Cellular functions are governed by molecular machines that assemble through protein-protein interactions. Their atomic details are critical to studying their molecular mechanisms. However, fewer than 5% of hundreds of thousands of human protein interactions have been structurally characterized. Here we test the potential and limitations of recent progress in deep-learning methods using AlphaFold2 to predict structures for 65,484 human protein interactions. We show that experiments can orthogonally confirm higher-confidence models. We identify 3,137 high-confidence models, of which 1,371 have no homology to a known structure. We identify interface residues harboring disease mutations, suggesting potential mechanisms for pathogenic variants. Groups of interface phosphorylation sites show patterns of co-regulation across conditions, suggestive of coordinated tuning of multiple protein interactions as signaling responses. Finally, we provide examples of how the predicted binary complexes can be used to build larger assemblies helping to expand our understanding of human cell biology. Here the authors explore the ability of AlphaFold2 to predict structures across the human protein-protein interactome and the limitations thereof. Protein interactions vary from transient interactions that regulate an enzyme to permanent interactions in molecular machines.Proteins are key cellular effectors determining most cellular processes. These rarely act in isolation, but instead, the coordination of the diversity of processes arises from the interaction among multiple proteins and other biomolecules. The characterization of protein-protein interactions (PPIs) is crucial for understanding which groups of proteins form functional units and underlies the study of the biology of the cell. Diverse experimental and computational approaches have been developed to determine the PPI network of the cell , with hundreds of thousands of human protein interactions determined to date5. The structural characterization of protein complexes is a critical step in understanding the mechanisms of protein function, and in studying the impact of mutations8 and the regulation of cellular processes via the post-translational tuning of binding affinities12.The structural characterization of the human interactome has lagged behind, with experimental and homology models currently covering an estimated 15 protein interactions14. The Interactome3D  repository currently lists 7,625 predicted models based on homology of domains, a number similar to the 8,359 pairs listed having an experimentally determined model. In addition, co-evolution-based information has been used to predict protein interactions and to guide structural docking for bacterial proteins15. Recently, neural network-based approaches have demonstrated the ability to accurately predict the structures of individual proteins17 and protein complexes21. These approaches can correctly predict the structures of up to 60% of dimers18, and have been used to predict structures of 1,506 Saccharomyces cerevisiae protein interactions22. However, the application of these neural network models for the large-scale prediction of human complex structures has not been tested yet.Computational approaches for predicting the structures of interacting protein pairs are primarily based on identifying structural similarity for pairs of proteins against experimentally determined protein complexesHere, we assess the possibilities and limitations of applying AlphaFold2 to modeling human protein interactions on a large scale. We predicted the complex structures for two sets of human interactions obtained using different experimental methods, comprising 65,484 unique human interactions. We show that it is possible to rank the models according to confidence, with 3,137 predicted structures ranked as highly confident. Further, we show that the higher-confidence predictions are enriched among those supported by a combination of experimental methods. We showcase the value of a structurally resolved interactome by studying disease mutations and phosphorylation of interface residues. Finally, we provide some indication that binary complexes can be used to build higher-order assemblies.2 and the Human Protein Complex Map (hu.MAP v.2.0)3. HuRI comprises protein interactions determined by yeast two-hybrid (Y2H) screening2 from which we modeled 55,586 pairs. From hu.MAP we selected 10,207 high-quality PPIs3. While HuRI is more likely to be enriched for direct protein interactions, including transient partners, the hu.MAP set is more likely to reflect stable protein interactions, including members of the same complex that may not be interacting directly. The overlap between the two datasets is small (309 pairs), and a comparison with two large-scale compendiums of structural models4 indicates that 62,019 of the combined pairs do not have experimental models nor can they be modeled easily by homology, suggesting a large potential gain in structural knowledge.We selected experimentally identified human protein interactions from the Human Reference Interactome (HuRI)18, based on AlphaFold2 (ref. 17). As in the FoldDock pipeline, we combined size and the predicted local Distance Difference Test (plDDT) scores of the interface into a single score to predict the DockQ score of a complex, dubbed pDockQ  pairs of protein chains in each complex were docked with each other. These pairs can be divided into the ones with direct interaction and those that do not interact directly. Here, we used a definition of more than 20 contacts of less than 8 Å between Calphas to exclude small interaction interfaces. When a complex contained multiple copies of identical chains, all interactions were included to allow for alternative interactions between the chains. The difference in pDockQ scores between the direct and indirect interacting pairs is striking, where only 6% of the indirect pairs have a pDockQ score > 0.5 compared with 38% of the directly interacting pairs Fig. . This sh23 database, the best manually curated database of mammalian protein complexes, and predicted the interaction of all pairs in the same complex. The average pDockQ score of CORUM is slightly higher than for hu.MAP, but the number of high-quality predictions is similar (16% versus 19%), indicating that the different databases of protein complexes have a similar fraction of high-confidence predictions and that HuRI is the outlier  in two ways. HuRI protein pairs contain more intrinsic disorder Fig.  and haveChemical crosslinking followed by mass spectrometry is an approach which can be used to identify reactive residues  that are in proximity, as constrained by the geometry of the crosslink agent used. The identification of such residues across a pair of proteins can help define the likely protein interface. To determine if the predicted complex structures agree with such orthogonal spatial constraints, we obtained a compilation of crosslinks for pairs of residues across 528 protein pairs with predicted models Fig. . In totaN(7)-methyltransferase (METTL) with its noncatalytic subunit (WDR4). The structure of WDR4 has not yet been solved experimentally but contains WD40 repeats, which are expected to form a β-propeller domain, as predicted here. The METTL domain is predicted to interact with the side of the WDR40, away from the ligand-binding pore. This orientation is supported by a crosslink between K122 (WDR4) and K143 (METTL) (18 Å). Finally, in Fig. In total, we have identified 479 crosslinks providing supporting evidence for 171 predicted complex structures with pDockQ > 0.5. Of these, 41 correspond to complex structures with no experimental structure or homology models, from which we selected some to illustrate Fig. . Figure 24. We then performed in silico predictions of changes in binding affinity upon mutations using FoldX25 and observed that mutations known to disrupt the interactions are predicted to have a strong destabilization of binding compared with mutations known not to have an effect .Next, we mapped human disease (from ClinVar) and cancer mutations (from The Cancer Genome Atlas) to the interface residues defined by the set of high-confidence protein complex predictions (pDockQ > 0.5) . The most significant difference to the cryoEM model is the relative positioning of the ERCC3 subunit. The exact final model obtained can vary depending on the aligned pairs, with multiple possible final conformations  and alternative predicted placements of the GTF2H1 subunit.The TFIIH core complex is composed of five subunits with 1-to-1 stoichiometry. All subunits can be modeled, with the final complex generally agreeing Fig.  with a c6VVO cryoEM structure  and the eukaryotic translation initiation factor 2B from Schizosaccharomyces pombe (PDB:5B04) 30 starting from dimers. However, for PDB:5B04 it was not possible as the chains started overlapping when we tried to build a larger model. However, if we build trimers and then use all three dimers from these trimers we can build a complete model with an r.m.s. deviation of 7.3 Å (TM-score 0.86), showing that it is sometimes necessary to use larger subunits to assemble the complexes. Results from a follow-up study31 show that it is often possible to build the structures of complexes if the subunits are well predicted. In summary, we find that it is possible to iteratively align structures of pairs of interacting proteins to build larger assemblies, but we also identified issues that limit this procedure at the moment.Two additional proteins where we could build a good model are Heterodisulfide reductase from e PDB:5B0  to rank higher-confidence models, providing an additional accuracy level to large-scale PPI studies, and in the future to provide additional high-quality targets for detailed studies of stable complexes. Experimental data from crosslink mass spectrometry experiments provide an ideal resource for further validating these predictions via orthogonal means.32), the current procedure cannot identify the exact pairing of the protein. For such cases, additional methods need to be developed.Based on comparisons with solved structures, we suggest that models with pDockQ > 0.5 are 80% likely to be correct. Additionally, models with lower scores (pDockQ > 0.23) are still 70% likely to contain many correct solutions and may highlight correct interfaces. Such lower-confidence models are likely to be useful for generating hypotheses and large-scale analyses of global properties. Equally important is the caveat that high-confidence predictions will still contain errors, and, in particular, we note that in protein complexes containing paralogous proteins  and qgraph (v.1.9), and further graphical editing was done using Cytoscape44.Human protein pairs known to physically interact were obtained from the hu.MAP dataset, retaining pairwise protein interactions with ≥0.5 confidence, and most interactions from the HuRI dataset. These interactions were further enriched by obtaining annotations on crosslinked peptides matched across pairs of interaction proteins, disease-related mutations and protein phosphorylation sites in the selected proteins. In addition, all nonhomologous pairs from 12 protein complexes (Supplementary Table 18 based on AlphaFold2 (ref. 17). We used the option of fused + paired MSAs and ran the model configuration m1-10-1 as this provides the highest success rate accompanied by a 20-fold speed-up. Both the fused and paired MSAs were constructed by running HHblits on every single chain against Uniclust30. The fused MSA was generated by simply concatenating the output of each of the single-chain HHblits runs for two interacting chains. The paired MSA was constructed by combining the top hit for each matching OX identifier between two interacting chains, using the output from the single-chain HHblits runs.To predict protein complexes of pairwise interactions, we used the FoldDock pipelineTo score models, we used features from the predicted complexes to calculate the predicted DockQ score, pDockQ. This score is defined with the following sigmoidal equation:x = average interface plDDT*log(number of interface contacts).45. The number of interface contacts is defined as elsewhere in this paper (any residues with an interface atom within 10 Å of the other chain), and the plDDT is the predicted lDDT score from AlphaFold2 taken over the interface residues as defined by the interface contacts.The parameters were optimized to predict the DockQ score using the dataset from ref. The building is started from a single dimer, by default the dimer with the highest pDockQ value. This is referred to as the ‘complex’.Exactly one chain of the dimer is identical to one chain in the complexThe structure of these two chains is similar enough (default TM-score > 0.8)The dimer is then rotated so that the two chains overlapThe second chain in the dimer does not clash with more than 25% of its residues (Cα-Cα distance < 5 Å) with any chain in the complex.All other dimers in the set are then tried to be added to the ‘complex’. Starting with the one with the second highest pDockQ, a chain is added to the complex if:If a chain is added, the procedure is started over again and repeated until no more chains can be added.A simple procedure to build larger complexes from a set of paired models was developed. All dimers in the set are by default ranked by their pDockQ values.26. Kinases associated with phosphorylation of interface residues were obtained from the PhosphositePlus database, and over-representation analysis of kinases was performed using a hyper-geometric test. Highly regulated interface phosphosites were defined as those with more than twofold change in phosphorylation in more than two perturbation conditions across a collated phosphoproteomics dataset comprising a range of physiological conditions and drug treatments29. Pearson correlation was calculated amongst these regulated phosphosites and clusters of co-regulated phosphosites were identified using hierarchical clustering (‘Ward’ method) of Euclidean distances of the correlation matrix. Phosphosite clusters were created by cutting the dendrogram at the appropriate level using the cutree (h = 17) function in R. Phosphosite clusters that were significantly regulated in each perturbation condition were identified by a Z-test from the comparison of fold changes in phosphosite measurements of all phosphosites in a cluster against the overall distribution of phosphorylation fold changes across the condition. Gene ontology over-representation of each cluster was performed separately using a hyper-geometric test in R. The gene ontology terms were obtained from the c5 category of the Molecular Signature Database (MSigDBv7.1)46. All over-representation analyses were performed using the enricher function of the clusterProfiler package (v.3.12.0)6 in R.Phosphosite residues in interfaces were identified from a previously published comprehensive list of known human phosphosites47 to retrieve subcellular localization, STRING48 for coexpression and other interaction data, and gtex49 for tissue-specific expression.All proteins used here were mapped to UniProtFurther information on research design is available in the Any methods, additional references, Nature Portfolio reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41594-022-00910-8.Supplementary InformationSupplementary Figs. 1–4.Reporting SummaryPeer Review FileSupplementary TablesSupplementary Tables 1–6.Supplementary Data 1Supplementary Data (cytoscape session) to Supplementary Table 1.


Most proteins function as part of various complexes, forming via stable and dynamic protein–protein interactions (PPIs). The profiling of PPIs expands the fundamental knowledge about the structures, functions, and regulation patterns of protein complexes and intracellular molecular machineries. Protein interactomics aims at solving three main tasks: (1) identification of protein partners and parts of complex intracellular structures; (2) analysis of PPIs parameters ; (3) the study of the functional role of novel PPIs. The purpose of this work is to update the current state and prospects of multi-omics approaches to profiling of proteins involved in the formation of stable complexes. Methodological paradigm includes a development of protein-extraction and -separation techniques from tissues or cellular lysates and subsequent identification of proteins using mass-spectrometry analysis. In addition, some aspects of authors’ experimental platforms, based on high-performance size-exclusion chromatography, procedures of molecular fishing, and protein identification, as well as the possibilities of interactomic taxonomy of each protein, are discussed. Most of the protein molecules in living systems function not alone but as part of various protein complexes—from the simplest dimeric structures to complex molecular systems consisting of dozens of subunits. The formation of protein complexes is realized by protein–protein interactions (PPI). Therefore, the scientific field called protein interactomics is aimed at solving three main problems: (1) identification of protein partners and parts of complex intracellular structures; (2) establishing the parameters of PPI ; (3) study of the functional role of the identified PPI. Successful solutions of these problems can significantly expand our fundamental knowledge about the structure, principles of functioning, and regulation of protein complexes and intracellular molecular machineries. In terms of application, it is important to find PPIs that can be used as targets for innovative drugs that selectively regulate biological processes at the level of PPIs.The initial task of protein interactomics is the systemic analysis of PPI and cataloging of protein complexes. The complexity of this task is due to the multilevel architecture, structural and functional dynamism, and flexibility of the protein interactome as a result of molecular rearrangements in the cell as a response to the action of many endogenous and exogenous factors.Diverse protein complexes can be characterized using a number of criteria: (1) the number of individual proteins (subunits) involved in the complexes—dimers, trimers, tetramers, etc.; (2) subunit identity—homo- and hetero-dimers, etc.; (3) functional significance—mono- or multi-functional complexes; (4) the number of proteins interacting with the target protein in the oligomeric complex.D), association and dissociation rate constants , and half-life (t1/2) of a complex [Functionally significant protein complexes can be conventionally divided into stable (long-lived) and dynamic  ones. This assessment of protein complexes is based on the following PPI parameters: equilibrium dissociation constant (Klf-life t/2 of a c complex . Dynamic complex . Moreovehttp://humap2.proteincomplexes.org/), CORUM v.3.1 [https://mips.helmholtz-muenchen.de/corum/), and Complex Portal (accessed on 5 September 2022) [https://www.ebi.ac.uk/complexportal/home) are important web-based tools for cataloging and interpreting interactome-profiling data and contain information on 6965, 5134, and 2472 protein complexes, respectively. However, one of the main problems of protein interactomics is the lack of information about the involvement of most proteins in the formation of stable protein complexes. This problem is further complicated by the fact that the spectrum of PPIs of a particular protein may differ depending on its subcellular and tissue localization, as well as in normal conditions and in the presence of a pathological process [Databases such as hu.MAP v.2.0  (accesseUM v.3.1  (accesseer 2022)   in a separation column packed with chemically inert sorbent. The larger the molecule, the smaller the depth of its penetration into the sorbent granule containing pores with different sizes. As a result, the path of large molecules in the sorbent is shorter than that of small molecules. Therefore, large molecules leave the separation column faster than small ones. The principle of high-performance SEC is to separate the lysate material into multiple fractions, each containing proteins and multiprotein complexes of the same molecular weight (MW). As a result of subsequent analysis of the protein content in the fractions, protein elution profiles can be obtained depending on the properties of the “molecular sieve” used. To do this, after SEC fractionation, proteins in each fraction are subjected to enzymatic proteolytic cleavage  according to standard protocols, followed by mass-spectrometric analysis (LC-MS/MS) of proteolytic peptides and bioinformatics data processing.A typical example of a chromatogram of a high-performance SEC fractionation of a whole tissue lysate is shown in Further, the distribution matrix of individual proteins, identified by LC-MS/MS analysis in the test lysate fractions covering the range from 15 to 700 kDa, is presented in Formally, three main types of protein states can be distinguished according to SEC elution profiles: (I) the protein is present only in the monomeric form and does not participate in the formation of stable protein complexes, (II) the protein is present in the form of homodimers and/or heterodimers, (III) the protein participates in the formation of multimeric complexes of a higher order. Intermediate types of protein states (IV–VI) include different combinations of types I, II, and III. https://string-db.org/), one can explore data on the mutual positioning of two proteins in the PPI network . It is also advisable to include additionally in the general protocol the computer modeling of possible 3D structures of the proposed dimeric complexes using the methods of molecular docking [The generation of hypotheses on the composition of stable protein complexes is often based on the correlation of protein co-elution profiles. The tissue and cellular lysates used for SEC fractionation contain a heterogeneous mixture of thousands of individual proteins and protein complexes with different compositions. In order to predict the compositions of possible stable protein complexes present in each fraction, a number of bioinformatic algorithms are used to analyze the LS-MS/MS data ,37. Thes docking  and mole docking , includi docking ,45. In t docking .D) of protein complex.A variety of methods can be used to verify stable protein complexes identified by panoramic interactome profiling of proteins in tissue and cellular lysates. One approach that we have successfully used to identify protein partners that form stable complexes with a target protein is the direct-molecular-fishing procedure. The protocol of this procedure is based on the affinity isolation and mass-spectrometric identification of potential protein partners from the whole lysate (or from its SEC fractions), where a target protein (“bait” protein) is covalently immobilized on a solid support (chromatography resins or paramagnetic nanoparticles) ,48,49. HAs an example of the implementation of an approach, based on the intersection of SEC profiling and molecular-fishing data, interactomic analysis of microsomal cytochrome b5 (CYB5A) as a target protein can be demonstrated. Previously, a semi-quantitative assessment of the distribution of proteins co-fractionated with CYB5A in SEC fractions of liver tissue lysate was performed . We founDetermination of the spectrum of disease-associated proteins and their mapping in PPI networks is important for understanding the mechanisms of pathogenesis. It is also useful for finding ways to influence key molecular points, the functioning of which is impaired both in infectious and non-infectious diseases . This rePreparative SEC-based separation of proteins in lysates followed by LC-MS/MS analysis was used to detect quantitative changes in the elution profile of proteins in HT29 intestinal adenocarcinoma cell culture when treated with the HSP90 inhibitor tanespimycin . New HSPStudies of stable protein complexes encounter an extremely important methodological aspect consisting of the transformation of a biomaterial (tissue samples or cells) into a liquid phase (lysate). Therefore, the methods of sample preparation of biomaterial for the analysis of the protein interactome as a whole and the preparation of tissue lysates, in particular, carry elements of customization. This is especially true for the choice of the composition of lysis buffers, since there is no standardization in these approaches that are focused on the stabilization of the specific native protein complexes. The existence of a large number of lysis buffers and protocols for the preparation of lysates increases the objective differences in the results of interactome profiling, which complicates the comparative analysis of data from different sources.Another problematic aspect of protein interactome studies is the participation of not only canonical forms of proteins in PPIs but also their various proteoforms arising as a result of alternative splicing and post-translational modifications. Moreover, the appearance of specific post-translational modifications of protein partners is possible, which occur only after the formation of stable complexes. To date, there is no systematic information on the relationship between the formation of protein complexes and the presence of specific proteoforms , which rIn conclusion, it should be noted that there is a positive outlook for the deeper fundamental understanding of protein-interactome-profiling data. This is an opportunity to carry out partial interactome taxonomy of each identified protein. The participation of the protein in the formation of stable complexes (the stable part of the interactome) can be characterized by two groups of parameters. The first group  includes the following: (1) the fact that the protein participates in the formation of stable protein complexes; (2) the fact that the protein is involved in one or more complexes; (3) the size of the complexes in which the protein participates; (4) the fact that the protein can be in the free monomeric state; (5) a list of proteins found in the same fractions along with the analyzed protein (co-fractionation hypotheses); (6) a list of potential protein partners (in case of the sum of the molecular weights of the partner protein and the target protein corresponds to the average MW of the SEC fraction); (7) hypotheses about the possible functional role of the protein. The second group includes the following semi-quantitative parameters: (1) the percentage distribution of the protein between the fractions (monomeric form and complexes of different sizes), (2) the ratio of the amount of each protein in the SEC fraction to the amount of the analyzed protein, (3) the list of potential protein partners based on semi-quantitative estimates.


Rabies is a neurological disease that causes between 40,000 and 70,000 deaths every year. Once a rabies patient has become symptomatic, there is no effective treatment for the illness, and in unvaccinated individuals, the case-fatality rate of rabies is close to 100%. French scientists Louis Pasteur and Émile Roux developed the first vaccine for rabies in 1885. If administered before the virus reaches the brain, the modern rabies vaccine imparts long-lasting immunity to the virus and saves more than 250,000 people every year. However, the rabies virus can suppress the host’s immune response once it has entered the cells of the brain, making death likely. This study aimed to make use of disorder-based proteomics and bioinformatics to determine the potential impact that intrinsically disordered protein regions (IDPRs) in the proteome of the rabies virus might have on the infectivity and lethality of the disease. This study used the proteome of the Rabies lyssavirus (RABV) strain Pasteur Vaccins (PV), one of the best-understood strains due to its use in the first rabies vaccine, as a model. The data reported in this study are in line with the hypothesis that high levels of intrinsic disorder in the phosphoprotein (P-protein) and nucleoprotein (N-protein) allow them to participate in the creation of Negri bodies and might help this virus to suppress the antiviral immune response in the host cells. Additionally, the study suggests that there could be a link between disorder in the matrix (M) protein and the modulation of viral transcription. The disordered regions in the M-protein might have a possible role in initiating viral budding within the cell. Furthermore, we checked the prevalence of functional disorder in a set of 37 host proteins directly involved in the interaction with the RABV proteins. The hope is that these new insights will aid in the development of treatments for rabies that are effective after infection. Rhabdoviridae family. There are 10 viruses in the Rabies serogroup, but most are not pathogenic to humans. Rabies lyssavirus and Australian bat lyssavirus are the only two rhabdoviruses that have been known to cause disease in humans [Rabies lyssavirus is a bullet-shaped, negative-sense, unsegmented, single-stranded RNA virus of the n humans . French n humans . The virn humans . The virn humans .All RABVs encode for five major proteins, as well as four more isoforms of the phosphoprotein that are generated through alternative initiation. The major proteins are the phosphoprotein (P-protein), matrix protein (M-protein), glycoprotein (G-protein), nucleoprotein (N-protein), and polymerase, or the large protein (L-protein). A total of five phosphoproteins can be made through alternative initiation . The RNAOnce the virus reaches the cell, the G-protein initiates endocytosis. The low pH inside the endosome initiates the fusion of the membrane with the endosome, resulting in the release of the nucleocapsid into the cell. The P-protein acts as a cofactor to the L-protein, creating the functional viral polymerase . The M-pThe P-protein has an important role in the infection and propagation of RABV. It binds to the dynein light-chain proteins DYNLL1 and DYNLL2 in humans, allowing the virus to be transported along the axon to the nucleus. During transcription, alternative initiation using a ribosomal leaky scanning mechanism can create five variations of the P-protein, termed P1–5 . This enκB family of proteins, which results in the further suppression of the antiviral immune response [The M-protein has been shown to be significant in regulating viral budding. It has been shown to bind to the plasma membrane even in the absence of other viral proteins, which suggests that the protein acts to connect the plasma membrane to the nucleocapsid. The M-protein has been shown to be able to induce vesicle budding without necessitating interactions with other proteins ,12. The response . The G-presponse . The G-presponse . This biAs is typical for other viruses, all RABV proteins are multifunctional. Such multifunctionality is crucial, as it allows a very small set of viral proteins to manage and control each and any aspect of the viral “life”, from entry to replication to the formation and exit of new infectious particles, and regulate each and any aspect of virus interaction with the host. In fact, a typical protein repertoire of a typical virus includes a minimal set of specific structural proteins that are crucial for the viral particle assembly and a set of non-structural proteins needed for the hijacking of many functional pathways of the host cell. This is why many viral proteins are multifunctional. They are typically engaged in numerous interactions with various host cell components. In the protein universe, multifunctionality and binding promiscuity are typically associated with protein intrinsic disorder. This directly follows from the recognition that many protein functions do not need a unique 3D structure ,31,32,33Importantly, based on computational analyses of the abundance of intrinsic disorder in various organisms, it has been concluded that the proteomes of viruses have the largest variability in the content of disordered residues in comparison with all other kingdoms of life ,38. The The Universal Protein Resource (UniProt) is an annotated database of protein sequences . EntriesThe amino acid sequences can be analyzed through various methods to identify regions in the protein that have a predisposition towards intrinsic disorder. Through a comparison of these regions of intrinsic disorder and the function of the protein, an analysis can be made of the disorder-based functionality of the protein. Determining the structure of the protein is important for mapping the intrinsic regions of disorder to regions on the protein. Intrinsically disordered regions are flexible. As a result, these regions are not recorded in the crystal structure of the protein. Regions of the protein that do not show up in the crystal structure are indicative of intrinsic disorder .® VL3, PONDR® VLS2, PONDR® VLXT, PONDR® FIT, IUPred_Short, and IUPred_Long predictors.For this study, amino acid FASTA sequences for the Rabies lyssavirus PV proteome were gathered through UniProt. The sequences were run through a series of disorder prediction tools to generate an estimate of the intrinsic disorder of each residue. These predictions of intrinsic disorder were averaged to form an overall prediction of intrinsic disorder for that residue. ®) is meta-prediction software that can analyze an amino acid FASTA sequence on a per-residue basis to predict regions of intrinsic disorder. This study made use of the PONDR®VLXT, PONDR®VL3, PONDR®VLS2, and PONDR® FIT meta-predictors from the PONDR family, as well as IUPred_Short and IUPred_Long [Predictor of Natural Disordered Regions , was used to obtain the intrinsic-disorder-related characteristics of the query proteins . This to® VLXT , PONDR® T [® VL3 , PONDR® [® VLS2B , PONDR® B [® FIT , IUPred2B [® FIT ,82. FurtB [® FIT ,84,85 anB [® FIT .The outputs of the per-residue predictors were averaged, and proteins were grouped based on their percentages of predicted intrinsically disordered residues (PPIDR) using accepted classification criteria . Protein2P2 database  [2P2 database uses the outputs of IUPred [® VLXT [® VSL2B [2P2 displays 9 colored bars representing the location of disordered regions, as predicted by these different disorder predictors. In the middle of the D2P2 plots, the blue–green–white bar shows the predicted disorder agreement between the nine disorder predictors , with blue and green parts corresponding to disordered regions by consensus. Above the disorder consensus bar are two lines with colored and numbered bars that show the positions of the predicted (mostly structured) SCOP domains [Complementary disorder evaluations, together with important disorder-related functional information, were retrieved from the Dst 2022) , which ist 2022) . The D2Pf IUPred ,82, POND [® VLXT , PrDOS [ [® VLXT , PONDR® [® VSL2B , PV2 [88[® VSL2B . The vis domains ,92 using domains . The yel domains , whereas domains , which ihttp://string-db.org/, accessed on 15 August 2022). STRING generates a network of protein–protein interactions based on predicted and experimentally validated information on the interaction partners of a protein of interest [Information on the interactability of human proteins that interact with RABV proteins was retrieved using Search Tool for the Retrieval of Interacting Genes  [The propensity of the RABV proteins to undergo liquid–liquid phase separation was evaluated by FuzDrop  .All computer-generated structures of the RABV proteins analyzed in this study were generated using SWISS-MODEL  and ExPa® VSL2 outputs, roughly 49% of the protein residues were predicted to be intrinsically disordered . Furthermore, almost 46% of its residues were predicted to be flexible , indicating that 95% of this protein is expected to be either disordered of structurally flexible. The P-protein of RABV, which is a 297-residue-long catalytic polymerase cofactor and regulatory protein that plays an important role in viral transcription and replication, was shown to display a significant amount of intrinsic disorder see . In factHigh levels of intrinsic disorder in the RABV P-protein are further evidenced by the analysis of its X-ray crystal structure (PDB ID: 3OA1). In fact, although the 69–297 fragment of this protein was used in the crystallization experiments, the structure was eventually solved for less than half of this construct (residues 192–297), with the entire N-terminal half (residues 69–191) representing a region with missing electron density . Furthermore, even within the solved structure of the C-terminal domain of the P-protein, some short regions were not modeled or incompletely modeled as well  (PDB ID: 3OA1).https://www.uniprot.org/uniprotkb/P06747/entry, accessed on 15 August 2022), most of the IDD2 region of the RABV P-protein is expected to be engaged in binding to cytoplasmic dynein light-chains 1 and 2 . Furthermore, ANCHOR analysis [Importantly, as per the manually asserted information inferred from the sequence similarity and available in the UniProt database  regions for interaction with the STAT proteins of cells. In fact, although the RABV P-protein binding site responsible for the STAT1/2 interaction is located within the ordered C-terminal domain vely see A. Similavely see A. This, Another level of structural and functional complexity of this protein is given by the fact at it has multiple isoforms generated by the alternative initiation of the P-protein during viral transcription. In fact, alternative initiation generates isoforms P2, P3, P4, and P5, which differ from the canonical isoform P1 due to a lack of N-terminal residues 1–19, 1–52, 1–68, and 1–82. An obvious consequence of this truncation is the elimination of the first MoRF region of P1, suggesting that these isoforms might be characterized by different interactability. Curiously, although P3, P4, and P5 have all lost an N-terminal MoRF as expected, P2 was predicted to behave differently. In fact, despite missing the N-terminal residues predicted to be the MoRF in P1, this isoform gained three new N-terminal MoRFs .The functional diversity of the RABV P-protein is further increased by the phosphorylation of its serine residues S63 and S64 by an unknown kinase (denoted rabies virus protein kinase (RVPK)) and residues S162, S210, and S271 by protein kinase C (PKC) , all loc® VSL2, N-NES and NLS are located within IDPRs  located within the long central IDPR of this protein (residues 133–199). A recent systematic analysis revealed that this region indeed plays a crucial role in the formation of viral Negri body (NB)-like structures in infected cells [Since the P-protein participates in the formation of Negri bodies  and can ed cells . Furthered cells .LLPS values of 0.6561 , 0.6832 , 0.4938 , and 0.3526 , indicating that alternative initiation dramatically affects the LLPS potential of the P-protein. According to the FuzDrop developers, proteins with pLLPS ≥ 0.60 are droplet-drivers, which can spontaneously undergo LLPS. Droplet-client proteins have pLLPS < 0.60 but possess droplet-promoting regions, which can induce their partitioning into condensates [Shorter isoforms of the P-protein, P2, P3, P4, and P5, showed pdensates . TherefoFuzDrop also indicated that P1 contains nine regions with context-dependent interactions . These regions can potentially undergo disorder-to-order or disorder-to-disorder transitions but maintain conformational heterogeneity in the bound state and show sensitivity to the cellular context or post-translational modifications, potentially serving as regulatory engines of cellular pathways .κB pathway [The M-protein is a 202-residue-long protein that plays a crucial role in the assembly and budding of the virion and engages in complete coverage of the ribonucleoprotein coil to keep it in a condensed bullet-shaped form see . It was  pathway  may induThis is in line with the results of several studies on the potential roles of intrinsic disorder in proteins that form the shells of several viruses  for viral transmission, immune evasion, and virulence ,119,120.https://www.uniprot.org/uniprotkb/P08671/entry, accessed on 15 August 2022) binding, includes an IDPR , indicating that the intrinsic disorder  of this region can contribute to its interactability. Furthermore, the M-protein contains a PPxY motif (residues 35–38), which is commonly found in viral proteins capable of manipulating the autophagic machinery to prevent the autophagic degradation of viruses [Curiously, region 115–151, which is essential for the glycoprotein  to create homology models for this protein. Although no structural information is currently available for the M-protein of RABV, we used SWISS-MODEL (LLPS = 0.2220), this protein contains one C-terminally located DPR (residues 199–202), which is included in the IDPR (residues 182–202), indicating that the M-protein can act as the droplet-client. FuzDrop also identified regions 16–29, 121–131, and 172–190 as regions with context-dependent interactions [Finally, FuzDrop analysis indicated that although the M-protein shows a low probability of spontaneous LLPS  and does not include any DPRs, this RNA-binding protein is invariantly present in Negri bodies (NBs) [Although, based on the FuzDrop analysis, the N-protein is expected to have a low probability of spontaneous LLPS (pes (NBs) . It is les (NBs) . Furtheres (NBs) . These oes (NBs) . It is kes (NBs) ,125. As The G-protein is a 524-residue-long type I transmembrane protein with a long extravirion region (residues 20–459), a transmembrane helix (residues 460–480), and an intravirion domain (residues 481–524). Being located on the surface of RABV particles, the G-protein controls receptor binding and the release of the viral ribonucleoprotein (RNP) in the cytoplasm via pH-dependent membrane fusion, thereby playing a crucial role in the cell entry and in vivo spread . Furtherhttps://www.uniprot.org/uniprotkb/P08667/entry, accessed on 15 August 2022) that is engaged in the interaction with the matrix protein [The G-protein is synthesized as a precursor with the N-terminal signal peptide (residues 1–19), which is removed during the maturation of this protein. Similar to the proteins found in the envelopes of other viruses, the RABV G-protein forms homotrimers on the surface of the virion that are responsible for the attachment of the virus to the host cellular receptors, such as the muscular form of the nicotinic acetylcholine receptor (nAChR), the neuronal cell adhesion molecule (NCAM), and the p75 neurotrophin receptor (p75NTR). There are approximately 400 such trimeric spikes, which are tightly arranged on the surface of the virus. The C-terminal domain of the G-protein (residues 258–505) is essential for trimer stability [ protein . Several protein . There aThere is currently no structural information for the G-protein from the RABV strain PV. Therefore, we used SWISS-MODEL to create a homology model for this protein. Furthermore, the authors of this study present a structure for another form of this protein (PDB ID: 6LGX), which shows a different pattern of missing electron density regions . Since these two structures were resolved under different conditions  and at ~pH-8.0 in free form (PDB ID: 6LGX)), these observations suggest that this G-protein structure is characterized by its noticeable sensitivity to environmental conditions. The authors pointed out that the basic-to-acidic pH change results in large re-orientations of the three domains found in the G-protein from the RABV strain CVS-11, leading to concomitant domain-linker reconstructions that switch from a bent hairpin conformation into an extended conformation . These lLLPS = 0.1351) and does not include any DPRs but contains five regions with context-dependent interactions , which overlap, include, or are included in the disordered/flexible regions of this protein   see A.With an amino acid sequence of 2124 residues, the L-protein is the longest protein in the RABV proteome. This protein is an RNA-directed RNA polymerase (RdRp) that catalyzes the transcription of viral mRNAs as well as their polyadenylation and capping. It has several functional regions, such as an RdRp catalytic domain (residues 611–799), a mononegavirus-type SAM-dependent 2′-O-MTase domain (residues 1674–1871) included in the C-terminal region that is involved in the interaction with the P-protein (residues 1562–2127) and contains a disorder-based interaction site, and a MoRF (residues 1631–1638). In addition to interactions with the P-protein, the L-protein may form homodimers. Although the L-protein includes many disordered or flexible regions, its overall intrinsic disorder level is relatively low see A.It is possible that the need for interplay between ordered and disordered features in this protein reflects its purpose to serve as a low-fidelity viral polymerase, as intrinsic disorder and structural flexibility likely define the lower fidelity of the polymerase, which results in a high mutation rate and therefore higher flexibility in the ability of the virus to adapt to host defenses . The polLLPS value of 0.11860 and does not contain any DPRs. However, this protein possesses 24 regions with context-dependent interactions . Many of these regions are related to the IDPRs identified in the L-protein .FuzDrop indicated that the L-protein has a low pIn addition to being involved in interactions with the RABV L- and N-proteins and viral ribonucleocapsids, the P-protein is known to bind to a multitude of host proteins, such as dynein light-chain 1 and 2 (DYNLL1 (UniProt ID: P63167) and DYNLL2 (UniProt ID: Q96FJ2), respectively) , as wellκB pathway [Several human proteins that interact with the RABV M-protein were established. The list includes RelAp43 (which is a splicing variant of RelA (UniProt ID: Q04206) and other proteins in the NF- pathway , as well pathway . Note th pathway .Very few host proteins were shown to act as physical partners of the RABV N-protein. These include heat shock 70 kDa proteins 1A  and 1B , prefoldin subunit 1 , and CCTγ (UniProt ID: P49368), with CCTγ being a shared partner for the RABV P- and N-proteins .2−α10 and β2−β4 3(β2)2, (α4)2(β2)3, (α3)2(β4)3, α4α6β3(β2)2, and (α7)5). Therefore, there are multiple possibilities for the binding of the G-protein to nAChR. Physical interactions of nicotinic acetylcholine receptor alpha 1  [The major biological function of the G-protein is cell entry via interaction with RABV entry receptors, such as nicotinic acetylcholine receptors (nAChR), the neuronal cell adhesion molecule , the low-affinity p75 neurotrophin receptor , and metabotropic glutamate receptor subtype 2  ,138,139. P02708)  and nACh P02708)  with the P02708) .The only human partner of the L-protein is DYNLL1, which is shared with the P-protein .2P2, and AlphaFold. This revealed a very high level of disorder in the majority of the proteins from this dataset, with the entire set being characterized by a mean PPIDR of 41.6 ± 20.9% .These observations are further illustrated in Therefore, 15 human proteins that interact with the RABV proteins are predicted to contain very noticeable levels of disorder . This correlates well with the results shown in The apparent discrepancies between the data shown in Not surprisingly, all 37 proteins in the analyzed set were found to form a rather dense PPI network see A, in whip = 3.49 × 10−8), immune effector process , interspecies interaction between organisms , intracellular transport , and cellular component organization . Among the molecular functions, the five most significantly enriched were ubiquitin-like protein ligase binding , protein binding , ubiquitin protein ligase binding , enzyme binding , and binding . Finally, the five most significantly enriched cellular components were postsynapse , cytosol , synapse , cell junction , and neuron projection .In this intraset PPI network, the five most significantly enriched biological processes were viral process . Looking at the functional enrichment of this network based on Gene Ontology (GO) terms revealed that the five most significantly enriched biological processes were interspecies interaction between organisms , viral process , symbiotic process , translational initiation , and cellular response to organic substance . The five most significantly enriched functions were structural constituent of ribosome , protein binding , binding , enzyme binding , and RNA binding . Among the cellular components, the five most significantly enriched were cytosol , protein-containing complex , cytosolic ribosome , cytoplasm , and ribosomal subunit .We also analyzed the set-based interactivity of these human proteins. To this end, STRING was used to generate a PPI network that includes first-shell interactors see B. The reAlthough a detailed description of the potential roles of intrinsic disorder in the functionality of human proteins shown to interact with the RABV proteins is outside the scope of this study, it is clear that all of the proteins analyzed here  contain very significant levels of intrinsic disorder. This is further illustrated in All Rabies lyssavirus PV proteins contain IDPRs, most of which are expected to aid in the flexibility of the virus and its ability to evade host antiviral defenses. All human proteins found to be RABV interactors also contain high levels of intrinsic disorder, with most of these proteins being highly disordered. This disorder-centric layer of complexity in RABV and its interaction with host proteins adds a new angle to the search for potential targets for anti-rabies drugs. Once the virus has infected a host cell, there are virtually no effective treatments available that can be used to destroy the virus. Although the modern RABV vaccine has largely eradicated the virus in developed countries, many people around the world are unable to seek treatment until the virus has already crossed the blood–brain barrier. Without the immediate usage of the vaccine upon infection, the RABV-related mortality rate is close to 100%. The advent of bioinformatics approaches in a clinical setting has inspired many developments that have led to the creation of new drugs. It is likely that targeting regions of intrinsic disorder within the viral proteins or host proteins that directly interact with the RABV proteins will help in creating novel drugs that can target the virus once it has infected the central nervous system.


Dr Cyrus Chothia FRS was a pioneer and one of the founding figures of theoretical and computational biology, nowadays commonly known as the field of bioinformatics (a term which Cyrus never quite got used to). To cite some of his numerous contributions to the field, the work of Cyrus and co-workers on the relationship between the divergence of sequence and divergence of structure in proteins supported the development of methods of homology modelling ; his worGilodi et al. coupled experimental methodologies with in silicio design and showed the potential of developing an antibody able to recognize the RNA binding regions of TDP-43. TDP-43 aggregates have been proposed as a potential cause of ALS and their sequestration has been proposed as a therapeutic strategy.As mentioned above, Cyrus’ work included diverse computational research areas including antibody canonical loop classification. Two articles in this collection focus on the computational study and design of antibodies. Antibodies can be used to target toxic molecules. In their work, Fernández-Quintero et al. investigated this relationship and found that CDR loops reveal conformational transitions in the micro-to-millisecond timescale, while the interface and elbow angle dynamics occur on the nanosecond timescale.Although the variable domains of an antibody contain the complementarity-determining regions (CDRs) that shape and host the antigen binding site (ABS), the elbow angle and the relative interdomain orientations of the variable and constant domains also influence the shape of ABS . TherefoChen et al. used a novel approach based on the study of domain-mediated protein-protein interactions, rather than a traditional focus on individual domains, for the structural profiling of bacterial effectors. These are proteins injected by the bacteria into the host cells that are critical for their virulence and intracellular survival. Their approach led to novel quantitative insights into the structural basis of effectors that might aid the design of effective and selective inhibitors of their pathogenic mechanisms.The next three studies discuss and attempt to identify general features of protein domains, in the spirit of Cyrus’ analyses of protein structures and sequences. Dubreuil and Levy discussed these constraints while emphasising relevant works of Cyrus. Subsequently, they focused their attention on the evolutionary rate of disordered regions and the role of cellular abundance in their sequence conservation. They found that disordered regions are equivalent to super-accessible surface residues, and they confirmed the strong divergence interdependency between surface and core residues and the weak evolutionary coupling of disordered and domain regions. Finally, they observed that protein abundance impacts the conservation of residues in core, surface and disordered regions with constraints of similar effect size.As studied by Cyrus particularly in later years of his career, interconnected functional, biophysical, and structural constraints drive the purifying selection leading to variable levels of conservations along protein sequences. In their work, Konagurthu et al. tried to answer the following question: ‘What is the architectural “basis set” of the observed Universe of protein structures?’ The authors used an information-theoretic inference method to identify automatically conserved sets of secondary structural elements within any given collection. By applying this method to the ASTRAL SCOP domains, they created an architectural dictionary of 1,493 substructures and used it to dissect the protein data bank (PDB). They made the entire dictionary, associated information and all the concept instances from the analysis of the PDB, publicly available on a webserver (http://lcb.infotech.monash.edu.au/prosodic).In the spirit of Cyrus’ global approach to analysing the protein Universe, Bhattacharya et al. presented the current advances of the protein homology detection field driven by the use of machine learning in Inter-Residue Interaction Map Threading.Homology modelling is one of the most established approaches to protein structure prediction and a longstanding tool for Cyrus and co-workers on important structures such as the model of the T cell receptor based on antibody structures . HomologJabeen et al. designed a method for homology modelling of GPCRs that exploits this biophysical characteristic, as well as other GPCR-specific features. Their method was validated with a number of published benchmarking datasets and a case study on an olfactory receptor is presented in the article. Furthermore, it was implemented in the form of a free tool called Bio-GATS (https://github.com/amara86/Bio-GATS).Some classes of proteins present a low sequence identity among homologs limiting the use of sequence-based methods for their homology modelling. G protein-coupled receptors (GPCRs) represent one such example. However, GPCR sequences with similar patterns of hydrophobic residues are often structural homologs, even with low sequence identity. In their study, Savojardo et al. investigated whether and to what extent single-amino acid pathogenic variants (PVs) could be associated with their solvent exposure. Solvent-Accessible Surface Area (SASA) is indeed a key characteristic of proteins in determining their folding and stability. Savojardo et al. mapped PVs onto a curated set of structures and determined that PVs occur more frequently in residues which are less likely to be accessible by the solvent, and that they are not evenly distributed among the different residue types. Using an in-house deep learning method for the sequence-based prediction of residue SASA, the authors confirmed these results in 12,494 human protein sequences for which no 3-D structure was available.Di Renzo et al. investigated the interactions between amino acids on the protein surface and the solvent (water) to characterise their solvation properties. Although many descriptors of such properties exist, the local environment of each residue in the context of the protein is complex and often overlooked by existing methods. Based on molecular dynamics simulations, Di Renzo et al. developed a method to characterize the dynamic hydrogen bond network at the interface between protein and solvent, from which they derive the solvation properties of each amino in the protein environment.On a related topic, Bordin et al. presented some of Cyrus’ accomplishments in the context of the history of protein structure classifications. The authors particularly focused on SCOP and CATH, two major protein structural classifications databases, and the evolutionary insights these two classifications have brought. They conclude their piece by discussing how the growing volume of data, and integration of protein sequences into these structural classifications, is helping to predict new functions in Metazoan organisms.Finally, in their review The articles in this collection cover very diverse areas of Structural Bioinformatics, reflecting the broad impact of Cyrus’ research.As a final remark, Cyrus never forgot that one’s life is about the journey and not only the destination, and was ahead of his time in the open-minded way that he collaborated with scientists of all backgrounds and nationalities as well as across disciplines. The editors and authors of this collection express their deepest gratitude to Cyrus for his enormous contribution to the field of Bioinformatics and for being a generous, supportive, and inspiring colleague and friend.


We present a structure-based method for finding and evaluating structural similarities in protein regions relevant to ligand binding. PDBspheres comprises an exhaustive library of protein structure regions (‘spheres’) adjacent to complexed ligands derived from the Protein Data Bank (PDB), along with methods to find and evaluate structural matches between a protein of interest and spheres in the library. PDBspheres uses the LGA  structure alignment algorithm as the main engine for detecting structural similarities between the protein of interest and template spheres from the library, which currently contains >2 million spheres. To assess confidence in structural matches, an all-atom-based similarity metric takes side chain placement into account. Here, we describe the PDBspheres method, demonstrate its ability to detect and characterize binding sites in protein structures, show how PDBspheres—a strictly structure-based method—performs on a curated dataset of 2528 ligand-bound and ligand-free crystal structures, and use PDBspheres to cluster pockets and assess structural similarities among protein binding sites of 4876 structures in the ‘refined set’ of the PDBbind 2019 dataset. However, when a new structure is obtained, it may be the case that little is known with regard to potential binding sites on that structure. Numerous studies have focused on understanding protein–ligand interactions e.g. ,2] to fu to fu2)]mple, in . Our metmple, in , (ii) thmple, in , or  that are already observed as protein–ligand binding sites in other proteins . This approach ensures that any structural shape that might resemble a cavity in evaluated protein models—e.g. holes within interchain interfaces, missing fragments in coordinates or other structural imperfections in geometry—will not be reported by PDBspheres searches as cavity candidates, simply because they are not evidenced as In the PDBspheres method, binding sites are identified exclusively based on the structure similarity between regions from the query protein and pocket spheres from the PDBspheres library. Ligand placement within a predicted pocket is calculated based on a protein–sphere superposition, i.e. an agreement in structure conformation between atoms from the query protein and protein atom coordinates from the template sphere. The main premise of structure template-based binding site prediction methods is that the number of pockets is limited ; therefoin silico’ drug discovery; it can be used in compound screening/docking efforts as a pre-filter for selection of most promising compounds for a more detailed and expensive computational evaluation. Currently, there are millions of compounds screened using docking or molecular mechanics-generalized Born surface area approaches. These methods are widely used to estimate ligand-binding affinities in identified binding sites of targeted proteins to find good candidates for a further  analysis of potential inhibitors.Knowing the correct ‘core’ conformation of the ligand within the pocket (i.e. the pose of the conserved part) can significantly improve ‘et al.  experiments  , an atteet al. ,26. It cet al. . The autet al. , templatet al.’s  analysis . The PDBspheres system has three main components: (i) the PDBspheres library of binding site templates; (ii) a structure similarity search algorithm to detect similarity between evaluated local structural regions (e.g. binding sites); and (iii) numerical metrics to assess confidence in detected similar regions.Currently, the PDBspheres library (ver. 2021/10/13) contains 2 002 354 compound binding site models (template spheres) and 67 445 short-peptide binding site models derived from protein structures deposited in the PDB database .The Local–Global Alignment (LGA) program  is used de novo ligand poses within a binding site. However, further relaxation of the predicted protein–ligand complex can be considered as the next step to improve a ligand placement within a pocket (which is a subject of continuing PDBspheres system development).When applied to predict protein binding sites in a given protein structure, PDBspheres detects ligand–protein binding regions using the pocket/sphere templates from the PDBspheres library constructed from all available structures deposited in the PDB database. After a binding pocket is detected, the ligand(s) from matching template(s) is/are inserted into the identified pocket in a query protein to illustrate an approximate location of the ligand. The location is approximate because it is based on the alignment of the query protein with the template protein spheres, and no docking or any energy minimization or structure relaxation is undertaken. Thus, it should be noted that PDBspheres is not a docking system to predict In the process of detecting pocket candidates within protein structures, similarity searches can be performed using all templates in the PDBspheres library , or if time is prohibitive, searches can be performed on a preselected subset of the sphere templates. For example, the preselection process can be based on specific targeted ligands (e.g. their names or sizes) or can be limited to those template spheres from the PDBspheres library that come from homologous proteins. The computational time of the structure processing can vary depending on the size of the protein or the number of template spheres from the PDBspheres library preselected by the system for the pocket detection and similarity evaluation. An exhaustive search (against the entire PDBspheres library) can take >1 day on a single processor machine; however, with the above-mentioned preselection procedures, the calculations can be completed in <1 h for medium size proteins. For example, the processing of three protein structures discussed in the manuscript and illustrated in Figure The clustering of identified pocket–sphere matches and their corresponding ligands characterizes distinct pocket regions within the protein by the sets of residues interacting with inserted ligands. The clustering of interacting residues allows identification of overlapping parts of ligands that approximate the ligand’s ‘core’ conformation within detected pockets and for each cluster defines a representative set of residues forming a consensus pocket. An example of an identified cluster of pocket spheres is shown in Figure Each protein–ligand template sphere in the PDBspheres library is a subset of the records from a corresponding PDB entry, consisting of the coordinates of all atoms belonging to a given ligand and coordinates of all protein atoms belonging to residues near that ligand (water atoms are excluded). The protein residue is part of the sphere if at least one of its atoms is within 12.0 Å of any atom of the ligand. Previous research indicated that distances of 7.5 Å are sufficient to capture informative functional properties for clustering purposes ; howeverIn the PDBspheres library, the 12.0 Å ‘sphere’ entries have been constructed for each ligand in the PDB, including peptides, metals and ions, although the library includes only peptides containing 25 or fewer residues. The library is updated weekly in coordination with new PDB releases. As of 13 October 2021, the library consisted of 2 069 796 spheres (binding site templates).The primary use of the PDBspheres library is to identify ‘sphere’ protein structures that are structurally similar to regions of a query protein structure. The query protein structure may be a complete, multimeric assembly, or it may be a single subunit of such an assembly, or even a fragment of a protein as long as it carries enough structural information  to reflect a structural shape of a putative binding site.The fundamental identification method used by PDBspheres is structural alignment of a sphere with the query protein structure and the assessment of the structural match. For a general use, a comprehensive search—i.e. a structural alignment-based search of the entire PDBspheres library (over 2 million sphere templates)—would be computationally expensive. One means to address this issue is to conduct an initial search for matches to a query protein structure on a subset of sphere templates. Then, if needed, the search can be expanded to additional templates from the library. In its current implementation for possibly faster processing, the PDBspheres library searches can be restricted to a subset of template spheres selected based on (i) ligand specificity, e.g. ligand names, their sizes or similarities, or (ii) sequence similarity between the query protein and PDB proteins from which the PDBspheres library entries were derived. In the latter approach, the sequence similarity searches are conducted using the Smith–Waterman algorithm against FASTA-format sequences of all proteins contributing to the PDBspheres library entries. In the current version of PDBspheres, the Smith–Waterman algorithm ‘ssearch36’  is used.In the PDBspheres library, each sphere template entry includes a number of characteristics such as the ligand name (PDB ligand ID), the number of heavy atoms in the ligand and the number of residues in the protein fragment forming the protein–ligand ‘sphere’ (i.e. the size of the pocket template). Thus, the list of matching spheres can be screened using specific criteria related to the expected pocket size, exact ligand name or ligand similarity. A selection of the template spheres based on the estimated similarity between the expected ligand and the PDB ligands from the PDBspheres system can be quantified by the Tanimoto or Tversky similarity indexes .After a selection of template spheres is completed, each member of this set is evaluated by assessing its structure similarity with a query protein. The evaluation is performed in two steps: (i) detection of the region resembling the binding site shape and (ii) assessment of the similarity in residue conformations including a conformation in their side chain atoms. The primary search involves a calculation of structural alignment between template spheres and a query protein. The structural alignment is conducted using the LGA program and is calculated using a single point representation of aligning residues . The structural similarity search returns similarity scores (LGA_S) and alignments between template spheres of various pockets and the query protein. The final evaluation of identified pockets is done by assessing similarities in the conformation of all atoms (including side chain atoms) using the GDC metric.The PDB ligand from the sphere template is translated and rotated according to the transformation that results from aligning the sphere template atoms to the query protein atoms. The ligand atoms are not considered in this alignment, and their conformation is not altered. It means that potential steric clashes between protein residues and atoms of ‘inserted’ ligands can be observed. The number of possible clashes is reported.PDBspheres reports the following set of characteristics for all pockets detected within a given protein:PDB identifier of the protein structure from which a template sphere matching the query protein pocket was derived.PDB identifier of the ligand from the template sphere matching the query protein pocket.List of residues in the query protein in direct contact with the inserted ligand (residues within the distance ≤4.5 Å).Coordinates of the centroid of the ligand inserted into the detected pocket in the query protein.Because the pocket searches can match template spheres to different regions of the query protein, all identified pocket candidates are organized into clusters. Within the PDBspheres approach, the merging and clustering of individually detected pockets  is performed to satisfy the following two criteria:Within each cluster, the pockets are grouped together based on sets of query protein residues interacting with inserted ligands. When >80% of predicted contact residues are the same, then these sets are merged to define residues forming the pocket.Pockets and the sets of residues identified as in contact with ligands are merged when the inserted ligands overlap (the distances between ligand centroids are not larger than 2.0 Å).This grouping of pockets and their corresponding inserted ligands allows to define sets of residues interacting with similar ligands and also helps identify overlapping parts of different ligands. Such overlapping parts can approximate the ligand’s ‘core’ conformation, whose protein contact residues can be used to define representative (consensus) pockets within the protein.For a given protein, the following set of measurements and scorings are provided to help assess the confidence level of the pocket prediction .N4: the number of predicted protein–ligand contact residues (i.e. query protein residues observed within 4.5 Å of the inserted ligand).cl: the number of query protein residues that may have possible steric clashes with inserted ligand’s atoms.First, to be considered a predicted site candidate, there must be at least 10 aligned residues (Nc ≥ 10) that are conserved between the query protein and the sphere. Second, the structural similarity measured by GDC , which cWe may expect a higher confidence in predicted binding sites when Nc ≥ 25, GDC ≥ 65 and cl ≤ 1. Note that these thresholds cannot be considered as absolute requirements. For example, possible clashes indicated by ‘cl > 0’ may suggest a need to correct the placement of the inserted ligand. Ideally, a score of ‘cl = 0’ would enhance the confidence in ligand–protein binding prediction; however, in many cases when ‘cl > 0’ the ligand placement within the pocket can be fixed by additional adjustment or refinement of protein side chain atoms through docking or molecular dynamics simulations. It is also important to keep in mind that the query protein structure may not be in its ‘holo’ conformation to properly accommodate the ligand (i.e. without clashes), so the conformation of binding site residues of the protein may require more substantial optimization. Indeed, recent studies of ligand binding site refinements show significant success in generating reliable ‘holo’ (ligand-bound) protein structures from their ‘apo’ (ligand-free) conformations .If N4—the number of protein atoms within 4.5 Å of the ligand—is very low, then this indicates that the current placement of the ligand does not show strong interactions with residues of the protein binding site. This may suggest that the pocket is too large . Of course, it may also indicate that the identified pocket is incorrect, or the location of the inserted ligand is wrong, but these conclusions can only be confirmed by more detailed inspection. In such cases, it can be informative to check the overlap of the template sphere pocket and the query protein pocket (i.e. check the Nc number of conserved superimposed pocket-forming residues). Higher overlaps (e.g. Nc ≥ 25) indicate greater size of the region forming the pocket and thus the higher confidence in reported pocket similarities (more residues identified as conserved). However, such thresholds cannot be decided upfront because in cases of shallow cavities or interface sites on the surface of the protein, the Nc number can be low.The application of the PDBspheres for binding site detection in papain-like proteinase PL2pro) from COVID-19 is illustrated in Figures pro from In Figure In Figure The benchmark dataset used to assess the performance of PDBspheres is the ‘LBSp dataset’ described in ,26. It cThe main criterion often used for the assessment of the accuracy of predictions of the binding site is the agreement in the set of residues predicted to be in contact with experimentally confirmed ligands [residue contacts derived from protein–ligand co-crystals from the PDB ]. To estF scores as calculated by Equations (Both MCCs and quations  and 2)  F scoreser (DCC)  and discmentclasspt{minimaer (DCC)  as they F scores and MCCs. The F scores and MCCs provide a good description of the relative success of evaluated algorithms by assessing whether or not a method produced a predicted binding site that contains residues in common with the reference UBS list of ligand contact residues. They assign high scores not just when at least one residue in a given site is identified correctly, but rather reward methods with more correct and less false predictions of contact residues implying which of the algorithm pocket predictions are close to the ‘correct’ location on the binding surface of the protein. Note that a similar evaluation procedure using MCC scores between predicted and observed contact residues was also applied in CASP experiments.In , predictet al.  and ∼0.7 (mean), as measured by As mentioned in , we expeIn Figure In this section, we describe how PDBspheres can be used to perform structure-based clustering and structure similarity analysis of binding sites from the PDBbind dataset   ver. 20. Here, wTo what extent can structurally similar binding pockets having similar ligand placement allow inference of binding affinity from one pocket–ligand pair to another pocket–ligand pair?To what extent can clustering of detected pockets and calculated structure similarities among clustered pockets from different proteins provide functional information for protein annotation?In our analysis, we focus on the ‘refined’ 2019 dataset (4852 structures), which we expanded by adding 24 structures from the previous PDBbind release that are not present in the 2019 version. Hence, in total the dataset of evaluated binding sites consists of 4876 structures. Since we are interested in the assessment of similarities between specific pockets listed in proteins from PDBbind , we restricted our structure similarity searches and evaluations to only those regions in PDBbind proteins that encompass targeted pockets. Note that many protein structures in the PDBbind refined dataset are multidomain complexes with total sizes of >2000 residues, so they can have multiple binding sites in addition to the targeted ones. Therefore, in our approach to evaluate similarities and cluster pockets from PDBbind, each of its protein structures was reduced to the region in the close vicinity of a reported ligand (residues having any atom within 16 Å of any ligand atom), and each ligand binding site reported in PDBbind was associated with its corresponding PDBspheres template sphere (residues having an atom within 12 Å of a ligand atom). We performed an all-against-all PDBspheres detection and pocket similarity evaluation using the 16 Å protein region representation of each PDBbind protein. Results from the pairwise pocket similarity evaluation are provided in As an example of results from PDBspheres clustering, Figure In Kd values, in Figure Kd values of each pair of 1544 protein–ligand complexes when GDC is >95% and aligned ligand centroids are within 0.5 Å. The R2 and Spearman values of 0.5 and 0.7, respectively, indicate the strength of the relation. Although reported scores are not very high, they can still suggest that with required similarity constraints the similar protein–ligand pairs tend to have similar Kd values. Similarly, in Figure Ki values and the same similarity constraints show the R2 and Spearman values of 0.44 and 0.574, respectively, for 5137 evaluated protein–ligand pairs.Another important question is: Can we transfer binding affinity scores from one ligand binding site to another if the pockets and the ligand placements within the pockets are similar? Results from PDBspheres analysis suggest that pockets from PDBbind that share high structure similarity and have similar ligand placements (distances between the centroids of inserted ligands are no greater than 0.5 Å) show also similarities in reported protein–ligand binding affinities. In particular, for PDBbind entries with known R2 and Spearman scores for 194 pairs with Kd values are 0.5 and 0.738, and for 411 pairs with Ki values are 0.69 and 0.772, respectively. Even if we relax the similarity constraints by requiring a ligand centroid’s distance cutoff of 1.0 Å and GDC pocket similarities as low as 90%, for pocket pairs that bind the same ligands, as shown in Figure Kd subset of 287 pairs the R2 and Spearman values are 0.46 and 0.703, and for the Ki subset of 631 pairs the scores are 0.64 and 0.752, respectively.These similarities in binding affinities are even higher between pockets from different proteins when they bind the same ligand. The corresponding Results from the PDBspheres clustering of the PDBbind dataset suggest that the structure similarity between pocket/ligand placements is a significant characteristic that can allow prediction of similar binding affinity values. In future work, we anticipate enhancing current measurements adding information about the specific atom location of protein residues interacting with the ligand, which may improve our predictions. Complete results from PDBspheres analysis of pairwise similarities in binding sites between proteins from PDBbind are provided in While developing PDBspheres, we focused on two goals: (i) binding pocket detection and (ii) identification of characteristics and scores to assess similarities between pockets to help further protein functional characterization and clustering.In particular, with regard to binding pocket detection, we find that PDBspheres’ strictly structure-based approach can correctly predict binding site regions in protein structures known to be in a ‘holo’ (i.e. ligand-binding) conformation as well as in protein structures in an ‘apo’ (without a ligand present) conformation. While we are not directly comparing our method with other template-based binding site prediction methods, we can estimate that PDBspheres has very similar accuracy when assessed by MCC between predicted and observed binding residues. CASP experiments CASP8–10) indicated that in comparison with other methods the template-based methods were performing on the top with reported average MCC scores slightly above 0.7. For example, the average scores for the best-performing template-based methods at CASP10 Firestar and SP-ALIGN were 0.715 and 0.707, respectively. Results from testing PDBspheres on LBSp dataset, reported in Table 0 indicatSince local regions in functionally similar proteins are remarkably conserved in their structural conformations, the PDBspheres method allows detection of similar binding sites even in proteins that share very low sequence similarity. Based on this observation and our tests on the LBSp benchmark dataset with restricted libraries, we can expect that structure template-based methods may successfully predict 96% of pocket locations and reach the accuracy level in predicted binding residues measured by the average MCC of ∼0.7 for proteins that have no close representation in used libraries.With regard to characterizing and evaluating binding pocket similarities among proteins, we find that a high level of sequence similarity between different proteins is not essential to identify structurally similar binding sites and that proteins even significantly different by sequence may perform similar functions. In the structure-based detection of binding sites, the similarity assessed based on calculated structural alignment using C-alpha atom positions is sufficient, and the use of other residues (e.g. C-beta atoms or other points representing residue) in the similarity assessment does not yield better results. Structurally similar binding pockets having similar ligand placements allow inference of binding affinity from one pocket–ligand pair to another pocket–ligand pair. PDBspheres-based clustering of detected pockets and calculated structure similarities among pockets from different proteins provide information that can significantly help protein function annotation efforts.https://github.com/LLNL/PDBspheres.All supplementary data, libraries and files with the PDBspheres system’s installation and usage instructions are publicly available at lqac078_Supplemental_FilesClick here for additional data file.


PLP1 encodes myelin proteolipid protein (PLP), the most prevalent protein in the myelin sheath surrounding the central nervous system. X-linked dysmyelinating disorders such as Pelizaeus–Merzbacher disease (PMD) or spastic paraplegia type 2 (SPG2) are typically caused by point mutations in PLP1. Nevertheless, numerous case reports have shown individuals with PLP1 missense point mutations which also presented clinical symptoms and indications that were consistent with the diagnostic criteria of multiple sclerosis (MS), a disabling disease of the brain and spinal cord with no current cure. Computational structural biology methods were used to assess the impact of these mutations on the stability and flexibility of PLP structure in order to determine the role of PLP1 mutations in MS pathogenicity. The analysis showed that most of the variants can alter the functionality of the protein structure such as R137W variants which results in loss of helix and H140Y which alters the ordered protein interface. In silico genomic methods were also performed to predict the significance of these mutations associated with impairments in protein functionality and could suggest a better definition for therapeutic strategies and clinical application in MS patients.The X chromosome gene  This protective layer is formed by Schwann cells in the peripheral nervous system, while oligodendrocytes form the sheath in the CNS . The myediseases . Human adiseases . Other mdiseases ,7,8. Smadiseases . PLP1 gene have been described in patients with clinical symptoms consistent with an MS diagnosis, such as an amino acid substitution at residue 31 of PLP (L31V) in a female patient with primary progressive MS [PLP1 exon 2 that changed leucine to arginine (L31R) was reported in a mother and her son through sequencing of the PLP1 gene [Multiple sclerosis (MS) is a chronic demyelinating inflammatory condition affecting the human central nervous system (CNS) . It is ussive MS . MutatioLP1 gene . In a reLP1 gene . MoreoveLP1 gene . The traLP1 gene .PLP1 gene in spontaneous myelin and axonal damage, we retrieved all mutations in the PLP1 gene related to MS. Using in silico structural and functional analyses, this study aimed to describe potential disease-associated variants of the PLP1 gene. The structure and function of the native protein may be significantly altered by missense mutations, particularly those in the coding area that modify the amino acid configuration. In order to determine the impact of each nonsynonymous single nucleotide polymorphism (nsSNP) in a related protein, it is common practice to functionally compare mutant proteins with their wild-type counterparts associated with specific traits in vitro . HoweverPLP1 gene related to MS. The methodology we followed is divided into four distinct levels, including (i) primary data collection, (ii) creation of the 3D protein structures, (iii) 3D protein structure comparison process (iv) variant functional analysis. A baseline method raised from pure bioinformatics approaches was utilized as a benchmark for validation. A variety of different computational approaches was used to screen out the functional effects of the variants in the PLP1 gene and specifically the association of three of them with multiple sclerosis were analyzed in our study. The transcript sequence and the protein encoded by the PLP1 gene were retrieved from the Ensembl database [Five variants of database . Then, tdbNSFP database was used for functional prediction and annotation of potential non-synonymous single-nucleotide variants (nsSNVs). The current version of this high-performance variant annotation tool can be queried to extract prediction scores from 38 algorithms ,23. dbNShttps://annovar.openbioinformatics.org/en/latest/, accessed on 16 September 2022), KGGSeq , VarSome , UCSC Genome Browser’s Variant Annotation Integrator , Ensembl Variant Effect Predictor , SnpSift  and HGMD  were used to strengthen the analysis, and the outcomes were validated using each platform separately. The algorithms utilized in this study are publicly accessible for all academic, non-commercial uses. Further computational tools such as ANNOVAR  assessments of the effects of single mutations on protein stability than other well-established methods. In addition, the DynaMut server defines ΔΔG ≥ 0 as stabilizing and ΔΔG < 0 for comparison purposes. In addition, Site Directed Mutator (SDM) server was used to estimate the change in protein stability following mutation [The correlation between mutations and protein stability was analyzed based on a lesser decrease in free energy (ΔG or dG). Alterations in protein stability are determined by differences in free energy (ΔΔG or ddG) between wild-type and mutant proteins . The Dynmutation . http://consurf.tau.ac.il/, accessed on 7 October 2022) was utilized to identify highly conserved functional areas of the PLP1 gene-encoded protein [ConSurf server , the secondary structure of PLP1 was predicted [Using the PSIPRED server  for the optimum sequence–template alignment to construct the model . Phyre-2Once the mutations were identified, the construction protein-based structures in PDB format were followed. I-TASSER and DynaMut servers were used to perform the transformation of the amino acid sequences to 3D protein models. Then, for each model, structural alignment was carried out and the structural similarity score was calculated. The TM-align and TM-score algorithms were selected for the alignment and the similarity score calculation, respectively . http://molprobity.biochem.duke.edu, accessed on 19 October 2022). It provides the number of residues that are located in the allowed, favored, and outlier regions. If a significant fraction of residues resides in the allowed and favored region, it is projected that the model is accurate [Three-dimensional structures were validated using Ramachandran plot analysis  [PLP1 has been previously characterized in PMD patients, and numerous mutations have been detected in a number of amino acids [PLP consists of 276 amino acid residues and four hydrophobic transmembrane domains, and its expression is restricted to oligodendrocyte cells. The area of the utation) . In caseutation) . The ideno acids . R137W mno acids . DetaileThere are numerous assessment strategies for missense variants and recent databases include results from a variety of techniques to assist the evaluation of the impact of variations predicted to modify the peptide sequence of a gene. Herein, using dbNSFP, we investigated the functional consequences of missense SNPs, including whether they are normal, disease-causing, or effective by chance. As We strengthened our analysis using further computational tools such as ANNOVAR, KGGSeq and VarSome and the outcomes were validated using each platform separately.The results of MutPred2 demonstrated that these variants may alter the function of protein structures . MutPredDynaMut predicts the change in stability by calculating the changes in unfolding Gibbs free energy (ΔΔG), as summarized in According to ConSurf analysis, the variants located at position 31  were found in a highly conserved region with a conservation score of 9 . Based oThe alpha helix, beta sheet distribution and coils for PLP1 were calculated according to PSIPRED protein structure prediction server. Among the exposed secondary structures, the highest in percentage was alpha helix (65%) followed by coils (30%) and no beta-sheet (0.0%) .https://www.ebi.ac.uk/pdbe/pdbe-kb/, accessed on 10 October 2022), there was no known crystal data of this protein of the appropriate length. Once the mutations were identified, the next step was to construct protein-based structures to represent these variants. Since the 3D protein feature view was not determined through experimental methodologies, established computational tools and databases such as Uniprot , Swiss-Model, Phyre-2, I-TASSER, C-I-Tasser, PDBeFold and Dynamut were evaluated for predicting the mutated structures and calculated the effect of these domain mutations on the 3D protein structure. Using four distinct homology modeling techniques, the three-dimensional (3D) structures of the PLP1-encoded protein were reconstructed. Since only 3% of residues 45–53 is represented on the protein data bank  collection of homologous sequences, (2) screening of fold library, (3) modeling of loops, (4) ab initio folding simulation Poing for multiple template modeling, and (5) placement of side chains . TertiarThe next step in our pipeline was to construct the structures for these mutations using I-TASSER and DynaMut . The DynThe Ramachandran plot was used to examine the conformation of the protein’s backbone. It represents an x-y plot of the phi/psi dihedral angles between NC-alpha and Calpha-C bonds. The Ramachandran plot of the wild-type protein in the AlphaFold model revealed 259 residues (94.2%) in the favored regions, 268 (97.5%) in the allowed regions and seven residues in the outlier region . The mutIn this study, the majority of tools indicated that MS-associated PLP1 mutations would have a significant impact on the protein structure, stability and function. Our analysis employed several computational approaches to predict the effects of the PLP1 gene variants, and important results were obtained. Examination of the modified protein structure revealed a destabilizing effect and an increase in flexibility. Loss of protein thermodynamic stability can reduce the ability of its structure to perform normal functions. Furthermore, precise analysis using MutPred2 revealed that these variants may affect protein functionality and structure. We used this machine learning-based approach to integrate data to reason probabilistically about the pathogenicity of amino acid substitutions. The resulted predictions for L31P, L31V and L31R indicated that these mutations may lead to an altered transmembrane protein. The R137W variant may also cause loss of helix, while the H140Y variant may alter the ordered interface of transmembrane protein. The findings of this study provide important insights for future investigations aimed at determining the role of PLP1 in MS.Missense mutations have a substantial effect on protein functionality. A comprehensive computational examination of the phenotypic characteristics associated with specific variants can reveal the vulnerabilities that interfere with the normal protein activity. This study suggests that mutations in myelin-related genes may play a role in the development of MS. There are two putative PLP1-related MS mechanisms: PLP1 mutations could damage oligodendrocytes , generatIn the present study, for most of the known variants, the 3D structures of the proteins are not experimentally known, so there is a clear lack of experimental evaluations of variant effects. Prediction methods can help close the sequence-annotation gap, but with respect to deep annotations of function, in silico methods remain limited. These methods are mainly oriented towards intrinsically disordered proteins and clustered data are based on sequence identity thresholds, retaining a single representative sequence from each group. This approach results in models that resemble having learned a concept instead of a probability distribution. Well-defined theoretical support for this situation is an open problem that will formalize and improve understanding of this long-standing practice in computational biology. MS is a persistent autoimmune inflammatory disease of the human central nervous system (CNS). It is characterized by loss of motor and sensory function resulting from immune-mediated inflammation, demyelination and sequelae destruction of nerve axons. Along the axon, there are intermittent points that are not surrounded by myelin and are called junctions of Ranvier . MS showPLP1 gene indicated a clinical scenario similar to MS in humans. The area of PLP1 gene encoding residue 31 appears to be a hotspot for mutation as L31P has been linked to severe PMD. The L31V mutation shows the least effects on PLP expression, trafficking, or UPR induction is a conservative mutation, as we already stressed [Although the pathogenesis of MS remains unclear, multiple genes, generally of poor penetrance, have been related to MS susceptibility, and their nature suggests autoimmunity causes disease development in most cases . MS is astressed . It is nstressed . Severalstressed ,49. SuchPLP1 plays an important role in myelin structure and stability, an insulating lipoprotein which helps transmit nerve impulses. Numerous computational tools were utilized in the present in silico analysis, which demonstrated that the amino acid changes L31V, L31R, and R137W of the PLP1 protein are functionally detrimental. The L31V and L31R variants of PLP1 reside in the conserved domain of the protein. To examine the stability of mutant and wild-type PLP1 proteins, we also calculated the changes in their free energies. Our findings provide evidence for the functional role of these three variations, which facilitates the establishment of accurate insights for drug targeting and future clinical application in patients with multiple sclerosis. Alteration of overall cellular activity often arises as a consequence of altered function of one or more individual proteins. Identification of more variants as specific targets may provide a better understanding of conformational dynamics for future studies, while molecular recognition specific to mutated proteins will play an important role in broadening the scope of intracellular mechanisms involved in inflammatory demyelinating diseases.PLP1 mutations would have a significant impact on the protein structure, stability and function.The majority of computational tools indicated that MS-associated Loss of protein thermodynamic stability can reduce the ability of its structure to perform normal functions.The resulted predictions for L31P, L31V and L31R indicated that these mutations may lead to an altered transmembrane protein.The R137W variant may also cause loss of helix, while the H140Y variant may alter the ordered interface of transmembrane protein.