Proteins perform their function by accessing a suitable conformer from the ensemble of available conformations. The conformational diversity of a chosen protein structure can be obtained by experimental methods under different conditions. A key issue is the accurate comparison of different conformations. A gold standard used for such a comparison is the root mean square deviation (RMSD) between the two structures. While extensive refinements of RMSD evaluation at the backbone level are available, a comprehensive framework including the side chain interaction is not well understood. Here we employ protein structure network (PSN) formalism, with the non-covalent interactions of side chain, explicitly treated. The PSNs thus constructed are compared through graph spectral method, which provides a comparison at the local and at the global structural level. In this work, PSNs of multiple crystal conformers of single-chain, single-domain proteins, are subject to pair-wise analysis to examine the dissimilarity in their network topologies and in order to determine the conformational diversity of their native structures. This information is utilized to classify the structural domains of proteins into different categories. It is observed that proteins typically tend to retain structure and interactions at the backbone level. However, some of them also depict variability in either their overall structure or only in their inter-residue connectivity at the sidechain level, or both. Variability of sub-networks based on solvent accessibility and secondary structure is studied. The types of specific interactions are found to contribute differently to structure variability. An ensemble analysis by computing the mathematical variance of edge-weights across multiple conformers provided information on the contribution to overall variability from each edge of the PSN. Interactions that are highly variable are identified and their impact on structure variability has been discussed with the help of a case study. The classification based on the present side-chain network-based studies provides a framework to correlate the structure-function relationships in protein structures.  •Monomeric, single domain protein structures can exhibit non-rigid behaviour and be highly variable.•The comparison of protein structural networks can better discriminate conformations with similar backbones.•Specific interactions between solvent accessible and inaccessible residues are poorly preserved.•Network edge-variation offers insights on which interacting residues are likely to influence their dynamics and function.•These side-chain network-based studies provide a framework to correlate protein structure-function relationships. In the case of crystal structures, they are also determined in different crystalline states, different crystallization conditions, etc. Three dimensional coordinates, thus obtained, represent snapshots in various conditions. Even though some of the native conformations of a protein are not crystallisable or have not yet been crystallised, differences observed in the available tertiary structures under various conditions reflect intrinsic flexibility in its overall structure. Depending on the inherent dynamics of the protein, variations in the 3D structure of a protein may be as small as subtle sidechain variation or a very large deviation of the backbone conformation.The pioneering work of GN Ramachandran  on the . A recenThe alteration of protein structure due to dynamics is characterized by variation of inter-residue interactions. The information of inter-residue interactions is vital to understanding protein function and is used in studying protein folding and stability , homologSeveral tools have been developed to compare and quantify the difference between PSNs . The conAdvancement in algorithms and computing power has led to the development of graph spectral methods to handle weighted matrices. This allows us to analyse PSNs using graph spectral features, incorporating the edge differences at the local level and the differences in modes of clustering at the global level. One such approach is the comparison of networks using the network similarity score (NSS) , which aThe main focus of this work is to characterise the extent of diversity in structures of a protein (or its ligand-complex) under varying conditions obtained from multiple crystal conformers, using the network formalism. We analyse inter-residue interactions within each protein by employing PSN that are constructed from coordinates of all non-hydrogen atoms from multiple crystal structures available for a given protein. The deviation in backbone is measured using the conventional root mean square deviation (RMSD) and changes in structural network are studied using the NDS. It is known that the 3D structure of some proteins may have several regions that are rigid while other regions, generally relating to their function, may show mobility . Likewis22.1,   that are,  is not i2.2ith and jth residues of the given protein), with respect to the maximum number of such contacts that are found between the pair of corresponding amino acids from the entire dataset. Such a ratio translates the interaction strength between the two connected residues into edge-weight between the two corresponding nodes. A proximity-based measure of edge-weight  of a crystal conformer is constructed from the 3D structure coordinates retrieved from the protein data bank (PDB). The amino acid residues in the structure are considered as nodes and undirected weighted edges are drawn between each pair of interacting residues based on the strength of their interactions. The edge-weight between nodes in the PSN is equivalent to the fraction of the number of contacts made by proximal atoms . In this work, the structural divergence between a pair of conformers is calculated as the root mean square deviation of C-alpha atoms, computed using the TM-align tool . The paiIt should be noted that the method of calculating RMSD has several limitations . For insAn in-house python program is used to compute the NDS between any pair of PSN. The score is calculated by obtaining its three components – EDS, EWCS and CRS. The edge difference score (EDS) directly calculates the difference in their edge weights. The correspondence score (CRS) and eigen value weighted cosine scores (EWCS) are calculated using the spectra of their networks, capturing local and global clustering changes of residues in the PSN respectively. Using these components, NDS is formulated as in Equation The NDS can range from a value of zero, that indicates absolute identical networks, to a value of 2.4A network that contains a subset of the nodes and edges of the original network makes a sub-network. All PSN are decomposed into sub-networks by choosing specific nodes and edges based on a given criteria. Two types of sub-networks are defined, the first decomposition is based on solvent accessibility of residues and the other is based on secondary structures.Sub-networks based on solvent accessibility: naccess tool  obtained from the global analysis of protein structure networks (PSN). Here, we provide a classification scheme for protein structural domains, based on these analyses.Crystal conformers of a protein present their native conformational states which have been used for the analysis of their structural variability. A dataset of 56 proteins is assembled with more than five crystal structures for each protein (913 PDB entries) that are of a resolution better than 3 Å. PSN for all the crystal conformers are constructed. All pairs of conformers of a protein are subjected to pairwise structural network comparison resulting in 12,251 network dissimilarity scores (NDS). Similarly, Cα-atom root mean square deviation (RMSD) is computed to obtain 12,251 pairwise backbone-structure comparisons. All the computed structure and network comparison scores are listed in 3.12) is plotted. A power equation is found to best fit the data inferring that no linear relationship exists between the RMSD and NDS. The Pearson correlation between the data is found to be 0.59, that supports the understanding that there is no strong linear relationship shared between the scores.A scatter plot of the comparison scores is illustrated in It should be noted that in the scenario where we observe low RMSD scores, NDS is found to vary between a range of small to large values. This implies that, even though there is not much change in the backbone structure of a protein, the variation in sidechain interactions impart a large change into its structural network. However, the converse is not always true. As RMSD increases, NDS has only higher values. This is because variation in backbone will inevitably bring changes to the underlying sidechain interactions.3.2The scatter plot is partitioned into four quadrants based on the statistical average that is computed for the entire dataset. The third quadrant, where both RMSD and NDS are lower, corresponds to the comparison of conformers that are highly superposable in terms of backbone and sidechain. Contrarily, both the scores are higher in the first quadrant. If the compared conformers have preserved sidechain interactions but vary only in backbone atom positions, i.e., RMSD is high and NDS is low, the points fall into the fourth quadrant. Also, when the backbone is preserved and there is variation only in sidechain interactions the points fall in the second quadrant.1.Rigid (R)2.Preserved network, with variation in backbone (N)3.Variable network, with preserved backbone (B)4.Flexible in backbone and network (F)5.Mixed (M)The data points (scatter) corresponding to individual proteins are analysed. Data points in the third quadrant correspond to conformers with a preserved network and structure and the protein is known to be of rigid type. Likewise, when the scatter spread across other quadrants on the plot, they are classified as non-rigid proteins. Using the scatter from all conformers of each protein, they are grouped into five categories based on the nature of their structural variability:i.e., all comparison scores of the protein should be lower than the sum of mean and standard deviation of the dataset. Similar criteria are used in the segregation of proteins into each of the categories. The criteria for classifying a protein into each of the categories are discussed in detail with the help of examples.Rigid category: In the discussed scatter plot for each protein, if more than 60% of the scatter lies in the rigid area and none of the data points regress with comparison scores greater than the sum of the mean and standard deviation of the entire dataset, the protein is categorised as a rigid protein. For example, the individual plot of Lysozyme C is shown in Preserved network category: If the interactions (mostly sidechain) in a protein are preserved even when the backbone shows divergence, they are categorised as proteins with preserved network. In the individual scatter plot of such a protein, excluding data points in the rigid area, more than 60% of the scatter lie in the preserved network area that lies on the bottom right of the plot. Also, none of the network comparisons have NDS greater than 0.181 (sum of mean and standard deviation in NDS of the dataset). The nature of structural variability in the protein is a flexible backbone with a preserved network. Four proteins from the dataset, listed in Variable network category: On the individual scatter plot for each protein, excluding the data points from the rigid area, if more than 60% of the scatter lie in the variable network area and none of the structure comparisons have RMSD greater than 0.64 Å (sum of the mean and standard deviation of the entire dataset), the nature of structural variability of these proteins is of a flexible network with a preserved backbone. Eight proteins from the dataset fall under this category which have been summarised in the Flexible category: In individual scatter plots, after excluding the data points in the rigid area, if more than 60% of the scatter from a protein has NDS and RMSD greater than the mean of the dataset then the nature of structural variability of these proteins is flexible. Twenty-one proteins from the dataset are found to be flexible and are detailed on Mixed category: Proteins that do not fall into any of the above categories are grouped as mixed category of proteins and the nature of structural variability of the mixed category of proteins is of the non-rigid type. The remaining 13 uncategorised proteins are classified as mixed category and are listed in , distinct clusters are observed as shown in 4PNJ (deoxy state) and PDB ID: 2Z6S (oxy state) that share a NDS of 0.143 and RMSD of 0.46 Å. Likewise, in the comparison of different conformational states of the protein, it is observed that the scatter is spread across different clusters as shown in If a protein exists in more than one structurally deviant conformational state then the data points corresponding to the comparison appear as more than one cluster on the scatter plot of the individual protein. This is recognised in many non-rigid type proteins and is discernible from the scatter plots shown in 3.3The nature of a residue such as solvent accessibility and secondary structure are frequently conserved during evolution in order to preserve the tertiary structure of the protein and retain its function . The infUsing the solvent accessibility information of residues in a protein structure, the all-atom PSN is decomposed into three sub-networks as detailed in the methods section Section . The thrIn a similar kind of analysis, information of secondary structures is used in constructing sub-networks. The residues of a protein are distinguished as ordered and non-ordered residues based on whether they form ordered secondary structures such as helices and sheets or if they make up the non-ordered secondary structures such as coils and turns. Three sub-networks  are generated for each conformer as described in the methods section Section . All pai3.4The conservation of structural interactions within the protein structure is essential for maintaining its function. Consequently, perturbations that alter the intra-connectivity of amino acids can modify the stability  or functPseudomonas putida are illustrated on a scatter plot shown in The method has been discussed with Camphor 5-monooxygenase (Cytochrome P450 protein) as a case study. The data points corresponding to pairwise comparison of all pairs of nineteen crystal structures of Camphor 5-monooxygenase obtained from 4The flexibility in protein structure enables them to bind to a wide variety of molecules and undergo conformational alterations, to perform its function in living cells. The extent of deviation changes from protein to protein. Native states of proteins that are captured using structure determination methods, such as X-ray diffraction, pave the way in understanding its conformation diversity. The flexibility in atom positions across several conformations of a protein constitutes its structural variability.•Proteins belonging to the family of protein kinase, catalytic subunits (SCOPe family: d.144.1.7) which are Cyclin dependent kinase 2, Casein kinase-II and Mitogen activated protein kinase are all grouped as flexible proteins.•Orthologs of Myoglobin and dihydrofolate reductase are predominantly characterised as flexible although few are in the ungrouped mixed category.•Mycocyclosin synthase has network variation with preserved backbone structure, while its homologs NADP nitrous oxide-forming nitric oxide reductase and Camphor 5-monoxygenase are found to be flexible.•The four members of the fatty acid binding protein family (SCOPe family: b.0.1.2) in the dataset are all grouped in different categories. While the Heart fatty acid binding protein is found to be rigid, Retinol binding protein is flexible. The Liver fatty acid binding protein which is categorised as mixed is observed to have strong network variation. On the other hand, Adipocyte fatty acid binding protein has limited network variation even when there is backbone structure deviation.The nature of structural variability may vary depending on protein function even when the fold is conserved. Hence, the structural variability of a given protein can be utilized as a metric to correlate with protein structure-function relationship. In this study, proteins have been characterised as rigid or non-rigid based on how diverse their conformers are in terms of their topologies. The mean value along with standard deviation information from the entire dataset is employed in formulating a criterion to segregate the proteins. Some of the salient features emerging from our analyses are presented below.It will be interesting to follow a similar protocol in identifying the variability across proteins with difference in sequence such as homologs.2Z6S) and de-oxy (PDB ID: 4PNJ) states of myoglobin. The highest absolute difference between their Fiedler vectors also correspond to the nodes in the PSN with the most change in local clustering. We identify these residues in the structure of myoglobin protein and find that they are in the vicinity of the HEME cofactor that is known to undergo a structural change between the oxy and deoxy states as shown in The PSN comparison method used here is shown to be effective in capturing the variation of the overall network. What has not been discussed in detail is how this method is also effective in capturing minute differences in the network such as a change in local or global clustering. The method has been discussed in detail with the help of examples before . We reviThe analysis of sub-networks based on solvent accessibility show that, in all proteins the sub-network of buried residues (B–B) sub-network, forming the core of the protein is well preserved. It is interesting to note that the average NDS is much higher in the connections between buried and exposed residues (B-E) than in connections between exposed residues (E-E) alone. This may imply that the displacements in exposed residue pairs are more associative than the displacements between buried-exposed connections. Likewise, in the analysis of sub-networks based on secondary structure information, ordered residue connections are found to be substantially well preserved (in forty-seven proteins) as expected. However, the N–N sub-networks and the O–N sub-networks are better preserved than O–O sub-networks in five and four different proteins respectively and the corresponding proteins are listed in the legend for Analogous to the edge weight mean square fluctuations (EW-MSF) discussed in reference Ghosh et al. , here we5The advantages of studying global and local connectivity within protein structures using graph spectral methods have been exploited in our analysis of structural variations in monomeric proteins. Ensembles of multiple crystal structures of 56 proteins are collected in a dataset for the analysis of structural variability by employing protein structural networks. The conformational diversity is described from pairwise comparison of their backbone structure and network topology that is used to group them into categories based on nature of their structural variability. Most of the proteins in the dataset are categorised as either rigid or flexible conformations. Furthermore, in certain proteins it is observed that the network of edges (mostly sidechain) may be variable even when the backbone positions are preserved, and the vice versa. This is an advantage using a method such as network dissimilarity to study sidechain connectivity rather than just by looking at the backbone structure deviation to understand diversity in protein conformations.Sub-network analysis reveals that connections of non-ordered secondary structures and solvent exposed residues depict high dissimilarity in inter-residue interactions thus imparting less rigidity to the structure. In a case study, it is seen that edges made with polar residues that are predominantly exposed show greater variability than their counterparts. Such an analysis can also be used as a basis for understanding the variability brought about by external perturbations that may influence the structure and dynamics of a protein.DBT-COE, 10.13039/501100004541Ministry of Human Resource Development, DST-FIST, UGC Center for Advanced Study, Bioinformatics and Computational Biology centre support from 10.13039/501100001407DBT and the DBT-IISc Partnership Program. NS is a J.C. Bose National Fellow. SV is an Honorary Scientist of NASI .VG is supported by CSIR-RA fellowship. Research from NS group is supported by the following agencies or programs of the Government of India: NS conceived the concept and idea for the work SV conceived the concept and idea for the work. VMP took care of all the calculations VG took care of all the calculations. All the authors were involved in formulating the Manuscript.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


Saccharomyces cerevisiae (S. cerevisiae) is a well-established model system for studying protein aggregation due to the conservation of essential cellular structures and pathways found across eukaryotes. However, limited structural knowledge of its proteome has prevented a deeper understanding of yeast functionalities, interactions, and aggregation.The budding yeast S. cerevisiae proteome. We used Aggrescan 3D (A3D) and the newly released protein models from AlphaFold2 (AF2) to compute the structure-based aggregation predictions for 6039 yeast proteins. The A3D algorithm exploits the information from 3D protein structures to calculate their intrinsic aggregation propensities. To facilitate simple and intuitive data analysis, A3DyDB provides a user-friendly interface for querying, browsing, and visualizing information on aggregation predictions from yeast protein structures. The A3DyDB also allows for the evaluation of the influence of natural or engineered mutations on protein stability and solubility. The A3DyDB is freely available at http://biocomp.chem.uw.edu.pl/A3D2/yeast.In this study, we introduce the A3D yeast database (A3DyDB), which offers an extensive catalog of aggregation propensity predictions for the The A3DyDB addresses a gap in yeast resources by facilitating the exploration of correlations between structural aggregation propensity and diverse protein properties at the proteome level. We anticipate that this comprehensive database will become a standard tool in the modeling of protein aggregation and its implications in budding yeast. Saccharomyces cerevisiae (S. cerevisiae) as an ideal eukaryotic model organism. S. cerevisiae is also a reference expression system for the heterologous production of recombinant proteins. One of the major advantages of S. cerevisiae resides on its well-characterized and fully annotated genome. Indeed, several databases dedicated to yeast have been published, including the Saccharomyces Genome Database (SGD) [S. cerevisiae stands out as one of the most widely employed model systems for studying protein aggregation in vivo. Notably, the yeast proteome is relatively small compared to higher eukaryotes, which makes it a privileged model for developing systematic studies and high-throughput screenings targeting protein aggregation [The notable conservation of essential cellular structures and pathways such as cell cycle regulation, DNA repair, RNA processing, signal transduction pathways, metabolism, protein quality control mechanisms, or stress response, has positioned the budding yeast se (SGD) , the Yease (SGD)  or YEASTse (SGD) . Among tse (SGD) . Indeed,regation . DespiteThe identification of intermolecular interactions mediated by solvent-exposed aggregation-prone regions (APRs) embedded in the protein sequence has proven successful in predicting protein aggregation –10. ThisS. cerevisiae [Over the years, accurate predictions of structure-based aggregation propensities in yeast at the proteome level were hampered by the limited availability of structural information. However, the newly developed AlphaFold2 (AF2) database has released the prediction of thousands of structures from different organisms, including revisiae . The overevisiae . TherefoS. saccharomyces protein models from the AF database [Herein, we present the A3D yeast database (A3DyDB), which compiles the structure-based aggregation propensity predictions for 6039 database . Since mS. cerevisiae proteome. To perform aggregation predictions, we used a large dataset of structural models generated with AF2, which were downloaded from the AF database [http://biocomp.chem.uw.edu.pl/A3D2/yeast). The A3DyDB is endowed with a search tab on the front page, which allows users to query for the content by using the gene, protein name, or Uniprot Accession  and the profile and additional annotations for transmembrane and intermembrane proteins can be found in the subsequent sections. For each entry, two different structural models are provided in the Structure tab . These precalculated models can be directly accessed from the Custom Jobs tab. The A3DyDB implementation also allows users to model the effects of custom mutations on the stability and aggregation propensity of a given particular protein entry using the FoldX force field [Custom Jobs tab it is possible to evaluate the effects of single or multiple mutations in a custom A3D analysis. These new jobs will be immediately listed and accessible in the A3DyDB queue.A significant number of proteins from the A3DyDB contain structurally disordered regions. This is consistent with the observation that eukaryotes have a higher proportion of IDPs relative to bacteria or archaea . AlphaFoce field . Using tS. cerevisiae proteome, and we have tested the performance of the database in a variety of case reports. Below, we provide selected case examples demonstrating the suitability of our comprehensive repository in diverse scenarios.Protein aggregation is increasingly recognized as a contributing factor to various pathologies in eukaryotes  and consYeast organisms live in a wide range of different environments which require local adaptations to transient conditions . For thiHerein, we used the data from the A3DyDB to investigate possible differences in structural aggregation between the 180 foci and 27 non-foci forming proteins described by Narayanaswamy et al.   [The endoplasmic reticulum (ER) network is built up by tubules with high membrane curvature in cross-section, which are generated and stabilized by reticulons and receptor expression-enhancing proteins (REEPs). Reticulons and REEPs are integral membrane proteins resident at the ER that are evolutionary conserved across all eukaryotes , 43. Themammals) , 45. HerYOP1 is a yeast reticulon that is highly enriched in the tubular portions of the ER and virtually excluded from other regions, which induces high membrane curvature of tubules by an unknown mechanism. Indeed, deletion of the reticulons and YOP1 in yeast has been linked to the loss of tubular ER. Previous studies have suggested that YOP1 generates high membrane curvature by hydrophobic insertion and scaffolding mechanisms, which is mediated by the insertion of the TM regions on the highly hydrophobic lipid bilayers –49. The We have analyzed the presence of membrane segments at the transmembrane regions tab from the A3DyDB output. We found that YOP1 has two pairs of trans-membrane (TM) segments, which is one of the characteristic features of reticulons Fig. a. These S. cerevisiae as a model organism in aggregation, we see A3DyDB as a valuable resource to inspect STAPs in yeast proteins and find associations between aggregation propensity and other functional aspects of yeast biology. Besides, A3DyDB can be used to analyze the effect of mutations on the 3D surface of proteins and engineer variants that could become more stable and soluble. The presented here in silico approach can serve to make faster and more cost-efficient yeast mutants for different applications such as reconstructing metabolic networks, improving the solubility of endogenous proteins, recombinant protein production, and anticipating improved protein variants in synthetic biology approaches.The A3DyDB provides a unique repository of structural aggregation predictions for thousands of yeast structures collected in the AF database, a resource that for a long time remained elusive due to the limited amount of available structural data. Given the importance of S. cerevisiae (UP000002311) protein structures (n = 6039) were downloaded from the AF database  and run with A3D in static mode with a distance of aggregation analysis of 10Å and FoldX-based energy minimization for stability calculations. Custom jobs were created for all predicted structures with two defined AF cutoffs 50 and 70, with residues of pLDDT < = 50 or < = 70 removed for the A3D aggregation prediction.The user interface of the A3DyDB online database was developed utilizing HTML and integrated with custom JavaScript functions to enhance interactivity. The visual design of the website is a combination of standard Bootstrap components along with a touch of custom CSS styles. An Apache2 web server is employed to host the website, leveraging MySQL integration to handle data storage, retrieval, and querying of the pre-calculated A3DyDB entries. Interactive plots are dynamically generated using the D3.js library, while molecular visualization tasks are handled by the Open Source PyMOL tool . AdditioFoci (n = 180) and non-foci (n = 27) forming proteins were obtained from . StructuGLK1 mutation analysis was performed with A3DyDB mutation mode under the Custom jobs tab. Structural representations of A3D mutants were obtained from the Structure tab visualization tool.


E. coli and human interactomes found in the PrePPI database. A number of examples that illustrate how these tools can yield novel functional hypotheses are provided.We introduce ZEPPI , a framework to evaluate structural models of a complex based on sequence co-evolution and conservation involving residues in protein-protein interfaces. The ZEPPI score is calculated by comparing metrics for an interface to those obtained from randomly chosen residues. Since contacting residues are defined by the structural model, this obviates the need to account for indirect interactions. Further, although ZEPPI relies on species-paired multiple sequence alignments, its focus on interfacial residues allows it to leverage quite shallow alignments. ZEPPI performance is evaluated through applications to experimentally determined complexes and to decoys from the CASP-CAPRI experiment. ZEPPI can be implemented on a proteome-wide scale as evidenced by calculations on millions of structural models of dimeric complexes in the  These methods have been used to predict protein structures5 and, more recently, to predict interaction partners and interfacial residues involved in PPIs9. The underlying premise is that functional interactions between two residues will result in their coevolution, which should be reflected in species-paired multiple sequence alignments (MSAs) of putative orthologues and detectable through mutual information (MI) based metrics between the two positions in the alignment. A complication is that the correlation between two residue positions i and j, i.e., two columns in the MSA, may result from an indirect coupling of i and j through their interaction with a third residue k. To solve this problem, methods such as Direct Coupling Analysis (DCA)10, sparse inverse covariance (PSICOV)11, EVcouplings8, and Gremlin7, have been developed. However, these methods rely on the availability of deep MSAs and thus have almost exclusively been applied to bacterial systems. In contrast, as we demonstrate below, ZEPPI can be applied on a genome-wide scale to eukaryotic proteomes with relatively shallow MSAs.The past decade has seen continuing developments in the prediction of protein-protein interactions (PPIs). One can trace these advances to the use of amino acid coevolution to predict inter-residue contacts12 (AFM) has fundamentally changed the landscape of the prediction of structures of multi-protein complexes. There have been continuing improvements in AFM-based methods14 as is evident from the substantial progress in the recent CASP-CAPRI16 experiment17. An underlying problem for MSA-based methods is that, for a heterodimeric pair, it is generally necessary to carry out a species-based matching of the two query sequences which limits application to eukaryotic organisms due to the relatively limited number of sequences available for a paired MSA. Recently, RoseTTAFold/AlphaFold, was used to screen 4.3 million potential yeast PPIs with alignments containing >200 sequences and proteins with < 1500 amino acids per pair18. This was enabled in part by the large number of fungal genomes available but, as the authors point out, by the extensive amount of structural information embedded in RoseTTAFold18. However, applying deep learning to predict whether and how two proteins interact for entire eukaryotic proteomes remains computationally challenging.AlphaFold-Multimer22 but have not been applied on a proteome-wide scale or to predict whether two proteins interact. Template-based modeling23 is an alternate approach where the structures of individual proteins are superimposed on structurally similar proteins that appear in a complex present in the PDB24. In a series of papers, we reported the PrePPI (Predicting Protein-Protein Interactions) algorithm and database27 that rely on template-based modeling and, through a highly efficient scoring function, leverage structural information on a truly proteome-wide scale. For example, PrePPI effectively screens the ~200 million possible pairwise combinations of human proteins which, in practice, amounts to billions of possible interactions among full-length proteins and protein domains. Based on a false positive rate of <0.005, 1.3 million high confidence predictions appear in the PrePPI online database with 370K corresponding to direct binary interactions 27.Docking-based methods predict models of protein dimers based on the structures of the constituent monomersE. coli and human proteomes to examine the extent to which simple evolution-based metrics are informative even in those cases for which the multiple sequence alignment (MSA) depth is shallow. Once an interface is defined, we expect that MI calculations alone would be sufficient, even for eukaryotic proteins, as the deep MSAs required for DCA analysis would no longer be necessary. Our method, ZEPPI , uses paired MSAs to determine coevolutionary information across interfaces but also leverages sequence conservation which provides an additional signal as to the reliability of a predicted interface. An essential feature of ZEPPI is the comparison of evolutionary metrics derived from MSA positions corresponding to residues in predicted interfaces versus positions corresponding to randomly chosen residues.Here we use PrePPI predicted complexes in the 28. Most notably there is a strong inverse correlation between ZEPPI scores and false positive rates (FPRs) for PrePPI predictions thus providing strong support for the reliability of ZEPPI’s efficacy and applicability to proteome-wide interactomes. We use a combined PrePPI/ZEPPI screen to identify a large number of novel interactions that do not appear in any database. A number of examples are discussed below.Our focus on interfacial residues leads to a significant speedup in the evaluation of dimeric complexes that allows us to apply ZEPPI on a proteome-wide scale. Similarly, our finding that DCA is not needed for evaluating heterodimeric complexes effectively removes the need for deep species-paired MSAs. As shown below ZEPPI is extremely effective in distinguishing correct from incorrect protein-protein interfaces as indicated by tests on PDB structures and on a CASP-CAPRI benchmark set29 metric for each. The resulting values are then averaged over the interfacial contacts yielding 6 metrics. in addition, the highest single-contact score for each metric, denoted as “top”, is retained, resulting in a total of twelve metrics that characterize a predicted interface. Columns in the MSA corresponding to contacting interfacial residues are colored in purple. For example, the residues in columns P1-a and P2-d are almost completely conserved and would give a strong Con signal but a weak MI signal. Columns P1-b and P2-e show no obvious Con or MI signal but P1-c and P2-f show a clear MI signal.The next step is to carry out the same procedure for a set of randomly chosen surface residues that are not in the interface. These are denoted in orange and are treated as if they were interfacial so that, for example, the metrics calculated between columns P1-a and P2-d are replaced by those between P1-g and P2-j. Each contact in the real interface is replaced in this way by fake contacts as indicated in the figure. Note that when the number of surface residues outside the interface is less than the number of residues in the interface, buried residues, e.g. P1-m, are included in sampling. This occurs for < 10% of PPIs evaluated. A hundred fake interfaces with corresponding values for the twelve metrics are generated in this way. A Z-score for the predicted interface is then calculated for each of the metrics based on the values for the real interface as compared to the values obtained for the 100 fake interfaces. In practice, based on results for PDB complexes (see next section) DCA is only used for homodimers where it makes an important contribution and where the computational cost is small given the relatively small number of homodimers in a proteome.24. We tested performance with both prokaryotic and eukaryotic proteomes which, overall, have very different MSA depths. As described in Dimeric PDB complexes were collected from the first bioassembly, as defined in the PDB structure file, for both bacterial and human complexes metrics  recovers metrics , A and B metrics , C and D metrics –S2. This9.For homodimers, the two coevolutionary metrics perform the best with DCA performing better than MI at high Z-values for bacteria while MI is, overall, the best performer for human. The improved performance of coevolution for homodimers is likely due to the fact that MSA sequence depth for homodimeric complexes is much larger (reflecting two copies of a single protein) than for heterodimers. It is interesting that in all cases, MI performance is comparable to or better than that of DCA, except for bacterial homodimers which are associated with the deepest MSAs. But even in that case the differences manifest only at high Z-scores. Most importantly, since DCA contributes very little for human complexes and, given its need for deep MSAs and the extra computer time required in its use, below we only use DCA for homodimers. Of note, Bitbol has reported that, using an iterative pairing algorithm, MI alone performs at least as well as DCA in the sequence-based identification of protein-protein interaction partnersMSA). The histogram (green) displays the number of interfaces as a function of NMSA. On average, ZEPPI score is seen to increase with increasing MSA depth although there are examples where ZEPPI scores > 2 are obtained for very shallow MSA depths (NMSA < 10). Most of these result from significant sequence conservation of interfacial residues but there are cases where even MI yields a significant signal. Although these few cases may well be statistical anomalies, there are many high-scoring interfaces of relatively shallow depths with values of NMSA in the range of 10 to 100. Note that NMSA here is a raw number that does not include the low-weighting of redundant sequences. It is generally accepted that, for most applications, NMSA should be at least the sum of the number of residues in each protein32 and is typically taken to be greater than 500 or 1000 for predictions of protein-protein interactions31. Our results highlight the success of ZEPPI in leveraging even shallow MSAs, made possible by the evaluation of interfacial residues in experimentally determined structures.28, which was derived from targets from the CASP-CAPRI experiment. The score_set contains docking models predicted by 47 different groups for proteins from bacteria, yeast, vertebrates and artificial design. We considered 13 widely studied targets which, overall, have 18,538 corresponding decoys: 10% represent docking predictions of acceptable, medium or high quality based on CAPRI-defined criteria. We combine these three categories and annotate the group as “acceptable+”, whereas the remaining 90% are annotated as “incorrect.” Even though two of the targets, T53 and T54 contain designed proteins, they both have MSAs with NMSA values of 2110 and 198, respectively. Table S1 reports MSA depth for all targets along with the number of acceptable+ and incorrect decoys, and the area under the ROC curve (AUROC) for each target. In contrast to the results with PDB complexes, the top metrics contribute to the ZEPPI score to a greater extent likely because the interfaces for acceptable+ models have inaccuracies. It is clear from the table that shallow MSA depths (<100) can produce good AUROCs, particularly for T47 which has an AUROC of 0.93 and NMSA of only 24.We tested the performance of ZEPPI in differentiating good versus poor models in a widely used decoy set, score_set38, most of which are based on deep learning. The data for other methods was taken from Table S8 of Réau et al.34  dataset for human PPIs. HINT-HQ-LC datasets are designed to contain high confidence binary interactions39. ROC curves were reported for testing the PrePPI human and E. coli models on the human and E. coli HINT-HQ-LC datasets using 10-fold cross-validation. These yielded AUROC values of 0.83 and 0.88, respectively, thus, attesting to the overall high-quality of the predictions.In recent work we reported PrePPI calculations for the human and −4, the percentage of predicted PPIs with a ZEPPI score > 2, 3, 4 is 94%, 81% and 67%, respectively. The comparable numbers for PDB structures (see discussion of E. coli PPIs (out of the 5.4 million for which a model can be built) and the number of proteins that comprise these interactions for different FPRs and ZEPPI scores. At FPR < 0.01 PrePPI predicts 71K PPIs involving 3.5K proteins, and these numbers are significantly decreased when more stringent PrePPI FPRs and ZEPPI scores are applied. 2.3K PPIs satisfy the highly restrictive criteria of FPR < 0.0001 and ZEPPI score > 4.Table 2A lists the number of E. coli . Although PrePPI provides structural models for many experimentally determined interactions, its value is also in hypothesis generation as many of its predictions are novel. At the highest confidence level , there are 2983 novel human PPI predictions. ZEPPI can be used to discriminate predictions at different PrePPI confidence levels, as indicated in the following examples.As is the case for E. coli , most Pr41 (PDB ID: 4k81), PrePPI makes predictions (FPR ≤ 0.005) for K-Ras interactions with Grb7, Grb11, and Grb14. However, ZEPPI is significant (Z = 3.5) for only KRAS-Grb742, the one for which there is evidence of an interaction (in DBs as defined above). This is a case where the structural models are too similar to be distinguished from one another but where there is a clear sequence signal that ZEPPI detects among interfacial residues.An issue with PrePPI and other PPI prediction methods is that they encounter difficulties in predicting binding specificity when closely related homologs are involved. For example, based on a relatively small number of templates, there are many predicted interactions for the small GTPase K-Ras with GTPases, GAPs, GEFs, and other signaling proteins. Based on the X-ray complex for H-Ras/Grb1443. Altogether, our predictions . The CELA1-APP model (Chymotrypsin-like elastase family member 1 (CELA1) is a secreted elastase with high pancreatic expression. Recent studies have implicated peptides produced from the amyloid precursor protein (APP) in metabolic diseasesPP model  suggests47. Cathepsin F (CTSF) was observed to have an anti-tumor effect in lung adenocarcinoma (LUAD)48 whereas Cystatin-SN (CST1) promotes the epithelial-mesenchymal transition in LUAD cells49. The CST1-CTSF model  human PPI interfaces predicted by PrePPI. As suggested by the results in To demonstrate its computational efficiency, we have applied ZEPPI to 5.4 million 17. Of course, alternatively, ZEPPI can be used independent of PrePPI to evaluate any predicted model of a complex. For example, it could be used to distinguish direct physical from indirect interactions in experimentally determined multi-protein complexes.The vignettes provided above indicate the ability of ZEPPI to aid in the discovery of novel and potentially important functional hypotheses. In this regard, ZEPPI/PrePPI can be viewed as a hypothesis generating method that could be followed up with slower structure prediction methods ranging from docking to AF-multimer to methods based on their combination as evidenced from the most recent CASP-CAPRI experimentThe combination of PrePPI with ZEPPI suggests a general approach to the proteome-wide prediction of whether and how two proteins interact. ZEPPI can be used to score PrePPI models and thus provide an independent metric for their accuracy. In addition, if one is interested in evaluating specific PPIs, predicted for example by deep learning or docking-based methods, the post-prediction application of ZEPPI would appear to offer an efficient and accurate option. However, in genome-wide applications, we suggest using structure-based approaches to provide interactome-wide yes/no answers along with 3D models and then turning to increasingly accurate deep learning methods for a more limited set of interactions of particular interest.50. From the SIFTS PDB chain taxonomy file, PDB chains that correspond to only one taxonomy ID were selected and then filtered to bacterial and human PDB chains, respectively. The taxonomy list of bacteria was collected from searching both the UniProt proteome51 and the NCBI Taxonomy databases52. The union of the two searches provided 521,897 unique bacteria taxonomy IDs.Taxonomy and UniProtKB summary files for all PDB chains were downloaded from the Structure Integration with Function, Taxonomy and Sequence (SIFTS) project24. PDBs that are protein-only as the polymer entity type, and either from X-ray with resolution ≤ 4 Å or from EM with resolution ≤ 4.5 Å are selected. NMR structures are not used. Further, through reading the PDB file header, PDBs where the oligomer state of the first BioAssembly (BIOMOLECULE annotations) defined as “DIMERIC” by either the author or software with resolved sequence lengths longer than 30 amino acids are selected. Different PDB dimer structures for the same UniProt ID pairs are collapsed by keeping the structures with better structural resolution or longer chain-concatenated length (at least twice as long). Lastly, to remove closely related homologous protein pairs, we compared the pairwise sequence identities and removed sequence redundant structures where both protein sequences have 90% sequence identity with another structure. The detailed pipeline is provided in the From the SIFTS PDB chain UniProt file, PDB files with only two UniProt IDs for heterodimers and one UniProt ID for homodimers with both chains longer than 30 amino acids are selected. PDBs that have any single chain mapped to ≥ 2 UniProt IDs are excluded to avoid fusion or chimera proteins. Structure resolution information is obtained through the PDB API service53. An interface is defined as long as the buried ASA is larger than zero. The interface between proteins A and B consists of contacting residues where the distance between any heavy atoms is less than 6.0 Å. All the residue indices from the PDB are updated after mapping the PDB sequences to their full UniProt sequences using hhalign of the hh-suite package54.The accessible surface area (ASA) of residues for individual chains A and B, and their complex AB are obtained using our in-house program of surfvThe positions of interface residues on proteins A and B in the concatenated MSA are replaced, one by one, with positions for randomly chosen surface residues of the same protein as indicated in 55. This database includes 4,445 prokaryotic reference genomes selected from original 25,038 bacteria genomes, and 477 eukaryotic genomes. Homologous sequences are searched using Jackhmmer (hmmer-3.2.1)56 with 5 iterations and the default E-value of 0.001. In the final outputted multiple sequence alignment, only the sequence with highest identity to the query is kept as the representative sequence for each species.To avoid biased sequence sampling due to over-studied model species, we carried out homolog sequence search on 5,090 representative proteomes that were carefully curated and selected in EggNog 5.0The MSAs of two proteins, p1 and p2, are paired based on the shared common species. Sequence rows that cover less than 50% of surface residue positions of p1 or p2 are excluded from the paired MSA. MSA columns, either for interface residue or surface residue positions, that have more than 50% gaps are excluded.3. The final direct coupling information is quantified using a similar definition as in the mutual information except For two positions y S . The dirulations .(Eq. 1The average product correction (APC) is applied to measurements as denoted throughout the text. Taking MI as an example, the APC term between position he other . The APCFor each interface contact of a given interface between protein p1 and p2 and the generated 100 random interfaces, the following six measurements are calculated: mutual information, conservation, direct coupling information and their corresponding APC-corrected terms. Of all the interface contacts, we choose the top and the mean as the representative metric for each measurement, denoted as E. coli PPIs is integrated from several major resources including Interactome3D57, HINT39, APID58, STRING40 and Ecocyc59, as well as previously known large-scale E. coli PPI high-throughput screening with experimental methods such as APMS60 and Y2H61. Another well-known experimental database BioGrid62 is not included due to the lack of E. coli (NCBI Taxonomy ID: 83333) PPIs included. Before their integration, each database was pre-processed by selecting only E. coli K12 proteins (proteome size: 4391) and sorting the uniport IDs for each pair of PPIs. During the integration, redundant PPIs were removed. Note that Interactome3D also includes homology-modeled PPIs and the STRING database has many inferred PPIs, which are not determined by direct physical interaction experiments but inferred by other methods such as gene-related methods or species PPI transfer. By excluding these two contributions, we also built a purely experimental PPI database of E. coli based on direct physical experiments. In all, there are 565,007 PPIs in the integrated experimental database set and 45,634 PPIs in the physical experimental PPI dataset.The experimental database of 63, the high throughput experimental PPI set from Threpp , the Y2H PPI set from Rajagopala et al.61 . For the physical experimental PPI dataset, only physical links in the STRING database with experimental score >0 are included; only the PDB subset of Interactome3D is included; the other datasets remain the same as in the integrated experimental database.In summary, the integrated experimental database set includes: all HINT binary and complex PPIs (updates of 2021/11), all APID PPIs (updates of 2021/11), all Interactome3D PPIs (updates of 2021/11), all STRING PPIs (v11.5), the gold standard dataset used in Zhang and coworkers’ Threpp workm Threpp , the Ecoet al.61 , the hig62 PPIs, all HURI43 PPIs, and the HC-2016 set from PrePPI26. In total, there are 6,068,248 PPIs collected from the above-mentioned experimental databases with the large majority derived from STRING.The integrated human experimental PPI database consists of the following resources: all HINT binary and complex PPIs, all Interactome3D PPIs, all APID PPIs, all STRING PPIs (v11.5), all BioGridSupplement 1


The identification of protein surfaces required for interaction with other biomolecules broadens our understanding of protein function, their regulation by post-translational modification, and the deleterious effect of disease mutations. Protein interaction interfaces are often identifiable as patches of conserved residues on a protein’s surface. However, finding conserved accessible surfaces on folded regions requires an understanding of the protein structure to discriminate between functional and structural constraints on residue conservation. With the emergence of deep learning methods for protein structure prediction, high-quality structural models are now available for any protein. In this study, we introduce tools to identify conserved surfaces on AlphaFold2 structural models. We define autonomous structural modules from the structural models and convert these modules to a graph encoding residue topology, accessibility, and conservation. Conserved surfaces are then extracted using a novel eigenvector centrality-based approach. We apply the tool to the human proteome identifying hundreds of uncharacterised yet highly conserved surfaces, many of which contain clinically significant mutations. The xProtCAS tool is available as open-source Python software and an interactive web server. The characterisation of the human interactome has been fundamental for our understanding of cellular processes. Tens of thousands of human protein-protein interactions (PPIs) have been detected using a range of experimental techniques ,2. To daVarious sequence-based and structure-based features have been analysed to discover protein interfaces ,7,8,9. SSeveral conservation-based interface prediction tools have been developed, and despite the similar underlying goal, each approach differs on the algorithm level. PatchFinder ,16 searcAn alternative approach to conservation-based methods is to study binding-site-level features based on geometric properties and identify binding sites by finding protein surface cavities . Other axtracting Protein Conserved Accessible Surfaces), to take structurally resolved or modelled protein regions and define conserved accessible surfaces by combining the structural and evolutionary data. The xProtCAS framework is available as open-source software and a user-friendly web server. The tool has been applied to produce a dataset of putative functional surfaces in the human proteome.Intuitive descriptive tools for defining conserved surfaces on protein structures simplify the task of biologists by characterising protein functionality. The advent of deep learning approaches for protein structure prediction has resulted in high-quality tertiary structural models for any protein. In this study, we have developed a novel method, xProtCAS . The structural model is preprocessed to define autonomous structural modules. This allows intramolecular interface surfaces to be ignored and each autonomous functional unit to be analysed separately. Autonomous structural modules are extracted from the AlphaFold2 structure model by running a graph-based community detection algorithm on the AlphaFold2 predicted aligned error (PAE) matrix [In the first step of the pipeline, the AlphaFold2 model of a full-length protein of interest is retrieved from the AlphaFold protein structure database ,34 (http) matrix . First, ) matrix  is used ) matrix . Higher ) matrix  were notThe residue accessibility of each structural module is calculated by Delaunay triangulation which generates a triangle-based tessellation of a protein surface. This approach has the advantage, relative to classical solvent accessible surface area (SASA) metrics, of discriminating between side chain and backbone accessibility. Delaunay triangulation ,39 takesConservation scores are derived from orthologue alignments created using the GOPHER orthologue discovery software  on a datA directed graph is constructed encoding the residue accessibility, residue conservation, and residue proximity in the three-dimensional space of the structural module B. The toAlthough filtering residues using a simple conservation score cutoff might seem sufficient for finding functional regions, it yields several residues spread on the surface of the protein. Hence, finding conserved patches instead of single residues is crucial to pinpoint the more likely functional regions. Eigenvector centrality  measuresEigenvector centrality scores are processed using a hierarchical clustering approach to extract the most conserved cluster of residues on the protein surface. Initially, each node in the graph is considered a separate cluster on its own. Next, pairs of clusters are successively combined into larger clusters by minimising the variance of centrality scores in the merged clusters. The hierarchical clustering produces two low variance clusters, one with the highest central scoring residue nodes and the other with the remaining surface accessible residue nodes in the TAC graph. Finally, the connected residues are extracted from the highest centrality-scoring cluster by starting with the node with the highest centrality score and recursively finding connected neighbours in the graph that are also in the high-scoring cluster to define the most conserved accessible surface.p-value of the relative patch conservation p-value calculated as a Mann–Whitney U test for the alternative hypothesis that the distribution underlying patch conservations is stochastically greater than the distribution underlying non-patch conservations.Three distinct conservation metrics are calculated evaluating the conserved accessible surface: (i) absolute patch conservation: the average residue WCS conservation of the patch representing the absolute conservation of the patch in the orthologous species set; (ii) relative patch conservation: the difference between the average of WCS conservation in the patch and the average of WCS conservation in the non-patch surface representing the relative conservation of the pocket compared to the remainder of the structural module surface; and (iii) the associated Identified conserved accessible surfaces are cross-referenced with functional annotation from a range of sources. Domain family overlap information is defined using Pfam domains  data colAs proteins or domains can have multiple interaction interfaces, centrality scoring, and hierarchical clustering can be performed iteratively after the removal of the residues in the initial patch from the graph representation to define additional interfaces.The xProtCAS pipeline can be applied beyond AlphaFold models to any PDB structures. All pipeline steps except for the “definition of the autonomous structural modules” are performed for PDB structure analyses. When required, PDB structures are mapped to UniProt  sequencepocket residues, were determined in each set as residues with heavy atoms less than 6 Å distant from the bound partner. The remainder of the residues on the protein surface not found in the pocket residues set are defined as non-pocket residues. Residues that are defined by the Delaunay triangulation as accessible are defined as surface residues. Residues that are filtered by the Delaunay triangulation as inaccessible with side chains in the hydrophobic core of the protein are defined as core residues.The xProtCAS pipeline was optimised and benchmarked on three datasets of PDB structures : (i) 407The xProtCAS tool was applied to 20,395 proteins of the Human Proteome   to definhttps://github.com/hkotb/xprotcas. The xProtCAS framework is also accessible as a web server at http://slim.icr.ac.uk/projects/xprotcas.All the pipeline steps were implemented in Python. A stand-alone open-source software with the core functionality of the pipeline is available at The PDB benchmarking dataset represents an evaluation set to benchmark xProtCAS’s ability to define conserved accessible surfaces and whether these regions are likely to represent functional sites on proteins.p-value: 5.66 × 10−44) compared to the three other orthologue alignments sets . Next, we evaluated metazoa-based scoring separately on the domain-domain (DDI), SLiM-domain (SDI), and the active site PDB benchmarking dataset. We observed a significant difference between the pocket and non-pocket residues for both DDI and SDI sets     on orthologue alignments from four sets of proteins from proteomes of species with different levels of evolutionary divergence  , see sup× 10−44) B,C. Inte× 10−14) B,C. Acti× 10−33) B. Finall× 10−33) D.We benchmarked the discriminatory power of the per-residue eigenvector centrality-based scores to the WCS and ConSurf Rate4Site scores. The eigenvector centrality-based scores show better discrimination between the pocket and non-pocket regions at the residue level compared to the WCS and ConSurf Rate4Site scores D. CentraThe xProtCAS pipeline was applied to 20,395 UniProt-reviewed human proteins to define a set of potential novel interfaces in the human proteome. The autonomous structural unit definition resulted in 31,702 autonomous structural modules . The toprelative patch conservation p-value , leaving approximately half of the structural modules . The high-accuracy structural module set was filtered by metabolic crises, recurrent, with rhabdomyolysis, cardiac arrhythmias, and neurodegeneration (MECRCN), and it is not characterised as a known interface or active site  and Predicted Aligned Error (PAE). Both scores are AlphaFold metrics for scoring structure prediction confidence and accuracy.relative patch conservation p-value cut-off to return significantly conserved accessible surfaces.The xProtCAS web server represents a fast, simple, and intuitive tool to analyse protein surface conservation. The two comparable available web-based tools for conserved accessible surface discovery, PatchFinder, and FuncPatch web servers, were no longer functional at the time of publication. There are overlaps with the functionality of the ConSurf server. However, the definition of the most conserved accessible surface and integration with AlphaFold2 models of the xProtCAS server adds key functionality not available with the ConSurf server. The xProtCAS server uses AlphaFold2 models as input. In our experience, the full-length AlphaFold2 structures reduce noise resulting from intramolecular interaction surfaces that are uncomplexed when domains are characterised independently. Furthermore, when an experimental structure is available and has been used in training, the AlphaFold2 model rarely diverges significantly from the experimental structure. However, if a non-AlphaFold2-derived structure is required the standalone software is freely available. An additional use case of the standalone software is to define multiple surfaces. Structural modules with multiple functional surfaces are an issue for many reasons. Firstly, the xProtCAS pipeline can find functional protein interaction surfaces yet incur a penalty in benchmarking if the surface is not part of the testing set. This also makes the definition of a negative set difficult. Secondly, as the centrality-based approach returns the most conserved surface in the graph, highly conserved surfaces can be discarded. The xProtCAS pipeline can be applied in an iterative manner to remedy this issue by removing the most conserved surface with each iteration. The number of iterations can be constrained by applying a In summary, we have developed xProtCAS, a graph-based pipeline to define conserved accessible surfaces in protein structures. The xProtCAS pipeline provides a novel tool to the biological community that allows rapid analysis of the surface properties of a protein to define putative functional pockets, pinpoint potential interaction interfaces, aid in experimental design, and prioritise proteins for functional characterisation.


Peptide informatics is a rapidly growing field that is at the intersection of bioinformatics, chemistry, and biology. Peptides are short chains of amino acids that play important roles in a wide variety of biological processes, such as protein folding, signal transduction, and immune function. Peptide informatics is the use of computational methods to study peptides and their sequence, structure, function, and interactions.Recent advances in peptide informatics have led to a number of new discoveries and applications. For example, new methods have been developed to predict the structure of peptides, which can be used to design new drugs and therapies. New methods for identifying peptide-protein interactions have also been introduced, which can be used to understand the molecular basis of disease.Cates et al. developed a web-based resource for exploratory analysis of the data generated from peptide immunoarray experiments. The platform, named “EPIphany”, enables users to upload data files and perform data preprocessing and normalization, followed by intuitive visualization options to gain insights. It is aimed at biological users and provides the greatest opportunity to extract biologically meaningful information from the immunoarray data, e.g., highlighting potential epitopes.Moody et al. first predicted the B cell epitopes in SARS-CoV-2 proteins, with the subsequent identification of the association between host proteins, autoimmunity, and cross-reactivity of the predicted epitopes. They isolated more than 100 human proteins that are immunologically likely to be cross-reactive with the COVID-19 pathogen.In a quest to predict cross-reactive human protein components with SARS-CoV-2, Kadam et al. developed a novel algorithm called “AbCPE” to predict the potential antibody class (es) to which a sequential B cell epitope may bind. By utilizing a knowledge base of epitopes that bind to various antibody classes , AbCPE derives dipeptide composition, among others, as features for prediction. The authors explored Random Forest and AdaBoost classifiers along with Binary Relevance and Label Powerset techniques. The best performance based on the lowest Hamming loss was achieved using the Binary Relevance model, along with Random Forest and AdaBoost.Qian et al. discussed the effectiveness of compound-protein interaction (CPI) prediction for drug discovery and proposed enhancements using deep learning methods. The article introduces “CAT-CPI”, a molecular image-based model that combines a Convolutional Neural Network (CNN) and a transformer. CAT-CPI utilizes the former to extract local features from molecular images and employs the latter to capture semantic relationships among these features. By integrating these approaches, CAT-CPI offers an improved framework for accurate CPI prediction based on state-of-the-art performance metrics on diverse datasets and comparisons with other existing tools.Yang et al. reviewed recent advances in the use of macrocyclic peptides in the context of cancer treatment and the opportunities and challenges they present. Macrocyclic peptides have garnered significant interest by virtue of their moderate size, enhanced selectivity, and strong binding affinities, making them promising therapeutics for targeting dysregulated protein-protein interactions. In addition to three major categories and mechanistic details, this review catalogs those in clinical trials along with FDA-approved candidates. This research opens up new perspectives in the pursuit of effective cancer therapies.Tufféry and Derreumaux. The study combines the Debye-Hückel formalism and coarse-grained potential to account for peptide structure prediction in systems beyond neutral pH. Benchmarking with established algorithms shows that this method has advantages in the case of poly-charged peptides and those with missing homologous sequences in public domain repositories. This research sheds light on the improvement of structure prediction techniques for a wide range of polypeptides.The importance of understanding the conformational ensemble of polypeptides under different experimental conditions for biological applications is discussed in the research article by Bertoline et al., outlined recent groundbreaking advances to predict protein structures, which include the groundbreaking AlphaFold2 and protein language models. Although these tools have limitations, these advances hold promise for revolutionizing structural biology and uncovering the hidden secrets within protein sequences. The authors provide a brief landscape of protein structure prediction approaches, pre- and post-AlphaFold2 release.Another review, this time by Peptide informatics, despite its challenges, holds immense promise with a multitude of potential applications. The advancement of computational methods dedicated to peptide research will unlock new discoveries and opportunities in the fields of biology, chemistry, and medicine. We invite readers to explore the diverse Research Topic in our Research Topic, which encompasses various applications of peptide informatics, and discover the exciting possibilities it offers.


Protein-protein and protein-peptide recognition are among the most important processes that make signal transmission and transduction possible in a cell; they also play a role in cellular processes involving diseases. Theoretical approaches in the study of these processes from a structural and thermodynamic viewpoint have focused on computational methods aimed at modelling, predicting, and characterizing the interface between the interacting proteins. Indeed, computational techniques represent potent tools for understanding PPI mechanisms at the atomic level: this information can then be exploited for designing molecules able to interfere with them.However, designing molecules to directly targeting protein-protein interactions (PPI) is a difficult task. Differently from other computationally driven small molecule discovery efforts, PPI targeting presents unique challenges: high resolution structural information on targets is not easily available or predictable; PPI surfaces deviate from the conventional druggable sites targeted by therapeutic small molecules, being more hydrophobic, shallow and larger, thus having higher degree of conformational freedom. The unique nature of PPI is also reflected by known PPI inhibitors, which tend to have different physicochemical features from typical small compound drugs.In this Research Topic, a few approaches are presented that try to overcome the complexity of designing PPI inhibitors or modulators by providing new computational methods or analysis tools to assist PPI drug discovery, as well as facilitate integration with experimental data.Ranaudo et al. addressed this problem for affitins, a class of easily engineerable proteins that can be used as molecular probes able to specifically recognize biomarkers, for the diagnosis of serious diseases. In their work, they proposed a protocol for discriminating the correct from incorrect predictions of binding poses of different affitins in complex with several protein partners, obtained through an initial stage of rigid docking calculations. They demonstrated that a consensus approach based on Molecular Dynamics simulations and the prediction of potential interacting residues, through the Matrix of Local Coupling Energies method, leads to the identification of the correct binding pose. Their approach represents an effective tool for engineering affitins which are specific for different protein targets and, therefore, that could be used for diagnosis applications.Currently, no computational method can reliably predict the three-dimensional structure of protein complexes. Molecular docking is still an open challenge for protein-protein interactions, with one of the critical steps being the post-docking processing for determining the correct binding modes among the different models. He et al. studied an oncogenic system for which regulation is mediated by protein-protein interactions in a ternary complex. They explored the molecular and atomistic details of long-range allosteric effects in the G13D oncogenic mutant of K-Ras4B in complex with Son of Sevenless (SOS) protein. Their results suggest that in the ternary complex K-Ras4BG13D•SOScat•K-Ras4BG13D–GTP, a GTP-bound version of Ras can allosterically activate the distal Ras bound to SOS. The authors used a set of well-established computational methods including network analysis to investigate conformational changes associated with the long-range allosteric mechanism. The novel insights provided by this study contribute to the current efforts to understand allosteric regulation in oncogenic protein complexes with the goal of informing drug development of inhibitors and modulators of protein-protein interaction.Catalytic activity in proteins is often allosterically regulated. Ikeda et al. present a novel chemical library database system (DLiP) to design PPI inhibitors, consisting of more than 32,000 PPI-related compounds. Among them, a significant component derives from in silico screening of a diverse set of protein-protein interfaces, through computational docking, while the rest is a Research Topic of experimental information from the literature or retrieved from other databases. Coming with a web interface that currently allows for several analyses and search, it promises to provide users with a download tool to be used in the future for prediction models.The advanced application of drug discovery methods, among which of AI-assisted approaches, critically relies on the availability of annotated databases of compounds with given features such as chemical structures and physicochemical properties to drive the design of new hits. This is particularly true for the challenges posed by designing inhibitors targeting protein-protein interfaces, which typically deviate from the common features of small molecule inhibitors, e.g., in terms of molecular weight. Vasile et al. the transmembrane protein E-cadherin is the principal actor in inter-cell adhesion and its downregulation gives the possibility for cancer cells to spread across the body and form metastasis. The formation of the complex between the extracellular moieties of E-cadherin could be a good target to prevent the reassociation of cancer cells. Towards this aim, the research of a new class of molecules able to interfere with E-cadherin homophilic interaction was carried out by combining in silico and experimental techniques. The in silico study was conducted with an initial analysis of the cavities and fragment-based drug discovery (FBDD). This technique allows us to identify which class of chemical fragment have a good chance to establish stable and strong interaction with the amino acids in the target protein. The Saturation-Transfer Difference Nuclear Magnetic Resonance (STD-NMR) experiments were used to validate the result obtained during the in silico simulations. With this experimental technique, it is possible to identify which part of the molecule interacts with the target protein. This research lays the foundation for further investigations towards the discovery of potent inhibitors.Finally, an application stresses the importance of experimental validation of interaction predictions in the context of a pharmacologically fundamental problem. The protein-protein interaction lies at the heart of cell adhesion and the aberrant behavior of this mechanism is responsible for the spread of cancer cells and metastasis formation. As discussed in the paper by Overall, this Research Topic presents a variety of problems and approaches, demonstrating a lively research area: the strategies and concepts suggested herein are likely to prove precious in increasing the possibility of generating new chemical tools useful in the biomedical and therapeutic fields. The increase in computational power and the development of new algorithms and computational strategies has significantly improved our understanding of experimental data on PPI targeting. In turn, experimental techniques have provided novel stimuli for the development and improvement of computational methods focused on the challenges of PPI targeting.


This new interface empowers users to apply ATLIGATOR by providing easy access with having all parts directly connected. Moreover, we extended the web by a design functionality so that, overall, ATLIGATOR web facilitates the use of ATLIGATOR with a more intuitive UI and advanced design options.A key functionality of proteins is based on their ability to form interactions with other proteins or peptides. These interactions are neither easily described nor fully understood, which is why the design of specific protein–protein interaction interfaces remains a challenge for protein engineering. We recently developed the software ATLIGATOR to extract common interaction patterns between different types of amino acids and store them in a database. The tool enables the user to better understand frequent interaction patterns and find groups of interactions. Furthermore, frequent motifs can be directly transferred from the database to a user-defined scaffold as a starting point for the engineering of new binding capabilities. Since three-dimensional visualization is a crucial part of ATLIGATOR, we created ATLIGATOR web—a web server offering an intuitive graphical user interface (GUI) available at  The specific recognition of binding partners in protein–protein or protein–peptide interactions is established by mutual interactions of amino acid residues. While each residue’s contribution shapes the binding affinity and specificity to agonist or antagonist binders, certain residue–residue interactions are more crucial than others. With this in mind, it is likely that in the context of optimized binding partners, pairwise interactions with a higher influence will be found more often than others. Following this hypothesis, Singh and Thornton  already atlas. By varying the input structures, the structural and evolutionary context of underlying data can be modulated and compared. Moreover, by mining the atlas for frequent interaction groups, interaction motifs can be extracted, visualized, and analyzed. Additionally, ATLIGATOR enables direct grafting of frequent interaction motifs (pockets) onto a user-defined scaffold protein. Since working with three-dimensional interaction motifs or designing binding pockets is a highly visually demanding task, it also allows to plot atlas or pocket data.Another idea is to investigate groups of residues that act as a binding partner for single residues. For this approach, we created the software package ATLIGATOR . It extrhttps://atligator.uni-bayreuth.de and creates an intuitive starting point for working with the software.To enable everyone to try and use ATLIGATOR instantly without installation, we developed a web interface. This graphical interface connects all data structures visually and even extends the design options of ATLIGATOR. It is freely accessible at atlases and pocket collections that can be explored in detail. All data can be visualized similarly to the python API. However, a unique aspect of the web interface is the connection between the different sections, i.e., an atlas to its input structures, pockets to its underlying atlas, etc. This connection is accomplished mainly by hyperlinks to superior data structures. Additionally, the representation of the plotted data points clearly highlights these connections in several ways: The original structural environment of data points is directly displayed by clicking on data points and, in this visualization, all atoms are labeled with their origin.ATLIGATOR web expands the functionality of the ATLIGATOR python package by a user-friendly interface, thereby providing easy access and supporting users to utilize its features instantly. For the analysis of pairwise interactions, we provide a list of pre-generated pockets can be grafted directly onto the binder chain. Additionally, manual mutations can be applied to extend or fine-tune grafts with new mutations.While the focus of ATLIGATOR web is the representation of data structures in a more intuitive way, we additionally provide the opportunity to design protein interaction sites. This is useful, because ATLIGATOR data can comprise potential starting points for shaping binding surfaces. Hence, by supplying a protein–protein or protein–peptide complex and defining ligand and binder chains, Below, we will elaborate the web interface and each section in more detail to showcase potential usage and highlight special features. To do so, we will guide through the data structures of ATLIGATOR web with the same example as examined before . Namely,Structures, Atlases, Pockets, Scaffolds, and Designs  identifier 5AEI] is not classified in the SCOPe database (version 2.08) because of its synthetic nature. However, since it originates from natural armadillo repeat proteins (a.118.1.1 in SCOPe), we can utilize the structures attributed to a.118 (α-α-superhelix). The structure collection comprises 2,584 structures, each hosting a binder chain and all potentially interacting ligand chains. Each three-dimensional structure can be observed and downloaded individually.Interaction data stored in Atlases are based on pairwise interactions derived from single structure collections. Thus, atlases in the web interface contain a link to the corresponding structure collection. As a landing page, atlas statistics visualize the frequency of the data points. The a.118-based atlas contains 43,645 data points, i.e., pairwise interactions. Our design target amino acid leucine (Leu) comprises 3,577 interactions—with other Leu (705), histidine , and aspartate  being the most frequent binding partners , His (H), and Leu (L), was observed in interaction with 10% of all Leu ligand residues of the initial atlas.Frequent groups of interactions found in an  pockets . On the pes Fig. C of whicpocket and its data points are visualized in different representations: First, the most representative pocket, which corresponds to the pocket instance with the lowest deviation from the item set clusters’ centroids [atlas are initially shown in atlas representation with ligand and binder Cα and Cβ atoms as bubbles (Fig. atlas contains all data points and represents a filtered instance of the original atlas. It exposes three spatially restricted clusters for His Cα positions as well as two clusters for each Leu and Asp (see Fig. pocket instances provide promising starting points for our redesign and can be downloaded as .pdb files or used directly for grafting onto a user-defined scaffold.The entroids , is visules Fig. D. The posee Fig. D and prosee Fig. D. The fuWhile the analysis of frequent motifs helps to extract ideas for the redesign of interaction interfaces, we additionally included design tools to directly use this information on specific binding interfaces. Those tools will be briefly introduced below.pockets shall be grafted are called scaffolds. Users can upload their own protein structures, hosting two or more polypeptide chains. One of these chains has to be defined as the ligand chain, comprising the residue on which basis the pocket should be selected. The chain has to be defined as the binder chain that is mutated eventually. Afterwards, mutable positions must be selected to finalize the scaffold preparation.Proteins on which ATLIGATOR manual graft, two ways of mutating the scaffold are implemented. The first method is based on choosing a pocket from the pocket collection for each ligand residue that will be grafted automatically onto the scaffold (Fig. A design is defined by the scaffold and the residue types that will be used in mutable ligand positions. Each design can harbor multiple design tasks where the binder can be altered by applying different mutations. After selecting a pocket collection for the design task, the user can start designing. Within the tool old Fig. . The graSince grafted mutations incorporate the side chain conformer of the pocket data point and the manual mutations do not include a side chain conformer at all, the rotamers do not resemble realistic conformations. Thus, a refinement of the designed interface might be necessary to judge the design’s quality. Manual graft offers to repack the mutable interface using Rosetta’s fixbb protocol to generate more realistic side chain conformations .atlas data points to the underlying structural data and an advanced design tool for pocket grafting and rational design. In summary, the easy access via this graphical interface enables a broader user base to apply ATLIGATOR and helps to understand its principles. Thus, ATLIGATOR web might also be a starting point and encourage users to directly apply the python package for extended analysis and design.The development of ATLIGATOR opens up the possibility to collect and learn from protein–protein interactions in a streamlined and automated fashion. ATLIGATOR web empowers users to leverage its functionality in a user-friendly and easily accessible environment. Additionally, the web interface extends ATLIGATOR tools with novel functions like connecting The web interface for ATLIGATOR is implemented in the Python 3 web framework Django  in combiatlas and pockets as well as full atom plotting of these [The JavaScript library plotly.js is used for responsive plotting of statistics data. Three-dimensional plotting is performed by two JavaScript libraries. Plotly.js is handling of these . NGL vieof these .structures, atlases, and pockets are openly accessible for discovering the data in the pre-built data structures. The protein structures were collected by searching the SCOPe database [structure collections were chosen based on known peptide binding capabilities, a diverse classification in SCOPe and multitude of available structures in the PDB. The protein structure files were preprocessed to generate files with one protein chain and cropped ligand chains. The resulting structure files were used for atlas generation and subsequent pocket mining. All processing parameters were used as previously described [scaffolds and designs can be fully explored via a user account that only requires a username, email address, and password. We also offer a scaffold and design example that correspond to the ones discussed above.The sections database  with theescribed . The desThe web server includes a tutorial mode that offers comments and explanations for the different features and sections directly on the web pages. It also links to videos for showcasing sections of ATLIGATOR web to enable an easier start.