated with other diseases such as Proteus syndrome and Cowden syndromes. Hence, this study aims to identify the deleterious AKT1 missense SNPs and predict their effect on the function and structure of the AKT1 protein using various computational tools. Methods: Extensive in silico approaches were applied to identify deleterious SNPs of the human AKT1 gene and assessment of their impact on the function and structure of the AKT1 protein. The association of these highly deleterious missense SNPs with different forms of cancers was also analyzed. The in silico approach can help in reducing the cost and time required to identify SNPs associated with diseases. Results: In this study, 12 highly deleterious SNPs were identified which could affect the structure and function of the AKT1 protein. Out of the 12, four SNPs—namely, G157R, G159V, G336D, and H265Y—were predicted to be located at highly conserved residues. G157R could affect the ligand binding to the AKT1 protein. Another highly deleterious SNP, R273Q, was predicted to be associated with liver cancer. Conclusions: This study can be useful for pharmacogenomics, molecular diagnosis of diseases, and developing inhibitors of the AKT1 oncogene. In-depth insight into genetic variation has emerged as a capacious challenge in the field of genomic research, owing to the presence of a large number of genetic variations in the genome of humans. Assessment of the variations in the genome could serve as a foundation stone to obtain better insight and understand the role of these variations in disease susceptibility, as well as their response to personalized treatment . AdditioHomo sapiens), the molecular location of this gene spans from 104,769,349 to 104,795,748 base pairs (bp) on chr14 (NCBI). This gene translates to one out of three members of the human AKT serine/threonine protein kinase family, which are usually stated as protein kinase B alpha (α), beta (β), and gamma (γ). These highly identical AKT proteins encompass the C-terminal regulatory domain, N-terminal pleckstrin homology domain, and serine/threonine-specific kinase domain. Moreover, the protein encoded by this gene becomes phosphorylated via phosphoinositide 3-kinase (PI3K) [In general, a single nucleotide polymorphism (SNP) is an alteration of a single nucleotide, i.e., adenine (A)/cytosine (C)/guanine (G)/thymine (T) at the genetic (DNA) level. Recently, a type of SNP called non-synonymous SNP (nsSNP) has gained the interest of researchers as it could serve as a potential biomarker for the diagnosis of various diseases. These nsSNPs are responsible for the substitution of amino acids within the sequence of the protein, which further imposes a negative effect on physiochemical properties of natural proteins such as dynamics, protein–protein interaction, and stability ,4. ThereAn extensive investigation of this gene has unveiled that AKT is an essential signaling hub that involves interaction with more than 100 downstream target substrates and is involved in tumor cell survival. This occurs as survival factors suppress the apoptosis through a transcription-independent manner by activating AKT1 which later becomes phosphorylated and inactivates the function of machinery responsible for apoptosis ,10,11. AOver the past decades, various in silico approaches have been developed for screening, identifying, and predicting the function of SNPs and the effect of deletion of nsSNPs in the candidate protein. Based on in silico approaches, the functional disease susceptible nsSNPs in various genes such as DEFB1, LEP, AGT, EPHX1, GJA3, GSTM1, HLA-G, GSTP1, TYR, and GSTT1 have been identified successfully ,21,22,23The schematic representation of the in silico analysis performed in this study is shown in https://www.ncbi.nlm.nih.gov/snp/, accessed on 15 July 2021). The dbSNP is an archive of several SNPs, microsatellites, and deletion insertion polymorphisms which can be used for a wide range of biological research, such as association studies, pharmacogenomics, evolutionary studies, and functional analysis [The dataset for the SNPs present in the human AKT1 gene was retrieved from the dbSNP database http://provean.jcvi.org/index.php, accessed on 17 July 2021). This webserver helps to predict if the single or multiple amino acid substitutions/deletions/insertions will have any effect on protein function [PROVEAN  webserver version 1.1 was used (function . PROVEANfunction . The SNPfunction . The lis(b)https://sift.bii.a-star.edu.sg/www/Extended_SIFT_chr_coords_submit.html, accessed on 17 July 2021) helps to filter a multitude of SNPs using the sequence homology method based on the assumption that the amino acid substitutions in the evolutionarily conserved regions have a higher probability of affecting the protein function [SIFT (Sorting Intolerant from Tolerant) (function . The SIFfunction . The nsSfunction . The lis(c)http://genetics.bwh.harvard.edu/pph2/, accessed on 18 July 2021) [PolyPhen-2 (Polymorphism phenotyping version 2) classifies the SNPs into three categories: benign, possibly damaging, and probably damaging based on the probabilistic score, ranging between 0 and 1, determined using the machine learning method (ly 2021) . This sely 2021) . The pro(d)https://rostlab.org/services/snap2web/, accessed on 18 July 2021). The amino acid sequence of the AKT1 protein in FASTA format was used as input. This tool uses a neural network approach to classify variants as either effective or neutral on the basis of the SNAP-2 score which is calculated by evaluating various sequence and variant features [SNAP-2 was another webserver used to analyze the functional effects of the nsSNPs (features . The SNAfeatures .(e)http://mutationassessor.org/r3/, accessed on 19 July 2021) uses evolutionary conservation patterns to determine the functional impact score which is then used to classify the amino acid substitutions as having a low, medium, or neutral functional impact [Mutation Assessor webserver (l impact . The Uni(f)http://pantherdb.org/tools/csnpScore.do, accessed on 19 July 2021)  webserver takes the input as the protein sequence in FASTA format and substitution information in the standard amino acid substitution format. It uses the position-specific evolutionary preservation (PSEP) method to predict the causal role of the nsSNPs in human diseases by classifying the nsSNPs as probably benign or probably damaging .PANTHER ((g)http://structure.bmc.lu.se/PON-P2/, accessed on 20 July, 2021) webserver takes the UniProt ID of the protein along with the substitution information as inputs. It uses a machine learning-based method trained using the VariBench database to classify the amino acid substitutions as neutral, pathogenic, or unknown [PON-P2 ( unknown .(h)http://mmb.irbbarcelona.org/PMut/analyses/new/, accessed on 21 July 2021) webserver uses a neural network-based method trained using the SwissProt dataset to determine the pathological nature of SNPs and classify them as disease-causing or neutral [PMut  sites in the AKT1 protein, the MusiteDeep webserver was used . The amihttps://consurf.tau.ac.il/, accessed on 22 July 2021). It uses either the maximum likelihood method or empirical Bayesian inference to analyze the evolutionary conservation of amino acids in proteins which can be useful in determining the regions which can be vital for protein structure and function [The conserved amino acids in the human AKT1 protein were predicted using the ConSurf webserver . I-Mutant uses a support vector machine (SVM)-based method to predict the protein stability due to single point mutations [For predicting the effects of most deleterious nsSNPs on the stability of the AKT1protein, two webservers, I-Mutant and MUpro, were used . The AKT1 protein sequence in FASTA format was provided as input. This server uses an artificial neural network trained using various experimentally determined protein structures to determine whether the amino acids are buried or exposed residues [For the prediction of surface accessible amino acids in the AKT1 protein sequence, the NetSurfP 3.0 webserver available at residues .https://www3.cmbi.umcn.nl/hope/, accessed on 25 July 2021). The UniProt accession code of the AKT1 protein and mutation information were provided as input. The HOPE webserver collects and combines data from various databases such as UniProt, and other web services . The HOPE webserver provides information about the schematic structure of wild and mutant residues, the difference in the properties of the mutated and wild-type amino acids, the impact of the different properties of wild and mutant residues on the flexibility and interactions of the proteins along with several other pieces of information, figures, and animations [The effects of the most deleterious SNPs on the 3D structure of AKT1 protein were predicted by Project HOPE (Have your Protein Explained webserver available at imations .https://string-db.org/ accessed on 31 August 2021. The UniProt protein identifier of the AKT1 protein (P31749) was provided as input and Homo sapiens was selected as the organism. The STRING server predicts the protein–protein interactions on the basis of information available from data mining, different experiments, and databases [The STRING database  was used to determine the AKT1 protein association network. The STRING database is available at atabases . The prohttps://cansarblack.icr.ac.uk/ (accessed on 31 August 2021). The database was searched using the UniProt protein identifier of the AKT1 protein (P31749).The association of highly deleterious AKT1 missense SNPs with cancer was predicted by searching the mutation profile generated with the CanSAR Black database available at http://gepia.cancer-pku.cn/index.html, accessed on 31 August 2021). It provides information about tumor/normal differential expression analysis, patient survival analysis, and other information by analyzing RNA sequencing data from different sources such as the Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression projects [As the substitution R273Q was found to be associated with liver cancer, the effect of AKT1 gene expression in liver cancer was analyzed. Furthermore, the effect of the overexpression or low expression of the AKT1 gene in liver cancer patients was also evaluated. For the analysis of gene expression and survival rate, the Gene Expression Profiling Interactive Analysis (GEPIA) webserver’s boxplot and survival analysis tools were used  was used as a template for modeling all the mutant proteins and it has 99.77% sequence similarity with the query protein. Mutant proteins were modeled by manually replacing the mutant residue at the related position of the wild-type protein’s FASTA file.The mutations at the conserved positions are more likely to be damaging the structure and function of proteins. Hence, the effect of mutations at 100% conserved residues on the structure of the AKT1 protein was studied. Altogether, 4 mutations—G157R, G159V, G336D, and H265Y—were at 100% conserved positions. These four SNPs were also predicted as highly conserved (ConSurf score: 9) using the ConSurf tool for comparison of the structure of wild-type protein and mutant proteins, and the 3D structures of mutant proteins were generated using the SWISS-MODEL online server available at https://zhanglab.ccmb.med.umich.edu/TM-align/, accessed on 31 August 2021). This TM-align algorithm generates a RMSD value and TM-score (ranges from 0 to 1) which can be used to determine the structural similarity between superimposed proteins [First of all, the 3D structure of the wild AKT1 protein and the mutants of AKT1 proteins were compared using PyMOL software. PyMOL software was used to identify and compare different types of interactions among the residues in the wild and the selected mutant proteins. The bonds and interaction between the residues are important for the structure and function of proteins. After comparing the structures with PyMOL software, the structures were further compared by aligning the protein structures. The protein structure alignment to determine the structural deviation of superimposed wild and mutant AKT1 proteins was determined using the TM-align webserver.  and package protr [The FASTA sequence of the AKT1 protein was deployed to check for the effect of point mutations. We deployed R software  and 3DLigandSite  [The ligand binding sites on the AKT1 protein were predicted using COACH (er 2021) ,48. The er 2021) . The 3DLhttps://bioinfo3d.cs.tau.ac.il/PatchDock/php.php accessed on 5 September 2021 [Further study to analyze the effect of SNPs on the interaction between ligand and wild-type and mutant proteins was undertaken by performing a molecular docking study using the PatchDock webserver available at the website: ber 2021 . The Patber 2021 . For molber 2021 . The 3D ber 2021 . While dber 2021 .The association between miRNA and SNPs was evaluated using the PolymiRTS (polymorphisms in miRNAs and their target sites) database . The Polhttps://portal.gdc.cancer.gov/ accessed on 9 June 2023) was explored for analyzing the AKT1 SNP association with different cancers. In the GDC portal, the gene name AKT1 was searched under the “Gene” option of the “Exploration” page. Then, the mutations option was clicked to see different mutations in AKT1 that have been reported to be associated with cancer.The data from The Cancer Genome Atlas program (TCGA) available at the Genomic Data Commons (GDC) Data Portal  were located at residues with conservation score of 8. Furthermore, the neural network algorithm of ConSurf predicted the SNP variants F217L, G336D, L321P, and H405P were located at the structural and buried residues, while the remaining highly deleterious AKT1 missense SNPs were at exposed and functional residues.MUpro predicted that all these SNPs will decrease the stability of the protein. I-Mutant also predicted a similar decrease in stability except in the cases of three mutations—G336D, H265Y, and T312I—where the mutations G336D and H265Y were found to have a neutral effect on the protein’s stability and the mutation Thr312Ile was predicted to increase the protein’s stability. The effect of these SNPs on the AKT1 protein stability as predicted by both I-Mutant and MUpro webservers is listed in In our study, the NetSurfP webserver predicted that 10 of the 12 highly deleterious missense SNPs, both the mutated and their respective wild-type variants, were located at buried residues . These hThe Project HOPE server obtained the structural information for predicting the effect of the SNPs on the 3D structure of AKT1 protein from the analysis of PDB ID: 6HHJ. The Project HOPE server predicted that all the mutations are located on the protein kinase domain of the AKT1 protein. It also predicted the effect of the amino acid substitutions on amino acid hydrophobicity, amino acid size, amino acid charge, protein function, protein flexibility, protein interactions, and protein structure. The detailed effects of each highly deleterious AKT1 missense SNPs on the protein structure are given in The STRING analysis showed that the AKT1 protein interacts with nitric oxide synthase (NOS3), glycogen synthase kinase-3 beta (GSK3B), 3-phosphoinositide-dependent protein kinase 1 (PDPK1), serine/threonine-protein kinase mTOR (MTOR), forkhead box protein O3 (FOXO3), serine/threonine-protein phosphatase 2A catalytic subunit alpha isoform (PPP2CA), tuberin (TSC2), phosphatidylinositol 4,5-bisphosphate 3-kinase catalytic subunit alpha isoform (PIK3CA), rapamycin-insensitive companion of mTOR, subunit of mTORC2 (RICTOR), and heat shock protein HSP 90-alpha (HSP90AA1) proteins refer . The STRThe CanSAR Black webserver predicted that the substitution of arginine residue at position 273 with glutamine is moderately associated with liver cancer. For other SNPs, their association with cancer could not be determined.p value was above 0.05  using boxplot analysis showed that the case of LIHC had relatively low expression of the AKT gene . The surove 0.05 .For G157R, 92.72% of residues were in Ramachandran’s favored region and 1.88% of residues were in Ramachandran’s outlier region. For G159V, 91.78% of residues were in Ramachandran’s favored region and 1.64% of residues were in Ramachandran’s outlier region. For G336D, 92.49% of residues were in Ramachandran’s favored region and 1.17% of residues were in Ramachandran’s outlier region. For H265Y, 90.38% of residues were in Ramachandran’s favored region and 1.41% of residues were in Ramachandran’s outlier region.The interactions among the wild-type and four mutant proteins were compared. The results show that there were variations in the number of hydrogen bonds formed by wild-type and mutated residues with other residues in the wild-type and mutant proteins. For the variant G159V, the wild-type residue, glycine, formed two hydrogen bonds with glycine at 162 positions whereas for the mutated protein there was only one hydrogen bond between the mutated residue valine at 159 positions and glycine at 162 positions. In variant G157R, both the wild-type residue (glycine) and mutated residue (arginine) formed two hydrogen bonds each with valine at 164 positions in both wild and mutated proteins. In variant G336D, the wild-type residue glycine at 336 positions formed three hydrogen bonds with tryptophan at 332 positions, glycine at 334 positions, and tyrosine at 340 positions. However, in the mutant protein, one hydrogen bond was lost, and mutated residue aspartic acid at 336 positions formed only two hydrogen bonds with glycine at 334 positions and tyrosine at 340 positions. Similarly, in the variant H265Y wild-type residue, histidine at 265 positions formed five hydrogen bonds with aspartic acid at 262 positions, leucine at 261 positions, asparagine at 269 positions, aspartic acid at 331 positions, and valine at 270 positions. Whereas in the mutant protein, the mutated residue tyrosine formed only two hydrogen bonds with leucine at 261 positions and proline at 51 positions. Furthermore, the structure of wild-type and mutant protein structures were compared by the TM-align tool. The TM-score of the superimposed and mutant AKT1 protein structures suggests that all the structures are in the same fold because for all the superimpositions the TM-score is above 0.5 and below 1. The mutant H265Y showed the highest root mean square deviation suggesting higher structural deviation in comparison to other mutants. The TM-score and RMSD value for the superimposed wild AKT1 protein and mutant AKT1 proteins are shown in A point mutation simply refers to a single alteration of a residue in a sequence; however, a single change may lead to catastrophes at the genetic level in humans. Therefore, it becomes mandatory to check and identify the potential of a macromolecule to have point mutations in its sequences. Functional effects of single amino acid residue substitutions/alterations have been identified and represented in the form of a heatmap refer . The redIt is evident that AKT1 is susceptible to showcasing strong altered effects on its functioning in the case of a single amino acid alteration in its sequence. Every residue in the AKT1 protein sequence corresponds to showcase an effect with a greater score (>50). This result validates our point that the greater the number of mutations in AKT1, the poorer is the stability of the structure. The 3DligandSite webserver predicted the residues at positions 161, 177, 179, 211, 227, 228, 229, 230, 278, 279, 281, 291, and 292 as ligand binding sites on the AKT1 protein. Similarly, the COACH webserver predicted the residues at positions 156, 157, 158, 164, 177, 179, 211, 227, 228, 229, 230, 234, 278, 279, 281, 291, 292, and 438 as ligand binding sites on the AKT1 protein. The residues at positions 177, 179, 211, 227, 228, 229, 230, 278, 279, 281, 291, and 292 were predicted as ligand binding sites by both webservers. The COACH webserver predicted the residue at 157 is involved in ligand binding. Hence, the SNP G157R could affect the ligand binding in the mutant AKT1 protein. To further assess the impact of the mutations on ligand binding, a molecular docking study was performed.The molecular docking study showed that the interactions between ligand and receptor proteins were not affected significantly by the mutations . In the Using PolymiRTS, the association between miRNAs and 14 3′UTR SNPs was determined. Among these variants, two SNPs were identified as disrupting the conserved site of miRNAs. Additionally, for four SNPs the ancestral allele could not be determined. Interestingly, three SNPs were found to be involved in the creation of a new miRNA site. Finally, five SNPs were determined to either disrupt the conserved site of miRNAs or create new miRNA sites. The list of 3′UTR SNPs of the AKT1 gene that affect miRNA is provided in In the GDC portal, information about 99 mutations in the AKT1 protein is available, out of which 50 mutations are missense SNPs. The position of different missense SNPs on the AKT1 protein and the number of cancer cases associated with the individual AKT1 SNPs are shown in With the advancement in high-throughput sequencing technologies, the identification of genomic variants in the human genome has increased rapidly. The dbSNP database maintained by NCBI  contains more than 1 billion SNPs and 9.6 million of these SNPs are missense. The missense SNPs can influence disease susceptibility, alter protein structure and function, and are relevant in various pathological conditions . The detAKT1 is a serine/threonine kinase belonging to the protein kinase B family . AKT1 isThe data for 293 missense AKT1 gene SNPs were obtained from the dbSNP database. Then, these SNPs were filtered out to predict the highly deleterious SNPs using eight different tools, namely, PROVEAN, SIFT, POLYPHEN, SNAP2, Mutation Assessor, PONP-2, PANTHER, and PMUT. The combination of these different webservers will improve the accuracy and reliability of the prediction. Overall, 12 SNPs—G157R, H405P, G159V, F217L, G336D, R391G, H265Y, R273Q, L321P, G345S, T312I, and G286R—were predicted as highly deleterious from the set of 293 AKT1 missense SNPs. Hence, these 12 SNPs were selected for further analysis. Next, ConSurf analysis was performed to predict the evolutionarily conserved residues in the protein as the conserved residues can be important for protein folding, structural stability, and function . The ConTo further validate the in silico results, the effect of these 12 highly deleterious SNPs on the stability of the AKT1 protein was predicted. Maintaining protein stability is crucial for the proper structure and functioning of a protein . The sta2, a plasma membrane protein [The solvent-accessible area refers to an area of a protein that can come in contact with polar solvents such as water. The analysis of the surface accessible area of amino acids could be of significance during the functional annotation of disease-associated SNP variant proteins . The SNP protein . Similar protein .After identifying the highly deleterious AKT1 missense SNPs and predicting their effect on the structure and function of AKT1 protein, their association with human diseases was predicted. The STRING webserver showed that the AKT1 protein interacts with different proteins belonging to diverse biological pathways. Hence, the mutations in the AKT1 protein are likely to affect interactions of the AKT1 protein with other important proteins and affect different pathways, thus resulting in different diseases. By analyzing the effect of the point mutation in AKT1 functioning, we discerned and validated that AKT1 is susceptible to showcasing strong altered effects on its functioning in the case of a single amino acid alteration in its sequence. Therefore, the greater the mutations in AKT1, the poorer is the stability with major disordered regions in its structure, thus causing insignificant and altered functioning. This may lead to the progression of several genetic diseases in humans.Different studies have shown the association of mutations in the AKT1 gene with Proteus syndrome, Cowden syndrome, and cancer ,64,65. AmiRNAs, which are short non-coding RNAs approximately 22 nucleotides in length, play a crucial role as post-transcriptional regulators. They achieve this by binding to the 3′UTRs of messenger RNAs (mRNAs) . The effAfter the extensive analysis of 293 AKT1 missense SNPs using a multitude of in silico tools, 12 highly deleterious SNPs were identified. These missense SNPs can affect the structure and functions of the AKT1 protein. Out of the 12 highly deleterious SNPs, 4 SNPs, G157R, G159V, G336D, and H265Y, were predicted to be located at highly conserved residues. It was also predicted that one of the highly deleterious SNPs, G157R, could affect the ligand binding to AKT1 protein as the residue at 157 positions was predicted to be involved in ligand binding using the COACH webserver. Another highly deleterious SNP, R273Q, was predicted to be associated with liver cancer. The deleterious effect of these SNPs is being reported for the first time in this study. As these results suggest, the potential of the deleterious missense SNPs to affect the structure and function of AKT1, are based on in silico experiments. Further in vitro, in vivo, and population-based studies are required to corroborate these findings. This study can be useful for pharmacogenomics, molecular diagnosis of diseases, and developing inhibitors of the AKT1 oncogene.


InterPro and ConSurf revealed domains and conserved variants respectively. After stability analysis, we concluded that almost all the mutations were responsible for decreasing the protein stability. HOPE predicted that all the selected damaging nsSNPs were present in the domain which is essential for the functioning of VEGFA protein. Constructed Ramachandran plot and ERRAT validated the quality of all the models. Based on the interactions predicted by STRING database, we performed Protein–Protein docking between VEGFA and VEGFR2. We found few conserved interactions and new polar contacts among wild-type and mutants with VEGFR2. From the simulations, we concluded that mutant R108Q was the most stabilizing mutant among all others whereas R82Q, C86Y, and R108W complexed with VEGFR2 were comparatively less stabilizing as compared to the wild type. This study provides insight into pathogenic nsSNPs that can affect VEGFA protein structure and function. These high-risk variants must be taken into consideration for genetic screening of patients suffering from RA.Rheumatoid arthritis (RA), is marked by joint inflammation leading to pannus formation which results in cartilage destruction promoting bone erosion. The pathological hallmark of RA includes synovial hyperplasia and synovial angiogenesis. Active tissue neovascularization is observed in RA. Vascular endothelial Growth factor A (VEGFA), an endothelial cell-specific proangiogenic molecule is triggered by hypoxic cells and its levels are upregulated in RA. The aim of this study was to investigate functional and pathogenic VEGFA variants and to identify the impact of point mutation in VEGFA’s interaction with VEGFR2 and how these polymorphisms affect the susceptibility and severity of RA. We investigated impact of these point mutations on the stability of VEGFA using various computational tools. These mutations were further identified by conservational profile as they are highly involved as structural and functional mutations. Furthermore, these selected variants were modelled and docked against targeted domain regions IGD2 and IGD3 of VEGFR2. Further molecular dynamic simulations were performed using Gromacs. Out of 168 nsSNPS, 19 were highlighted as highly pathogenic using  RA is characterized by synovitis, damage to articular cartilage and several other bone issues3. RA accounts for 0.5–1% in general population4. 0.55–1.9% of population in Pakistan is affected by RA5. RA is 2 to 3 times more prevalent in females as compare to males6. Synovial angiogenesis is also observed in RA patients which is responsible for synovitis. The major proangiogenic factor is Vascular growth factor A that is released under hypoxic conditions by the cells which are deprived of oxygen to fulfil oxygen requirement7. Hypoxia inducible factors (HIF) are group of heterodimeric transcription factors which are the key regulators to show active response against varying oxygen conditions in body and angiogenesis is the major response against HIFs to fulfil the oxygen requirement. Figure RA is an inflammatory autoimmune disease and it is one of the major reasons for disability8. The VEGF family is comprised of VEGF-A, VEGF-B, VEGF-C, VEGFD, VEGFE, VEGF-F and PLGF . Genome sequencing of VEGF have confirmed that all the families of VEGF have 8 cysteine residues which are conserved at fixed position that are closely related to PDGF family9. VEGF-A is also known as VEGF10. VEGFA is a homodimer protein which is secreted by different types of cells like endothelial cells, smooth muscle cells, macrophages, fibroblasts, neutrophils, platelets and many tumor cells. VEGFA protein is formed by vegfa gene. This gene undergoes alternative splicing to give different isoforms of VEGFA protein. There are 6 different transcripts of VEGFA reported these transcripts generates 16 different isoforms. The reported transcripts of vegfa gene are VEGFA-111, VEGFA-121, VEGFA-145, VEGFA-165, VEGFA-189, VEGFA-206. VEGFA-165 was the first isoform which was identified and it is the most extensively studied isoform11. The transcript selected for this research is VEGFA-206. This transcript is the largest among all others. This transcript is composed of 232 amino acids12. In VEGFA-206 all the exons remained conserved from 1-8a except for exon 8b that is removed by alternative splicing hence it is considered as largest isoform11. VEGFA initiates the process of angiogenesis upon binding with VEGFR2.The Vascular Endothelial Growth factor (VEGF) is located on chromosome 6p12. It is comprised of 8 exons and 7 introns and the coding region spans around 14 kb11. Immunoglobulin like sub domains including IgD2 and IgD3 (141–320 aa) are involved in tight binding of VEGFA dimer to VEGFR2 for its dimerization and activation13. Figure 15.Vascular Endothelial Growth factor receptor (VEGFR-2) is a Kinase Insert domain (KDR gene) receptor is transmembrane glycoprotein which is located on chromosome locus 4q11-12. It possesses 7 extracellular Ig-like domains (immunoglobulin like domains) one transmembrane helix and in the cytoplasm there is a split tyrosine kinase domain17. A single nucleotide polymorphism can affect the overall protein stability, functioning, pathogenicity, protein–protein interactions and protein nature. Vegfa is reported as highly polymorphic gene possessing high number of genetic polymorphisms i-e. 30 functional SNPs in promoter region, 5’UTR region, 3’ UTR region19. Due to polymorphic nature of VEGFA it may influence the expression of this gene between different individuals. According to different functional studies, VEGFA variants were directly associated with mRNA levels and the protein expression in patients with different angiogenic diseases20.VEGFA polymorphism has also been reported to have association with RA in different populationsFigure homosapians) was retrieved from NCBI and Ensemble. All the SNPs were classified by Ensemble into 10 comprehensive groups depending upon their genomic location including splice acceptor variants, splice donor variants, non-synonymous variants, synonymous variants, splice polypyrimidine variants, splice region variants, 3′ UTR and 5′ UTR variants, coding sequence variants and intron variants.Primary Sequence and SNPs of VEGFA gene 25, Cadd26, Revel27, Mutation assessor28, and MetaLR the meta predictor logistic regression (LR)29, After using these six web tools we neglected those mutations which were showing neutral effect and selected those nsSNPs which were observed with pathogenic effect. Further we used PHD-SNP, PANTHER, SNAP231, SNPs and GO (single nucleotide Polymorphism Database & Gene Ontology)32, PMut33, MutPred 234 to select highly pathogenic nsSNPS.To determine the pathogenic nature of nsSNPs, twelve webserver and tools were employed. All tools were used according to their defined protocol and default settings. We used SIFT35.Interpro server was used to classify and predict the functional domains of VEGFA protein. Protein’s FASTA sequence was subjected to the tool to identify the conserved domain present in VEGFA. After retrieving domains of our protein, the position of the selected nsSNPs were visualized via PyMOL36. Protein sequence of VEGFA was subjected for identifying the conserved residues involved in structure and function of VEGFA gene.The conservation profile of VEGFA protein was obtained using ConSurf server. This tool uses an empirical Baysian method to give the conservation scale of each residue. The conservation score ranges from 1 to 9 where the score ranging from 7 to 9 are considered as conserved residues37.HOPE web server was used to analyze the effects of point mutation on the phenotype of VEGFA gene. HOPE finds homologs sequences from different databases by using BLAST algorithm and generates output by giving information about changes in protein structure, hydrophobicity, size of protein and electron density39 I-mutant 2.040, INPS-MD41 and iStable42 were used by comparing on Gibbs free energy.To determine the stability, change of protein after an amino acid substitution have an important role either mutation/ substitution will have effect on configuration of protein or not by increasing or decreasing the protein stability. Pathological mutations and those substitutions that are specifically in the conserved region of a protein can affect the stability of a protein. In order to determine impact of nsSNPs on overall stability of VEGFA MUpro43.The protein–protein interaction of VEGFA were identified by using STRING database. The database predicts both direct  and indirect  interactions. The server is linked to several other databases to provide in-depth and detailed map of protein–protein interactionThe homology modelling also known as Comparative modelling was performed by using Swiss-Model, which builds 3D structure of protein having known sequence but the structure is unknown. We superimposed wild-type model with all the mutants to visualize the tertiary confirmational change and root mean square deviation (RMSD). After successfully generating wild and mutant models they were saved in.pdb format for visualization using PyMOL.45. ERRAT helps to determine overall model quality by giving a score percentage46.For further validation of protein models obtained from Swiss-Model Saves-Procheck server for construction of Ramachandran Plots and ERRAT was used. By Ramachandran Plot we can predict which secondary structure is associated with given amino acid of a protein47. VEGFA wild and mutants were docked against IgD2 and IgD3 domains of VEGFR2. The VEGFA molecules specifically binds to these domains of VEGFR2 and intracellular cascades are activated which triggers angiogenesis. So, the aim of this step was to check the impact of interactions of selected non-synonymous mutations of VEGFA protein with VEGFR2. The structure of IgD2 and IgD3  protein was retrieved from PDB and submitted to HDOCK to carry out binding interaction mechanism. Pymol was used for visualization of these docking complexes to check interactions of wild and mutants with VEGFR248.In order to identify the Protein–Protein interaction, molecular docking was performed using HDOCK server49, a visualization program was used to analyze the trajectories.MD Simulations were carried out by using GROMACS . Simulations were performed for 50 ns (ns) for all the mutants and wild type. After performing simulations, the resultant data was retrieved and analyzed including Root mean square deviation (RMSD), Solvent-Accessible surface area (SASA), Root mean square fluctuations (RMSF). MD simulations were performed to comprehend structural outcomes of all the mutants that affect VEGFA- IgD2 and IgD3 domains of VEGFR2 complexes interactions with respect to time. We predicted behavior and motion of protein in fourth dimension under principles of Newton’s laws. After that VMDEnsemble provided total 26 splice variants (transcripts) of VEGFA gene. VEGFA-206 was selected for this study. This transcript is based on 784 base pairs and consist of 232 amino acids. It comprises of 8 exons and 4,172 allelic variants. Missense/Non-synonymous variants were selected for further in silico analysis. Graph in Fig. All the non-synonymous variants were analyzed through 6 different computational tools to identify the pathogenic SNPs. The tools used for mining of nsSNPS were SIFT, Polyphen, CADD, Revel, MetaLR and Mutation Assessor. 168 nsSNPs of VEGFA were submitted to SIFT, out of them 83 nsSNPs were deleterious. The threshold Index for SIFT was 0.5. SNPs having score less than 0.5 were considered as damaging. Among the deleterious SNPs 40 were having score of 0 and were categorized as highly deleterious Table . PolypheAfter refining results from 12 different computational tools, we neglected those mutations which were resulting as benign in most of the tools. We highlighted those mutations which were sharing common results in maximum number of tools used for analysis of their pathogenicity. After manually screening method 19 nsSNPS were selected with high robustness for further study.According to Interpro results VEGFA contains two domains including PDGF/VEGF domain (39–135 aa) and Heparin-binding domain (183–232 aa) as shown in Fig. Protein residues which are highly conserved are more likely responsible for structural and functional integrity of the protein. ConSurf predicted the evolutionary conservation profile of VEGFA Fig. A, accordAccording to the predictions of InterPro a loop between two domains confirmed that it possesses highly variable residues with lowest conservation score. Similarly, we selected 9 nsSNPS that would be of prime significance in VEGFA association analysis with Rheumatoid Arthritis. Furthermore, the first domain  opted conserved residues sited for missense mutations, other exposed residues with functional effect and buried residues with structural effect are located in the PDGF/VEGF domain., Both of the domains cannot be selected because variable region can form loop in structural analysis, for all these reasons we picked out 9 nsSNPs from the first domain for further study.To predict the effect of all the deleterious nsSNPs on the stability of VEGFA protein four tools were used: MuPro, I-Mutant 2.0, INPS-MD and iStable. We analyzed stability change in VEGFA by free energy values. According to results of MuPro and iStable and INPS-MD, 8 out of 9 nsSNPs showed decrease in protein stability after mutation whereas only rs1208889729 showed increase in stability  of VEGFA so structural analysis of nsSNPs present in this domain was carried out. The homologous model was generated using a template 5fv1.1.D. The model predicted by Swiss Model was comprising of residues from glu39- arg136 amino acids Fig. After structural prediction of VEGFA (native and mutants) we further superimposed native VEGFA with all the mutants to confirm deviation of mutants from wild Fig. . High RMThe quality of all the predicted models was verified by ERRAT which predicted that overall quality factor was more than 95% for wild and mutated models Fig. A. RamachSTRING was used to perform the protein–protein interaction (PPI) network analysis. According to the results VEGFA was interacting with 30 other proteins Fig. B. Among Protein–Protein docking was performed using H-dock server. Comparative interaction analysis of native VEGFA and mutants docked with IgD2 and IgD3 domain of VEGFR2 Fig.  showed sWe performed stability analysis by calculating RMSD Fig. A, which To explore the dimensional discrepancy caused by mutants we calculated Solvent accessible surface area (SASA) of the protein structures Fig. B. Accord50. To understand the flexibility changes of individual residues that are incorporated due to point mutation on VEGFA, we calculated RMSF. The fluctuation distribution of all the mutants and native structure interacting with VEGFR2 are shown in the Fig. Flexible nature of a protein allows it to behave accordingly in the environmental changes. Protein’s flexibility regulates different functions such as regulating the protein activity, Enzymatic activity. So, if protein’s flexibility is disrupted it affects the function of particular proteinth residue. After 50th residue there is change in behavior of C86Y mutant. From 50 to 80 residue in mutant, showed high fluctuation with the highest one showing fluctuation of 1.6 Å. It was observed that residues in 100–140 position constituted a comparatively highest flexible region (2 Å) as compare to wild type. The mutant E90K also showed similar patterns of fluctuation, residues from 50 to 80 showed a flexibility in fluctuation (2.4 Å). The residues between 80 and 100 were comparatively more stabilizing with mean fluctuation of 1 Å. After 100 residue the RMSF peak got increased to 3 Å till almost 120th residue which indicated that residues after 100 were highly unstable. By the introduction of mutation, the mutant C86Y and E90K has lost its stability as compare to wild type. The mutant R82Q was showing less fluctuation as compare to former two mutants. The residues from 50 to 80 showed highest RMSF of approximately 0.5 Å which was closely related to the wild type. The introduction of point mutation  changed the flexibility of residues from 0.1 A˙ to 0.8 A˙ respectively. There was not a significant change in flexibility of mutant but it effected other residues to change their flexibility. All the residues after insertion of Met at position 78 showed increase in fluctuation which means that stability of all the residues hence the whole complex decreased. The highest RMSF was recorded from residue 100–120 of 1.6 Å. The mutant R108Q showed decrease in RMSF from residual position 50–60 with minimum RMSF recorded 0.2 Å which was similar to wild type. The highest fluctuation was recorded from 70 to 100 residue was 0.8 Å. The residues in the mutant R108W also depicted high fluctuations as compare to wild type which indicated that this mutation also have decreased the overall stability of complex. The mutant V59M and E90K showed abnormally high RMSF which indicated that the complex is highly destabilizing hence they were ignored. The RMSF of R108Q recorded in 50 ns time period showed that fluctuation of residues was almost similar to wild type. The RMSF of all the mutants and wild type at different peaks are given in supplementary tables  on which our VEGFA molecule actively bind to initiate downstream pathways to trigger angiogenesis. We performed protein–protein docking using H-Dock server. VEGFA wild and mutants were docked on the IgD2 and IgD3 of VEGFR2. According to the results only few polar contacts remained conserved in all the mutants with respect to wild type. Number of new interactions among mutants with VEGFR2 were observed that were actually not present in wild type.We identified the domains of VEGFA using Interpro server we concluded that VEGFA is divided into two domains PDGF/VEGF domain which start from 39th amino acid and ends at 135th and Heparin-binding domain that range from 183rd to 232nd amino acid. Between these two domains (136–182 aa) there was unidentified loop region. The template for this region is not available on PBD hence the structure from amino acid 136 to 182 of VEGFA is unpredicted. Hence, we were aimed to select one particular domain because loop region can cause hindrance in further structural and docking analysis. Evolutionary conservational analysis using Consurf predicted that most of the conserved amino acids having highest conservation score were present in first domain so we selected 9 mutations present in PDGF/VEGF domain for further analysis. From stability analysis of VEGFA mutants by MuPro, I-Mutant 2.0, INPS-MD and iStable we predicted that overall protein stability was decreased by all the mutations except for the rs1208889729. So, decrease in protein stability refers to the phenomena that all the mutations were destabilizing which means that after introduction of point mutation the stability of VEGFA was decreased. HOPE server predicted change in phenotype of VEGFA after each mutation. According to results of HOPE server, all the mutations were causing drastic change on overall protein’s structure. The model generated by Swiss-model was covering residues from glu39- arg136 which means that Swiss-Model generated the 3D structure of only first domain. A total of 98 residues were covered by this protein model. QMEAN score of our structure was 0.75 which confirms the reliability of our model for further analysis. We further generated the models of all the mutants as well. In order to check the change in structure after mutation we superimposed wild type with all the models and visualized them in PyMol. We calculated Root Mean square Deviation (RMSD) which tells how much our mutant structure is deviated from the wild type The higher RMSD value means there is high deviation of mutant alpha Carbon backbone from its wild-type. If the RMSD value is minimum it means that mutated structure is almost similar to its wild type and there is not much impact of mutation on structural chemistry of proteinIn order to validate our docked models, we performed Molecular Dynamics Simulations to check the docked interactions in dynamic environment. The simulation was performed using Gromacs for 50 ns. We determined RMSD, RMSF, and SASA for wild and mutants. According to the our findings mutant R108Q was showing RMSD pattern similar to wild type which suggest that this mutant was showing behavior almost similar to wild type. This mutant also showed less RMSF in comparision to other mutants. The least RMSF means that the mutant is highly stable. The SASA analysis depicted that mutant V78M and R108Q less deviation pattern similar to wild type as compare to other mutants which confirms their stabilizing nature.This study predicted high-risk SNPS in VEGFA. We confirmed by docking and simulations how these mutations can disrupt the interactions of VEGFA with VEGFR2. However, there is need to perform invitro study on these mutations in different populations to check their presence in patients with RA.Current study concluded that, after performing screening analysis using different bioinformatics approaches, we got 2 nsSNPS of VEGFA that can be damaging and these mutations can affect the structure and/ or function of VEGFA. The in-depth analysis revealed the significance of mutations in PDGF/VEGF domain which can disrupt the interaction of this particular domain with the second domain. These nsSNPs can be further studied for purpose of therapeutic strategies and development of personalized medicines for patients suffering from RA. Among all the mutants the higher binding energy of R108Q with VEGFR2 showed that this complex was significantly more stable which confirms that if this mutation is present in a population, it can be more dangerous. As higher binding of VEGFA with VEGFR2 is directly related to high angiogenic rate, so presence of wild type and mutant R108Q can cause angiogenesis at high rate because there is need to identify drugs or biologics which can disrupt the binding of wild and mutant R108Q with VEGFR2 and act as anti-VEGF. So, biologics which can bind with VEGFR2 with similar binding energy as wild type and mutant R108Q, can slow down/ stop the angiogenic process and ultimately reducing pathogenesis of RA. Furthermore, functional analysis of these mutations is required to understand biological mechanisms of these polymorphisms in pathogenesis of RA, therefore it is needed to interpret downstream effects of all these pathogenic mutants on cellular signaling pathway and other biological processes. Protein expression, cell culture studies and enzymatic assays can provide more insight about the pathogenicity of the filtered mutants and their role in RA. There is a need to perform invitro population studies for these nsSNPs to check their prevalence in Pakistani population and their association with Rheumatoid arthritis.Supplementary Information.