The development and improvement of methods for comparing and searching for three-dimensional protein structures remain urgent tasks in modern structural biology. To solve this problem, we developed a new tool, SAFoldNet, which allows for searching, aligning, superimposing, and determining the exact coordinates of fragments of protein structures. The proposed search and alignment tool was built using neural networking. Specifically, we implemented the integrative synergy of neural network predictions and the well-known BLAST algorithm for searching and aligning sequences. The proposed method involves multistage processing, comprising a stage for converting the geometry of protein structures into sequences of a structural alphabet using a neural network, a search stage for forming a set of candidate structures, and a refinement stage for calculating the structural alignment and overlap and evaluating the similarity with the starting structure of the search. The effectiveness and practical applicability of the proposed tool were compared with those of several widely used services for searching and aligning protein structures. The results of the comparisons confirmed that the proposed method is effective and competitive relative to the available modern services. Furthermore, using the proposed approach, a service with a user-friendly web interface was developed, which allows for searching, aligning, and superimposing protein structures; determining the location of protein fragments; mapping onto a protein molecule chain; and providing structural similarity metrices .  A number of tools, such as BLAST ne 2023) , which i023 , whiHowever, in nature, the three-dimensional structure of a protein and its spatial topology are more conserved than its amino acid sequence; therefore, the search for and comparison of structures based on their three-dimensional topologies are important elements for understanding the functions of proteins and classifying protein families. This task is particularly urgent given the recent significant increases in the number of three-dimensional protein structures available in various databases (Protein Data Bank (PDB), AlphaFold, and CATH, among others).To solve the problem of searching for and aligning three-dimensional structures, predictors of the various properties of protein structure are employed, including position invariant descriptors obtained from spherical harmonics and Zernike polynomials ,4 based The present article introduces a novel tool, SAFoldNet, which employs an amino acid sequence-independent algorithm for searching and aligning protein structures using a neural network and a SA comprising 23 prototypes described earlier . A neurahttps://psskb.org/similar-search (accessed on 3 June 2023).In addition to the high speed of searching for structures of arbitrary lengths, the tool accurately determines the coordinates of a given structure in the detected protein, performs pairwise alignment and superposition of the detected structures, and provides the expected value  metrices of the statistical significance of a structural similarity and root mean square deviation (RMSD). In addition, the service provides key characteristics useful to the user for each identified protein structure, including taxonomic affiliation, name, and biological function of the protein; PDB ID; and experimental parameters . The SAFoldNet service is available to users at We developed SAFoldNet to swiftly search for protein structures and their fragments in PDB using neural networking and BLAST. The neural network encodes three-dimensional protein structures in a SA sequence. The developed tool uses a multistage process involving a preprocessing stage using a neural network to represent the geometry of the protein structures in the SA sequence, a search stage to generate a set of candidates, and a refinement stage to calculate the structural alignment and overlay and determine the location of the structure or fragment in a protein.The present tool has a convenient web interface that allows for the visualization and evaluation of search results in real time.To assess the accuracy and sensitivity of the algorithm, we used reference datasets SCOP and CATH  to generThe SCOP superfamily is treated like true positives (TP) when used as a hit model for proteins in a query. Domains from another SCOP superfamily are considered false positives (FP). Although it is possible that homology relationships exist between some SCOP superfamilies (one rule for identifying such homologies was established by Julian Gough ), we ignMatches between the query superfamily and the search results for the CATH dataset were considered in the same way. For assessment, the relationship between the E-value of the first hit and the number of correct and false superfamily assignments was calculated for the test datasets. The assessment also included a coverage indicator, which is defined as TP/Total, where “TP” is the number of correct matches, and “Total” is the total number of structures in the superfamily.The ROC curve plots the TP and FP values as certain threshold changes, where larger areas under the ROC curve indicate better performances. We plotted the ROC curve using the expected value  as the threshold. TP and FP estimates were weighted by 1/(number of other homologs that belong to the query superfamily) to prevent bias in the estimates by large protein superfamilies .−5 to 10−15 , a search tool with the same SA implemented on the PDB website (3D-structure search) , and the well-known search service PDBeFold .Additionally, to evaluate the quality of the developed tool, we conducted a comparative analysis and evaluation of the search accuracy with other popular services: 3D-BLAST . The results of the comparison are presented in A comparison was performed for three structures representing a family of small proteins from different domains: ubiquitin, immunoglobulin, and PDZ. The total number of structures detected was analyzed, and the correspondence of the detected structures to the original domain was assessed according to CATH classification. The CATH superfamilies for search results were assigned using the FunFHMMer web server  and its fragment in the form of a PDB file  can be sThe user can visualize the starting structure in various views . When starting a search, the user can limit the search results using statistical significance .The search results page presents basic information regarding the starting structure in a tabular form: protein data ; experimental structure data ; and protein structural information  .The search is performed in real time, and the search progress indicator bar displays the current search status and progress. The search result block is organized in a tabular form and presents data on the number of similar protein structures identified, E-value, RMSD relative to the start structure, amino acid sequence (Seq ident), and secondary structure (Sec struc ident) alignment results. The user can upload the search results in “. csv” format. In addition to the presented CSV data, the table includes information on the secondary sequence of the protein.Each structure from the search results can be additionally analyzed by the user when going through the “View” link to the graphical representation of the protein structure , which aThe sequence and secondary structure identity blocks on the graphics card display the alignment results of the identified and start structures in terms of their amino acid sequence and secondary structure, respectively.Unlike analogs, our service provides clear information regarding the localization of the detected structure in the protein chain and defines clear boundaries of the locus. The graphical representation helps the user evaluate the quality of the identification.The SA described in  was usedThe architecture of the encoder for the vector representation of structures was based on a modified version of PSSNet developed by the authors earlier . This neFrom the array of coordinates, a graph is formed, each vertex of which corresponds to the Cα-atom of the primary protein chain, connected by edges with Cα-atoms . Each edge and vertex of the graph contain scalar and vector features that describe the orientation of the amino acid residue in three-dimensional Euclidean space.Signs of the graph vertex comprise torsion angles, single vectors that determine the direction of the previous and subsequent C and Cα atoms of the primary chain, single vectors that determine the direction of the side chain (direction to the Cβ-atom), and codes of the amino acid sequence of the protein.The graph edge features comprise a single vector that defines the direction of the edge, distance between graph vertices encoded using Gaussian radial basis functions, and edge position code encoded using a positional sinusoidal encoder.loss function is used to calculate the network loss function.All graph features are input into the network, which comprises two interconnected blocks: an encoder, which generates a hidden feature representation, and a decoder, which converts the hidden feature representation into SA elements. A negative log likelihood The data transformation algorithm and network architecture are described in detail elsewhere . The essA schematic representation of the network structure and data processing steps are shown in The training, test, and validation sets were retrieved from the PDB, encoded using the selected SA. Before training the network, the original set was cleaned, and structures with a short length (<16 AA) and low resolution (>3.5Å) were excluded. The resulting dataset, with a volume of ~90k copies (PDB chains), was divided into the training, test, and validation sets. The dimensions of these sets are listed in The network training lasted 46 epochs. During training, the mechanism of automatic stopping of the learning process (early stopping) was applied when the accuracy indicator reached a plateau. The hyperparameters used in model training are presented in Graphs of changes in the indicators depending on the learning epoch are shown in To search for a set of structures similar in conformation or their individual motifs, both a protein of arbitrary length and its fragment in the form of a PDB file can be input into the service.From the requested structure submitted to the input of the network, the features are extracted, and a structure graph (featurization) is created.The network encoder block generates a vector of hidden feature representation (embedding) for the requested structure. The input for the search can be either a full chain (as deposited in the PDB) or a fragment.The block decoder network (decoder) generates a sequence of SA for the requested structure.Using the programing interface provided by the BLAST package, areas of similarity between sequences of the SA of the requested structure in the SA database are determined, and the statistical significance of similarity  is calculated. At the output, an array is formed comprising the indices of PDB files, elements of the identified areas, and value of the statistical significance index. E-value is a parameter describing the number of matches that can be “expected” or detected by chance when searching or browsing the sequence database. This value decreases exponentially as the number of matches increases. Essentially, the E-value describes random background noise. For instance, an E-value of 1 may imply that, in a SA database, one would detect a match simply by chance by looking at the content of the database. The lower the E-value or the closer it is to zero, the more “significant” the match. The E-value can also be used as a convenient metric to create a threshold of significance in structural similarity searches and in search result reports. The methodology for calculating this indicator is presented in detail elsewhere .Next, the BLAST search results are sequentially processed, and each protein from the search results containing elements of the requested sequence is passed through the network encoder block (encoder) to obtain a latent representation vector that aggregates the geometric characteristics of the protein. This vector, together with the vector (embeddings) of the desired structure, is used to determine the exact location of the structure in the protein from the search results (this is relevant if a protein fragment is input and it is necessary to determine its exact location in the search results). The location is determined using a matrix of pairwise distances between embeddings, and the region with the smallest distance determines the location. This approach allows for accurately determining the position of a structure in the composition of large proteins.After the location is determined, the desired and detected structures are superimposed using the Kabsh algorithm , and the-Statistical significance ;-RMSD of atomic positions;-Structural coordinates within the protein;-FASTA matches (Sequence identity);-Secondary structure identity.At the output, a report is generated containing the detected structures, dropped and superimposed on the original structure, and indicators:An explanatory diagram of the search process is shown in We introduce the novel tool SAFoldNet for the searching, aligning, and overlaying of three-dimensional protein structures based on a library of local protein motifs, the so-called SA. A distinctive feature and advantage of the proposed method is the use of graphical neural networking to transform the geometric and topological characteristics of the structure into SA sequences, which ensures the high accuracy of protein structure approximation, and the use of the well-known BLAST algorithm to search and align SA sequences. The synergy of these approaches ensures the high speed of searching for structures and high accuracy of structural comparisons. The SAFoldNet service can compete with the other available structural alignment search services in performance and, in some cases, can even outperform them.https://psskb.org/similar-search link.The service has a modern and user-friendly web interface and is available to a wide range of researchers at Overall, the use of SA for searching, aligning, and analyzing the evolution of protein structures is a promising avenue in structural biology and bioinformatics. Our future studies will be devoted to the development of a new version of SA, which will enable more accurate approximations of local structural motifs.


The “Protein Abundances Across Organisms” database (PaxDb) is an integrative metaresource dedicated to protein abundance levels, in tissue-specific or whole-organism proteomes. PaxDb focuses on computing best-estimate abundances for proteins in normal/healthy contexts and expresses abundance values for each protein in “parts per million” in relation to all other protein molecules in the cell. The uniform data reprocessing, quality scoring, and integrated orthology relations have made PaxDb one of the preferred tools for comparisons between individual datasets, tissues, or organisms. In describing the latest version 5.0 of PaxDb, we particularly emphasize the data integration from various types of raw data and how we expanded the number of organisms and tissue groups as well as the proteome coverage. The current collection of PaxDb includes 831 original datasets from 170 species, including 22 Archaea, 81 Bacteria, and 67 Eukaryota. Apart from detailing the data update, we also present a comparative analysis of the human proteome subset of PaxDb against the two most widely used human proteome data resources: Human Protein Atlas and Genotype-Tissue Expression. Lastly, through our protein abundance data, we reveal an evolutionary trend in the usage of sulfur-containing amino acids in the proteomes of Fungi.  •PaxDb offers reference protein abundance data for an unrivaled collection of species.•The PaxDb human dataset aligns with benchmarks derived from two leading resources.•PaxDb data unveils a consistent evolutionary trend of sulfur avoidance in yeast. PaxDb is a comprehensive metaresource of protein abundance data across multiple organisms and tissues. Here, we describe the latest version 5.0, for which we have improved data standardization and expanded the number of individual datasets, organisms, and tissue groups, as well as the overall proteome coverage. We also present a comparative analysis of PaxDb human tissue proteomes against HPA and GTEx. Lastly, using PaxDb as reference, we uncovered a previously undescribed evolutionary trend in the amino acid composition in Fungi. Biological processes are regulated at multiple levels. Although many cellular changes are clearly detectable already at the transcriptome level, it is the protein level that most accurately reflects the cellular state since proteins act as the direct executors of biological functions. Apart from a protein’s expression level, further regulatory potentials lie in its posttranslational modifications, subcellular localizations, and biological contexts. In a complex multicellular organism with a system of coordinated organs, protein expression patterns largely conform to the specific requirements and activity of the tissue or organ. Furthermore, protein expression profiles can differentiate between healthy and disease states, providing important markers and targets for diagnosis and treatment.Thus, systematic measurements of protein expression levels facilitate both the understanding of fundamental biological processes and the design of new therapeutic strategies. Proteomics data collections have seen exponential growth in the last decade. Along with the data growth, analytical instrumentation and data processing methodologies for quantitative proteomics have rapidly progressed. Mass-spectrometry–based measurements provide the bulk of protein quantifications, with multiple workflows and modalities from stable isotope labeling to label-free quantification, from targeted, data-dependent acquisition (or shotgun) to data-independent acquisition modes, and involving multiple ion trap technologies—time-of-flight , linear Protein Abundances Across Organisms) is an integrative metaresource dedicated to absolute protein abundance levels in whole organism or tissue-specific proteomes . For datasets reporting protein abundances in the form of ortholog IDs from a closely related species (because mass spectrometry libraries and databases were available there), protein records were also blasted against the original species’ proteome and mapped via reciprocal best-hit matching.From publication supplements, abundance reports already aggregated to the protein level were mapped to the common identifier space using global alias file from STRING database. To recover any unmapped entries, additional steps were taken. In particular, protein IDs in the International Protein Index namespace (closed with its last update in September 2011) system were converted to UniProt ID with the mapping file (last release 2014-01) from UniProt Archive (UniParc). The converted Uniprot IDs as well as other unmapped IDs were searched through the NCBI E-utilities  to fetchIn bulk-download files from ProteomeXchange, most protein-centric data are in the MQ output format “proteinGroup.txt” . Any “COet al. , tissueontology , Plant Oontology , Cell Liontology , Cell Onontology , and Theontology .The Human Protein Atlas (HPA) normal tissue data based on version 21.1 and Ensembl version 103.38 was downloaded. Twenty-three tissues were subsequently mapped to their corresponding PaxDb tissue categories with identical labels for all but two tissues .Genotype-Tissue Expression (GTEx) proteome data were collected from Supplemental Table S2 (protein level) from . For eacThe HPA normal-tissue protein data were first filtered by the “Reliability” parameter: proteins in the “Uncertain” category were removed. The four protein levels: “Not detected,” “Low,” “Medium,” and “High” were then used for the comparison. A one-way ANOVA was performed for each tissue with PaxDb abundance ppm values, using HPA protein level as groups.The PaxDb human proteome data (excluding GTEx experiments) were compared against GTEx RNA and protein datasets. A tissue-specific z-score was calculated for each gene and each tissue in PaxDb integrated data and GTEx data to represent relative protein expression . The PeaFungi species as well as five reference species from other Eukaryotic clades  were included in the study. The “Simple Modular Architecture Research Tool” , the orthologous group was also not considered. For each species pair (Fungi species versus reference species), the protein abundance values were taken from the orthologous protein in S.cerevisiae, using its WHOLE ORGANISM integrated data. A Spearman’s rank correlation was then calculated between the amino acid usage ratios and protein abundances. For the visualizations in Evolution of Sulfur-Containing Amino Acids section, the data points were binned in six equally sized groups for the violin plots, and a linear regression was fit over all data points.For each orthologous protein pair, the amino acid usage was assessed within the aligned domain sections, and a ratio was computed. Only those orthologous pairs in which both proteins registered at least one amino acid of interest were considered. In cases where the orthology relation was complex . The PaxDb data integration pipeline involves a keyword-based discovery search for suitable projects and/or publications and automatic data processing for multiple input formats downloaded from repositories. Two hundred seventy-seven of the 410 newly imported datasets were derived from open-access publications’ supplementary files (“Curated”), and 133 were from data repositories (“Repository”). From a data processing point of view, 297 of the datasets were passed through our protein-centric import pipeline, which involves protein name mapping and where necessary also mapping via protein sequence comparison. In addition, 113 datasets were passed through the peptide-based import pipeline. Twenty-five of the latter were processed from msf files, and the rest from plain text inputs (B). The current PaxDb version 5.0 has nearly doubled its data content with respect to the number of datasets, as well as the number of organisms covered A. The Pat inputs B. Supplei.e., the best-estimate weighted combinations per species or tissue group). For example, the quality score and proteome coverage of 10 original human liver datasets range from 2 to 29.7 and from 12% to 52% respectively, while the human liver (integrated) dataset scores 30 with 80% proteome coverage.Based on the protein interaction information derived from STRING v11.5 , we compC for a comparison of for 93 integrated datasets with their counterparts in version 4). For existing integrations where no new datasets were added, the scores remained largely unchanged. However, with the inclusion of new datasets, the scores generally improved.Since genome annotations and name-spaces continually evolve, the datasets from version 4 of PaxDb also needed to be remapped and/or requantified. The scores were found to be mostly improved after the version update, when using the same STRING protein network as reference. For the integrated datasets, the interaction z-scores and proteome coverage nearly always increased when new datasets were added . The number of Archaea species increased from 1 to 22. While more species are included in Bacteria, Eukaryota encompasses a larger number of datasets owing to the diverse tissue-differentiated measurements. The top five species in terms of the number of datasets are H. sapiens (249), A. thaliana (59), M. musculus (106), and D. rerio (27). While ample datasets exist for these species and a few other model organisms, 104 species are represented by one dataset only (A). On the species-tissue level of complex organisms, the most tissue groups exist in H. sapiens (64), followed by M. musculus (47), A. thaliana (15), and D. rerio (13). Also, 143 out of 235 species-tissue groups consist of only one dataset, while datasets accumulate in frequently studied tissues and species, for example human cell lines (61), human liver (11), and mouse liver (8) (B).The 170 species in version 5.0 of PaxDb span the Archaea, Eukaryota, and Bacteria domains D. The nuset only A. On theiver (8) B.E. coli, H. sapiens, and B. subtilis rank highest in proteome coverage, with S. cerevisiae, P. falciparum, D. melanogaster, B. burgdorferi, H. pylori 26695, S. pombe, and M. musculus trailing closely, each covering over 90% of their proteome (C). In 63 out of 170 species, at least one dataset covers 50% proteome. On the species-tissue level of complex organisms, in 35 species-tissue groups, the proteome coverage exceeds 50%, most of them from H. sapiens, followed by T. aestivum root (77%), A. thaliana root (53%), and mouse cerebellum (51%) (D).proteome C. In 63 um (51%) D.H. sapiens: the GTEx project as well as the HPA, both of which contain tissue-specific protein expression data.To independently assess the validity of PaxDb data, it was compared to two comprehensive gene expression resources focusing on A). Due to the ubiquitous core biological processes, proteome-wide abundance has been observed to highly correlate across different tissue origins (B). From PaxDb version 4 to 5, correlation increased regardless of whether the pairs were matching, due to the increased dataset score and proteome coverage (B and C). As nonmatching tissue pairs could still show correlation, particularly in cases where the tissues do not exhibit tissue-specific patterns, we use the tissue specificity z-score as described in . At the RNA level, 9 out of 15 pairs clustered together, including adrenal gland, brain cerebral cortex, colon, esophagus , fallopian tube, kidney, testis, stomach, and spleen (A). The agreement between GTEx and PaxDb was stronger for protein data compared to RNA, despite the markedly smaller sample size in protein data. T tests for in-group and out-group Euclidean distances showed significance for both RNA  and protein . While over half of the organ pairs demonstrated similarity both at the protein and at the RNA levels, others did not. The discrepancy could be due to multiple tissue lineages within certain organs, such as the stomach, esophagus, and colon, which encompass epithelial and muscle layers. The proportions of these sampled tissues could influence the expression landscape. Certain organs, like the prostate, have previously been reported to be relatively indiscriminate . The order of protein groups ranging from “high” to “undetected” concurred with the PaxDb abundance group averages for all tissues, except for the GALLBLADDER and HEART.While PaxDb data are almost entirely derived from MS data, the HPA normal tissue datasets approximate the protein expression profiles with antibody-based tissue microarrays. PaxDb computes protein abundance in continuous ppm values, while HPA reports protein expression in four levels, from “high,” through “medium” and “low”, to “undetected”. The different characteristics of either technology may result in systematic biases in the results, but a global abundance trend is expected to be observable in both. The overlapping proteins were grouped using HPA abundance labels, and the distribution of protein abundance based on PaxDb data was visualized using a kernel density plot, with a vertical line representing the mean of each group. Two examples of such plots are shown in Figure 2in liver , C and De.g., unusual fractions of secreted proteins) or the result of genome-wide mutational biases, or it could suggest an adaptive response. To narrow down potential causes, we compared Fungi proteomes to a varied set of other eukaryotic proteomes and restricted the comparisons to strictly one-to-one orthologous gene pairs ; doing so should largely cancel out effects caused by differences in overall proteome functions. Then, to distinguish genome-wide mutational effects  from potentially adaptive effects, we further stratified proteins by their absolute abundance levels—adaptive changes in response to sulfur limitations should be visible particularly in highly expressed proteins.The relative frequencies of amino acids in the overall proteome can change at evolutionary timescales , and thene pairs . In addiC. elegans and D. melanogaster) and two unicellular eukaryotes .We systematically conducted this analysis on the proteomes of 179 Fungi species, for which the encoded proteomes, protein domain compositions and orthology relations have been established , 50, 51.i.e., the median ratio centered on 1.0, see S. cerevisiae) was taken as a reference for protein abundances for all subsequent analysis because of its highest quality and coverage in PaxDb, within the Fungi clade.When comparing the ratios of S-containing amino acids across orthologous gene pairs, we observed for the majority of proteins expressed at low to medium levels, that the overall usage of sulfur was roughly similar . Assuming that the observed sulfur avoidance in the Saccharomycotina might be adaptive, i.e., perhaps related to recurring episodes of sulfur limitation, a multicellular lifestyle would have been advantageous as it could provide mobility to escape the limiting environments. In the Fungi kingdom, the marker for multicellularity is the development of hyphae, long tubular substrate-seeking extensions, which allow the organisms to survive and migrate away from nutrient-poor areas . However, upon excluding the Saccharomycotina clade, the correlation disappeared entirely . This indicates an associated effect of the lower GC content of Saccharomycotina clade. We further investigated the environment/host associations of the Saccharomycotina and the closely related Taphrinomycotina species. Habitat and/or lifestyle information for Fungi is not systematically available; we approximated it by the annotated sources of the first isolation of the type strains, as described in ATCC ; we also marked potential symbiotic lifestyles with “c”, parasites with “p”, and free-living lifestyles with “f”. For Taphrinomycotina (showing little sulfur avoidance), two out of six species were parasitic, while for Saccharomycotina (strong sulfur reduction), 11 out of 33 species were symbionts, six of which were parasitic. Overall, a clear association of sulfur avoidance with free-living or parasitic lifestyles was not observed. As the environments of the isolated type strain cannot fully represent the major habitat of the species, the underlying cause of the clade-wide sulfur avoidance was not established.Assessing the strength of this effect across all 179 Fungi, a differential sulfur depletion pattern was observed . Separatder C in , containor areas . We askeion B in  and a fe A in in  are alsoThis update of PaxDb v5.0 reports a nearly three-fold increase in the number of species covered, and a two-fold increase in the number of original datasets and publications. Decreased evolutionary distances between the species will enable higher-resolution cross-species comparisons. Likely due to general improvements of the genome/proteome reference annotations, the re-mapping of older datasets from the previous version of PaxDb mostly resulted in dataset quality improvements.Using two independent human-centered data collections—HPA and GTEx, we verified the overall data quality of PaxDb, in terms of quantitative agreement and dataset tissue matching. We compared the PaxDb integrated tissue-level protein abundances with matched GTEx RNA and protein abundances. While both showed strong correlation between the matched labels, the protein-level comparison contained fewer indiscriminate correlations and more in-group correlations than the RNA-level. Comparisons with antibody-based protein abundance estimates from HPA reached qualitatively similar conclusions. Using protein–protein interaction data as another, independent arbiter of data quality, we show that the PaxDb integration of multiple lower-coverage, lower-quality datasets enhances the data quality and provides a boost to overall proteome coverage.Chlamydomonas reinhardtii  applies to all content of PaxDb resource.The PaxDb database is freely accessible at This article contains The authors declare no competing interests.


AlphaFold is making great progress in protein structure prediction, not only for single-chain proteins but also for multi-chain protein complexes. When using AlphaFold-Multimer to predict protein‒protein complexes, we observed some unusual structures in which chains are looped around each other to form topologically intertwining links at the interface. Based on physical principles, such topological links should generally not exist in native protein complex structures unless covalent modifications of residues are involved. Although it is well known and has been well studied that protein structures may have topologically complex shapes such as knots and links, existing methods are hampered by the chain closure problem and show poor performance in identifying topologically linked structures in protein‒protein complexes. Therefore, we address the chain closure problem by using sliding windows from a local perspective and propose an algorithm to measure the topological–geometric features that can be used to identify topologically linked structures. An application of the method to AlphaFold-Multimer-predicted protein complex structures finds that approximately 1.72% of the predicted structures contain topological links. The method presented in this work will facilitate the computational study of protein‒protein interactions and help further improve the structural prediction of multi-chain protein complexes. An efficient computational method detects the topological links in the protein complex structures and finds that the topological links nearly do not exist in PDB experimentally determined structures but exist in the AlphaFold2-Multimer predicted models. Reliable structure prediction of single-chain proteins has further inspired and facilitated the prediction of the structural details of protein‒protein interactions (PPIs)4, which is pivotal for both the understanding of biological functions and intervention in diseases. Several recent studies have presented methods to predict the component identities and interaction modes of PPIs that are built upon AlphaFold8.Predicting the three-dimensional (3D) structure of a protein from its primary sequence has long been a topic of great interest in biology. Recent studies using end-to-end deep neural network (DNN) methods such as AlphaFold4 to study PPIs and generated large-scale datasets of predicted protein‒protein complex structures. We found that some of the predicted complex structures, including the top-ranked ones with the highest confidence scores, contained unusual topologically intertwining links formed within short backbone fragments in chains at the interface 16 including predicted ones19, which are considered to be associated with particular thermodynamic and kinetic properties20. Algorithms that identify and classify the topological features of these nontrivial protein structures are well established28, and some researchers have attempted to apply them directly to multi-chain protein complexes. The identification of topological features for a set of closed curves is mathematically complete; however, protein chains are open curves with N- and C-termini such that determining how to close the curves represents a major difficulty. Existing strategies for closing a protein chain can be mainly classified into two categories: finding loops 30 and creating loops 26. The topological feature of a curve is deterministic, but it may vary according to different ways the curve is closed. Therefore, the key step is to create properly closed loops in a protein chain without changing its original topological feature. To this end, approaches such as the KMT algorithm31, minimal surface analysis32 and the Gauss linking integrals (GLN) method33 are commonly used to determine the topological types and locations for protein structures with “closed” loops.Topologically complex elements such as knots, slipknots, lassos and links have been well studied for single-chain protein structures34 established a systematic classification of the topological links in protein structures with three categories: deterministic links, probabilistic links and macromolecular links. Deterministic links and macromolecular links are both topological links in structures that already have loops closed by covalent bonds ; for such structures, there is no need to handle the chain closure problem. Probabilistic links are topological features in probabilistic form for structures with random closures, circumventing the chain closure problem. Another method of measuring the entanglement of protein complexes was established by observing the behavior of chains when pulling at both termini of each chain35, where the closure of the chains and the results of identification are determined by the pulling directions. In addition, the Gauss discrete integral over open chains was suggested for measuring the entanglement in domain-swapped protein dimers without the need for closing curves36, which is essentially an approximate estimation of the topological feature of two closed chains formed by directly connecting the N- and C-termini of each chain.For topological link detection in multi-chain protein complex structures, determining how to close at least two curves without changing the original topological features is much more challenging. The LinkProt databaseAs the number of available experimental protein‒protein complex structures is limited, existing methods have only focused on the global topological feature for the whole structure when detecting entanglement in multichain protein complexes. Currently, the explosively growing number of predicted structures of protein‒protein complexes, with real or fictitious interactions together with their more complicated interfaces, bring new opportunities and challenges for the detection of topological features of protein complexes. We find that the existing methods may fail to identify many topological links observed in the AlphaFold-Multimer-predicted structures. Therefore, there is an urgent need for methods of identifying topological links in protein‒protein complex structures in an accurate and robust manner.To fill this gap, we propose an efficient method of detecting topologically linked structures in protein complexes, by introducing geometric constraints when measuring topological features from a local perspective. Our method provides a solution to the chain closure problem based on the Gauss integral, by systematically sliding windows in chains instead of focusing on the closures of the termini of chains as in other studies. This is a fast and reliable method with deterministic results regarding how many topological links exist and where the links occur for a given protein‒protein complex structure. We demonstrate how this method can detect topologically linked structures in several sets of predicted protein complex structures. Our work may further facilitate the improvement of protein structure predictions and computational PPI studies.34 and the GLN method23, as well as the pulling method35. These false inferences are caused by the closure of termini, which creates artificial topological links, indicating the low specificity of the link identification of these methods. For the predicted structures with topological links, LinkProt correctly identified three with Hopf links, but one  was unlinked , indicating a possible topological link formed between the fragment and the other chain, the middle atom of the fragment, which contributes most to the topological link, will be selected and marked for further analysis in the next step.In the second step, we focus on the detection of atoms that form topological links in each chain. We systematically create locally closed loops under geometric constraints, by sliding windows along each chain to create fragments with varying lengths, in which each considered fragment is formed by several adjacent atoms together with their covalent bonds and is closed by directly connecting its two ends. The topological feature of each fragment in the focused chain with respect to the other chain is measured by the GLN value, and the larger the absolute GLN value is, the more severe the entanglement of the two corresponding curves. All the fragments, each represented by its beginning and ending atoms, correspond to the form of a matrix; thus, the topological features can be measured by the GLN matrix constructed in this way. We restrict the systematic scan to fragments with lengths ranging from D, Ts, Re, and Rb).In the third step, we infer the number of topological links of the protein complex structure with a comprehensive analysis of the marked atoms. Usually, the same topological link can be identified repeatedly around a set of marked atoms that have covalent bonds with each other, forming a consecutive fragment. Therefore, we calculate the number of consecutive fragments formed by the marked atoms in each chain and use the maximal one as the number of topological links in the structure, which is an indicator of whether a structure is topologically linked. In this way, the algorithm would not only identify whether the complex structure contains topological links but also locate the exact regions in which they occur. We note that the algorithm is robust with respect to the four user-definable hyperparameters  was identified as containing topological links  dataset39, where protein‒protein interactions were confirmed through experimental measurements such as two‒hybrid screening and co‒immunoprecipitation (co-IP)39. The sizes of the protein complexes range from 132 to 1,534 residues.Our algorithm allows quick identification of topologically entangled structures from a large number of predicted protein‒protein complex structures. We used AlphaFold-Multimer to predict protein‒protein complex structures for 1,669 experimentally established interacting protein pairs and quantified the number of topologically linked structures. Specifically, we generated two PPI datasets, consisting of 841 protein pairs from Homo sapiens and 828 protein pairs from Drosophila melanogaster, respectively. These datasets were obtained from the positive PPIs benchmark datasetsWe generated 21,025 and 20,700 predicted structures for the 841 and 828 PPIs using AlphaFold-Multimer (v2.2.0), respectively, with 25 predicted structures for each PPI pair. We applied our method on these datasets and found that 641 and 565 structures contained topological links, respectively Table . Besides41. To investigate whether AlphaFold-Multimer predictions for protein complexes with unknown interactions would contain topological linked elements, we generated a dataset by reorganizing protein‒protein complexes from some known PPIs in the Escherichia coli protein interactome and predicting their structures with AlphaFold-Multimer. We randomly selected 20 PPIs from a benchmark set of the E. coli protein interactome42, with sizes of the complexes between 167 and 1422 residues  and then focus on detecting them. We developed an algorithm to identify topologically linked structures for protein complexes by providing a solution to the chain closure problem from a local perspective, that is, systematically sliding windows along each chain instead of closing the N- and C-termini. The algorithm has high sensitivity, as all the topologically linked structures it identified in this work were confirmed by manual inspection. The method runs in seconds per structure, although the exact runtime varies depending on factors such as the size of the protein complex and the degree of entanglement. For example, using a single Intel Xeon E5-2650 v4 CPU core, the average computation time is 8.10 s per complex structure (with an average of 891.2 residues) for the two PPI datasets and 4.37 s per structure (with an average of 721.5 residues) for the 6. When focusing solely on AlphaFold-Multimer predictions with pDockQ > 0.5, 271 out of 5483 and 156 out of 2488 complex structures (4.9% and 6.3%) are identified as containing topological links in the two PPI datasets, respectively  structures from the predictions.In addition to the characterization of the topological features of complex structures, our method provides a measurement for evaluating complex structure prediction programs. For example, AlphaFold-Multimer v2.2.0 generated 1.72% topologically linked structures on average during the prediction of protein‒protein complexes, while a previous version (v2.1.0) produced 30.54% topologically linked structures  chains can be split into M(M − 1)/2 combinations of two-chain complexes. Only backbone heavy atoms  together with their covalent bonds are considered, and only atoms within a cutoff distance D of each other’s chain together with the atoms between them are preserved; their coordinates are used for further analysis with the indexes rearranged. The first 15 residues from either the N- or C-termini of each chain are removed to reduce the possible impact of their flexibility.A protein complex with two chains (chain A and chain B) is taken as an example, as a complex composed of To take a local perspective on topological links and to reduce the computational complexity, one chain (A) is systematically scanned locally in a sliding-window manner, and the other chain (B) is closed by directly connecting its two endpoints. Any fragment formed by adjacent atoms in chain A with a reasonable scanned window length is closed by directly connecting its two endpoints. Together with the simply closed chain B, the detection of the topological feature of the two closed curves is straightforward. The contribution of topological features from the fragment rather than the end-connected part can be represented by the GLN value; the larger its absolute value is, the more severe the winding of the fragment to the closed chain B. Fidelity in link identification is ensured by filtering out inappropriate connections of fragment ends, which can be achieved by setting a threshold score for the GLN values.N1 and N2 represent the numbers of atoms in chain A and chain B, respectively. Let kth row and the lth column of S1, represents the GLN value of the fragment of chain A from the lth atom to the kth atom winding around chain B. Note that S1 is a lower triangular matrix, i.e., Let Rb to Re . Let ith atom to the (i + j)th atom in chain A, where Ts is an empirical threshold score, then the ⌊i + j/2⌋th atom, i.e., the middle atom of the corresponding fragment, is selected and marked because it may form topological links in chain A with respect to chain B. Note that one atom may be marked several times in fragments of different lengths.To detect the atoms that form topological links, we focus on the fragments of the open chains whose lengths range from Rb, Re] in each chain, the same topological link can typically be identified repeatedly around a set of marked atoms, which are next to each other and form a consecutive fragment. Therefore, we calculate the number of consecutive fragments formed by the marked atoms in each chain and use the maximal one among all the chains to define the number of topological links in the structure of the protein complex. We call one structure a topologically linked structure if the number of topological links exceeds zero. In addition, the rearranged indexes of the marked atoms are mapped back to the whole structure, and the locations of the topological links in the form of residue indexes are returned by the algorithm.After a systematic scan of all the fragments in the length interval [Re) and lower (Rb) limits of the length range, the threshold score (Ts) and the cutoff distance between chains (D). The default values  are empirically set without being elaborately crafted, which seems to be suitable for detecting topologically linked structures on the protein‒protein interaction interfaces in this work. We do note that among these four hyperparameters, Re, which is the maximal fragment length in the scanning windows, is the most sensitive, so we smoothed the method by automatically adding 3 to Re for structures whose maximal absolute GLN value for the searched fragments was less than Ts but greater than 0.9Ts.There are four user-definable hyperparameters in the algorithm, i.e., the upper , except that the sequences of the 40 antibodies were taken from PDB (https://www.rcsb.org/), SARS-CoV-2 NSPs were taken from the NCBI database (https://www.ncbi.nlm.nih.gov/), human virus were taken from PDBe (https://www.ebi.ac.uk/pdbe/) and human membrane proteins were taken from the human protein atlas (https://www.proteinatlas.org/). Detailed sequence information is provided in the Supplementary Data https://ftp.ebi.ac.uk/pub/databases/uniprot/previous_releases/release-2019_10/uniref/), BFD (https://bfd.mmseqs.com/), Uniclust30 v2018_08 (https://wwwuser.gwdg.de/~compbiol/ uniclust/2018_08/), and MGnify clusters v.2018_12 (https://ftp.ebi.ac.uk/pub/databases/metagenomics/peptide_database/2018_12/). The sequence search tools were JackHMMER and HHblits (v3.3.0). For MSA pairing, we used UniProt (https://ftp.ebi.ac.uk/pub/databases/uniprot/current_release/knowledgebase/complete), downloaded on Feb 11, 2022. For the template search, we used “pdb_mmcif” from the PDB database as of Feb 10, 2022. The template search tools were hmmsearch and hmmbuild of HMMER (v3.3.2). All predicted structures were refined using the built-in Amber force field.The two PPIs datasets were obtained from the positive PPIs benchmark datasetsAll quantitative results of topological link detection were presented as integers, with the same outcome when the calculations were repeated at least two additional times. Although the predicted structures generated by AlphaFold-Mulimer for a specific protein sequence may be influenced by random factors, the percentage of predicted structures containing topological links is expected to yield similar results when tests are conducted at the current data scale .Further information on research design is available in the Peer Review FileSupplementary InformationDescription of additional supplementary filesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Reporting Summary


Numerous computational methods based on sequences or structures have been developed for the characterization of protein function, but they are still unsatisfactory to deal with the multiple functions of multi-domain protein families. Here we propose an original approach based on 1) the detection of conserved sequence modules using partial local multiple alignment, 2) the phylogenetic inference of species/genes/modules/functions evolutionary histories, and 3) the identification of co-appearances of modules and functions. Applying our framework to the multidomain ADAMTS-TSL family including ADAMTS  and ADAMTS-like proteins over nine species including human, we identify 45 sequence module signatures that are associated with the occurrence of 278 Protein-Protein Interactions in ancestral genes. Some of these signatures are supported by published experimental data and the others provide new insights (e.g. ADAMTS-5). The module signatures of ADAMTS ancestors notably highlight the dual variability of the propeptide and ancillary regions suggesting the importance of these two regions in the specialization of ADAMTS during evolution. Our analyses further indicate convergent interactions of ADAMTS with COMP and CCN2 proteins. Overall, our study provides 186 sequence module signatures that discriminate distinct subgroups of ADAMTS and ADAMTSL and that may result from selective pressures on novel functions and phenotypes. The vast majority of proteins have a modular multidomain organization. Domains are conserved building blocks of proteins that are widely used to characterize and predict protein functions. However, the organization of multidomain proteins underlies a great complexity and their biological role is generally not the sum of the functions attributed to each domain. To address this problem, it is necessary to develop new methods to better identify functional signatures. Here, we developed a framework based on partial local multiple alignments (to find conserved sequence modules) and phylogenetic reconciliation methods . Application of our framework to the search for functional sequence modules in extracellular matrix proteins from the ADAMTS  and ADAMTSL (ADAMTS-like) family, enabled to highlight sequence signatures potentially involved in Protein-Protein Interactions. C. elegans to 26 genes in H. sapiens, suggesting that the ADAMTS-TSL protein family undergoes a large expansion in the number of paralogs during metazoan evolution. ADAMTS-TSL proteins are usually described according to their domain/motif composition as shown for the human paralogs in The ADAMTS  and ADAMTSL (for ADAMTS-like) are secreted proteins involved in extracellular matrix remodelling. They are associated with a wide range of human diseases, including cancer, fibrosis, arthritis, and cardiovascular disease , 2. Thessequence modules maintained during evolution, at least in a subset of sequences, by selective pressures, and thus likely to be important functionally. To identify which of these modules are associated with specific functions, we propose here to use phylogenetic inference methods to reconstruct ancestral histories of both the modules and the functions, and to predict their co-appearance along the ADAMTS-TSL evolutionary tree. The consideration of both sequence similarities and the evolutionary history of genes to propagate functional annotations along their phylogenetic tree was pioneered by Eisen et al. [reconciliation of the species, gene and domain phylogenies and enable to estimate ancestral domain compositions.Because of their complex structure and their biochemical properties, the functional characterization of ADAMTS-TSL using classical experimental approaches remains a challenge . In addin et al. . Subsequn et al. –21. Thesn et al. –24. ThesHere we present an innovative approach that relies on conserved sequence modules instead of domains. We apply the above mentioned methods to estimate the co-appearance of functional traits, i.e. Protein-Protein Interactions (PPIs), with conserved sequence modules, given the ADAMTS-TSL gene phylogeny. In a given ADAMTS-TSL ancestor, all the gained ancestral modules form a signature that is possibly involved in a gained ancestral function. Applying this new approach to ADAMTS-TSL proteins enabled us: 1) to identify 186 sequence module signatures that discriminate distinct sub-groups of ADAMTS-TSL; 2) to infer 45 signatures associated with the emergence of 278 PPIs in 45 ADAMTS-TSL ancestors; 3) to identify non-trivial convergent interactions of ADAMTS-TSL with COMP and CCN2 proteins; 4) to observe that module changes during evolution mainly occurred in the N-terminus part and in the ancillary domain, highlighting the importance of both regions in the divergence of ADAMTS-TSL specificities, and 5) to characterize ADAMTS-5 by a sequence signature that might explain its functional specificity with respect to other hyalectanases.The general workflow of our new approach is summarized in Homo sapiens, Mus musculus, Bos taurus, Gallus gallus, Xenopus tropicalis, Danio rerio, Ciona intestinalis, Caenorhabditis elegans, and Drosophila melanogaster, because they are representative of the evolutionary history of the ADAMTS-TSL family from the bilateria ancestor to Homo sapiens and they have complete proteome and genome annotations in the Gene Feature Format (GFF) datasets (Orthofinder software [orthogroups. An orthogroup is defined as a group of sequences from different species descending from a common ancestor gene. We identified 22 orthogroups containing at least one of the 159 RefSeq sequences of known human ADAMTS (102) and ADAMTSL (57) proteins, 17 orthogroups corresponding to ADAMTS proteins and 5 to ADAMTSL proteins. We also selected the 2 orthogroups corresponding to 7 RefSeq sequences of 4 human ADAM genes to use them as an outgroup enabling to root the ADAMTS-TSL phylogeny. Like ADAMTS-TSL, the ADAM  proteins belong to the adamalysin family. The resulting dataset, called dataset-708, is composed of 708 protein sequences  from 24 selected orthogroups and is detailed in To build the set of input sequences, we selected 9 species: datasets . Providisoftware  . Each protein sequence is associated with its locus according to its coordinates as described in the GFF file. In a species, two protein loci overlapping by at least one residue on the same genome strand were considered as two isoforms encoded by the same gene. The longest isoform sequence of a gene was then selected as the representative protein without significant loss of information as shown in H. sapiens ADAMTS-TSL genes are represented in the dataset  and the tree was rooted using ADAM proteins as the outgroup, resulting in the final phylogeny that we named the reference gene tree. To analyse the uncertainty resulting from the taxon and gene sampling, and to identify robust bi-partitions in the reference gene tree, we analyzed several ADAMTS-TSL protein samples by varying the content of the outgroup and by adding ADAMTS-TSL sequences of species not considered in our 9 species , a new and faster re-implementation in modern C++ of Paloma  computed by the program dialign2–2 [−5 in random sequences). The multiple sequence alignment was built from this set of pairwise alignments of interest as follows. First, the candidate diagonals were sorted by the default heuristic of Paloma-D, putting forward those with the stronger evidence of conservation across all the sequences. Then, they were iteratively added in this order to the PLMA, with transitive alignment and local extension of diagonals, if and only if the consistency of the resulting induced alignment [The PLMA of the 214 sequences contained in the align2–2 , of lenglignment  was presmodules to be all the sets of segments that were aligned by these PLMA blocks, provided that their length was greater than 5 residues . Then, each module tree was corrected by TreeFix [SEADOG-MD [n of the reference gene tree, enabling us to infer an occurrence of an ancestral segment of this module at the ancestral gene n.First, the phylogenetic tree of each module was inferred from the gapless alignment of its segments  by Maximum Likelihood software PhyML  (v3.3.20 TreeFix  (v1.1.10EADOG-MD  (defaultEADOG-MD ). This rn of the reference tree was assigned an ancestral module composition Mapping all the modules, each ancestral gene node The step 4 consists in collecting functional traits associated with sequences.H. sapiens, 28 for M. musculus and 106 for C. elegans) and interactions between proteins from different species (e.g. 65 for H. sapiens/M. musculus). The 66 interactions involving ADAMTS-TSL and complexes are excluded because of the lack of protein identification from complexes. After filtering redundant identifiers, 461 different PPIs were annotated  involve papilin (mig-6) and the remaining four PPIs involve madd-4 (orthologs of ADAMTSL1 and 3) and adt-2 (ortholog of ADAMTS2), but there is no PPI shared with human. In addition, 8 interactions between human and mouse proteins are redundant with each species and the remaining 8 PPIs involve human proteins and proteins from other organisms such as viruses and bacilli. In conclusion, sequence queries of the nine species yielded very little information about the non-human species and we chose to retain only the PPIs from H. sapiens in the PSICQUIC results. Importantly, we observed that many recently identified substrates of ADAMTS were not described in molecular interaction databases and we decided to complete the list of PPIs by manual curation of the literature. Our review of the literature enabled us to identify an additional set of 303 PPIs  as the phenotypic trait of interest. The conversion of the RefSeq ID of the 386 ADAMTS and 226 ADAMTSL sequences from dataset-708 enabled us to retrieve 494 UniprotKB ID that were used to query molecular interaction databases using the Proteomics Standard Initiative Common QUery InterfaCe (PSICQUIC) . The retnnotated . They in303 PPIs . In this PPIs 32/ involve ractants .The step 5 consists in building the evolutionary history of the PPI, according to the reference gene tree.PastML  [PastML on the ability of a protein at a leaf of the tree to interact with each partner: “able to interact” (1), “not able to interact” (0) or “missing annotation” (?); each missing annotation being later inferred by PastML as able to interact (1) or not able to interact (0). As stated in step 4, we focus on H. sapiens PPIs. We set the status of H. sapiens representative protein to “able to interact” (1) for each interacting partner listed in PPIhuman and “not able to interact” (0) for the other human proteins. Given the very limited amount of information on PPIs in non-human species, we chose to set the status at the leaves to “missing annotation” (?) for each PPI.Ancestral ADAMTS-TSL Protein-Protein Interactions along the reference gene tree were inferred with setting) . Three kPastML associated to each node n  with missing annotation a set of interactants PastML proposes different coherent evolution scenarios based on marginal probabilities of character (PPI presence in this study) that were also recorded. Here we focus only on the proposed most likely scenario.Given these leaf annotations and the reference gene tree, The reference gene tree annotated with ancestral interactants at each node represents the inferred scenario of evolution of ADAMTS-TSL interactions with proteins.By integrating the module compositions (from step 3) and ADAMTS-TSL PPI annotations (from steps 5) onto the reference gene tree, we can simultaneously examine the inferred evolutionary histories of genes, module compositions and ADAMTS-TSL protein interactions.n of the reference tree, the sets n with respect to its ancestor at node a. A module was added to n were computed similarly from To focus on changes across the evolution of ADAMTS-TSL genes, we computed for each node n, module signature of the node. The more significant module signatures are those co-appearing with a phenotypic trait at a node. Then we focused on the co-appearance of protein interactions and modules, i.e. on nodes n such that n.The set of modules gained at a node https://itol.embl.de/tree/13125419158431781652196295. An HTML popup window provides detailed information about each gene node, including the name of the gene, the nature of the gene (ancestor or leaf), the PPIs associated with the gene and PPI gain/loss with respect to the ancestor, the module composition of the gene and module gain/loss with respect to the ancestor. The Itol tree also provides annotations including the number, the composition and the transfer of modules, the domain composition and the presence of PPIs. In particular, each gene has its own module signature annotation in which “gained” modules are represented by green boxes on the descendants. All module signatures can be visualized within their domain contexts by first enabling the domain composition annotations. All the inferred module signatures for the ADAMTS-TSL family are available at https://github.com/OcMalde/PhyloCharMod_publ , using the Interactive Tree Of Life software, Itol . The ADATo explore the ADAMTS-TSL evolutionary history according to their diverging module compositions and PPIs, we first inferred a phylogenetic tree from ADAMTS and ADAMTSL protein sequences and rooted the tree using some ADAM protein sequences as the out-group. This reference gene tree is shown in H. sapiens ADAMTS-TSL sequences is detailed in We then inferred the evolutionary history of the gene module composition. Using Paloma-D, the partial local alignment of the 214 sequences identified 1059 conserved sequence modules . As expeH. sapiens ADAMTS-TSL and one of their 471 H. sapiens partners. Among the 471 partners, 119 have interactions with at least 2 ADAMTS-TSL  exhibited one PPI but no module gain and 45 ancestral gene nodes exhibited both module and PPI gains . Among tNext, we looked at the module location of the 45 signatures along the human protein sequences descending from the corresponding ancestor genes . Of noteTo further explore the ability of our model to predict associations between conserved sequence modules and PPIs, we focused on two specific cases: first, ADAMTS-7/ADAMTS-12 and their common substrates CCN2 and COMP and, second, the hyalectanase group. In both cases, we compared the model predictions with experimental data to validate the method as well as to propose new insights into the underlying evolutionary processes. The CCN2/COMP PPI case highlights the convergent evolution of PPIs during ADAMTS-TSL evolution while the hyalectanases case explores the sequence evolution of an entire gene subgroup up to ADAMTS-5, a member of this subgroup that shows significant interaction specificity.H. sapiens ADAMTS-TSL proteins, the literature curation (step 4) identified ADAMTS-3, ADAMTS-4, ADAMTS-7 and ADAMTS-12 as interacting with the cartilage oligomeric matrix protein COMP and/or the connective tissue growth factor . The role of ADAMTS-7 and ADAMTS-12 in the pathogenesis of arthritis was firstly supported by their ability to cleave COMP [H. sapiens, B. taurus, M. musculus ADAMTS-4 and the CCN2 PPI was gained at G315, the last common ancestral gene of the H. sapiens, B. taurus, M. musculus, G. gallus ADAMTS-3.Among the 26 ave COMP , 44. Morave COMP , 46. Sinave COMP . Howeverave COMP  and ADAMave COMP , respectTo characterize the PPI changes associated with the ancestral genes G161, G15 and G315, we focused on the modules predicted to appear in those ancestors. We first investigated the module signature at G161 and we observed that the 25 modules gained were primarily localized in the TSP1-repeat of the ancillary region . ImportaThe hyalectanase family is composed of 7 members  which degrade proteoglycans such as aggrecan (ACAN), versican (VCAN), brevican and neurocan . The lasin vitro [According to the literature, ADAMTS-4 (aggrecanase-1) and ADAMTS-5 (aggrecanase-2) are the main hyalectanases involved in the degradation of aggrecan (ACAN), the major component of articular cartilage. While ADAMTS-1  and ADAMin vitro , ADAMTS-in vitro –11. The in vitro  and bothin vitro , 55 and in vitro . In addiin vitro  or such in vitro . It is iin vitro .H. sapiens  to illustrate the block conservation with regard to close and distant paralogs of hyalectanases. The B781 block .H. sapiens ADAMTS-5 belonging to G65 and G96 module signatures are shown in H. sapiens ADAMTS-5 protein. AlphaFold 2.1.14 [H. sapiens ADAMTS-5. Since ADAMTS-5 is activated in the extracellular space [colabfold_batch with the parameters --num-recycle 3 --num-models = 5 --amber. The two resulting first ranked relaxed models colored by the location of G65 and G96 modules are shown in The full set of segments of d 2.1.14  was usedd 2.1.14  to prediar space , we explβ6-β7-β8 loops from the spacer domain. Note that the specific G65 module segments present in the C-terminal TSP1 and the propeptide are co-localized near the central TSP1 domain in the predicted structure suggesting a regulatory role for the propeptide.Interestingly, although module segments may be distant in the sequence, they are all located in the same area of the predicted 3D structures at the interface of the protein, around a conserved ADAMTS-TSL core consisting of the central TSP1 domain and the C-term of the cystein-rich domain, with the exception of the module segment in the catalytic domain. In particular, the segments of the G65 and G96 signature modules from the cystein rich and spacer domains seem to form a localized sub-unit made of a beta sheet in the spacer facing the conserved segments in the cystein rich domain. The segment of the G65 module in the cystein rich domain is also interestingly predicted in a loop with an orientation similar to the β1-β2, β3-β4 and β9-β10 hypervariable loops identified by Santamaria et al  and the β1-β2 region of ADAMTS-5 provided novel insights into the spacer domain. Moreover, these conserved modules in the spacer are likely to be functionally linked to the specific modules in the cystein rich domain from their respective orientation in the predicted 3D structures. Besides, our data highlight also specific conservations predicted to be spatially co-localized in the propeptide and C-term TSP1 regions, which are challenging to interpret and are of interest to guide further studies.Together our data summarized in Emerging new combinations of domains plays a major role in the evolution of modular organization of proteins, and the emergence of adaptive traits. This phenomenon has been shown to be predominant in the evolution of proteins involved in the extracellular matrix, an interconnected network composed of hundreds of macromolecules that includes the multidomain protein family ADAMTS-TSL –66. HereThe evolutionary history of the ADAMTS-TSL protein family has previously been studied –72. UsinAn interesting new finding is the monophyly of the ADAMTSL group while papilin, a member of the ADAMTSL protein family, clustered in a distinct group related to ADAMTS-6 and ADAMTS-10. Note that these two distinct groups were not supported in all the gene re-sampling assays See . HoweverMany studies have used domain composition as a proxy for protein function, as they represent independent structural and functional units . A module signature thus consists of one or several independent modules, so that one of their distinguishing feature is the possibility of being discontinuous along the sequences.Protein-Protein Interactions play fundamental role in the structure of the ECM , 66 and The identification of functional sites in a protein is a prerequisite to understand its molecular action and to develop efficient therapeutic approaches . NumerouOur method is supported by the identification of module signatures that matched functional motifs previously identified, such as PPIs involving COMP and ADAMTS-7/ADAMTS-12 , 44, or Like other ADAMTSs, ADAMTS-5 shows high variability in the propeptide domain  that hasWe propose here a new approach bringing together advanced sequence analysis and phylogenetic methods for identifying conserved sequence modules associated with protein function. This approach is based on an original integration of the evolutionary histories of species, genes, modules and on the prediction of modules and phenotype co-appearances to reveal sets of (non-contiguous) conserved sequence regions defining phenotype specific signatures. We applied this strategy to search for conserved modules in the ADAMTS-TSL family using Protein-Protein Interaction data as phenotypes. Based on a set of experimentally supported ADAMTS-TSL protein interactions we provide novel insight into the evolution and specialization of ADAMTS-TSL interactions. Our framework is generalizable to other protein families as well as to other phenotypes, giving new perspectives to the functional characterization of other multi-functional, multi-domain protein families.S1 Appendix(PDF)Click here for additional data file.S2 Appendix(PDF)Click here for additional data file.S3 AppendixItol tree screenshots and usage examples.(PDF)Click here for additional data file.S1 Table(CSV)Click here for additional data file.S2 TableThe whole isoform sequences were examined as splicing graphs and the unconsidered information for all isoforms was quantified.(CSV)Click here for additional data file.S3 Table(CSV)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.S6 Table(XLSX)Click here for additional data file.S7 Table(CSV)Click here for additional data file.S8 Table(CSV)Click here for additional data file.S9 Table(CSV)Click here for additional data file.S1 Fig(PDF)Click here for additional data file.S2 Fig(PDF)Click here for additional data file.S1 File(PDB)Click here for additional data file.S2 File(PDB)Click here for additional data file.


The server can assess the impact of mutations on the protein sequential and structural properties and apply a virtual screening to identify potential inhibitors that could be used as a highly individualized therapeutic approach, possibly based on the drug repurposing. PredictONCO integrates predictive algorithms and state-of-the-art computational tools combined with information from established databases. The user interface was carefully designed for the target specialists in precision oncology, molecular pathology, clinical genetics and clinical sciences. The tool summarizes the effect of the mutation on protein stability and function and currently covers 44 common oncological targets. The binding affinities of Food and Drug Administration/ European Medicines Agency -approved drugs with the wild-type and mutant proteins are calculated to facilitate treatment decisions. The reliability of predictions was confirmed against 108 clinically validated mutations. The server provides a fast and compact output, ideal for the often time-sensitive decision-making process in oncology. Three use cases of missense mutations, (i) K22A in cyclin-dependent kinase 4 identified in melanoma, (ii) E1197K mutation in anaplastic lymphoma kinase 4 identified in lung carcinoma and (iii) V765A mutation in epidermal growth factor receptor in a patient with congenital mismatch repair deficiency highlight how the tool can increase levels of confidence regarding the pathogenicity of the variants and identify the most effective inhibitors. The server is available at  In 2020 alone, more than 19 million cases of cancer were diagnosed, of which 10 million were mortal .Personalized medicine-based techniques have their limitations. Decisions are often based on next-generation sequencing technologies, which can generate a large amount of genomic or transcriptomic data. It can be difficult to analyze and interpret this amount of data in a clinically feasible manner. This leads to a divide between the generation of experimental data and their application during the decision-making process. For example, when targeted next-generation sequencing panels are used for analyzing multiple genes simultaneously, various coding sequence mutations are typically found. And when no previous data on the effect of such mutations on the expressed protein are available, the data cannot effectively support decisions since there is usually little space to study each mutation in sufficient detail .With the recent advances in protein modelling , 6, e.g.On the one hand, the traditional method of deliberation and discussion by multiple experts is naturally time-consuming. On the other hand, an alternative consisting of performing all the aforementioned analyses requires significant knowledge of computational biology and bioinformatics. It would also entail considerable time if performed manually. Here, we introduce PredictONCO, a web server for fully automated and fast analysis of the effect of mutations on protein structure, stability and function in 44 known protein targets relevant for oncology. PredictONCO applies computational methods and bioinformatic analyses and produces actionable reports within a few days. To ensure the high quality of the results, the entire pipeline was thoroughly validated using 108 clinically and experimentally characterized mutations covering all 44 proteins. The final report includes visualizations to help with data comprehension.The workflow of PredictONCO consists of two major steps: (i) selection of target protein and mutation and (ii) data analysis . In the The first step of the workflow is the specification of an input as one of the 44 pre-defined oncology-relevant proteins . CliniciAll of the 44 proteins available to the users were previously curated, following a number of steps. The most suitable calculation parameters were optimized for each target to increase the reliability of the results. First, the protein sequence and annotations were fetched from the UniProt database . The essOnce the protein is selected, users are requested to specify the mutation by typing in the substitution or selecting the position of the substituting amino acid using a visualized protein sequence. These calculations usually take up to 48 h, depending on the protein sequence length and other structural parameters (could be longer under heavy workload of the server). This time threshold was carefully selected as a balanced spot between time constraints of medical boards and the possibility to use the most reliable and accurate computational methods. However, all finished calculations are stored in a database and when a previously computed mutation is requested, the calculation output is immediately served from saved results. All 913 mutations used during the training and testing of the predictor see  are pre-Once the job is submitted, all calculations are executed automatically. The specific analyses depend on the region where the mutation is located and on the availability of a structure information for that region. If a high-quality 3D structure is available and the mutation is located in the cytoplasmic domain responsible for ligand binding, all analyses are performed, including the function-related and drug-related calculations to assess the potential inhibiting role of small molecules. The function-related and drug-related calculations are omitted if the mutation is in the extracellular unit. Only sequence-based analyses can be performed for the transmembrane and undefined regions, as these regions often lack the experimental structural information required for precise force-field calculations. Finally, the effect of the mutation is predicted using a newly developed machine learning-based predictor see .Ka change of catalytic residues using PROPKA3 [Initially, a mutant structure is constructed using the MODELLER  software PROPKA3  and iden PROPKA3 . In the  PROPKA3  is perfo PROPKA3 .The most important sequence analysis is a prediction of an effect of a mutation on a protein function performed by the meta-server PredictSNP , which cThe next step is the extraction of important and relevant information from publicly available database. We use the UniProt database  as the pIn the end, all obtained values from the structure and sequence analyses were used as features for our newly trained predictor: essentiality of the mutated residue (yes—1/no—0), the conservation of the position (the conservation grade and score), the domain where the mutation is located , the PredictSNP score, the number of essential residues, FoldX and Rosetta ddg_monomer scores, whether the residue is in the catalytic pocket (yes—1/no—0) and the pKa changes . The predictor is based on the XGBoost classification model and returns the probability of the oncogenic effect of a mutation. This model consists of a collection of small decision trees, trained sequentially by fitting each one of them to the gradients of the loss function from the previous iteration. On every iteration, the predictions of the current set of decision trees (the probabilities of a mutation being oncogenic) are weighted and evaluated against the ground truth to calculate the loss function. Therefore, even despite having weak predictive power separately , when combined they often show state-of-the-art performance, comparable with advanced methods from deep learning.https://zenodo.org/records/10013764), showing the areas under the receiver operating characteristic curve (ROC AUC) of 0.96 and 0.93 and the average precision from the precision–recall curve of 0.99 and 0.93 for the structure-based and sequence-based predictions, respectively  is a crucial molecule mediating the cell cycle . The CDKmelanoma . The mosmelanoma .Ka of catalytic residues are predicted to be negligible. Based on visualization, we can observe that the mutation is on the edge of the binding site and can have some effect on the interactions with critical molecules. Moreover, the protein loses an important interdomain salt bridge interaction. Therefore, an impact on the structure and/or substrate binding affinity can be expected based on these analyses.The tumour-derived DNA from a patient with melanoma was analyzed by whole-exome sequencing, and amongst other findings, CDK4 K22A alteration was identified. The mutation K22A previously showed to significantly decrease CDK4 binding to cyclin D1 and tumour suppressor p16 , 47 but Ebind = −9.5 kcal/mol), and, therefore, it is the top hit for potential treatment. Moreover, several other drugs were predicted as even better binders, with lumacaftor  being the top hit indicating a potential for the drug repurposing strategy  and palbociclib being the most commonly used. In the case of K22A, all four associated drugs show no significant difference between affinity in wild-type and mutant proteins. According to the docking results, none of the four drugs is directly influenced by the mutation, and their inhibitory effect should not be compromised. The highest affinity of the four associated drugs was observed for palbociclib  E1197K substitution. Since there is only limited evidence regarding the variant’s significance in the clinical databases of genetic variants, PredictONCO analysis was performed to evaluate the variant’s impact. The prediction results suggested that the variant is oncogenic with a 100% confidence score, and it affects the protein stability as predicted by both FoldX and Rosetta. Within the provided summary, substantial differences between the wild-type and mutant residue in their size and charge and a high level of evolutionary conservation of the wild-type residue are highlighted, underscoring the potential implications of this substitution. In terms of therapeutic options, the binding energies of second- or third-generation inhibitors such as ceritinib, alectinib and lorlatinib, which could be considered for subsequent treatment, do not appear to be significantly affected by the presence of the variant. This suggests that these inhibitors may remain viable options for following clinical management.The PDF report with all results for the E1197K mutation is available in the in silico evidence, PredictONCO analysis was conducted. The overall assessment returned a benign prediction with a 55% confidence score. In alignment with the overall prediction, PredictSNP and FoldX deemed this mutation to have a neutral impact on both sequential and structural levels, respectively. Stability prediction performed by Rosetta yielded discordant results, indicating a slightly deleterious effect on protein stability. In terms of evolutionary conservation, the residue displayed no significant conservation across species. When considering the entirety of the PredictONCO analysis, it did not corroborate the potential impact of the EGFR V765A variant. Consequently, this variant was not pursued further as a predictive biomarker.The DNA from a tumour of patient with congenital mismatch repair deficiency (CMMRD) was subjected to NGS analysis. CMMRD, characterized by a disruption in one of the fundamental DNA repair mechanisms, led to the presence of hundreds of variants in the tumour DNA. These variants often possess unknown functional significance, as they are randomly scattered throughout the DNA sequence . PatientThe PDF report with all results for the V765A mutation is available in the PredictONCO 1.0 is a novel web server for rapid analysis of the effects of missense mutations detected by exome sequencing in tumour tissue. It enables the analysis of important protein targets with a known relation to oncological diseases. The tool covers several important areas of analysis: (i) computational analysis of sequential and structural properties of the mutant enzyme, (ii) virtual screening of 4380 FDA/EMA-approved drugs and (iii) verified knowledge aggregated in the UniProt database. To make the tool accessible to researchers outside the bioinformatics community, the graphical user interface was designed to be highly interactive and visualize the critical information obtained from computations. PredictONCO provides the data in an easy-to-understand format, using various visual aids and comprehensible language. To meet the demands for a short execution time required by clinical practice, only the tools with high predictive accuracy and low computational time were selected, and they are executed in a highly parallel manner using our high-performance computing environment. Moreover, all previously calculated results are stored for their immediate re-use. To ensure the quality of provided data and prediction, the server was thoroughly validated using 108 experimentally characterized mutations with known clinical association with tumour.One of the limitations of the current version is its limited ability to target the mutations causing loss of function. Even though these mutations can be successfully identified during the calculations, the server itself cannot provide actionable insight into the treatment strategy. Repurposing the drugs, which are mostly inhibitors, on an already non-functional protein will not bring helpful effects. To overcome this limitation in the next version, we plan to incorporate knowledge from the biological pathways that will also allow inhibiting proteins downstream in the pathway. The second drawback of our method is its inability to predict effect of insertions and deletions even though they are also often related to oncological diseases. Unfortunately, this problem is still generally unresolved and there are no reliable tools available. However, we are actively monitoring newly published tools, and as soon as any promising options emerge, we will work to integrate them into PredictONCO. The third challenge concerns missing and low-quality regions of protein structures. We have already attempted to use models that AlphaFold 2  predictePredictONCO is a web server that analyzes the effects of mutations on proteins frequently altered in various cancer types.The server assess the impact of mutations on the protein sequential and structural properties and apply a virtual screening to identify potential inhibitors that could be used as a highly individualized therapeutic approach.PredictONCO integrates predictive algorithms and state-of-the-art computational tools combined with information from established databases.The server currently covers 44 common oncological targets and provides a fast and compact output ideal for the often time-sensitive decision-making process in oncology.Three use cases of missense mutations highlight how the tool can increase levels of confidence regarding the pathogenicity of the variants.report_ALK_E1197K_bbad441Click here for additional data file.list_of_protein_targets_bbad441Click here for additional data file.