Intrinsically disordered proteins/regions (IDPs/IDRs) are crucial components of the cell, they are highly abundant and participate ubiquitously in a wide range of biological functions, such as regulatory processes and cell signaling. Many of their important functions rely on protein interactions, by which they trigger or modulate different pathways. Sequence covariation, a powerful tool for protein contact prediction, has been applied successfully to predict protein structure and to identify protein–protein interactions mostly of globular proteins. IDPs/IDRs also mediate a plethora of protein–protein interactions, highlighting the importance of addressing sequence covariation-based inter-protein contact prediction of this class of proteins. Despite their importance, a systematic approach to analyze the covariation phenomena of intrinsically disordered proteins and their complexes is still missing. Here we carry out a comprehensive critical assessment of coevolution-based contact prediction in IDP/IDR complexes and detail the challenges and possible limitations that emerge from their analysis. We found that the coevolutionary signal is faint in most of the complexes of disordered proteins but positively correlates with the interface size and binding affinity between partners. In addition, we discuss the state-of-art methodology by biological interpretation of the results, formulate evaluation guidelines and suggest future directions of development to the field. Structure prediction tools showed an improved performance in the Critical Assessment of Structure Prediction (CASP) experiment upon the incorporation of coevolutionary information10. More recently, evolutionary couplings were observed across protein–protein interfaces, mostly in prokaryotic complexes13.Many positions in protein sequences do not evolve independently from each other, but rather bear a pattern of inter-relationship since some changes are accepted in evolution only if there is a compensatory change somewhere else in the protein which ensures that structure and/or function are maintained. The correlation pattern between positions appears as the result of a process of concurrent mutations and it is generally known as “coevolution”. Coevolving residues are usually in spatial proximity to each other, therefore analyzing the correlated residue variation in a protein family became a common technique for contact prediction in globular domains17.The bottleneck of a covariation analysis is usually the quality and size of the multiple sequence alignment (MSA) used. The sensitivity of methods depends on the number and diversity of sequences in respective MSAs, and each method has its own requirements to achieve meaningful predictive performancesFor a great number of globular proteins it is possible to generate such alignments, but it is very difficult for intrinsically disordered proteins (IDPs) and proteins with intrinsically disordered regions (IDRs). Coevolutionary information in IDP complexes has not been systematically analyzed because of the technical difficulty to obtain reliable results, e.g. to generate a well-aligned MSA from a large number of divergent homologs since standard tools have been optimized for globular proteins.20, and these proteins mediate a plethora of protein–protein interactions21, highlighting the importance of addressing their sequence covariation-based inter-protein contact prediction.The estimated fraction of disordered proteins in eukaryotic proteomes is over 40%23.Analysis of coevolved inter-protein covarying residue pairs is possible if the paired MSA containing paired orthologs has an effective size, and the coevolutionary signal is strong enough. Clearly, this requirement makes the analysis of disordered protein complexes much more challenging and restricted. Up till now, the use of inter-protein covariation signal has only been demonstrated to be effective in modeling the complexes of globular proteins, as demonstrated in the Critical Assessment of Prediction of Interactions (CAPRI) experiment24 and the mutual folding induced upon binding database (MFIB)25. We analyzed 287 complexes involving disordered proteins or regions that fall into two flavors: (1) complexes between a disordered protein and a globular domain and (2) complexes where the two chains are disordered on their own and become structured upon binding.In the present work, we perform an analysis of two databases of IDP complexes: the database of intrinsically disordered binding sites (DIBS)Through these analyses, we established that coevolutionary signals between interacting proteins are generally faint but positively correlate with the affinity of binding. By way of biological interpretation of the results, we also thoroughly discuss the challenges and possible limitations of the analysis, and formulate guidelines to the community on how to carry out the critical evaluation of the underlying methods. In essence, this analysis opens novel avenues of understanding protein–protein interactions and suggests future directions of development to the field.The three-dimensional structure imposes strong constraints on amino acid replacement during the course of evolution. Phylogenetically related positions evolve in a coordinated manner, leaving a recognizable footprint in sequence alignments. By analyzing the sequence variability within a homologous protein family, it is possible to infer the coevolution between positions (columns in an MSA) from their covariation.In this work, we evaluated the coevolution between IDPs (or IDRs) and their partners in protein complexes. To this end, we analyzed two databases that present data on disordered regions in complexes: DIBS that contains complexes between an ordered and a disordered partner, and MFIB that contains complexes between two IDRs that undergo mutual folding when they bind to each other.It is assumed that the vast majority of coevolving pairs are in contact with each other, so a reasonable approach to compare the performance of different methods is to measure their capability of predicting residue contacts.To distinguish true coevolutionary couplings from the noise of covariations, we tested a naive predictor based solely on residue distances in the sequence, without the use of evolutionary (i.e. MSA-based) or structural information. The results of the naive predictor suggest the importance of filtering out at least five neighboring residues to have an unbiased evaluation of the performance of contact prediction  , it is paradoxical that they could possibly coevolve with all different binding partners at the same time. As an example, Fig. One of the multiple causes behind this phenomenon may be the promiscuity of disordered proteins31 which is faster than that of their ordered counterparts. Another consideration is the fuzziness of the IDP complexes32. It is widely accepted that IDPs exist as dynamic ensembles of conformations, rather than having a single conformation33, hence the structures evaluated might not represent the dominant or only conformation the proteins adopt in their complexes. In case of X-ray complexes, crystal packing might pose some artificial constraints on the complex, while both NMR and X-ray structures may have been measured under conditions far from being physiological. This can also lead to the overestimation of the stability of the complex, i.e. the affinity of binding. Many of these complexes may arise from transient, short-lived and unstable interactions, not strong enough to leave a covariation signal. Supplementary Figure Another factor could be that globular proteins and IDRs do not evolve at the same rate. IDRs are known for their rapid evolutionAnother type of IDP/IDR complex is represented by structures deposited in the MFIB database. This database contains the structures of complexes that form upon the binding of two disordered partners to each other. We performed a two-sample t-test of means with unequal variances to compare the relevant AUC values. The statistics of analysis showed that the mean AUC of inter-protein contact prediction in the MFIB database is higher than the inter-protein AUC mean in the DIBS database, and is closer to the performance of intra-molecular contact prediction on globular proteins (ordered partner of DIBS), although the number of analyzed MFIB complexes is small Fig. . SupplemThis result agrees with our observation that the three-dimensional structure of MFIB complexes are often similar to proper folds of globular proteins. In fact, some complex structures (IDR and globular partner together) belong to known fold families of monomeric ordered proteins  as it also stores experimentally determined affinities measured by a diverse set of techniques . We should not miss, though, that comparing Kd values from different sources may introduce additional noise into the analysis, i.e. the results presented here should be taken with care. Nonetheless, we also found correlations between the area of the interface (interaction surface) between the partners and both the AUC and Kd values  of an IDR interacting with a single globular protein, and inter-protein coevolution, for complexes in the DIBS database. Figure This result suggests that complexes between IDPs/IDRs and globular partners with higher binding affinity (lower Kd) might have coevolved more recognizably on the residue level, and an evolutionary footprint can be better seen in their sequences as covariation between pairs of interface residues.Saccharomyces cerevisiae. This complex has a high AUC for inter-protein contact prediction (translated as coevolution) and a low Kd. Snu17 binds Bud13 through a large interface, in which Bud13 adopts a U-like conformation interacting with two helices of Snu1737. Four of the contacting residues between these two proteins are among the top 20 covarying pairs in our analysis, including two pairs involving residues close to the conserved Trp232 of bud13, which is known to be important for binding37.As an example, Fig. A general observation about structures in the MFIB database is that complexes formed by coupled folding upon binding of two or more disordered chains often form a complex resembling a known globular domain. If this is the case, inter-protein coevolution of mutual folds are expected to approximate intra-protein coevolution of single-chain globular domains; this is actually what we observed in the previous section Fig. . We offeIn this study, we present the first comprehensive analysis of coevolution between residues of intrinsically disordered proteins in complexes. We evaluated the performance of intra- and inter-molecular contact prediction as a proxy for coevolution in a benchmark of three established covariation methods and a naive predictor. The analysis was carried out on two manually curated datasets of IDP/IDR complexes that are already accepted in the community.Our systematic evaluation of inter-protein residue covariation shows that the signal is weak in interactions between disordered and ordered chains, as well as between two disordered chains. However, we have seen a difference in the interaction between a disordered region and a globular domain (DIBS database) and the interaction between the two disordered chains upon binding to each other rendering a globular-like domain (MFIB database). In fact, complexes of the latter category are more similar to intra-molecular interactions observed in globular domains.It was reassuring to be able to validate our hypothesis that the coevolutionary footprint becomes more prominent in those proteins that have a greater affinity and larger interface, laying the groundwork to develop methods to determine how stable or transient an interaction is.We note that there are several potential causes of the weak signal observed, such as the accelerated evolutionary rate of disordered proteins, their binding promiscuity, and structural heterogeneity, dynamics and instability of their complexes.For the future, we suggest performing an upscaled version of this study with algorithms parameterized to work with disordered proteins as soon as a larger (or different type of) dataset of IDP complexes becomes available. The results we provide can be enhanced in the future when technical limitations are mitigated.Our analysis might have eliminated some of the mentioned technical biases but not the obstacles of the limited number of available IDP complexes (287 interactions) and the controversial reliability of the covariation calculation methods. Moreover, we should also emphasize that the sequence-alignment algorithms and homology search tools for the identification of orthologous complexes have been trained solely on globular domains and might not ideally fit with approaching disordered proteins. As the experimental techniques of structural biology for IDPs are becoming more advanced, it is expected that in the near future a significant growth of the number of available IDP complexes will be achieved. Furthermore, specific tools to optimally align IDPs will be developed, and may as well be used and made available by Pfam or other databases in the forthcoming years. As a result of all these advances, we may expect significant improvements in the covariation analysis of IDPs/IDRs.https://dibs.enzim.ttk.mta.hu/). Each DIBS entry has two or more chains, consisting in two in the majority of the cases . To characterize the database, we measured the intramolecular contacts per residue in each partner, being more than 10 in the globular partner while less than 10 in the disorder partner  is a repository for protein complexes that are formed by intrinsically disordered proteins whose tertiary structure is induced by the assembly of the complex. It has 186 entries, 98 of which are homo-, the rest are hetero-complexes with two or more chains.MFIB . Complexes with one or more chains not matching with any Pfam family, were filtered out. Each of the sequences of the complexes are taken as the reference sequence.To map proteins to Pfam domains, we used the following strategy: For each complex, we extracted Uniprot IDs and sequences. The sequences are usually small segments that are not suitable to be used to scan the Pfam database. Therefore, we extracted the full protein sequences from Uniprot and used them to query Pfam. As each query sequence might match multiple Pfam domains, we only considered those that overlap with the annotated sequence in DIBS or MFIB databases. If more than one Pfam overlapped the sequences, the one with the longest overlap was taken. The pipeline procedure can be seen in Supplementary Figure Mapping and filtering resulted in a total of 228 complexes from DIBS. We ignored homodimers and duplicated MSAs due to DIBS hereto-multimer complexes .38, while X-ray floppy terminal regions not forming non-local contacts were removed. Mapping and filtering resulted in 59 heterodimeric chains from the MFIB database that we could analyze.MFIB has 45 heterodimers and 14 tri-, tetra- and pentameric heterocomplexes. Those complexes with more than two chains were separated into dimeric units for each contacting heteromeric chains. Disordered tails not belonging to the mutual fold were trimmed. NMR structures were truncated based on MobiDB’s annotation as mobileStructures of MFIB complexes often appear to match those of regular protein domains. In order to compare MFIB complexes with PDB monomers and dimers, we created two sets of PDB structures as controls (respectively). We compared the radius of gyration (Rg) and compactness  of the two PDB control sets and MFIB protein complexes.PDB-dimers control set: we downloaded the 90% identity clustered protein X-ray dimers with high resolution (< 2 Å), then in order to obtain a length distribution similar to MFIB proteins, we took complexes with size ≤ 450 residues. This control dataset of PDB dimers constitutes 209 complexes.PDB-monomers control set: we downloaded the 30% identity clustered protein X-ray monomers with high resolution (< 2 Å), then in order to obtain a length distribution similar to MFIB proteins, we took complexes with size ≤ 450 residues. We ended up with 375 complex structures. We clustered at 30% due to the large number of complexes obtained when clustering at 90% identity.We looked for DIBS and MFIB sequences (reference sequence) in the Pfam database .12. This way, we ensured that both proteins belong to the same organism, a minimal condition for their putative interaction. If more than one protein had the same NCBI taxid, the most similar to the reference sequence was taken. We ended up with paired MSAs for 228 complexes from DIBS and 59 complexes from MFIB for the analysis.For each complex, we concatenated proteins from each Pfam alignment that had identical taxonomic ID (NCBI taxonomy)39. Supplementary Figure It is known that coevolution methods require a great number and diverse sequences to give meaningful results. To know the diversity of the MSAs, sequences were clustered at 62% and 80% identity with Hobohm-1 clustering algorithm15; N/L ≥ 3 sequences for CCMPRED9, where N is the number of sequences and L is their length—for a 100 residues protein, the MSA should have 300 sequences—or ≥ 1000 sequences less than 100% identical26.Different methods perform better at different number of sequences and diversity in the MSA: e.g.: 400 clusters at 62% identityWe also compared the number of clusters obtained at 62 and 80% identity for both the individual MSAs (order and disorder part) and the concatenated MSAs of DIBS complexes. The purpose of doing it was to determine if the number of clusters in the paired alignments changed due to the addition of the disorder part  curve (AUC)Residues close in sequence tend to give good covariation scores but these values are considered trivial as residues contiguous or close in sequence are actually always in spatial proximity. To evaluate sequence distance bias , we tested a naive predictor that scores residue pairs purely based on the distance between their positions in the query sequence. This means that it does not use any information from the MSA . Its scoring function has one naive assumption: local contacts always occur, the closer residues are in sequence, the closer they are in space. Contacts of residue pairs located distantly in the sequence are not attempted to be predicted. The formula of the scoring function:Supplementary Figure Evaluation of the performance of methods is highly dependent on the 3D distance cutoff applied to define a contact and on the number of residues set to consider a contact “non trivial” due to lack of sequence proximity. We assessed these parameters on the structured parts of DIBS complexes, thus intra-protein contacts of globular domains serve as a reference to evaluate the methods.The distance between residues in the structures was calculated with MIToS Julia package 41 using default parameters. We tested several distances between residues  to find a meaningful threshold to use in the covariation analysis.We also evaluated the sequence proximity between residues to consider a pair of residues truly coevolving instead of “trivial” contacts. In Supplementary Figure Supplementary Figure 26 and disregarding the prediction between five contiguous neighbors. We did not see any difference considering only side chain atoms or all atoms in structural distance measures. All tested thresholds to arrive at the final parameters to evaluate covariation can be seen in Supplementary Figure The choice of five residues apart is a good tradeoff between eliminating the trivial contribution to the signal coming from sequence neighbors, and having enough data to obtain robust results. From here onwards in the results section we will show the predictions at 4 Å and 6.05 Å (6.05 Å for comparison as it is a common distance threshold used in several papersSupplementary Information


Statistical potentials, also named knowledge-based potentials, are scoring functions derived from empirical data that can be used to evaluate the quality of protein folds and protein–protein interaction (PPI) structures. In previous works we decomposed the statistical potentials in different terms, named Split-Statistical Potentials, accounting for the type of amino acid pairs, their hydrophobicity, solvent accessibility and type of secondary structure. These potentials have been successfully used to identify near-native structures in protein structure prediction, rank protein docking poses, and predict PPI binding affinities.Here, we present the SPServer, a web server that applies the Split-Statistical Potentials to analyze protein folds and protein interfaces. SPServer provides global scores as well as residue/residue-pair profiles presented as score plots and maps. This level of detail allows users to: (1) identify potentially problematic regions on protein structures; (2) identify disrupting amino acid pairs in protein interfaces; and (3) compare and analyze the quality of tertiary and quaternary structural models.While there are many web servers that provide scoring functions to assess the quality of either protein folds or PPI structures, SPServer integrates both aspects in a unique easy-to-use web server. Moreover, the server permits to locally assess the quality of the structures and interfaces at a residue level and provides tools to compare the local assessment between structures.https://sbi.upf.edu/spserver/. Three-dimensional (3D) structures of proteins and protein–protein interactions (PPIs) are essential to understand most biochemical functions of cells and living organisms. Yet, the amount of experimentally determined 3D structures is limited, especially for protein complexes. Structural models derived by computational methods can be used to close the gap between the number of sequences and structures. In the recent CASP13 competition, we have observed a dramatic progress in the quality of the template-free models made by novel computational methods involving deep learning techniques .Evaluation methods can be classified into two categories: single- and multiple-model methods. Single-model methods only require one model as input, whereas multiple-model methods require several. The latter ones take advantage of the similarity between the distinct models to evaluate them, but they are not based on the properties of the model itself. In contrast, single-model methods are often based on the geometric and energetic analysis of the model coordinates, although some of them may also use additional information (e.g. for evolutionary related proteins) , 4.For single-model methods, the most common approach is to use knowledge-based potentials, i.e. scoring functions derived from the analysis of empirical data . SeveralMany scoring methods have been proposed to assess the quality of protein fold models –8, 14–18Here, we present the Split-Statistical Potentials Server (SPServer) featuring our SPs for the evaluation of protein structures and PPIs. The web server has been designed to facilitate its use and the interpretation of results. When analyzing protein folds, the server returns global scores and shows score profiles along the protein sequence to identify potentially problematic regions in the structure. When analyzing PPIs, the server returns global scores and score maps of the interfaces. The SPServer identifies stabilizing and disrupting residue pairs that can be used as starting point for follow up protein engineering.The overall implementation of the web server is summarized in Fig. As input, users have to provide the structures of one or more proteins or protein complexes. The server input is flexible; users can provide either PDB structures, mmCIF files or compressed directories containing the structures to analyze. Users also have to select the parameter used to define residue contacts (i.e.12 Å cut-off between their β-carbons—option Cβ—or 5 Å between any atom of each residue—option MIN—). Often the structures used as input are produced by modelling or fold prediction approaches, because we are interested in checking the quality of models rather than the quality of experimental structures. In the case of structures of single proteins or folds, the most common methods to produce them are by homology modelling (e.g. by MODELLER ), remoteThe first step of the scoring process is to identify the contacts between residues from the same protein (to score protein folds) or from different proteins (to score PPIs). These contacts consider the amino acids type, the distance between them, and environmental features such as the type of secondary structure or the degree of exposure of the amino acids. SPs provide a score for each one of these contacts. We obtain the score of a structure by performing the sum of scores of all its contacts. We can also get the scores of individual amino acids by performing the sum of scores of all the contacts of that residue. This can be used to define a score profile along the protein sequence. Residue scores can be averaged using a sliding window of size defined by the user along the protein sequence in order to smooth the profile.θ = . Then we define the potentials as in Eqs. B the Boltzmann constant, T the standard temperature (300 K), θa, and θb the features of amino acids a and b, and dab the distance between both residues. The terms P(·) denote the probabilities of observing interacting pairs (with or without conditions). For instance, P is the conditional probability that residues a,b interact at distance smaller than or equal to dab, and P(dab) is the probability of finding any pair of residues interacting at distance smaller than or equal to dab.We defined SPs in previous works , 10 usinPAIR, ELOCAL, E3D, E3DC, and ES3DC are obtained by summing the PMF with the corresponding subindex of each pair of interacting residues a, b, either of the same protein (for fold) or between two interacting proteins (for PPIs), as in Eq. The scores We proved  that theWith a residual ε that accounts for the reference state and becomes noise centered at 0 upon normalization . Hence, given that E3D nullifies when normalizing the scores and ε is irrelevant, we define another score, ECOMB, as:Furthermore, these potentials can be used to generate a profile per amino acid position along the sequence by summing the energies of the contacts of each residue.ES3DC considers residue frequencies along distances and their environments ; (2) E3DC considers frequencies along distances of pairs referred by the hydrophobicity of the amino acids and the rest of their environments; (3) PAIR considers amino acid frequencies along distances; (4) ELOCAL considers amino acid frequencies on a particular environment; (5) E3D considers the frequencies of any pair of residues along distances; and finally, (6) ECOMB combines ES3DC, ELOCAL and E3DC scores [In conclusion, the SPServer has 6 types of SPs available that differ on the environmental features considered for the contact definition: (1) C scores . AdditioFor a set of protein folds, the SPServer outputs: (1) the global scores  of all SPs; and (2) the scoring profile per residue  along the protein sequence. Global scores account for the overall quality of structural models, while per-residue score plots pinpoint problematic regions of the models that likely have either a wrong conformation or contacts with a wrongly modelled region.For PPIs, the server outputs: 1) global scores for the quality of the interface between the two interacting proteins; 2) a measure of the penetration between two proteins to assess for steric clashes at the interface; and (3) interface maps with the scores of residue contact-pairs between the two proteins. Global scores inform on the overall quality of the interaction (i.e. for ranking docking poses). The measurement of steric hindrances is indicated in a color legend depending on the relevance of the clashes  [We compared the native structure of Cysteine synthase A from TS321_1) . The gloThe interaction of BAX with BID mediates the insertion of BAX in the outer mitochondrial membrane, which induces apoptosis . The BAX. [We test the SPs of the SPServer on the CASP12  benchmar.  (Additiofined in ) and wrofined in . In Fig.In the Additional file Additionally, we compare the global Z-scores of SPServer with three quality metrics used as reference in CASP: Template Modelling (TM) score , Global We compared the SPServer in terms of input flexibility, user-friendliness, speed and intuitive visualization of results with other state-of-the-art functional web servers for protein fold assessment : Platform independent.Programming language: PHP, JavaScript, Python.Other requirements: Chrome, Safari, Firefox or any other modern browser.License: Open Source.Any restrictions to use by non-academics: None.Additional file 1. Data.Additional file 2.  Figure S1: Identification of steric crashes using GEPOL approach to calculate the surface. The two atoms are represented as light blue and light brown circles. The normal and position vectors are shown both in a case where there is no steric crash (a), and there is a steric crash (b). In the case (a) both vectors form and acute angle (i.e. < 90°) while in the case (b) they form an obtuse angle (i.e. > 90), and thus the sign of the two dot products will be negative.Additional file 3.  Figure S2: Residue scores of the native structure of Cysteine synthase A (green), the near-native model (blue) and the wrong model (red). The curves represent the smoothed PAIR scores with a sliding window of value 10.Additional file 4.  Figure S3: Difference between the residue scores of the native structure (reference) and the near-native (blue) and wrong (red) models. The curves represent the smoothed PAIR scores with a sliding window of value 10.Additional file 5. Figure S4: Local scores map of the interface of the interaction between BAX (Receptor) and BID (Ligand). Large cells are used for local scores (statistic energy) of the wildtype structure and upper  squares are for the mutant. Energies are shown by colors, from high (red) to low (blue), indicating the range in the label at the bottom. The scores are calculated with the PAIR potential, using a sliding window of 1 to smooth, being the optimal interactions those with most negative energy.Additional file 6.  Figure S5: Mean Pearson correlation values of the comparison between the global scores of the SPServer (ZES3DC and ZPAIR), DOPE and PROSA (Pair Z-score) potentials, and TM, GDT_TS and QCS quality metrics for the structures of CASP12 benchmark. The correlation values are extracted after performing a bootstrapping strategy of 1000 repetitions (described above). The Pearson correlation values of TM score, GDT_TS and QCS are negative because their score is higher when the model is more similar to the native structure .Additional file 7. Figure S6: Scatter plots of the global scores of the SPServer potentials ZES3DC (a) and ZPAIR (b) with respect to PROSA  for the structures of the CASP12 benchmark.Additional file 8. Figure S7: Scatter plots of the global scores of the SPServer potentials ZES3DC (a) and ZPAIR (b) with respect to DOPE for the structures of the CASP12 benchmark.Additional file 9.  Figure S8: Scatter plots of the global scores of the SPServer potentials ZES3DC (a) and ZPAIR (b) with respect to GDT_TS for the structures of the CASP12 benchmark.Additional file 10. Figure S9: Scatter plots of the global scores of the SPServer potentials ZES3DC (a) and ZPAIR (b) with respect to TM score for the structures of the CASP12 benchmark.Additional file 11. Figure S10: Scatter plots of the global scores of the SPServer potentials ZES3DC (a) and ZPAIR (b) with respect to QCS for the structures of the CASP12 benchmark.Additional file 12.  Figure S11: Histograms showing the residue correlations between the SPServer scoring functions (ES3DC and PAIR) and the PROSA (Pair) and DOPE scoring functions. Each correlation value corresponds to the correlation of all the residue scores of a structure from the CASP12 benchmark.Additional file 13.  Table S1: Global scores of the native structure of Cysteine synthase A and two predicted structural models.Additional file 14. Table S2: Global scores of the native structure of Cysteine synthase A and two of its models.Additional file 15.  Table S3: Comparison between global and quality metrics for the structures of CASP12 benchmark.Additional file 16.  Table S4: Comparison of local (residue) profiles between SPServer and state-of-art methods DOPE and PROSA for the structures of CASP12 benchmark.Additional file 17. Table S5: SPServer global Z-scores , PROSA (Pair Z-score), DOPE score, GDT_TS, TM score and QCS of the structures of the CASP12 benchmark.


The accurate annotation of protein functions is of great significance in elucidating the phenomena of life, treating disease and developing new medicines. Various methods have been developed to facilitate the prediction of these functions by combining protein interaction networks (PINs) with multi-omics data. However, it is still challenging to make full use of multiple biological to improve the performance of functions annotation.We presented NPF (Network Propagation for Functions prediction), an integrative protein function predicting framework assisted by network propagation and functional module detection, for discovering interacting partners with similar functions to target proteins. NPF leverages knowledge of the protein interaction network architecture and multi-omics data, such as domain annotation and protein complex information, to augment protein-protein functional similarity in a propagation manner. We have verified the great potential of NPF for accurately inferring protein functions. According to the comprehensive evaluation of NPF, it delivered a better performance than other competing methods in terms of leave-one-out cross-validation and ten-fold cross validation.We demonstrated that network propagation, together with multi-omics data, can both discover more partners with similar function, and is unconstricted by the “small-world” feature of protein interaction networks. We conclude that the performance of function prediction depends greatly on whether we can extract and exploit proper functional information of similarity from protein correlations. Proteins are the main component of cells and play an essential role in nearly all cell functions such as composing cellular structure. Biological functions are performed by groups of interacting and functionally associated proteins, instead of individual proteins. The accurate characterization of protein functions is a key to understanding life at the molecular level and has a profound impact on biomedicine and pharmaceuticals., resulting in biased outcomes and reduced confidence in PINs.Proteins of unknown function comprise a significant fraction of sequenced genomes . Thus, hGrAPFI method for the annotation of protein functions. INGA  balances between the propagation information and restart scores, VDit and VCit are two vectors at the t step to measure structural correlation and modular correlation between protein pi with the remaining proteins, respectively. Elements of the two vectors are initialized to 1/n . It was to note that it is possible to tune the functional similarity scores by defining two restart vectors RV_Di and RV_Ci byIntuitively, we wish to calculate functional similarity between proteins by propagation that takes both structural feature and modular feature of proteins into account. These two features are derived from domain annotation and protein complex information, respectively. Therefore, we performed a two-step propagation operation to calculate functional similarity between the protein In this study, we set α to 0.5 , 37. WheThe overall framework of network propagation algorithm can be illustrated as the Algorithm 1. The proof of convergence on the Algorithm 1 can be found in Additional file v, M_V is a module of the propagation network PN, which is composed of all neighbour nodes of v. The module fitness [M_V.WDM _ Vin denotes the total weight of edges contained entirely by a group of proteins in the module M_V, WDM _ Vout denotes the total weight of edges that connect the group with the rest of the network. β is a positive real-valued parameter, controlling the size of the module. To simplify operation, we set β to 1. The aim of this stage was to determine a module starting from protein v such that the inclusion of a new neighbour or the elimination of one neighbor from the module would lower fM_V. Thus for this purpose, we introduced the concept of neighbour fitness. Given a v’s neighbour u, the neighbour fitness of u in reference to the module M_V was calculated as follows:Intuitively, interacting partners are helpful to characterize target proteins. However, members of the same functional module are often more densely connected than those across functional modules . Therefo fitness  was intrM_V+ {u} and M_V-{u} represents the module obtained from M_V with neighbour u inside and outside, respectively.In eq. , M_V+ {uv were ranked in descending order according to the functional similarity to v. And then, all neighbours of v were visited and nodes with neighbour fitness greater than 0 were selected to form a candidate proteins set P = {p1, p2,…, pl}. Let F = {f1, f2,…, fm} be a list of functions of all proteins in P. The score of a candidate function fj in F can be calculated as follows:PN represents the functional similarity between u and v in the newly constructed propagation network. If u contains function fj, then tuj = 1, otherwise tuj = 0. Finally, candidate functions were ranked in descending order according to their scores and TOP K of them were selected to characterize the target protein v. In this study, the parameter K was set to the number of functions of the protein with the greatest functional similarity to v in the propagation network PN. The Algorithm 2 gave the overall framework of the proposed NPF method.First, neighbours of Saccharomyces cerevisiae (Baker’s yeast), because of their completeness, convincement, and widespread used in function prediction algorithms as gold standard data. The PIN data is derived from BioGRID database [To test the performance of NPF, we applied our method and six competing methods to infer protein functions in the protein interaction network of database , updateddatabase .The annotation data of proteins used for validation was downloaded from GO official website . The GO In order to better understand the behaviour of the proposed NPF method, we provided descriptive statistics on the constructed networks, including the Co-Neighbor network, Co-Domain network, Co-Complex network and PN (propagation network). Table GrAPFI [GrAPFI constructed a protein correlation network and characterized unknown proteins by integrating PINs and protein domain information.Two assessment criteria were adopted to compare function prediction performance of the NPF with six competing methods, including NC , ZhangDCGrAPFI . The NC Proteins in PINs were divided into two categories: the training set and the testing set. In one round of cross validation, the functions of each protein in the testing set are predicted according to the proteins in the training set. The validation process is repeated multiple times until each protein has a chance to become a member of the testing set. The final performances were evaluated by the average of all rounds. The first assessment criterion was leave-one-out cross-validation  which puTo assess the quality of predicted functions, we matched inferred functions with actual functions of target proteins. Precision and Recall were the commonly used measures to test the performance of function prediction methods. Precision is the fraction of predicted functions that are matched with known proteins while Recall is the fraction of known functions that are matched with predicted functions. In this study, true positive (TP), false positive (FP) and false negative (FN) represents the number of matched predicted functions, incorrectly matched predicted functions and missing matched known functions, respectively. Therefore, these two measures can be defined as follows:F-measure, as the harmonic mean of Precision and Recall, was another measure to evaluate the performance of a method synthetically, which was calculated as follows:GrAPFI. To ensure impartiality, we filtered out those GO terms whose number of annotated proteins is less than 10 or more than 200 proteins. After being processed by this step, the number of GO terms is 267. Out of all the 4113 proteins in the PINs, 2716 proteins were annotated. The average and median number of GO terms for these annotated protein was 2.1 and 2, respectively. The NPF method obtained 2146 functional modules for these 2716 training proteins on the BioGRID databases. The average size and fitness value of the detected functional modules is 13.48 and 0.5625, respectively.First, the leave-one-out cross validation was applied to verify the quality of predicted functions inferred by our NPF methods, as well as a representative set of competing methods: NC, Zhang, DCS, DSCP, PON and MP was the number of proteins successfully matching at least one known function, while PMP represented the number of proteins perfectly matching the known functions, yet ZP was the number of proteins with zero-error prediction. MMP denoted the number of proteins completely mismatching the known functions. In other words, none of the predicted functions match the known functions. From Table ZP of our method (885) is far more than NC’s (100). Figure GrAPFI, respectively.We first assessed the performance of NPF and six other competing methods on these target proteins by the average Precision, Recall and F-measure. The basic information about predicted functions by NPF and six other competing methods was presented in Table method 88 is far mGrAPFI, respectively. Then, the top K functions were selected and annotated target proteins. The Parameter K changed from 1 to 267. As for the methods of Zhang, DSC and DSCP, top N (N < =K) proteins which had the highest similarity value with target proteins were selected and K functions in these fell out proteins were selected in turn to characterize target proteins. For a given target protein and the parameter K, the precision and recall values can be calculated according to the definition in Eqs.  curve, whose vertical and horizontal coordination are the values of Precision and Recall, respectively. The PR curve is a standard for evaluation of the comprehensive performance of all methods in terms of different strategies of function selection. Predicted functions were ranked in descending order according to the values of functional similarity calculated by NPF, NC, PON and  in Eqs.  and 15)GrAPFI, rK varies. Fig. K (ranging from 1 to 267). The smaller slope of the FP/TP curve of a method was, the lower the noise ratio was, which resulted in a greater predicted accuracy of the method. From this figure we can see that, FP/TP curve of NPF has consistently been covered with that of all other methods. That is, NPF generated the fewest false positives among all the methods when matching the same number of known functions.For overall comparison, we counted the number of true positive and false positive functions predicted by NPF and competing methods. A more valuable comparison between these methods was presented by plotting FP/TP curves as parameter GrAPFI, no one function was inferred, let alone matched a known function. DCS and DSCP generated two predicted functions with one function matched by including neighbors or complex members for calculation of domain context similarities. The NC method annotated the protein YBR278W with functions of its all neighbors. Although the method successfully matched five functions, it introduced a large number of false-positive functions. Out of seven functions predicted by NPF, five functions were matched with known functions. This is due to the fact that we discovered more partners with similar functions through network propagation and got rid of some functionally unrelated proteins by detecting functional modules with high coupling. The example exhibited the highest predicting accuracy of NPF, compared to the results archived by other competing methods.To further analyze the difference between NPF and six other competing methods, we selected YNL262W, YBR278W and YPR175W as examples and inferred proteins using the seven methods. Table GrAPFI on PINs to get mean values and standard deviations of precision, recall and F-measure, as shown in Table In the previous section, we applied the leave-one-out cross-validation to exhibit the NPF’s improvement on function prediction compared to the state-of-the-art methods. However, in real-world applications, there are usually much more unknown proteins than just one. To do this we adopted the ten-fold validation to verify the validity of our method on PINs with less function information. The entire set of proteins was divided into ten equal sets randomly, nine of which were used for training and the remaining part was used for testing. The process is repeated 1000 times, each time using another testing set. We ran the functional annotation methods of NPF, Zhang, DCS, DSCP, PON and The accurate annotation of protein functions is the key to understanding life at the molecular level and plays an important role in disease treatment, new drug development. Limited by the quality of protein interaction data generated by high-throughput technologies, methods that infer protein functions in terms of protein interactions may not work well . A populIn this study, we proposed a novel protein functions annotation method based on network propagation, named NPF, which incorporates the topology of PINS and multiple biological data, such as domain annotation information, protein complexes information. Furthermore, we guarantee the NPF against false functions by detecting functional modules based on the neighbour fitness. Experimental comparison results between NPF and six state-of-the-art methods on yeast PINs showed that NPF significantly outperforms other competing methods. In our future study, we will take the hierarchical structure of GO Terms into account for further improvement of the performance of function prediction.Additional file 1 Algorithm convergence. This file provides the proof of the Algorithm 1 convergence about the effect of parameter α and ∂ on the speed of convergence.


The problem of finding the correct set of partners for a given pair of interacting protein families based on multi-sequence alignments (MSAs) has received great attention over the years. Recently, the native contacts of two interacting proteins were shown to store the strongest mutual information (MI) signal to discriminate MSA concatenations with the largest fraction of correct pairings. Although that signal might be of practical relevance in the search for an effective heuristic to solve the problem, the number of MSA concatenations with near-native MI is large, imposing severe limitations. Here, a Genetic Algorithm that explores possible MSA concatenations according to a MI maximization criteria is shown to find degenerate solutions with two error sources, arising from mismatches among (i) similar and (ii) non-similar sequences. If mistakes made among similar sequences are disregarded, type-(i) solutions are found to resolve correct pairings at best true positive (TP) rates of 70%—far above the very same estimates in type-(ii) solutions. A machine learning classification algorithm helps to show further that differences between optimized solutions based on TP rates are not artificial and may have biological meaning associated with the three-dimensional distribution of the MI signal. Type-(i) solutions may therefore correspond to reliable results for predictive purposes, found here to be more likely obtained via MI maximization across protein systems having a minimum critical number of amino acid contacts on their interaction surfaces (N > 200). Ingenious approaches based on the correlation of phylogenetic trees5 and profiles6, gene colocalization7 and fusions8, maximum coevolutionary interdependencies9 and correlated mutations11, maximization of the interfamily coevolutionary signal12, iterative paralog matching based on sequence energies13 and expectation–maximization14 have been developed and applied to resolve interaction partners in single or multiple  gene copies in the same genome. Despite these advances, the problem of protein partners prediction remains unsolved for large sequence ensembles in general, especially for the case of protein coevolution across independent genomes—examples are phage proteins and bacterial receptors, pathogen and host-cell proteins, neurotoxins and ion channels, to mention a few. The problem lacks any suitable solution especially because an effective heuristic to search for the correct set of protein partners across the space of M! potential matches still misses in case of large number of sequences M  has received great attention over the years15. When compared to other sources, the coevolutive information is the strongest signal to distinguish protein partners derived from coevolution within the same genome and, likely, the unique indication available in the case of protein interactions in independent genomes. We showed that physically-coupled amino acids at the molecular interface of A and B store the largest per-contact mutual information  that maximizes z*.In search of an effective heuristic to resolve specific protein partners based on MSAs with large numbers of sequences, the degeneracy of the per-contact mutual information ome see “. To approme see “ that staentclass1pt{minimaentclass1pt{minimaentclass1pt{minimaDespite presenting near-native values of ion see “”—Fig. 9 ion see “B. As a mion see “.Figure 316 discriminates type-(i) and -(ii) solutions with high accuracy ~ 82%, according to their nativelikeness across the space 15—as also indicated by the high accuracy ~ 82% in which such solutions are effectively classified by the KNN algorithm applied on the correlation space redefined in terms of the specific signals. Here, what is meant by coevolutive signal, as explained in15, is the surplus of MI stored in residue pairs at the interface (on average) when compared to the MI stored in residue pairs in general (on average), which is the evolutive, or phylogenetic, signal. For all cases, differentiation is far above the non-significant value of 50% thus supporting the conclusion that differences between optimized solutions based on TP rates may have a biological meaning associated with the amount of functional information recovered and its spatial distribution.To get further insights on the mismatch problem reported in Fig. 17. The condition N > 200 thus emerges here as one plausible threshold criteria for the classification of protein systems that are suitable for maximization of Given the importance that native-like solutions may have in predictive purposes, the propensity of protein systems to produce such optimized solutions was further analyzed according to the content of non-trivial errors. As shown in Fig. HAB see “”. AccordHAB see “C, this r19, each consisting of multiple sequences sharing significant homology at the primary and tertiary levels. Despite that signature, HK-RR pairs are highly specific within the same genome in consequence of evolutive pressures avoiding crosstalk between independent two-component pathways20—as shown by Rowland and Deeds, the evolution of new HK-RR pairs follows rapid sequence divergence immediately after duplication events21.So far, our results were obtained from a set of protein families involving unique sequence pairs per genome that may not have coevolved under strong selective pressures towards specificity. To better understand any implicit dependence of the results with that experimental condition, error sources (i) and (ii) were then further investigated in the context of the bacterial two-component system HK-RR featuring highly specific protein–protein interactions across multiple protein copies per genome. More specifically, histidine kinase (HK) and their respective response regulator (RR) are paralogous gene families24. Optimizations were performed with 6 replicates each, starting from a paired alignment with a randomized pairing within each species. All species were optimized together, which means that each optimization step benefits from the cumulative changes that happened in previous steps  mismatches and the great degeneracy involved. In previous works, Bitbol and coworkers developed an iterative pairing algorithm (IPA) capable of inferring protein partners using either direct coupling analysis (DCA-IPA)13, mutual information (MI-IPA)18, or phylogeny (Mirrortree-IPA)19. When benchmarked for paralog matching on the standard HK-RR dataset, DCA-IPA was as accurate as MI-IPA, and Mirrortree-IPA was even more accurate. The performance of these algorithms, however, drops considerably for species with more than 32 paralogs. The tendency is that the TP rate also drops to zero in a hypothetical genome with hundreds of paralogs19, a situation analogous to the results in Fig. Results in Fig. 15. In contrast to other coevolutionary signals that may also be considered in purpose14, Here, we investigate the hypothesis that the coevolutive information encoded on the interacting amino acids of proteins A and B  and -(ii) solutions are found to be functionally distinct from each other, with the former presenting a larger near-native content of mutual information correctly distributed among amino acid contacts. Particularly important, that finding supports the notion that their differentiation based on TP rates is not just a theoretical construct but instead has a biological meaning associated with how much functional information is recovered and how accurately distributed this information is. Type-(i) solutions may therefore correspond to reliable results for predictive purposes19 employing other more complex optimization algorithms, such as DCA-IPA13. As shown in Fig. Finally, as a special case of a highly specific system of paralogs, HK-RR interactions are resolved here at very low TP rates following Overall, the investigations performed in this work provide some clarifications into the general problem of protein coevolution from the perspective of sequence diversity. It is difficult to say to which point homologous sequences were selected to selectively bind to their native partners since there is a huge degeneracy in the space of possible sets of partners. Despite the intrinsic complexity of the problem of specific protein partners prediction for large sequence ensembles, the novel theoretical insights presented here might provide relevant information for future studies and should contribute to advancing our knowledge in the field.A and B. It is possible to construct two MSAs, MSA A and MSA B, containing M sequences from families A and B, respectively. A specific coevolution process l in MSA B to a sequence k in MSA A in a unique arrangement of size M  was selected as a representative for this system. The reason for including this system containing paralogous proteins is to have a baseline for comparison with previous related studies.Additionally, the HK-RR standard dataset containing around 5000 sequences, coming from around 450 bacterial genomes from the P2CS databasez of MSAs A and B. In each generation, the elite  reproduces and replaces the remaining 50% individuals with lower fitness with new individuals with genomes that are mutated copies of the elite. A mutation in the genome of an individual consists of swapping positions of two sequences on MSA B, and thereby slightly changing the concatenation z. The fitness of the individuals is calculated in each generation and corresponds to the total interface mutual information obtained considering an individual unique genome, i.e., a specific concatenation of MSAs A and B. The optimization was stopped after a predefined number of 50,000 generations was reached.The mutual information contained on the interface of the protein complexes, calculated as described in Eq. , was maxA slightly different optimization procedure was implemented for the special case of the HK-RR standard dataset Fig. . In thisThe optimal set of parameters for the GA were derived from a series of tests performed on six representative systems. In each test, one of these parameters varied, assuming a range of values while all other parameters remained fixed Table . All tes15, its value was maintained the same. As shown in Figure Figure B Figure A,B, the B Figure C, thus te Figure D and, siThe true positive (TP) rates of optimized concatenations obtained at the end of the genetic algorithm (GA) https://scikit-learn.org) parameters, except for the number of neighbors (K). Values of K were tested ranging from 2 to 20, but little variation in the accuracy score was observed, with scores ranging from 0.76 to 0.87. Therefore a value of K = 10 was chosen as a compromise between a possible overfit when considering too few neighbors and losing accuracy when considering too many neighbors  classifier was used to investigate if MSA pairing solutions with trivial and non-trivial error sources scattered differently in the space of relative Supplementary Information.


We address the problem of triggering dissociation events between proteins that have formed a complex. We have collected a set of 25 non-redundant, functionally diverse protein complexes having high-resolution three-dimensional structures in both the unbound and bound forms. We unify elastic network models with perturbation response scanning (PRS) methodology as an efficient approach for predicting residues that have the propensity to trigger dissociation of an interacting protein pair, using the three-dimensional structures of the bound and unbound proteins as input. PRS reveals that while for a group of protein pairs, residues involved in the conformational shifts are confined to regions with large motions, there are others where they originate from parts of the protein unaffected structurally by binding. Strikingly, only a few of the complexes have interface residues responsible for dissociation. We find two main modes of response: In one mode, remote control of dissociation in which disruption of the electrostatic potential distribution along protein surfaces play the major role; in the alternative mode, mechanical control of dissociation by remote residues prevail. In the former, dissociation is triggered by changes in the local environment of the protein, e.g., pH or ionic strength, while in the latter, specific perturbations arriving at the controlling residues, e.g., via binding to a third interacting partner is required for decomplexation. We resolve the observations by relying on an electromechanical coupling model which reduces to the usual elastic network result in the limit of the lack of coupling. We validate the approach by illustrating the biological significance of top residues selected by PRS on select cases where we show that the residues whose perturbation leads to the observed conformational changes correspond to either functionally important or highly conserved residues in the complex. Chemical and physical processes within assemblies of proteins in the cellular environment are events often encompassing multiple time and length scales. Therefore, different modeling tools are commonly used to describe the network of interactions featuring the protein dynamics . In thisIn vivo, the perturbation may arrive in the form of changing environmental conditions such as pH or ionic strength  . The metstrength , or it mstrength  or otherstrength . It is fstrength . PRS reqstrength . In PRS,strength  and ferrstrength  via PRS.strength . In a lastrength . PRS hasstrength . In a siIn this manuscript, we utilize these CG approaches to address a major challenge for structural biology in providing a mechanistic view of the behavior of molecular complexes and their conformational changes. Protein-ligand and protein–protein interactions (PPI) govern most of the cellular processes . Many stIn this work we address a reverse problem: How is it possible to trigger dissociation events between proteins which have already formed a complex? We study 25 sets of protein complexes utilizing PRS with the ENM harmonic potential to determine regions responsible for rendering known complexes incompatible. Elastic network construction helps one to probe conformational changes due to altered physical and chemical environment . AccordiThe conformational change was analyzed via PRS between two different conformers of a protein, one in its complexed form with another protein, and the other in its unbound form. The propensity to convert between conformations was examined for these two states of the protein by employing fictitious forces. The detailed theory of PRS has been laid-out in previous studies . In briekB is the Boltzmann constant and T is temperature. ΔF vector contains the components of the externally inserted force vectors on the selected residues; e.g., for the perturbation of a single residue i, –1H is the variance-covariance matrix which may be obtained from either the atomic coordinate trajectories of MD simulations of suitable length  entries. Any given pair of nodes interacts in accord with a conventional harmonic potential, if the nodes are within a cut-off distance, rc, of each other. This leads to a total of M interactions. Within the scope of an elastic network of residues that are connected to their neighbors by linear-elastic springs, one gets the 3N × M direction cosine matrix B –1 is the covariance matrix –1H for a given configuration, which is also an N × N supermatrix whose ijth element is the 3 × 3 matrix of correlations between the x-, y-, and z-components of the fluctuations ΔRi and ΔRj of residues i and j.where the subscripts 1 and 0 denote perturbed and unperturbed configurations of the protein, e length , or by imatrix B . BBT is H of the system has at least six zero eigen-values corresponding to the purely translational and rotational motions. The eigen-value distribution of the Hessian of proteins is such that the low-frequency region is more crowded than expected of polymers or other condensed matter . For each network structure, we ensure that the system has six zero eigen-values corresponding to the translational and rotational degrees of freedom of the protein. The smallest common rc at which we obtain six zero eigenvalues for all the proteins tested is 9 Å.d matter . Thus, t studied . For allR1 = ⟨R⟩1−⟨R⟩0. There is no a priori assumption on how a force might be generated at a particular point. Conversely, after finding the force/residue pair that best leads to the conformational change of interest, we relate this finding to possible causes. In this study, PRS is applied by scanning each residue in 500 random directions.PRS technique relies on repeating the above LRT calculation (Equation 1) by scanning the residues of the protein one-by-one and focusing further on those perturbations that overlap with the conformational change, Δi, we use the correlation coefficient between the predicted and experimental displacements, averaged over all the affected residues, k:To assess the quality of the predicted displacements of all residues resulting from a force applied on selected residue Sk are the displacements between the initial and the target forms obtained from the PDB structures, σS and σR are the respective standard deviations for experimental and calculated displacements. A value close to 1 implies good agreement with the experimental conformational change, while a value of zero indicates lack of correlation between experimental and theoretical findings. Several approaches were taken to select the residues that are effective in directing the protein toward alternative conformations depending on the distribution of the maximum of the Ci values, imaxC, obtained from the 500 perturbations and calculated through equation 2. We first list imaxC in ascending order: (1) If there is a sharp decrease in the Pearson correlation values, we list the top residues until that gap. (2) If there is a smooth decrease in the Pearson correlation values, we list the residues that are common among top 10 residues of all cut-off values. We also check the location of the residues that do not survive these selection criteria. We have found that the variable residues observed among top residues in different cut-off values are spatial neighbors of the listed ones.In equation 2, the overbar indicates the average, ΔWe analyzed a set of 25 protein complexes in their bound and unbound forms . The comS. In this study, we perturb the bound form of each protein by applying a random force to the Cα atom of each residue in the complex. We select residues whose perturbation leads to the ΔR vector (equation 1) that best resembles the dissociated proteins using equation 2 as the measure. For a given protein, we select the cut-off distance, ropt that yields the closest agreement with the displacement vectors from experiments for at least one residue , displays those encouraging the conformational change from the bound form to unbound form do not necessarily reside on these flexible structures. We have collected 161 effector residues from PRS calculations , are usually expected to dampen upon binding, especially at the binding interface residues, even when the protein conformation is unaltered . RMSFs o i and j  are calc i and j . The cutFrom the 8828 residues, 161 of them are selected by PRS. These residues, whose perturbation encourages the unbound over the bound form, are distributed among all amino acid types. The percentage of each amino acid type in our analysis pool and their contributions in PRS analysis are listed in In α atoms of the residues from the two sides of the pair residing within 7 Å cut-off distance of each other. We define first coordination shell residues as those located within 7 Å cut-off distance from any interface residue. All remaining residues are classified as remote, defined as those residing beyond the first coordination shell of the interface. We observe that except for the case of alpha amylase and its inhibitor . In this group, PRS top rated residues are all confined to one of the binding proteins. Thus, residues on this protein also control the conformational changes of the binding partner. Type II are the proteins in which residues confined to one of the proteins control the other binding protein and vice versa. Type III are the proteins in which each constituent controls its own dissociation event; therefore, essential residues are clustered on the “self” protein. Finally, in Type IV both partners are essential for the dissociation to occur, as residues signaling the dissociation are scattered on both binding partners. In a subset of the cases, perturbation of specific sites on only one of the constituents in the protein complex modulates dissociation. We label these as Type I group of protein complexes. The functional amino acids defined by PRS which are involved in disintegrating the contact network displays no specific perturbation location in Type I; they may be located on the interface, first shell or remote locations of the protein tertiary structure. Thus, the local perturbations which lead to global conformational shifts between bound and unbound states are not bundled in a specific region.To determine if these long range effects are controlled by electrostatics, we obtain the electrostatic potential distributions on the biomolecular surface using the APBS package . In APBSType II group represents another set of protein complexes with remotely controlled conformation changes from the bound to the unbound form. In this group, there is cross-controlled dissociation; i.e., residues that lead to the conformational change upon dissociation on one protein are located on the partner in the complex. We observe that perturbations in a stretch of consecutive resdues is required to trigger the interconversion between two conformational endpoints see , Type IIRemarkably, electrostatic effects provide an excellent description for the observed pattern in both Type I and Type II complexes. Electrostatic interactions are the primary factors of pH dependent processes in biochemical reactions. Particularly, we find that among the 16 protein complexes included into Type I and II groups, 13 of them belong to enzymes. Enzymatic activities are known to be pH dependent and protonation state of catalytic and active site residues are effective in potential distribution of the binding region. Consequently, charge distribution of these regions will modulate the interactions between the proteins and the reaction products. It has been reported that, enzymes make use of their preoriented environment to stabilize the transition state and the reduction in catalytic energy is accomplished by electrostatic stabilization of the active site of the enzyme .In the same vein, we propose that the electrostatic characteristics of residues top-rated by PRS might be found essential to specificity and ligand binding properties in enzymatic reactions. For example, in the Type I complex C3bot-RalA , arginine (13.3%), and tyrosine (12.3%) have the highest probabilities of occurrence . MoreoveWe find that dissociation events disclosed by PRS analysis may be classified into four main groups as summarized in This study is a step toward developing descriptors aimed at disrupting protein complexes with the aim of developing therapeutic approaches to alter the function of proteins working in tandem. In particular, targeting remote sites to destabilize interacting proteins using unique approaches will aid in the emerging field of allosteric drug design .All datasets generated for this study are included in the article/HA designed the research, conducted the computer experiments, analyzed the results, interpreted the data, wrote the manuscript, and constructed the figures. FJ wrote the manuscript, interpreted the data, and constructed the figures. AA and CA designed the research, guided the computer experiments and analyses, interpreted the data, guided the structure and contents of the manuscript, and edited the manuscript. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Cross-linking mass spectrometry provides a resource for the discovery of novel protein interactions of synaptic protein complexes. http://xlink.cncr.nl) to provide new entries into exploration of all protein interactions identified.Synaptic transmission is the predominant form of communication in the brain. It requires functionally specialized molecular machineries constituted by thousands of interacting synaptic proteins. Here, we made use of recent advances in cross-linking mass spectrometry (XL-MS) in combination with biochemical and computational approaches to reveal the architecture and assembly of synaptic protein complexes from mouse brain hippocampus and cerebellum. We obtained 11,999 unique lysine-lysine cross-links, comprising connections within and between 2362 proteins. This extensive collection was the basis to identify novel protein partners, to model protein conformational dynamics, and to delineate within and between protein interactions of main synaptic constituents, such as Camk2, the AMPA-type glutamate receptor, and associated proteins. Using XL-MS, we generated a protein interaction resource that we made easily accessible via a web-based platform ( N-ethylmaleimide–sensitive factor attachment protein receptor) protein interactions. At the postsynapse, the postsynaptic density hosts diverse functions in neurotransmission, such as anchoring ionotropic and metabotropic glutamate receptors, physically connecting and aligning pre- and postsynaptic elements by transsynaptic cell adhesion molecules, recruiting intracellular signaling effectors, e.g., Ca2+/calmodulin-dependent protein kinase II (CaMKII/Camk2) or G proteins, and linking these components to the actin cytoskeleton  offers the advantage of capturing both native protein structures and interactions by cross-linking reagents in a physiologically relevant subcellular context (http://xlink.cncr.nl). Using various biochemical and computational approaches, we validated the fidelity of our procedure and investigated protein interactions and dynamics for key synaptic proteins, such as Camk2 and the AMPA receptor (AMPAR). This extensive resource provides a novel perspective on protein structures, assemblies, and interactions of the synaptic proteome.In this study, we applied XL-MS to probe the complex architecture of the synaptic compartment. We identified 11,999 unique lysine-lysine cross-links from 2362 different proteins, representing one of the largest cross-linking datasets (available online at http://xlink.cncr.nl), including the number of samples in which each cross-link was found and the associated FDR , which allows the user to evaluate the reliability of specific cross-links. We also included filtering options that enable advanced visualization of experiment subsets, retrieving the origin of the cross-links, the FDR of the identification, and overlay with previously reported protein interactions.We applied proteome-wide XL-MS using the MS-cleavable cross-linker DSSO (disuccinimidyl sulfoxide) to reveal the architecture and assembly of synaptic protein complexes . To exteTo assess whether XL-MS correctly captured the spatial localization of the synaptic proteins, we investigated the existence of aberrant cross-links between intra- and extracellular regions of proteins . We retrTo reveal the coverage of synapse-specific proteins in the cross-linking repertoire, we interrogated the recently established synaptic Gene Ontology (GO) database SynGO  and 32 cR2 = 0.23) was found for the number of cross-linked lysines and the protein abundance corrected for the total number of lysine residues . No correlation was found between the number of cross-linked lysines and the total number of lysines of each protein (fig. S1D) for protein abundance and the total number of lysines . Together, these data indicated that high-abundance proteins were preferentially identified, while less abundant proteins were detected more sporadically.The depth and sensitivity of the XL-MS approach were interrogated by comparing the abundances of proteins cross-linked over the entire synaptic proteome. Protein abundance data were obtained from our previous study using label-free liquid chromatography–MS (LC-MS) quantification on equivalent preparations . This diThe formation of cross-links is bound to the spatial close proximity between two lysine residues embedded in the three-dimensional structure of protein complexes. Thus, the confidence of XL-MS datasets can be interrogated by inspecting the spatial distances of the cross-linked pairs on previously reported high-resolution structures. We matched the cross-link data with eight well-established synaptic, mitochondrial, and ribosomal protein complexes . More than 99% of the unique lysine-to-lysine cross-links within the structures  fell below the distance limit imposed by the DSSO cross-linker  domain, which further assembles into a large 12-mer holoenzyme via the C-terminal association domains. It undergoes large activity-dependent conformational movements, i.e., active or inactive in extended or compact conformation, respectively. The observed Camk2a and Camk2b cross-links were mapped onto three different Camk2 structures , includiP = 2.2 × 10−16). Similarly, the path distance between the annotated proteins for each GO term was shorter compared with the nonannotated proteins for the same term (fig. S8C). For each individual network, proteins were also found highly connected to proteins from the same cluster . Thus, these network topology analyses argue for the capability of XL-MS in capturing authentic protein connectivity.The identification of 1036 protein pairs cross-linked enabled us to construct an XL-based protein-protein interaction map of the synapse. The PPI network of individual subcellular fractions and brain areas are shown in figs. S3 to S6, and the combined network is shown in To compare the XL-based PPIs to previously reported protein interactions, we overlaid the cross-linked protein pairs to the experimentally defined interactions retrieved from the public PPI databases STRING . FurtherXL-MS provides information at the sequence level that can be used to reveal protein-protein binding interfaces. To explore this, we first investigated the distribution of cross-linked lysines regarding human protein interaction interfaces as predicted by Interactome INSIDER . The missing loops from the crystal structures of Stxbp1 (residues 506 to 531) and Stx1 (residues 10 to 26) were modeled with I-TASSER . The majFrom the cross-linked proteins identified, we investigated the AMPAR in more detail. The AMPAR is a key ligand-gated ion channel that mediates the fast neurotransmission and plasticity in the postsynapse. Its activity, function, and properties are tightly regulated by the interaction with several auxiliary proteins. Here, we combined structural modeling and interaction site investigation on the GO cluster “intrinsic component of the postsynaptic density” to provide further insights into protein interaction and assembly of the AMPAR . This clA typical MS analysis with data-dependent acquisition usually achieves 70 to 80% reproducibility at the protein level in technical replicates and 60 to 70% at the peptide level . The interaction network from all seven XL-MS experiments is also presented in fig. S11, including detailed information of each cross-link . These data resource and online tools can assist the exploration and selection of cross-links of interest for further biochemical and/or biological follow-up experiments.Together, from the seven XL-MS experiments performed in this study, we obtained 11,999 unique cross-links comprising connections within and between 2362 proteins, representing one of the largest cross-linking datasets. All the cross-links identified are available for inspection in our interactive web tool (http://xlink.cncr.nl). We used this recent type of data to investigate three different protein features, such as protein structure, protein interaction, and interaction sites, opening new avenues in revealing the architecture and assembly of protein complexes. For each of these features, we validated the reliability of the data by several approaches in key synaptic proteins, such as the structural dynamics of Camk2 and protein interaction sites of SNARE proteins. Last, we combined structural modeling and protein interaction site investigation based on XL-MS to extend our knowledge on the architecture of the AMPAR complex.In this study, we generated an extensive XL-MS dataset of synaptosomes and microsomes from mouse brains, yielding one of the largest cross-link collections to date . The complete dataset is available as a user-friendly resource for interrogation .Together, we applied XL-MS directly at subcellular fractions from mouse brain to open up new avenues in probing protein structures, assemblies, and interactions close to their native subcellular context. We extended the current knowledge on essential elements of the synapse, including Camk2 and the AMPAR, by a combination of structural modeling and protein interaction investigation. The reliability of the data was validated by several approaches, as we deemed necessary for this recent methodology. Given the molecular complexity of the synapse and the large amount and depth of the cross-linking data generated, additional hypotheses should emerge for other proteins and functional groups. To enable this exploration, we provided the complete dataset in table format (table S1) and as a user-friendly web-based platform  from two different mouse brain regions (hippocampus and cerebellum) to extensively cover the synaptic proteome. Because of the discovery purpose of these experiments, each of the four preparations for MS analysis was performed as a single dataset (dataset 1). In addition, we performed cross-linking experiments of three biologically independent replicates of hippocampal synaptosomes to gain insight into the reproducibility of XL-MS and provide further data to be used as a resource (dataset 2). The fidelity of the results was extensively investigated by comparing the XL-MS data with previously reported high-resolution structures, PPIs, and binding sites. For peptide array interaction assays, two independent and two technical replicates were performed.g for 10 min at 4°C, and the supernatant (S1) was divided for the different preparations. For the synaptosomal preparation, the supernatant (S1) was centrifuged at 100,000g for 2 hours at 4°C in a 0.85/1.2 M sucrose gradient. The synaptosomes were collected from the interface, diluted, and centrifuged at 18,000g for 20 min at 4°C. The pellet containing enriched synaptosomes was resuspended in homogenization buffer and kept for cross-linking. For the microsomal fraction, the supernatant (S1) was centrifuged at 20,000g for 20 min at 4°C, and the supernatant (S2) was ultracentrifuged at 100,000g for 2 hours at 4°C. The pellet containing enriched microsomes was resuspended in homogenization buffer and kept for the cross-linking procedure. All experiments were approved by the Vrije Universiteit Amsterdam Animal Users Care Committee and were performed in accordance with the relevant guidelines and regulations.Ten mouse hippocampi and cerebellum were dissected from 8- to 10-week-old C57BL6 mice and stored at −80°C. The subcellular fractions were prepared as described previously  and incubated at room temperature for 1 hour. The reaction was quenched by adding 50 mM tris buffer (pH 8.0) at room temperature for 30 min. The cross-linked samples were denatured with lysis buffer (8 M urea in 50 mM ammonium bicarbonate), reduced with 5 mM dithiothreitol at 56°C for 30 min, and alkylated with 15 mM iodoacetamide for 30 min in the dark at room temperature. Cross-linked proteins were digested with Lys-C for 4 hours at 37°C and subsequently digested by trypsin overnight. The resulting peptide mixture was desalted using Sep-Pak C18 cartridges (Waters), dried under vacuum, and fractionated by strong cation exchange (SCX) chromatography as previously described (z ≥ 3) were analyzed by LC-MS using an ultrahigh-performance LC Agilent 1200 system (Agilent Technologies), equipped with an in-house packed C18 column for reversed-phase separation  and coupled online to an Orbitrap Fusion mass spectrometer (Thermo Fisher Scientific). Mass analysis was performed using a previously described CID-MS2-MS3-ETD-MS2 acquisition strategy  parameters were enabled.For each preparation, 20 to 40 SCX fractions containing predominantly higher-charged peptides (http://xlink.cncr.nl) for further investigations.Peak lists (.mgf files) were generated in Proteome Discoverer (version 2.1) to convert each RAW file into three MGF files containing CID-MS2, ETD-MS2, and CID-MS3 data. During MGF conversion, the CID- and ETD-MS2 spectra were deconvoluted to charge state 1 using the MS2 Spectrum Processor add-on module in Proteome discoverer v2.1. The MGF files were used as input to identify cross-linked peptides with stand-alone XlinkX v2.0 (S ribosome (PDB 4UG0). The Gria2 homomeric structure was used since this subunit contained the highest number of cross-links compared with the other Gria proteins .The following structures were used for the spatial distance measurements: the AMPA-type glutamate receptor , the voltage-dependent calcium channel (PDB 5GJV), the vesicular adenosine triphosphatase (PDB 3J9V), the mitochondrial electron transport chain complexes I to IV , and the 80Caenorhabditis elegans (PDB 2BDW), and the compact conformation of human (PDB 3SOA). Homology modeling was performed with Maestro 11.4 . All manual docking, manipulation, optimization, and loop generation (G315-V345 for autoinhibited and T305-V345 for compact conformation) were created using the Loop Search tool implemented in SYBYL-X 2.1.1 . Energy minimization was performed using AMBER7 FF99 force field. Structure images and morphs between the different conformations were generated with PyMOL 1.8.4.1.The different conformational states of Camk2 were modeled on the basis of the structures of the extended Camk2 structure of rat (PDB 5U6Y), the autoinhibited kinase domain of The following available structures were used in the structural analysis of the AMPAR auxiliary protein complex: PDB 5IDE for AMPAR, PDB 5VOT for Cacng2/8, and PDB 5AMO for Olfm1. The structure of the DOMON domain of Frss1l was modeled on the basis of the sequence homology using the available structure with the highest sequence coverage (PDB 4ZEL). The side chains of AMPAR were modeled with Scwrl4, and the missing loop of Cacng2 (residues 39 to 56) was modeled with I-TASSER. Interaction space models were calculated using the DisVis Webserver, using a maximal Cα-Cα distance of 23.4 Å. Docking models were generated using HADDOCK2.2 for the Frrs1l DOMON domain and Olfm1. Cacng2 was structurally aligned in PyMOL from PDB 5VOT.The XL-based PPI network was generated on the basis of interprotein cross-links and visualized using Cytoscape v3.4.0 for each subcellular fraction and brain area, as well as for the different conditions combined. Myelin basic protein, which presented a high number of cross-links, was removed from the network to improve the readability of the figures, and its cross-links can be found in the table S1, our web-based platform, and fig. S11. Unsupervised edge-betweenness clustering was applied to the core component of the network, and each cluster was annotated according to the Cellular Component of the GO enrichment analysis, with all proteins identified as background . Disconnected modules were grouped using DAVID Gene Functional Classification using medium and low stringency, and each group was subsequently annotated. The connectivity between annotated proteins was tested by measuring the network path distances and the number of protein pairs directly connected with the same GO annotation (for cellular component terms with >50 proteins in the core component of the network). For the latter measurement, we performed graph rewiring while preserving the degree distribution (repeated in 100 permutations) as a control using the igraph R package. For each individual network and the replication experiment, this analysis was performed using the clusters from the combined network. The same package was used to calculate the modularity score of the network. Protein abundances were obtained from our previous proteomic study of synaptosomes and microsomes .Stx1a (NM_016801) was cloned into the pCMV6-Entry vector, yielding Stx1a-pCMV6-Entry including a C-terminal Myc-Flag tag (OriGene MR203927). Snap25 (NM_030991.3) was Gateway cloned into pDEST–EGFP (enhanced green fluorescent protein) vector, yielding Snap25-pDEST-EGFP including three N-terminal Flag tags. Stxbp1 (NM_013038.3) was Gateway cloned into pDEST vector, yielding Stxbp1-pDEST including three N-terminal Flag tags. Mouse Stxbp5 and Stxbp5l were fused to EYFP (enhanced yellow fluorescent protein) tag and cloned into a p156RRL vector, as described previously , 25 mM Hepes (pH 7.4), 150 mM NaCl, and EDTA-free complete protease inhibitor (Roche)]. Proteins were extracted by gently mixing at 10 rpm for 1 hour at 4°C and separated from the insoluble debris by two centrifugation steps for 20 min at 20,000g.HEK293 cells  were cultured in 10-cm dishes as described previously  were blocked with 3% bovine serum albumin (Sigma-Aldrich) in TBS-T  for 1 hour. After three washes with TBS-T, the arrays were incubated with protein extracts from expressing cells overnight at 4°C (one 10-cm culture dish for each array). Next, arrays were washed three times and incubated with primary antibodies (Flag or GFP tag for different proteins) for 2 hours at 4°C. IRDye 800 secondary antibody was used for the detection using an Odyssey Fc imaging system (LI-COR Biosciences). Images were obtained with Image Studio software (version 2.0.38) and analyzed with Protein Array Analyzer for ImageJ. The background was subtracted, and signals were normalized to the positive control spot of each array (antibody antigen). The arithmetic mean of the three spots with protein tags not present in the proteins probed  was used as negative control. The two technical replicates performed for each protein were averaged, followed by the subtraction of the signals of nontransfected (for Flag-tagged proteins) or GFP (for GFP-fused proteins) nonspecific binding controls. For low-signal spots, only the overlapping protein sequences of at least two different peptides with signals after the filtering were considered as positive hits. The following antibodies were used for the assay: Flag-tag , GFP , and IRDye 800CW goat anti-mouse .