Naegleria fowleri (N. fowleri) is a free-living thermophilic amoeba of fresh water and soil. The amoeba primarily feeds on bacteria but can be transmitted to humans upon contact with freshwater sources. Furthermore, this brain-eating amoeba enters the human body through the nose and travels to the brain to cause primary amebic meningoencephalitis (PAM). N. fowleri has been reported globally since its discovery in 1961. Recently a new strain of N. fowleri named Karachi-NF001 was found in a patient who had traveled from Riyadh, Saudi Arabia to Karachi in 2019. There were 15 unique genes identified in the genome of the Karachi-NF001 strain compared to all the previously reported strains of N. fowleri worldwide. Six of these genes encode well-known proteins. In this study, we performed in-silico analysis on 5 of these 6 proteins, namely, Rab family small GTPase, NADH dehydrogenase subunit 11, two Glutamine-rich protein 2 proteins (locus tags: 12086 and 12110), and Tigger transposable element-derived protein 1. We conducted homology modeling of these 5 proteins followed by their active site identification. These proteins were subjected to molecular docking against 105 anti-bacterial ligand compounds as potential drugs. Subsequently, the 10 best-docked compounds were determined for each protein and ranked according to the number of interactions and their binding energies. The highest binding energy was recorded for the two Glutamine-rich protein 2 proteins with different locus tags, and results have shown that the protein-inhibitor complex was stable throughout the simulation run. Moreover, future in-vitro studies could validate the findings of our in-silico analysis and identify potential therapeutic drugs against N. fowleri infections. Naegleria fowleri is a thermophilic amoeba that is free-living in nature. This single-celled microbe has a global presence, with a particular affinity for inhabiting soil and warm freshwater sources, for example, hot springs, rivers, and lakes , which often leads to fatalities  in recent years has emerged as a major hunting ground for this brain-eating amoeba. In 2008, the first PAM case emerged in Karachi , isolated from one of the reported cases  who had traveled from Riyadh, Saudi Arabia in June of 2019 to Karachi  operating platform was used.Protein-ligand docking aims to accomplish the objective of accurately predicting the location and orientation of a ligand within the binding pocket of a particular receptor. The docking of proteins to their ligands has seen widespread application in modern drug development. To determine the ligand interaction with receptor and to visualize docking of ligand compounds against protein active sites, the Molecular Operating Environment (MOE) software was used . MOE is N. fowleri. After that, docking, using MOE with default parameters, was performed involving proteins and ligand compounds that were prepared and minimized. The software generated a docking score (S) and the number of interactions for each ligand was used to select the best compounds with the highest S score and interactions. These ligands were then analyzed using PyMOL an open-source molecular visualization system  and active (bound by GTP) forms which they switch between, which assists in the recruitment of downstream effectors of different kinds to membranes that play an important role not only in the formation and fusion of vesicles but also in their movement and tethering. Furthermore, Rabs are involved in the early development of the autophagic vacuole in the endoplasmic reticulum .One of the 105 ligand compounds used in this study, 6,7-dichloro-5,8 dimethoxynaphthalene-1,4-dione showed the highest binding affinity for Rab family small GTPase with the binding energy value of −14.769 Kcal/mol. Furthermore, 2 of its interacting residues that are involved in protein interaction are Ser145 and Ser 149. The interaction can be seen in more detail in NADH dehydrogenase subunit 11 is an accessory subunit of the Complex I of NADH dehydrogenase that is present in the respiratory chain of the mitochondrial membrane, and it does not participate in catalysis. It is encoded by the NDUFB11 gene in humans. Electrons from NADH are transferred to the respiratory chain with the help of Complex I, and ubiquinone acts as the immediate acceptor of electrons in the enzyme. The molecular weight of NADH dehydrogenase subunit 11 protein is 17.317 kDa .According to the docking results , 5-hydroGlutamine-rich protein 2, encoded by the QRICH2 gene in humans , has a mTwo different ligand compounds 5,8-dihydroxy-2,3-dihydronaphthalene-1,4-dione and 1,4,5,8-tetramethoxynaphthalene show maximum binding affinity for the two Glutamine-rich protein 2 proteins encoded by different genes (Locus tags: 12086 and 12110). Their respective binding energies are −18.147 Kcal/mol and −20.890 Kcal/mol . FurtherTigger transposable element-derived protein 1 is encoded by the TIGD1 gene in humans and has a molecular weight of 67.299 kDa. In humans, it is part of the tigger subfamily which belongs to the superfamily of pogo DNA transposons. Furthermore, TIGD1 shares a relationship with fungal and Nematodal DNA-mediated transposons but is only vaguely related to the mariner and Tc1 transposases. Also, there is a strong similarity between TIGD1 and CENP-B . Still, the primary function of the TIGD1 gene is unknown .Based on binding affinity, 1,4,5,8-tetramethoxynaphthalene interacts with Tigger transposable element-derived protein 1 with the highest binding energy of any other ligand compound used in this study. With a value of −14.960 Kcal/mol. The interacting residues, include Arg 182, Asn 175. .2 with highest of 39.3162 Å2. This illustrates the protein residues with very stable behavior following the inhibitor engagement.Root mean square deviations of the complex (C-RMSD), root mean square deviations of the ligand (L-RMSD), and beta factor (-factor) were used to assess the output trajectory pattern. In N. fowleri in Karachi, Karachi-NF001, is a lethal strain that requires a treatment strategy. Therefore, in this in-silico study we docked the known protein products of 5 unique genes identified in the genome of Karachi-NF001 against 105 selected antibacterial compounds from the literature. The ligand compounds were ranked based on their binding affinity for these 5 proteins. Based on our findings, the ligand compounds showed the highest binding affinity for Glutamine-rich protein 2, from two different locus tags (12086 and 12110), and therefore should be considered as potential drug targets. MD simulation of protein with the inhibitor showed that protein inhibitor complex was stable. Furthermore, in-vitro studies can provide significant insight to validate the findings of our in-silico analysis followed by possible in-vivo studies of these compounds as alternative therapeutics against N. fowleri.The recently discovered strain of


Users can also provide novel sequences (unavailable in public databases) for analysis. MSALigMap is written in Python. There are several tools and servers available for comparing and mapping active-site amino-acid residues among protein structures. However, there has not previously been a tool for mapping ligand binding amino-acid residues onto protein sequences of interest. Using MSALigMap, users can compare multiple protein sequences, such as those from different organisms or clinical strains, with sequences of proteins with crystal structures in PDB that are bound with the ligand/drug and DNA of interest. This allows users to easily map the binding residues and to predict the consequences of different mutations observed in the binding site. The MSALigMap server can be accessed at  Recently, genome and mRNA sequencing revealed the possibility to identify a large number of genes and transcripts ,2. As seTranscription factors are important for genetic regulation in all organisms and recognize sequence-specific regions in DNA called transcription factor binding sites (TFBSs). Through this type of interaction, transcription factors regulate (induce or repress) the expression of their target genes . AnnotatIdentifying binding sites is essential to achieving an understanding of catalytic reactions and to the classification of enzyme proteins involved in various biological processes. The number of 3D protein structures deposited in RCSB Protein Data Bank (PDB) ,13 has gMany excellent tools have been developed for this purpose; however, they have the limitations that they can be only used for comparing individual sequences against structural data. FeatureMap3D, a web-based tool, allows protein features to be mapped onto protein structures separately for each sequence submitted . Many sthttps://albiorix.bioenv.gu.se/MSALigMap/HomePage.py.As protein sequences provide insights into protein function, the mapping of functionally important amino acids from three-dimensional complexes onto proteins with unknown function can assist in protein evolutionary analyses and protein design. This demands for tools that can be used to transfer annotated features from characterized protein sequences to novel sequences. Given the necessity of transferring binding-site information about ligands such as drugs, DNA, substrates, or cofactors for large uncharacterized sequences from homologous PDB 3D protein structures, we developed MSALigMap, a user-friendly Python-based tool that requires only two input files. This tool can be used to assist the user in mapping the binding residues onto homologous non-structure protein sequences based on the sequences of PDB 3D protein structures provided by the user. However, this tool can be used particularly when the sequence identity for the selected sequences with similar function is above the twilight zone, i.e., above 30% identity. The MSALigMap server can be accessed at https://albiorix.bioenv.gu.se/MSALigMap/HomePage.py) developed in Python (>3.0) for protein sequence alignment and the mapping of functionally important amino acids onto known and novel unannotated homologous sequences with similar function. The web interface was developed in HTML on the XAMPP server running on a Linux system. Python CGI programming was used for developing MSALigMap. This tool was not designed to generate alignment of its own; rather, the current version uses MAFFT [MSALigMap is a web-based tool  of the structure is used for extracting 2D details from the 3D protein structures. PDBsum is used for extracting the ligand-binding- and DNA-binding-site information. The command line version of ClustalO is used for performing the multiple sequence alignment of the user-specified protein sequences.The basic workflow is illustrated in The identified PDB codes are searched in the PDB database and downloaded A. The prThe protein sequences are extracted from the PDB atom file, and the non-structure protein sequences are aligned using the ClustalO tool, which is installed locally C. There The DSSP protein secondary structure annotations of the PDB 3D protein structures are extracted from the MRS server  using thFor protein–ligand and protein–peptide complexes, the ligand and peptide binding amino-acid residues whose interactions are classified as hydrogen-bonded or non-bonded are extracted using the LigPlot  output aThe mapping of functionally important amino acids is then performed with MSALigMap F. MSALighttps://albiorix.bioenv.gu.se/MSALigMap/HomePage.py. The input sequences are submitted through the URL. The current version of the server has options for separately analyzing protein–ligand, protein–DNA, and protein–peptide complexes  and substituted (blue) across all sequences.Arabidopsis thaliana (Arabidopsis) [The main features of MSALigMap are the color-coded secondary structure alignment of the PDB structure and the mapping of ligand binding amino-acid residues onto non-PDB sequences A. Next, idopsis)  ; hydrogen-bonded amino acids are designated in bold, and non-bonded interacting amino acids are underlined. Secondary structure information about the PDB 3D crystal structures is provided to facilitate a comparison of the conservation of secondary structure elements across the sequences of protein crystal structures. The results of the sequence alignment of both bonded and non-bonded amino-acid residues are shown below to illustrate the level of conservation of active-site amino acids among the proteins of interest. The alignment of amino acids of the binding site alone is provided, where identical and mutated/substituted amino acids are color-shaded in red and blue, respectively. In the current analysis, of the 13 amino acids that formed a hydrogen bond between the ligand NDP and the protein, 4 were highly conserved across the sequences used in the study. However, of the 33 amino acids that were identified as forming non-bonded interactions, 9 amino acids were identified to be highly conserved.Triticum aestivum (wheat) using the BLASTP program  extension proteins (LRXs) represent an example of proteins that bind to redundant signaling RALF peptides. The protein sequence of the LRX crystal structure of the LRX protein in complex with the RALF peptide in Arabidopsis (PDB code: 6QWN)  was used program B. Two ho program . The secTriticum aestivum (wheat) genome   Table 2C. A prev) genome . In Figu) genome A. The DN) genome B. Compar) genome  that binIt is not possible to use the tools presented here to distinguish whether mapped protein–ligand interactions are within the protein backbone or sidechain; for that purpose, the recently launched LiBiSco program can be used . The ligAn important application of functional annotation is the mapping of ligand and DNA binding amino-acid residues from characterized proteins onto novel protein sequences that are generated in genome sequencing projects. In this context, MSALigMap is presented as a tool for mapping the active-site amino-acid residues that bind to either ligand or DNA onto the sequences of proteins with unknown functions by transferring information extracted from structural data of protein structural complexes. These mapped residues can be then used by users to carry out further experimental studies for exploring the efficiency of these mapped amino acids; alternatively, any in silico tools, such as Variant Effect Predictor (VEP) , or avai


Bioinformatics, Volume 38, Issue 16, 15 August 2022, Pages 3968–3975, https://doi.org/10.1093/bioinformatics/btac440This is a correction to: Alicja W Nowakowska, Malgorzata Kotulska, Topological analysis as a tool for detection of abnormalities in protein–protein interaction data, In the originally published version of this manuscript the images for Figure 1 and Figure 2 were inadvertently swapped. Additionally in the fourth paragraph of the introduction two citations were incorrectly referenced as “study3, study4” after the sentence “Finally, it is even possible to try to predict new protein characteristics or functions on the basis of its network location, by combining the network analysis with other computational tools.” Additionally the header of the right hand column of table 3 included “(%)” in error.These errors have now been corrected.


Indeed, interactions between proteins and DNA regulate many biological processes such as protein synthesis, signal transduction, DNA storage, or DNA replication and repair. Despite their importance, less than 4% of total structures deposited in the Protein Data Bank (PDB) correspond to protein-DNA complexes, and very few computational methods are available to model their structure. We present here the pyDockDNA web server, which can successfully model a protein-DNA complex with a reasonable predictive success rate . The server implements the pyDockDNA program, as a module of pyDock suite, thus including third-party programs, modules, and previously developed tools, as well as new modules and parameters to handle the DNA properly. The user is asked to enter Protein Data Bank files for protein and DNA input structures (or suitable models) and select the chains to be docked. The server calculations are mainly divided into three steps: sampling by FTDOCK, scoring with new energy-based parameters and the possibility of applying external restraints. The user can select different options for these steps. The final output screen shows a 3D representation of the top 10 models and a table sorting the model according to the scoring function selected previously. All these output files can be downloaded, including the top 100 models predicted by pyDockDNA. The server can be freely accessed for academic use ( Proteins and nucleic acids are fundamental biological macromolecules whose functions and interactions are vital to regulating cell’s life. Their interactions regulate many biological processes such as protein synthesis, signal transduction, DNA storage, and DNA replication and repair, among others. Learning how protein and DNA interact is fundamental to fully elucidate many central biological processes and disease mechanisms, and can also support the discovery of novel therapeutic targets. Although 192,025 structures have been experimentally determined and deposited in the June 2022 release of Protein Data Bank (PDB), only 10,480 of them correspond to protein-nucleic acid complexes . Thus, the number of protein-DNA structures experimentally determined is clearly much smaller than the number of protein-DNA complexes that are expected to be formed in cells. This gap is partially explained by the difficulty of the experimental determination process, i.e. a very time-consuming process in the best scenarios or impossible in many cases due to limitations on the experimental techniques. For this reason, a computational approach on modelling protein-DNA interactions could be of enormous help.ab initio docking. Template-based modelling aims to model a complex based on the structure of a homologous complex. The popularity of template-based methods has increased in the past years, especially for modelling protein-protein complexes, thanks to the development and support of many structural databases of protein interactions that can provide the required templates, such as 3D Complex  community-wide experiment. During the past editions of the CAPRI experiment , targetsIn order to test the new pyDockDNA docking protocol, we used a previously developed protein-DNA docking benchmark (version 1.2) . The benAn additional set of case studies was compiled following the criteria selection used in the above described protein-DNA docking benchmark. This test set is composed of ten protein-DNA complexes, where both bound and unbound structures are available for each reference complex, and the sequences are different from those in the first protein-DNA docking benchmark. Protein-DNA complex and unbound structures were compiled from the Protein-DNA Interface Database (PDIdb)  and the https://model3dbio.csic.es/pydockdna/info/faq_and_help#extended_benchmark).This additional test set  is freelIn this first step, the input files with the coordinates in PDB format for the structures (or models) of a protein and a DNA molecule (which can be B-DNA or any other conformation) are checked for potential format errors. Missing side-chains in the protein are rebuilt with SCWRL 3.0 , and thenuc.dat).Finally, the protein-DNA docking poses are ranked using a scoring function composed of electrostatics, desolvation and van der Waals energy. This new pyDockDNA scoring function is adapted from the previously pyDock scoring function for protein-protein docking , which nThe program pyDockDNA is built as a module of the new pyDock 4.0 version (upcoming publication), thus include the same third-party programs, modules and tools from previous versions of pyDock as well as new functionalities to handle the nucleic acid structures properly. The user can select the chains to be docked, the energetic scoring function, and even include external information [from available experimental data or using predictive methods such as the DBSI server , for inshttps://github.com/victor-gil-sepulveda/pyRMSD/). A cut-off value of 4.0 Å was used for L-RMSD to define the clusters. For each defined cluster of models, the orientation with the lowest docking score is selected as the cluster representative.When testing this software (see Results) we have run several docking executions in parallel, using different initial random rotations for the input structures, and the best-scoring 100 resulting models for each individual run were merged into a single pool. To avoid redundancy in the final set, all docking orientations were clustered by pyProCT analysis software , which iN docking models, which is the % of benchmark cases in which a near-native (acceptable) solution is found within the top N docking models. A near-native solution is defined as a docking orientation model with L-RMSD ≤ 10 Å with respect to the reference structure.We have evaluated the predicted performance of pyDockDNA in different conditions based on the success rates for the obtained top The pyDockDNA web server has been tested on the 47 cases of a previously reported protein-DNA docking benchmark (see Methods). It is known that using different randomly rotated input structures can slightly affect docking predictions of FFT-based docking protocols as in FTDOCK, because this can modify the mapping of the atom positions on the 3D grids . To checpyDockDNA (no desolv) shows in We further analyzed whether a scoring function previously developed for protein-protein docking was really optimal for protein-DNA docking, since for the latter, electrostatics energy term is expected to have a larger contribution to binding energy due to the higher overall charge of DNA molecules. Moreover, desolvation atomic parameters were previously derived for protein-protein docking in pyDock, but they were not specifically optimized here for nucleotide atoms. To analyze the role of desolvation in protein-DNA scoring, we rescored the generated docking models with the pyDockDNA scoring function but excluding desolvation energy. This greatly improved the success rates, as the curve In addition, we have also tried other combinations of energy terms, for instance, increasing the factor for van der Waals to 1.0 , medium (1 Å ≤ unbound-bound RMSD <3 Å) and high (unbound-bound RMSD ≥3 Å) flexible cases. As for DNA flexibility, we defined these three categories: low (unbound-bound RMSD <3 Å), medium (3 Å ≤ unbound-bound RMSD <5 Å) and high (unbound-bound RMSD ≥5 Å) flexible cases. The results are shown in For further testing, we have applied pyDockDNA to a set of ten additional protein-DNA cases  where thFor each case study, we performed a single pyDockDNA execution on the randomly rotated unbound protein and DNA structures. This represented a realistic scenario, since the pyDockDNA server only provides results for a docking execution . Overall, we obtained predictive success rates of 10% (for the top 10 models) and 30% (for the top 100 models) when using pyDockDNA scoring function, and 10% and 60% , when using pyDockDNA without desolvation. Given the small number of cases of these additional set, these values are within the expected range according to the larger docking benchmark set.The most successful case is the complex between the DNA binding domain of Early B-cell Factor 1 (Ebf1) bound to a 22bp DNA (PDB 3MLO), where a near-native docking model (L-RMSD 3.33 Å with respect to the reference) is found with rank five when using pyDockDNA (no desolvation) scoring function . When usAnother case is the complex between the catabolite gene activator protein and a 11bp DNA (PDB 1O3R), where we found an almost acceptable docking model (L-RMSD 10.76 Å with respect to the reference) with rank 5, when using pyDockDNA either including solvation or not . This caIn summary, we present here the pyDockDNA web server to model protein-DNA complexes, which implements a docking method based on pyDock, with new scoring parameters for DNA. We have evaluated the performance on unbound proteins and modelled DNA molecules in canonical B-DNA conformation, using a known protein-DNA docking benchmark. The results show near 40% success rate for the top 10 models when using the pyDockDNA (no desolvation) scoring function, after merging the results from 10 docking executions using different randomly rotated initial structures, and clustering the models to remove redundant ones. The method has been applied to external case studies, with similar predictive performance.


Despite continued advances in canonical experimental methods for protein structure determination, they all still possess significant limitations, which have become more evident when applied to challenge systems . For exaThe success rate of resolving a protein structure is often inversely related to protein size and flexibility. Thus, predictions and measurements of protein dynamics are essential for effectively characterizing structural ensembles . Hence, The goal of this Research Topic is to present applications of experimental and computational strategies regarding integrative structural approaches. This collection of nine articles explores experiments of high-speed atomic force microscopy (HS-AFM), small-angle X-ray scattering (SAXS), hydrogen-deuterium exchange mass spectrometry (HDX-MS), crosslinking MS (XL-MS), time-lapse microscopy, circular dichroism (CD), applied with complementary modeling tools, such as molecular docking, normal modes, molecular dynamics (MD), and hybrid enhanced sampling methods.Soares et al. determined the molecular architecture of the complex formed between native DM64 glycoprotein and myotoxin II from Bothrops asper venom using an integrative structural modeling approach. Distance constraints from XL-MS guided the docking of DM64 domains to the myotoxin II crystal structure. SAXS and molecular dynamics simulations indicated that the complex is flexible and structured with an anisotropic shape. In addition, interprotein cross-links and limited hydrolysis analyses revealed inhibitory regions involved in the interaction with toxins.Molza et al. have developed a complete integrative modeling pipeline that incorporates theoretical knowledge with advanced interactive and immersive modeling tools. As a result, it is possible to extract biologically relevant information by combining automated and human-driven interactive steps.Almeida et al. there is a theoretical discussion on the subject, presenting the relative contribution of the hydrophobic effect versus the stabilization of proteins by surface forces, which can sometimes go unnoticed.Proteins are polymers composed of simple units–amino acid residues. However, the dynamic folding process is highly complex . In the Pattanayak et al. monitored conformational changes of an outer membrane efflux protein resulting from the interaction with kanamycin. The bound structure was predicted by molecular docking, and the conformational changes occurred during the MD simulations. CD data suggests that the protein is less flexible and more compact in the presence of kanamycin.Most proteins undergo conformational changes to perform their functions . The artDasgupta et al. modeled multiple conformations of a bacterial ClpB through HS-AFM images. The function of this protein relies on the interconversion between these different conformations. Therefore, even at low resolution, the knowledge of such states can help interpret dynamical-related properties. Still, on the subject, Fagnen et al. studied the dynamical properties of an engineered bacterial channel specifically designed to rest in an open conformation. Using an in-silico hybrid method that combines MD simulations and normal modes (The prediction/determination of relevant conformational states of proteins can be obtained by different methods. al modes , global Kaynak et al. presents a systematic comparison of the usefulness and limitations of four normal modes-based hybrid methods to explore the conformational space of proteins. They were applied to four well-studied proteins: triosephosphate isomerase, 3-phosphoglycerate kinase, HIV-1 protease, and HIV-1 reverse transcriptase.The major limitation of predicting protein dynamics using MD simulations is to sample rare long-time-scale events. Several enhanced sampling MD methods have been developed to address the problem . The artUrazbaev et al. demonstrated that the microtubule growth in living cells could be approximated by a constant velocity with large stochastic fluctuations through time-lapse microscopy and mathematical modeling.Ahmed et al. described an accurate prediction of residue burial aside from a quantitative prediction of specific residue contributions to protein stability and activity. However, this is a major challenge, especially in the absence of experimental structural information. Therefore, the authors resorted to the yeast surface display of a CcdB bacterial toxin saturation mutagenesis library to investigate the relationship between ligand binding and the level of expression of the displayed protein, with both in vivo solubility and in vitro thermal stability.


ELANE  gene are associated with severe congenital neutropenia, while the ELANE gene provides instructions for making a protein called neutrophil elastase. We identified disease susceptibility single-nucleotide polymorphisms (SNPs) in the ELANE gene using several computational tools. We used cutting-edge computational techniques to investigate the effects of ELANE mutations on the sequence and structure of the protein. Our study suggested that eight nsSNPs  are the most deleterious in ELANE gene and disturb protein structure and function. The mutants F218L, R34W, G203S, R193W, and T175M have not yet been identified in patients suffering from SCN and cyclic hematopoiesis, while C71Y, P139R, C151Y, G214R, and G203C reported in our study are already associated with both of the disorders. These mutations are shown to destabilize structure and disrupt ELANE protein activation, splicing, and folding and might diminish trypsin-like serine protease efficiency. Prediction of posttranslation modifications highlighted the significance of deleterious nsSNPs because some of nsSNPs affect potential phosphorylation sites. Gene-gene interactions showed the relation of ELANE with other genes depicting its importance in numerous pathways and coexpressions. We identified the deleterious nsSNPs, constructed mutant protein structures, and evaluated the impact of mutation by employing molecular docking. This research sheds light on how ELANE failure upon mutation results in disease progression, including congenital neutropenia, and validation of these novel predicted nsSNPs is required through the wet lab.Single-nucleotide polymorphisms in the  Neutropenia is a disease that clinicians face frequently, yet it can still be troublesome since neutrophils are the most fundamental and functionally vital component of innate immunity. Neutropenia leads to a defect in pathogen digestion, altered inflammation, an unusually severe course of infection, and a high rate of infection , 2.Clinicians encounter secondary neutropenia more frequently in patients due to viral infection, autoimmune disorders, and hormonal or cytostatic therapy. Severe congenital neutropenia is a rare hematological disease characterized by a selective decrease in circulating neutrophils, bone marrow maturation arrest at the promyelocyte stage, and the occurrence of infections .The causes of congenital neutropenia can be defects in neutrophil maturation and function, immune dysregulation syndromes (different familiar hemophagocytic lymphohistiocytosis), some severe combined immunodeficiencies  and PAC2 activation defect), and primary autoimmune neutropenia at different stages of neutrophils development. Usually, patients with congenital neutropenia require antimicrobial prophylaxis and treatment with granulocyte colony-stimulating factor, and radical cure is not possible without hematopoietic stem cell transplantation . There aELANE deficiency), GFI1 deficiency (SCN 2), HAX1 deficiency (SCN 3 or Kostmann disease), G6PC3 deficiency (SCN 4), VPS45 deficiency (SCN 5), type 1b glycogenosis (G6PT1 deficiency), X-linked neutropenia/myelodysplasia (WAS GOF mutation), P14/LAMTOR2 deficiency, Barth's syndrome  , Cohen's syndrome (VPS13 B deficiency), Clericuzio syndrome (USB1 deficiency), JAGN1 deficiency, 3-methylglutaconic aciduria (CLPB deficiency), G-CSF receptor deficiency (CSF3R), SMARCD2 deficiency, neutrophil specific granule deficiency (CEBPE), Shwachman-Diamond syndrome , HYOU1 deficiency, and SRP54 deficiency [Classical congenital neutropenia depends on the elastase function. Elastase defects lead to severe congenital neutropenia (SCN) type 1  encodes neutrophil elastase, a serine protease expressed in myelomonocytic cells and their precursors. Neutrophil elastase is primarily produced during the promyelocyte stage of neutrophil maturation and is retained in the neutrophil granules of azurophils, which are involved in microorganism destruction [truction , 8. Howetruction –12.ELANE is known to become the most abundant protein during the developmental stage of promyelocytes, reaching millimolar concentrations in neutrophils [ELANE p. G185R polymorphism is linked with impaired neutrophil differentiation to lower expression of genes encoding critical hematopoietic transcription factors, cell surface proteins, and neutrophil granule proteins [ELANE mutation, which leads to disruption of its synthesis and folding.The main mechanisms of neutropenia in neutrophil elastase defect are reduced to endoplasmic reticulum stress (unfolded protein response) when misfolded elastase accumulates in the endoplasmic reticulum and leads to the activation of death signals . ELANE itrophils , which strophils , 7, 8. Atrophils . The ELAproteins . HoweverA single amino acid change or naturally occurring mutations are linked to a variety of complicated illnesses, including cancer. Deleterious mutations at the genomic and/or proteome levels have serious consequences for human health. Biophysics-based computational approaches are useful for examining the effects of mutations on protein structure and function, and there is a lot of interest in such research right now –18. SeveELANE. We looked at a variety of mutations and defined how they affect the structure and function of ELANE protein, which may contribute to illness onset and progression.We used cutting-edge computational techniques to conduct an in-depth investigation of genomic and proteomic changes in ELANE's FASTA sequence was obtained from UniProt (UniProt ID: P08246). A list of mutations was compiled using the dbSNP [he dbSNP  and counhe dbSNP  databasehttps://genemania.org/) [GeneMANIA (ia.org/)  was usedhttps://sift.jcvi.org/ [First, the SIFT and PolyPhen2 techniques were used to determine if isolated nsSNPs are deleterious/damaging or tolerated. The SIFT method examines sequence homology and the physical characteristics of amino acid residues to assess whether a mutation is deleterious or not. It is dependent on the evolutionary conservation of amino acids in protein families. The highly conserved amino acids are often intolerant to replacements, but the less conserved ones can be replaced. A nontolerable mutation has a SIFT score of <0.05. SIFT may be accessed at cvi.org/ .https://genetics.bwh.harvard.edu/pph2/ [PolyPhen-2 is a tool for sequence-based mutation analysis that uses the FASTA sequence as input . This prdu/pph2/ .https://provean.jcvi.org/ [PROVEAN evaluates the impact of mutations on protein function based on the delta alignment score. The PROVEAN score for a deleterious mutation is <2.5, whereas scores for neutral nonsynonymous mutations are >2.5. PROVEAN Protein, PROVEAN Protein Batch, and PROVEAN Genome Variants are the three tools available on the PROVEAN online server. The SIFT tool's outputs are also delivered by the PROVEAN Protein Batch tool, which can process a higher number of protein variants. This program accepts amino acid replacements as input and works with public protein IDs such as NCBI RefSeq, UniProt, and Ensembl. PROVEAN may be found at cvi.org/ .ELANE protein would impact its activity. This platform analyzes a FASTA sequence and assigns a prediction score , reflecting the potential of a mutation affecting native protein function [SNAP2 is a classifier based on a neural network. It was used to predict how a single amino acid change in the function .https://mmb.irbbarcelona.org/PMut [P-Mut is one of the platforms used to identify illness phenotypes. P-Mut is a network-based classifier that uses datasets from the manually generated Swiss-Prot database. The tool's main attributes are its physiochemical properties and conservation parts of the sequence. If a mutation has a P-Mut score > 0.5, it is considered pathogenic. The new version now includes the ability to develop new predictors for specific protein families. It also includes a database of preestimated forecasts. P-Mut may be found at org/PMut .https://snps.biofold.org/snps-and-go/snpsand-go.html.SNPs and GO is a website based on SVM that detects deleterious nonsynonymous substitutions . It emplVarCARD was used to obtain findings from the MCAP (> 0.025) and FATHMM tools, FATHMM-MKL-coding-pred (> 0.5), LRT , METALR (> 0.5), FATHMM-pred (> 0.453), Meta-SVM, Mutation Assessor (> 0.65) (−5.545 to 5.975 (higher score > more damaging), VEST3, CAAD (> 15), DANN (> 0.5), and Mutation Taster (< 0.5). VarCARD is a genetic and medical database that includes coding variations in the human genome. A variety of genomic approaches and databases have been created to help in the study of genetic variations, particularly in nonsynonymous species. VarCARDs, on the other hand, make it simpler for scientists, researchers, general practitioners, and geneticists to collect data on a single variant or from several web platforms or databases .https://gpcr2.biocomp.unibo.it/cgi/predictors/I-Mutant3.0/I-Mutant3.0.cgi) is a predictor of the effects of single point protein mutation. The ΔΔG Mut dataset from ProTherm was used to pretrain the algorithm. A single-site mutation that depends on a protein structure or sequence can be calculated using the ΔΔG value . A ΔΔG value less than zero suggests that the variant reduces protein structure or a protein sequence [I-Mutant 3.0 (sequence .https://mupro.proteomics.ics.uci.edu/) is a tool that uses machine learning vector machines and neural networks to quantify the influence of single-site amino acid modifications on protein stability. Protein stability may also be predicted using simple sequence information or a mix of sequence and tertiary structure information. The G cut-off value in I-Mutant3.0 and MUpro is the same [MUpro  against the UniprotKB/Swiss-Prot database, and significant alignment was identified up to 100 sequences. Clustal Omega was used to do additional computational analysis on sequences that shared more than 50% identity and had an E-value less than 1.00 E-20 [The human .00 E-20 . The ami.00 E-20 .Project HOPE is a web server that investigates the structural consequences of the desired mutation. The HOPE Project offers the changed protein in an observable 3D structure by cooperating with UniProt and DAS prediction algorithms. The protein sequence is used as an input source in Project HOPE, and then a structural comparison with the wild type is performed. Project HOPE is a web server that investigates the structural consequences of the desired mutation. The HOPE Project offers the changed protein in an observable 3D structure by cooperating with UniProt and DAS prediction algorithms. The protein sequence is used as an input source in Project HOPE, and then a structural comparison with the wild type is performed .SOPMA  is a morhttps://SMART.embl-heidelberg.de). Searches for proteins containing specified combinations of domains in defined taxa are possible using the database's user interfaces. We utilized a protein sequence in FASTA format or a protein ID as a query to predict domains and motifs [SMART (Simple Modular Architecture Research Tool) is a tool for identifying and annotating genetically mobile domains as well as analyzing domain structures (d motifs .https://raptorx.uchicago.edu/BindingSite/) is a web portal that predicts the binding sites of a protein sequence using a RaptorX-predicted 3D model [P-value, uGDT (GDT), and uSeqID (SeqID). Higher values suggest that the expected pocket is more reliable, especially when the score is close to 40.RaptorX binding (3D model . RaptorXhttps://msp.biocuckoo.org/online.php) was utilized to predict methylation sites in the ELANE protein. We used NetPhos 3.177 (https://www.cbs.dtu.dk/services/NetPhos/) [https://gps.biocuckoo.cn/) [https://msp.biocuckoo.org/), BDMPUB (https://www.bdmpub.biocuckoo.org), and UbPred [https://www.ubpred.org). NetOglyc4.0 additionally predicts glycosylation sites using glycosylation [https://www.cbs.dtu.dk/services/NetOGlyc/). Sites of glycosylation with a score greater than 0.5 are more likely to be glycosylated.Posttranslational modifications (PTM) are used to predict the function of a protein.  GPSMSP vetPhos/)  and GPS koo.cn/)  to predid UbPred  (https:/sylation  (for furhttp://www.sbg.bio.ic.ac.uk/phyre2/html/page.cgi?xml:id=index) [https://zhanglab.ccmb.med.umich.du/TM-align/) [https://zhanglab.ccmb.med.umich.edu/I-TASSER%20R/) for 3D protein structure comparisons [Phyre2 is a 3D homology modeling tool that predicts 3D models for proteins (d=index) . The res-align/) . The temparisons –50. Chimparisons . PROCHECparisons  was usedx, y, and z). The ligand structure is employed to build a scoring grid, which shortens the computation time. AutoDock/Vina was used to bind proteins and ligands using protein and ligand information. AutoDock/Vina uses an iterated local search global optimizer, and during docking, both the protein and the ligands are treated as stiff [The graphical user interface tool AutoDock was used to perform intermediate procedures such as producing pdbqt files for protein and ligand preparation and generating grid boxes (ADT). ADT was used to determine the protein polar hydrogens, Kollman charges, solvation parameters, and fragmental volumes. The produced file was saved in PDBQT format by AutoDock. AutoGrid and a grid box were used to produce a grid map. The grid was configured to 40 × 40 × 40 xyz points with a grid spacing of 0.375 with grid centers of −49.009, 14.545, and 34.19 . It consists of 3646 SNPs, 301 of which are in coding nonsynonymous areas (missense) and 149 of which are in synonymous parts, all of which were present in Homo sapiens , shared domain with only 8 genes , physical interaction with 19 genes , and co-localization with 19 genes  .A total of 301 single-nucleotide polymorphisms (nsSNPs) were studied to see if they influenced protein structure or function. The first step was to determine which of the nsSNPs is responsible for the amino acid substitution. SIFT computes the effect of an nsSNP on protein structure and determines if the induced amino acid is acceptable at that location. SIFT predicted 21 nsSNPs to be deleterious, while 18 nsSNPs were predicted to be deleterious together by SIFT and PolyPhen out of a total of 301 nsSNPs .Both the SIFT and PolyPhen algorithms offer 18 common pathogenic nonsynonymous SNPs. To confirm the pathogenicity of the identified mutations, we analyzed the pathogenicity of nsSNPs through other tools. However, not every platform produces similar results.For example, the FATHMM-MKL algorithm also determined all nsSNPs detected by SIFT and PolyPhen as pathogenic . HoweverELANE and 11 as tolerant, neutral, or unknown.Among all the 21 SIFT nsSNPs predicted, the LRT and FATMANH algorithms predicted the least number of matching results. Both algorithms predicted only 10 pathogenic nsSNPs for The PolyPhen platform identified 18 deleterious nsSNPs; the VEST, CADD, and DANN platforms each predicted 19; MCAP and Mutation Taster each predicted 20 deleterious nsSNPs. Using the SNAP2 approach, 18 mutations were found to be damaging, whereas three had no association with pathology.ELANE gene were predicted to be highly disruptive, and 7 were considered neutral.For the PANTHER program, 17 nsSNPs were considered deleterious mutations, among which 10 nsSNPs were classified as probably pathogenic, 7 classified as possibly pathogenic, 2 marked as probably benign, and 2 variants with unknown significance  and S2. Mutation Assessor counted 20 nsSNPs as destructive, including 3 with high pathogenicity, 6 with medium, 12 with low, and 1 with an unknown value. P-Mut predicted 10 mutations as pathological, 10 with unknown significance, and 1 with no result. PhD-SNP predicted 13 mutations as pathological, SNP-GO 10, METALR 17, and MTA-SVM 15.ELANE gene: C71R, P139L, C151Y, T175M, G203S, G214R, R193W, and F218L.All current nsSNP pathogenicity assessment methods used together identified 8 overlapping common mutations in the P-value of 0.001.According to the software used, we know that the frequency of the C71R allele in Latinos is 3.655e-05 and T175M in Africans is 0.0002, in Latinos is 0.0023, in East Asians is 5.832e-05, in Nephinese Europeans is 3.632e-05, and in Latinos is 2.979e-05. The results of all prediction algorithms were statistically significant and closely related. The value of the Student coefficient between the instruments has a In addition to SIFT and PolyPhen2, the results of pathogenic mutation prediction by the above tools as well as the predictive significance of all tools are presented in ELANE gene and nsSNPs in different domains. SMART may look for proteins that have the same domain architecture as the query  or the same domain composition as the query . Proteins with similar or identical domain structures to the query protein may improve predictions of protein activities rather than domain functionalities. This database yielded the domain trypsin-like serine protease (29–242), with an E-value of 1.78444685325842e-71 for ELANE protein neutrophil elastase. Mutations in this domain induce congenital neutropenia, and one of our most dangerous nsSNPs is situated in this domain. The SMART online database was used to identify domains in the ELANE high-risk nsSNPs on protein stability and function. The results revealed that V101L and A166V had enhanced protein stability, whereas R34W, C71R, V101M, P139L, R143C, C151Y, A166T, T175M, R182H, V190M, R193W, G203S, L206F, N209K, G210R, G214R, F218L, P262S, and P262L had decreased protein stability. At the same time, MUpro showed all deleterious nsSNPs decrease the protein stability. I-Mutant 3.0 and MUpro results are provided in Supplementary The online software I-Mutant 3.0 was used to forecast the consequences of ELANE gene regulates the synthesis of neutrophil elastase, a protein. This protein is present in neutrophils, which are white blood cells that help fight infection and inflammation. Neutrophils secrete neutrophil elastase as the body develops an immunological response to combat an infection. To resist infection, this protein alters the activity of specific cells and proteins. The ELANE protein sequence was aligned using Clustal Omega with default parameters after blasting against UniprotKB/Swiss-Prot entries. TC protein sequences aligned with other phylogenetically related sequences from other living organisms, such as NP 001963.1 (Homo sapiens), AK172501.1 (synthetic construct), AK172502.1 (synthetic construct), AK172502.3 (synthetic construct), XP 034800823.1 (Pan paniscus), XP 034800822.1 (Pan paniscus), and PNJ05388.1 . Among these 22 missenses variants, 8 were located in the highly conserved positions, 2 (P139L and C71R) were predicted as functional and exposed residues, and the other 3  were predicted as buried and structural residues. Twelve  were predicted as exposed and the other 3  were buried residues. The results are shown in We investigated the degree of conservation of ELANE nsSNPs were considered high-risk mutations by prediction, the HOPE service was used to predict their impact. The HOPE was based on amino acids' size, spatial arrangement, charge, hydrophobicity, structure, and function. One mutant amino acid was smaller than its wild-type equivalent, while seventeen mutant amino acids were bigger. At three different places, the charge was altered from positive to neutral, from neutral to positive at four spots, and from neutral to negative at one location. The hydrophobicity of eight of the alterations increased, while the hydrophobicity of the other remained the same. This study suggests that amino acid modifications at these sites alter protein structure and interactions with other molecules, hence affecting protein function. The results are shown in the following graph , followed by 70 extended strands (26.22 percent), 56 alpha-helices (20.97 percent), and 13 beta-turns (4.87 percent) (SOPMA predicted the secondary structure of percent) . For thepercent) .ELANE sites would be affected. ELANE phosphorylation sites predicted by GPS 3.0 and NetPhos 3.1 are listed in Supplementary ELANE protein. Interestingly, the mutant ELANE sequence does not differ from the natural in terms of glycosylation sites. All the scores for the wild‐type and mutants are given in Supplementary GPSMSP 3.0 was utilized for this purpose, and it projected that no ELANE protein had the highest pocket multiplicity of 71, which were linked to the residues H70, L114, G196, V197, C198, F199, G200, S202, A216, S217, F218, V219, G222, C223, and D230 with an expected SO4, BEN, and ANH ligand. The ELANE protein had the 2nd highest pocket multiplicity of 46, which were linked to the residues N88, R91, E93, and R96 with ligand CA.When using the RaptorX binding server, the criterion for accurate prediction is a pocket multiplicity number greater than 40. The ELANE protein as well as four very harmful nsSNP mutations. Separate nsSNP substitutions in the ELANE protein sequence were produced and then submitted to Phyre2 [ELANE protein using Chimera 1.11. The validation findings for the wild and mutant versions of the 3D models were satisfactory, and the Ramachandran plots and Chi1-chi2 plots of the most detrimental nsSNPs can be seen in Supplementary Phyre2 was used to model 3D structures of the wild-type o Phyre2  which prThe docking conformations of all hits were visually evaluated using Discovery Studio and PyMol, and their interactions were calculated to discover the binding forces that were crucial in stabilizing the formation of receptor-ligand complexes. The ligand ANH was docked into the active site of the wild-type protein as well as four mutant proteins. The docking score for the wild type was −8.4 KJ/mol, and the 2D interaction showed that the wild type has two hydrogen bonds with SER202, as well as seven van der Waals and seven hydrophobic contacts Figures . The docELANE structure and function. However, there are far too many nsSNPs in ELANE that might be candidate disease indicators and have a role in the illnesses produced by this gene. In this study, the dbSNP database revealed 301 missense nsSNPs in the ELANE gene, with 5 of the known MAFs of nsSNPs in the ELANE gene being less than 1%. SIFT predicted 21 nsSNPs as deleterious, while PolyPhen overlapped with SIFT 18 nsSNPs in the ELANE gene as potentially deleterious. All of the state-of-the-art prediction approaches used in the study confirmed the top eight high-risk nsSNPs  in  in  as exceeELANE protein.There were few nsSNPs  that were novel but they were not deleterious by all of the in silico tools used in the study. These SNPs will need further confirmation to consider in a study for patients. ConSurf assesses if an amino acid is conserved, exposed, functional, or structured using a mix of evolutionary conservation data and solvent accessibility predictions. Based on their placements on the protein surface and core, highly conserved residues are considered to be structurally or functionally significant . Amino aELANE gene since they reduce protein stability. Various in silico investigations on genes and proteins such as ADA and GJA3 have been conducted [Protein stability was predicted using the I-Mutant and MUpro web server, and variants R34W, C71R, V101M, P139L, R143C, C151Y, A166T, T175M, R182H, V190M, R193W, G203S, L206F, N209K, G210R, G214R, and F218L were predicted to decrease the stability of the protein. These nsSNPs in the future will be important during the study of the onducted , 56.ELANE wild type), RAMPAGE values were 82.3% favored residues, 15.6% allowed, 2.2% generally allowed, and 0.0% disallowed, similarly for mutants C71Y , R34W , F218L , and G214R . Secondary structures include the alpha-helix, beta-sheet, and coil. PTMs are important in cell signaling, protein-protein interactions, and other processes in biological systems [ELANE protein. Several bioinformatics approaches were used to predict PTM sites in the protein under study. Methylation is a significant PTM because lysine residues in proteins are methylated, influencing their interaction with DNA and gene expression. The molecular switch is another protein regulatory mechanism that adjusts the protein to perform such as protein structure conformational changes, protein activation and deactivation, and signal transduction pathways [This type of study might lead to the discovery of novel treatment. RAMPAGE data were used to validate all of the simulated structures. For the structure given in  systems , 58. Thepathways –62.ELANE protein. Docking experiments revealed that both mutations drastically damaged ELANE's binding pocket with ATP. Normal protein was compared to mutant, and the most evident difference was found in F218L, showing a significant reduction in H-bond interactions inside the binding pocket residues. Because ATP is tightly bound to ELANE's binding cleft, these mutations interfere with the favorable interactions essential for ELANE's functional activity. Furthermore, the difference in the bound ATP molecule may reduce ELANE's catalytic efficiency. It is essential for protein function and stability. It serves as a structural component in protein-protein interactions. PTM predictions show that phosphorylation is the sole PTM that may have a significant influence on ELANE protein structure and function, with the Supplementary According to the ConSurf Conservation profile, several predictions are highly preserved, exposed, and functionally relevant, highlighting their importance. Ubiquitylation is a protein degradation mechanism that helps DNA damage repair . We perfELANE is the most interacting gene and coexpressed with diverse genes in our study. Because of their interaction patterns and coexpression profiles, any of the most deleterious nsSNPs in the ELANE gene will ultimately influence and impair the proper functioning of other linked genes. This emphasizes the importance of these interconnected and coexpressed genes in congenital neutropenia and other primary immunodeficiency diseases. Because our study was comprehensive, it included all of the necessary data and analyzes for identifying the most harmful nsSNPs. Every study, including ours, had limitations.According to GeneMANIA's predictions, ELANE gene's deleterious nsSNPs, protein 3D structure, PTM potential sites, and gene-gene interaction with other genes, all of which might be relevant in the future while studying the ELANE gene and its disorders. RaptorX predicted 15 ligand-binding sites, each of which is H70, L114, G196, V197, C198, F199, G200, S202, A216, S217, F218, V219, G222, C223, and D230 residues. As a result, the highly toxic F218L is identified in the ligand-binding domain and forms the catalytic coordination sphere, potentially altering the ELANE protein's binding affinity.Our study is based on mathematical and statistical approaches used in computer tools and web servers. As a result, further investigation will be beneficial to validate these findings. Our findings provide information on the ELANE protein structure and function, and these mutations can affect wild-type protein structure. These mutant structures exhibit significant differences in complex binding, which may impair ELANE protein activation and reduce the efficiency of trypsin-like serine protease. These differences could potentially compromise the validation of the secondary structure, threatening the stability of the protein. We also observed that the ability of the mutant proteins to bind ATP was lower than that of the wild-type protein. However, these five nsSNPs mutations have not yet been described in humans with autosomal dominant severe congenital neutropenia ELANE 1. As a result, these newly uncharacterized nsSNPs may cause disease by affecting protein activation or efficiency. The results of this study will help future genomic association studies to identify deleterious SNPs associated with different forms of autosomal dominant severe congenital neutropenia 1. Large-scale clinical trial-based studies, as well as experimental mutation studies, are needed to characterize these data on SNPs.Single amino acid changes are among the most common mutation variants associated with various disorders, including neutropenia. The wide variety of protein variants suggests different degrees of functionality and, in certain situations, determines the direction of pathology or disease resistance. An in-depth study of amino acid substitutions will correlate protein features and clinical conditions, develop effective therapeutic agents, and predict the course of the disease for the patient. This study describes five  nsSNPs that are lethal and have mutational effects on


As protein-protein interactions (PPIs) are also important in disease development, protein-protein interactions are highlighted in the pharmaceutical industry as possible therapeutic targets in recent years. To understand the variety of protein-protein interactions in a proteome, it is essential to establish a method that can identify similarity and dissimilarity between protein-protein interactions for inferring the binding of similar molecules, including drugs and other proteins. In this study, we developed a novel method, protein-protein interaction-Surfer, which compares and quantifies similarity of local surface regions of protein-protein interactions. protein-protein interaction-Surfer represents a protein-protein interaction surface with overlapping surface patches, each of which is described with a three-dimensional Zernike descriptor (3DZD), a compact mathematical representation of 3D function. 3DZD captures both the 3D shape and physicochemical properties of the protein surface. The performance of protein-protein interaction-Surfer was benchmarked on datasets of protein-protein interactions, where we were able to show that protein-protein interaction-Surfer finds similar potential drug binding regions that do not share sequence and structure similarity. protein-protein interaction-Surfer is available at  Proteins are involved in almost all essential biological processes. Biological functions of proteins are usually exhibited through interaction with other molecules such as DNA, proteins, hormones, and small chemical compounds. Therefore, a core concept of drug discovery is to control the interaction of proteins with other biomolecules. Traditionally, a small chemical compound was identified as a drug that binds to a well-defined pocket of a target protein. The compound typically competes with a natural binder of the target to inhibit or enhance its function. Even though the concept has been successfully applied to drug discovery since the mid-twentieth century, the efficiency of the research and development process has been decreasing . The maivia interaction with MDM2  is those which target interaction between p53 and mouse double mutant 2 homolog (MDM2). P53 is a tumor suppressor but it is downregulated in cancer cells ith MDM2 . Thus, cith MDM2 .Although PPIs have attracted lots of attention as drug targets, there are not many SMPPIIs successfully developed so far. From 2004 to 2014, about forty PPIs have been targeted, among which only six of them have further proceeded to clinical trials . PPIs arSMPPIIs also have distinguishing features from traditional drugs, which are summarized as the rule of four (RO4): SMPPIIs tend to have a molecular weight higher than 400 Da, logP higher than four, more than four rings, and more than four hydrogen-bond acceptors . These pSeveral computational algorithms have been developed to examine and classify PPIs. The methods can be categorized into two classes, ones that align PPIs first and the others which are alignment-free. One of the alignment-based methods is MAPPIS . The metPatchBag is an example of alignment-free methods . In PatcRecently, we have developed a series of surface-based molecule similarity calculation programs using three-dimensional Zernike descriptors (3DZD) . The notHere, we developed a new method, PPI-Surfer, which represents a PPI surface with 3DZDs and quantifies the similarity between different PPIs. Similar to PL-PatchSurfer, PPIs are segmented into overlapping surface patches and their physicochemical properties are represented by 3DZDs. Compared with the above-mentioned PPI comparison methods, PPI-Surfer is unique in that several physicochemical features mapped on a PPI surface are naturally described in the same fashion by using 3DZDs. Compared with existing methods discussed above, first, PPI-Surfer is an alignment-free method, thus different from the alignment-based methods which need to superimpose interacting protein complexes to compute their similarity. Compared with the two existing alignment-free methods , a novelPPI-Surfer was benchmarked on three datasets: On the 2P2I database , which sThe aim of the PPI-Surfer is to calculate the similarity between two PPIs. A PPI surface is defined by surface atoms that are closer than 5 Å from any heavy atoms of the binding partner protein. i and j are a surface point and an atom from a protein, respectively. fj is an atomic logP value from the XlogP3 parameter and rij is the distance between the voxel point i and the atom j.Indices The hydrogen-bond property of a surface point is assigned by considering the closest atom of the protein. If the closest atom is a hydrogen-bond donor or an acceptor, a value of 1 or −1, respectively, is assigned to the surface point. Otherwise, the point is assigned with zero.The visibility concerns the local curvature of a surface point . It has After assigning all the features to surface points the surface of a given PPI surface is divided into patches. Seed points are iteratively selected from protein surface points so that they are separated by more than 5 Å from each other. Then, a surface patch is segmented by a sphere of a 10 Å radius centered at a seed point. Patches can overlap and cover the entire PPI surface see .x) as a vector of coefficients. The similarity (or the distance) of two 3D functions, which represents a physicochemical feature on patch surfaces, can be simply computed by the Euclidean distance between the vectors. A 3D function f(x) is converted to 3DZD as follows:The physicochemical properties on a surface patch are represented by 3DZD  essentiax) is the Zernike-Canterakis basis function, composed of radial function θ,φ Eq. .Znlm=Rnn, l, m are integers and the conditions for the numbers are −l < m < l, 0 < l < n, and (n-l) is even.3D Zernike moment is further converted to 3DZD, which is rotationally invariant, by taking a norm:n, which also sets the resolution of the 3DZD representation. In PPI-Surfer, n is set to 15, making 3DZD a 72-dimensional vector.The dimension of 3DZD is determined by the order To quantify similarity of two PPI sites, which are represented by a set of patches, we optimize pairing of patches from the two PPI sites so that the following score (distance), PatchScore, is minimized:pDist, is a weighted sum of the Euclidean distance between 3DZD for matched patch A and B.The first term, i denotes physicochemical features of a patch, which are 3D shape, electrostatic potential, visibility, hydrogen-bond acceptors/donor distribution, and hydrophobicity. Relative weights of the features, wi, were trained on the protein structures that bind to multiple partner proteins, called a hub protein set, extracted from the PiSite database  is an average of a term X in Eq. kis) in Eq. SD, refers to the Size Difference between the PPI sites, which is inferred by counting the number of patches on each PPI site. The term is determined by dividing the difference between the number of patches of two PPI sites by the number of surface patches of the larger PPI site. PPI Score is a distance metric; similar PPI sites have a small value.To search similar patch pairs between PPI sites, a modified version of the auction algorithm, a bipartite matching method, is used . The algPD+kS×SDwhere thThe weight optimization of the scores was performed on the hub protein dataset. This dataset contains “hub” proteins, which bind multiple partner proteins using the same interface. We first obtained 180 complexes that consist of 69 hub proteins and their binding partners from PiSite . From thpDist , a collection of 1,520 off-patent small molecules, to the PPI sites using the GLIDE SP ligand docking program  were separated in the dendrogram and some entries were isolated from the rest of the subgroup members. Among five BD1 PPI sites, 2DVQBQ was not clustered together and placed close to the BD2 cluster (violet). The main reason for this separation turned out to be the number of patches, i.e., the size of the PPI interface. The PPI Score Eq.  includesThe PPI site size difference in the complexes also explains why 2E3KDR was separated far from two clustered BD2 members, BD42AB and BD32AB (violet). The length of the 2E3KDR histone peptide is 14, shorter than 16 that bind to the other BD2 PPIs. Moreover, the C-terminal tail of the 2E3KDR histone does not bind to the bromodomain, which resulted in only four PPI patches for 2E3KDR. This is much smaller than the average number of patches of the other three BD2s, 16.3, which largely influenced to the PPI Score between 2E3KDR and the rest.The next example of difference observed in the PPI Score-based clustering is for three PPIs of double mutant 2 proteins (red). In the PPI Score-based clustering, the cluster of double mutant 2 proteins included two additional PPI sites, von Hippel-Lindau disease tumor suppressor  and Kelch-like ECH-associated protein 1 . 2FLUXP has a β-propeller structure, complexed with a 16-residue-long peptide with two β-strands. A closer look at their PPI sites indeed identifies similarities in corresponding surface patches. The top five patch pairs identified by PPI-Surfer are shown in In terms of the drug ranking distance, 1TUEAB and 4GQ6AB have four compounds in common among their top 10 scoring docked compounds. Among the four common compounds, we can see two compounds have similar binding modes on these two PPI sites. First one show in pDist  from the PDB entry 1a4y and from 1dfj. The sequence identity between them is 77%. Three hotspot residues in RI have been experimentally confirmed, which are TYR434, ASP435, and TYR437 (the residue numbers are from 1a4y A chain). In order to evaluate the performance of PPI-Surfer on hotspot identification, we defined a hotspot patch as a patch that locates closer than 5 Å to any heavy atom of a hotspot residue when computed from its seed point. For example, seven hotspot patches were identified on RI with angiogenin (1a4yA).pDist distance  patches are colored in black, red, and blue, which distribute among all three hotspot residues.The second pair is proteins from the immunity proteins with colicin DNases, 1bxiA and 1znvA, which have a sequence identity of 59%. GLU30, ASP51, TYR54, TYR55, and PRO56 in 1bxiA are hotspot residues that were identified by experiments. MAPPIS identified all these five residues . LikewisThe results on this experiment show that PPI-Surfer detects hotspot patches since they are similar in physico-chemical properties among related proteins. The difference between MAPPIS and PPI-Surfer is that while MAPPIS takes protein complexes as input and identifies common interactions through structural alignments, PPI-Surfer identifies similar regions without the information of the binding partner, thus without structural alignment operation.D, of 1–20 nM  of human Angiotensin Converting Enzyme 2 (ACE2). The bound mini protein inhibitor interferes with the interaction of RBD with the SARS-CoV-2 spike protein . Many mi 1–20 nM . The ami 1–20 nM . As show 1–20 nM  and LCB3 1–20 nM  and then 1–20 nM . All theStarting from each of the eight binders, we constructed decoy proteins by sequence optimization using Rosetta FixBB , where tOn the library of the eight designed binders and their decoys, we calculated the surface similarities among their PPIs using PPI-Surfer and examined if the PPI Score can discriminate the binders from other generated decoy proteins. We first compared LCB1, LCB3, and their decoys in terms of the PPI Score. We started with these two binders because LCB1 and LCB3 has a low sequence identity of 18.3% and both have experimentally determined complex structures with RBD. We performed PPI site comparison in two directions. First, the PPI site of LCB1 was compared with the PPI site of LCB3 and its 10 decoys. Next, we computed the PPI site similarity from LCB3 with LCB1 and its 10 decoys.As shown in We further analyzed PPI sites of other six LCBs, LCB2, 4, 5, 6, 7, and 8. We compared the PPIs of the LCBs and their decoys with LCB1 and LCB3, the two binders that have crystal structures, in terms of the PPI Score by PPI-Surfer. To rank the PPIs, first, we computed Z-Scores of PPI Scores from LCB1 and LCB3 separately and averaged them. In th strongest binder among the 8 LCBs and prevented ∼20% of virus infections when ∼100 nM was treated  . LCB5 wa treated . By the ated  are not detected.Despite the importance and growing interests in PPI drugs, computational protocols for the development are not well established. PPI-Surfer would be a valuable tool for finding similarities and classifying PPI sites and aid protein-based PPI drug discovery.


Moreover, this protein is crucial for the normal development of the lung in the fetus two months after gestation leading to determining lung functions all over life. In this regard, mutations in ADAM33 have been linked with asthma risk factors. Consequently, identifying ADAM33 pathogenic nonsynonymous single-nucleotide polymorphisms (nsSNPs) can be very important in asthma treatment. In the present study, 1055 nsSNPs of human ADAM33 were analyzed using biocomputational software, 31 of which were found to be detrimental mutations. Precise structural and stability analysis revealed D219V, C669G, and C606S as the most destabilizing SNPs. Furthermore, MD simulations disclosed higher overall fluctuation and alteration in intramolecular interactions compared with the wild-type structure. Overall, the results suggest D219V, C669G, and C606S detrimental mutations as a starting point for further case-control studies on the ADAM33 protein as well as an essential source for future targeted mechanisms.ADAM33 is a zinc-dependent metalloprotease of the ADAM family, which plays a vital biological role as an activator of Th Asthma is a multifaceted lifetime pulmonary disease of the respiratory bronchi, described by fluctuating and repetitive symptoms, bronchi spasms, and reversible airflow obstruction . It consConsequently, there has been an increase in prevalence rates, albeit with a decline in asthma mortality ratios in several countries in recent years. For instance, 345 million people worldwide had asthma in 2019, compared to 183 million patients in 1990 . Althoug2 cytokines and growth factors [Many genetic investigations have been carried out to recognize genetic polymorphisms related to asthma vulnerability. In this regard, a gene called metalloprotease and disintegrin 33 (ADAM33), placed on human chromosome 20p13, is among the initially recognized asthma nominee genes. It belongs to the zinc-dependent metalloproteases of the ADAM family and participates in a vital biological task as an activator of Th factors . ADAM33  factors . The men factors .Furthermore, genetic investigations have revealed that ADAM33 can take part in ascertaining lung function during life, correlating with the elevated prospect of curative intervention in asthma . Moreovein vivo studies, computational approaches to functional nonsynonymous single-nucleotide polymorphisms (nsSNPs) have been regarded as an intelligent, helpful procedure before conducting empirical research [In compliance with various investigations, single-nucleotide polymorphisms (SNPs) in the ADAM33 gene can hinder the physiological functions deployed through the ADAM33 protein. A current extensive meta-analysis of populations has indicated the relationship between genetic variation with asthma progression . In thisresearch . In thisThe entire approach utilized in the present research is briefly illustrated in a flowchart .http://www.uniprot.org/uniprot/. In addition, the information relating to ADAM33 protein was obtained from InterPro databases  (https://www.ebi.ac.uk/interpro/) [http://raptorx.uchicago.edu/StructurePropertyPred/predict/) [The protein sequence information of the ADAM33 gene (ID: Q9BZ11) was attained from the UniProtKB database with the address terpro/) . Moreoveredict/) .http://www.ncbi.nlm.nih.gov/SNP/, and the nsSNPs were filtered out for additional studies.The SNP data of the human ADAM33 gene was attained from dbSNP-NCBI with the address The structural and functional impacts of damaging SNPs of the ADAM33 gene were analyzed using SIFT, PolyPhen-2, PROVEAN, PANTHER, SNAP2, Align GVGD, and PredictSNP online web tools, sequentially.http://sift.jcvi.org/) was carried out via the Sorting Intolerant from Tolerant (SIFT) online web server. SIFT employs the sequence homology-based method to predict an amino acid substitution that impacts protein function via calculating the amino acid conservation degree throughout evolution [http://genetics.bwh.harvard.edu/pph2, which utilizes a variety of structure- and sequence-based comparisons for prediction of the consequences of nsSNP on both protein function and structure [The functional influences of nsSNP were predicted by applying various computational tools. Prediction of the harmful impacts of nsSNPs  for the prediction of the functional consequence of an amino acid substitution  [An online server of PROVEAN with the address ertions) . The cuthttp://pantherdb.org/tools/csnpScoreForm.jsp determines the effect of nsSNPs on protein function via evolutionary conservation. In addition, this server calculates the alignment of various evolutionarily related proteins to provide the scoring system based on position-specific evolutionary conservation (PSEC) scores provided [The PANTHER PSEP  web server with the address provided .https://www.rostlab.org/services/SNAP/ discriminates neutral and effect variants via inspecting a range of variants as well as sequence traits, including secondary structure, evolutionary, annotation data, and solvent accessibility [The SNAP2 web server (Screening for Nonacceptable Polymorphisms) with the address sibility . The prohttp://agvgd.hci.utah.edu/ is the basis of biophysical traits of amino acids and protein multiple sequence alignments for predicting damaging amino acid substitution in proteins. It provides a variety of sorted variants  in which C65 is mainly probable to hamper function and vice versa [The Align GVGD web server with the address ce versa .https://loschmidt.chemi.muni.cz/predictsnp1/) gathers the input data from various tools for estimating the consequence of single amino acid substitution. This tool offers a consensus prediction with enhanced efficiency and precision rather than an individual integrated tool [The PredictSNP online tool (ted tool .We established a criterion to narrow the obtained predictive outcomes of the online tools. Due to the huge nsSNP input (about 5000 nsSNPs), only the most likely deleterious or damaging variations (the highest scores) predicted by all the used web servers were selected for subsequent analysis like structure stability changes, energy changes, and surface accessibility using different tools.http://folding.biofold.org/i-mutant/i-mutant2.0.html [http://mutpred.mutdb.org/ [http://mupro.proteomics.ics.uci.edu/) is an online software tool used to predict stability changes of single point mutation based on a method named support vector machine (SVM) [http://www.cbs.dtu.dk/services/NetSurfP/), which is a neural network-based algorithm, was used accordingly [http://snpeffect.switchlab.org/ offers structure- and sequence-based methods to predict the impact of nsSNPs on the structure and function of human proteins [The stability of the mutant protein was verified utilizing five servers: IMutant2.0, MutPred2, MUpro, NetSurfP.2, and SNPeffect.I-Mutant2.0 with the address 2.0.html . The contdb.org/ . MUpro (ne (SVM) . In ordeordingly . The SNPproteins . SNP effproteins .http://raptorx.uchicago.edu/BindingSite/. This online tool calculates the pocket multiplicity along with P value, uSeqID (SeqID), and uGDT (GDT), which is utilized to conclude the predicted pocket quality. In this context, the superior score is implied for the more precise predicted pocket, particularly when the score reaches over 40 [Ligand-binding sites of ADAM33 protein were forecasted using the online server RaptorX binding with the address  over 40 .http://genemania.org/) can discover other genes related to a group of input ones via a massive series of functional association information. Association data comprises genetic and protein interactions, protein domain similarity, coexpression, pathways, and colocalization [The gene-gene interactions were performed to highlight nominee genes that might be related to asthma disease. The GeneMANIA online tool  [https://servicesn.mbi.ucla.edu/PROCHECK/) [http://servicesn.mbi.ucla.edu/ERRAT/) [http://servicesn.mbi.ucla.edu/Verify3D/) [https://zhanglab.dcmb.med.umich.edu/TM-align/ to evaluate the structural deviation degree among natives and mutants [The quality of the obtained protein models was further assessed by ProSA-web, (osa.php) , PROCHECOCHECK/) , ERRAT (/ERRAT/) , and Verrify3D/) . Finally mutants . In this mutants .http://biosig.unimelb.edu.au/dynamut/. This server performed prediction by evaluating flexibility analysis and protein dynamics [Prediction of alterations in the stability of protein and interaction upon nsSNPs were carried out using the DynaMut server with the address dynamics .http://imods.chaconlab.org), in which the preset values of variables were utilized. This online tool is a rapid and user-friendly molecular dynamic simulation software program that can swiftly characterize potential conformational alterations [Normal mode analysis was carried out through the iMOD server (iMODs) , Reprolysin (210-409), Disintegrin_dom (417-503), ADAM-Cys_rich (502-645), EGF-like_dom (649-681), signal peptide (1-27), and cytoplasmic domains (726-813) was investigated in InterPro and UniProtKB databases. The structural information obtained from the UniProtKB database demonstrated distinctive parts involving an extracellular domain (30-701), a transmembrane domain with helical structure (702-722), and a cytoplasmic domain (723-813).α-helix, 17% β-sheet, and 67% coil for the ADAM33 protein. Moreover, there were three states of residue-relevant solvent accessibility, i.e., exposed, medium, and buried, which comprised 51%, 28%, and 20% of the ADAM33 protein, respectively. Moreover, a total of 188 residues (22%) were predicted as disordered.The RaptorX server predicted 15% A total of 7522 SNPs for the ADAM33 protein were achieved from the dbSNP database in which nonredundant SNPs were only considered. The recognized SNPs were categorized into various functional classes involving inframe deletion , inframeSeven tools, including SIFT, PolyPhen-2, PROVEAN, PANTHER, SNAP2, Align GVGD, and PredictSNP, were recruited for pathogenicity or deleterious prediction of nsSNPs by setting limitation criteria for all of the abovementioned tools due to the large number of nsSNPs. Accordingly, 31 nsSNPs among 1055 were forecasted unanimously to be deleterious nsSNPs in all bioinformatic tools .For forecasting the alterations in the stability of ADAM33 protein, the 31 mutants were selected from the previous step and subjected to structural evaluation using five distinctive web server tools. The obtained outcomes are tabulated in The outcome of SNPeffect 4.0 was obtained from 4 different structure- and sequence-based bioinformatic tools comprising TANGO, WALTZ, LIMBO, and FoldX. TANGO predicted the tendency for protein aggregation . In thisMUpro and IMutant2.0 predicted any stability change in the ADAM33 protein, where all nsSNPs decreased the stability of the ADAM33 protein. In contrast, only P678L and C573Y SNPs were predicted to increase stability, as identified by MUpro2 and IMutant2.0 software programs .P value below 0.05. In this regard, the scores with P < 0.05 and g > 0.5 designate an actionable hypothesis, the scores with P < 0.05 and g > 0.75 designate a confident hypothesis, and the scores with P < 0.01 and g > 0.75 designate a very confident hypothesis due to the nonsynonymous mutation on the basis of the mechanistic disruption. Therefore, all selected nsSNPs were regarded as a very confident hypothesis except for only the two mutations of C637Y and C371Y, which were considered a confident hypothesis. In addition, various molecular mechanism alterations were discovered, including altered transmembrane protein, altered metal binding, loss of catalytic site, and loss of disulfide linkage  as well as NetSurfP online software was used for the solvent accessibility of the ADAM33 protein. In this regard, the class changes from the exposed state to the buried one and the buried state to the exposed one were provided. The results showed that only the two mutations, including P678L and C360R, took part in class alignment change from the buried state to the exposed state with increasing RSA value, implying an increase in solvent accessibility .P value, and multiplicity. The threshold for acceptance of the predicted model(s) for each value is as follows: uGDT (GDT) ≥ 50, uSeqID (SeqID) ≥ 30%, P value ≤ 10−3, and multiplicity ≥ 40. Accordingly, only domain 1 met all the abovementioned criteria and was predicted as a good model. However, domain 4 could be considered a relatively good model despite its lower multiplicity value. Other predicted domains cannot be regarded as correct models due to not passing the criteria mentioned above (except multiplicity). The location of domain 1 is within residues 199-414, which consists of 2 pockets, and the following residues might be prone to mutations: T310, L313, T342, H345, H355, A374, A375, and D296. A similar pattern was also observed for the following residues: L484, E486, D498, V499, L419, N422, E426, E429, D432, and D481. However, no deleterious mutation was observed by our defined restriction criteria.The RaptorX server was employed to predict ligand-binding sites of ADAM33 protein, followed by an investigation of any mutation within the recognized ligand-binding sites. The results showed that four distinct domains were predicted in the ADAM33 protein . The resThe possible occurrence of posttranslational modification in ADAM33 protein was evaluated using various servers for ubiquitination, glycation, phosphorylation, and sumoylation sites. The BDM-PUB server revealed that 13 residues were predicted to be ubiquitinated Table . HoweverThe probability of the presence of phosphorylation sites in the protein sequence was evaluated for Thr, Tyr, and Ser residues. The scores above 0.9 were generally regarded as “very probable” to be correct phosphorylation sites. In this regard, 13 out of 93 identified residues were recognized as phosphorylation sites in the ADAM33 protein Table .GlycoEP predicts N-linked glycosylation, O-linked glycosylation, and C-linked glycosylation for a given protein sequence. This server predicted 5, 13, and 0 sites of N-linked glycosylation, O-linked glycosylation, and C-linked glycosylation, respectively .SUMOs fundamentally regulate different biological processes by adding SUMO-interaction motifs (SIMs) or SUMOylation sites in proteins. Accordingly, no SUMOylation site was predicted by adjusting the threshold to medium and high values by the GPS-SUMO 2.0 online server. However, by adjusting the threshold to the low value, some predicted SUMOylation sites with a The comprehensive study of evolutionary conservation analysis of 31 nsSNPs using the ConSurf server showed that out of 31 nsSNPs, 17 mutants had a conservation score of 9 (highly conserved residues). The remaining mutants (12 residues) had a conservation score of 8 (relatively highly conserved residues), followed by 2 mutants (conservation score of 7) that were predicted to be moderately conserved. Moreover, ConSurf provided prediction for functional or structural on the basis of solvent accessibility and conservation for amino acid residues. Among these 17 highly conserved residues, 14 mutations were predicted to be structural and buried, and the rest (3 mutations) were predicted to be functional and exposed .The GeneMANIA tool was used to analyze the gene-gene interaction of the ADAM33 protein. Results showed that the ADAM33 gene has interacted with a number of ADAM families along with DGCR2, CTSK, and CTSV genes. Moreover, the coexpression genes and any contribution to attaining similar functions or sharing similar protein domains are shown in α , MS4A2 (high-affinity immunoglobulin epsilon receptor subunit β), GSDMB (Gasdermin-B), and PHF11 (PHD finger protein 11) proteins which take part in immune system functions. Other interactions with proteins are illustrated in String revealed that ADAM33 protein has interacted with FCER1In order to determine which of the potential asthma deriver nsSNPs should be subordinated to homology modeling, all evaluation tools were used with a stringent threshold of deleteriousness/effectiveness  for building a 3D model. In this context, the mutations of D219V, C388Y, C444Y, C475G, C606G, and C669G were analyzed.Due to the not availability of the 3D structure of full-length ADAM protein in the protein data bank, the FASTA amino acid sequences, as well as mutated protein sequences, were submitted to the trROSETTA server to model the 3D structure of ADAM33 protein. The server provided 5 top models, from which model 1 underwent a structure refinement by the 3Drefine server. The same procedure was done for all mutated proteins. Additional evaluations were performed using PROCHECK, Verify3D, ERRAT, and ProSA programs to calculate the quality of the model, which revealed good results for model 1 . Then, tG by ENCoM among the wild-type and mutant ADAM33 protein. The C669G, C475G, and C606S mutants showed a decrease in the ΔΔG ENCoM value compared to the wild-type SHANK3. On the other hand, the ΔΔS ENCoM value decreased in D219V, C444Y, and C388Y mutants compared to the wild-type protein. Moreover, DynaMut predicted the decrease in ΔΔG for D219V, C669G, C475G, and C606S, implying destabilization , deformability, covariance map, and linking matrix. The eigenvalue indicates the total mean square fluctuations, which are straightly associated with the energy needed for the deformation of the structure and signify the stiffness of motion. In this regard, the lower eigenvalue implies easier deformation. The outcomes of the iMOD server disclosed that the eigenvalues of D219V, C669G, C388Y, and C606S were lower than wild-type proteins, which points out the distinct behavior of wild-type and mutant proteins  were selected for MD simulation. By recruiting coarse-grained (CG) models, additional information was obtained concerning the conformational structure of wild-type ADAM protein and alterations due to the abovementioned mutations in a 2000 ps time frame. Results showed that only the wild-type and C606S substitution demonstrated a small phase of constant decline in the UNRES (united residue) potential energy followed by a palpable steady state, which stayed up to the end of the simulation . HoweverOn the other hand, the radius of gyration plots which computes protein compactness exhibited that only mutants C669G had an extremely high radius of gyration in comparison to the wild-type protein. Nevertheless, D219V and C606S substitutions had a relatively higher degree of gyration radius than the wild-type protein . SimilarAlthough the ADAM33 protein has been recognized as an asthma vulnerability gene, its function in the progression and pathogenesis of the asthma disorder has not been completely known. ADAM33 is mainly expressed in mesenchymal origin cells, chiefly fibroblasts, myofibroblasts, and smooth muscle cells, signifying a probable function in airway remodeling .In recent years, the existence of detrimental SNPs in a number of asthma-associated genes , 18 has Nevertheless, the probable consequences of numerous other mutations have stayed obscure. In general, genetic investigations are labor-intensive, time-consuming, and costly, while bioinformatic studies offer superior intuition into the right pathway of empirical research and considerably diminish expense and time . TherefoThe prediction and further validation of the most damaging SNPs can be performed by merging various biocomputational-based procedures. In the present research, the scrutiny with seven prediction tools named SIFT, PolyPhen-2, PROVEAN, PANTHER, SNAP2, Align GVGD, and PredictSNP was carried out to get a blueprint of pathogenic nsSNPs of the ADAM33 gene. Due to the dependency of every algorithm on discrete parameters, 31 nsSNPs were selected  as highlIn general, a protein's activity, regulation, and function considerably rely on the stability of the protein molecule structure. Therefore, a reduction in the stability of protein leads to misfolding, degradation, and aggregation of proteins, resulting in posterior malfunction , 55. ForThe profile of evolutionary conservation of a protein contributes to ascertaining the harshness of a damaging mutation. The location of mutations in highly conserved regions is more likely to be harmful than mutations positioned in variable regions . The ConPrediction of structural and functional consequences of mutations on ADAM33 protein made up the central part of the present research; however, probing for the existence of damaging nsSNPs in the PTM and binding sites improves the heftiness of the obtained conclusion for the significance of that specific mutation. Various approaches have been employed to forecast ligand-binding sites, including structural templates, evolutionary data, and sequence conservation . In the Moreover, several researchers have reported that these alterations could considerably modify the protein's function by changing its stability, location, or interprotein interactions , 61, 62.Owing to the lack of human ADAM33 protein structure at the protein data bank, a protein modeling tool was applied to determine the 3D protein structure. The automated protein modeling tool called trROSETTA was used in which the whole protein FASTA sequence was an input file. The server offered five 5 top final structural models. The generated models demonstrated that D219V, C444Y, C388Y, C669G, C475G, and C606S mutants might result in noteworthy stereochemical aberration. Moreover, the quality of the forecasted models was validated via PROCHECK, Verify3D, ERRAT, and ProSAweb. Based on the default score of all validation tools, model 1 was chosen as the best tentative structure of the ADAM33 protein . FinallyBefore running the MD simulation, a preliminary analysis of structural stability alterations was performed using DynaMut and iMOD servers due to their advantages over other tools for stability change prediction. DynaMut identified D219V, C669G, C475G, and C606S as the most destabilizing mutations, whereas NMA results showed that only C475G and C444Y might cause structural stability in analogy to the wild-type protein. Therefore, the mutants D219V, C669G, and C606S were selected for MD simulation to detect the most deleterious mutations better. Literature research connected the stability of proteins with related atom fluctuations , 64. In It has become imperative to perform gene prediction with particular DNA sequence polymorphisms by combining variant alleles and wild-type genotypes that influence vulnerability to a disease chiefly via interactions with environmental and genetic parameters . In thisFurthermore, protein-protein interaction was also carried out using the STRING server to apprehend the functional interaction blueprint of the ADAM33 protein with other proteins within a cell. The findings demonstrated a strong interaction network with FCER1A, GSDMB, PHF11, NPSR1, and MS4A2 proteins involved in immune regulation and asthma disease –73. Thusβ signaling and various central receptors . Hence, ADAM33 delineates a candidate target for asthma, and nsSNPs of this protein may influence asthma susceptibility. In the present research, 31 of 1055 nsSNPs of the human ADAM33 gene were forecasted as deleterious mutations using diverse computational software programs. More structural analysis disclosed that 6 SNPs, including D219V, C444Y, C388Y, C669G, C475G, and C606S, considerably influence ADAM33 protein stability. In additional evaluations using DynaMut and iMOD servers, D219V, C669G, and C606S were the most destabilizing SNPs between the six recognized mutations. Coarse-grained (CG) MD simulations were also carried out to explore how these mutations impact the protein structure. Simulation findings disclosed various considerable structural modifications, principally for the C669G variant, which significantly leads to the loss of hydrogen and disulfide bonds in the EGF-like domain.The exact biological role of ADAM33 protein has not been well understood; however, it has been proposed that the ADAM33 protein may have a potential role in the remodeling of the airway owing to its expression in myofibroblasts, epithelium, and ASMCs as well as its function in furthering stimulation of cell differentiation and proliferation along with angiogenesis. Moreover, ADAM33 may intervene in airway inflammation induced by environmental exposure and remodeling, likely via the TGFInterestingly, this is the first systematic study of in silico evaluation of functional and structural nsSNPs in the ADAM33 protein. However, more clinical studies in various ethnic populations should be inspected in the future to confirm the outcomes of this evaluation. Furthermore, functional and structural inspections are also required to be performed in order to clarify the plausible mechanisms underlying the relation between nsSNPs and susceptibility to asthma disease.