We present an approach for the efficient docking of peptide motifs to their free receptor structures. Using a motif based search, we can retrieve structural fragments from the Protein Data Bank (PDB) that are very similar to the peptide’s final, bound conformation. We use a Fast Fourier Transform (FFT) based docking method to quickly perform global rigid body docking of these fragments to the receptor. According to CAPRI peptide docking criteria, an acceptable conformation can often be found among the top-ranking predictions.https://peptidock.cluspro.org/nousername.php.The method is available as part of the protein-protein docking server ClusPro at Bioinformatics online. Fast Fourier Transform (FFT) based sampling approaches have been shown to be very effective for modeling protein–protein interactions. If we assume that proteins undergo minimal conformational changes upon binding, we can exhaustively sample mutual protein orientations using a molecular mechanics like energy function in an efficient manner . The lowFFT based approaches have been successfully applied to rigid protein systems, however, they are not directly applicable to flexible systems, such as peptide-protein interactions. These transient protein complexes are important as they make up nearly half of protein interactions in the cell. To account for increased flexibility of peptides, existing global peptide docking methods instead use Monte Carlo and Molecular Dynamics-based approaches  and L (leucine) are fixed as amino acids, and X represents any amino acid. It has been shown experimentally that motif regions are limited to a small ensemble of potential conformations within a protein environment  grid based sampling using accurate molecular mechanics potentials (The key idea of our approach is to generate this ensemble by searching for proteins containing the motif of interest in the Protein Data Bank (PDB)  Clustering of fragments: In the next step the fragments are clustered using a greedy algorithm and a stringent clustering radius of 0.5 Å. The cluster center of each of the top 25 clusters is retained for docking in the next step [Similar to the 25 top-scoring fragments used in Rosetta ab initio modeling ((2) Docking of peptide fragments: Each of the (up to 25) fragments is docked to the receptor structure using an FFT based sampling protocol (see SI). The top 250 results of each run are combined into one ensemble.(3) Selection of models: The ensemble of models is clustered using a cluster radius of 3.5 Å, which was chosen to represent the resolution of basins of attraction, and the resulting clusters are ranked according to cluster size (see SI). Representatives of the top-ranking selected clusters are further locally minimized using CHARMM ((4) Overview of the protocol is presented in To demonstrate the efficiency and accuracy of this approach, we model diverse protein domain-motif interactions from the PeptiDB v2 dataset for which the interacting motif has been reported , a near-native peptide conformation is found among the top 10 PeptiDock predictions for 11 of the 16 complexes, and all apart from two cases are identified among the top 20 clusters. Similar performance is obtained for the additional validation set: for 4 out of 5 complexes, a conformation similar to bound is extracted using the motif based search, and for 3 out of the 5 cases a near-native structure is ranked first. The overall detailed assessment of PeptiDock performance is provided in accuracy .ab-initio docking of peptides to protein domains using an approach that capitalizes on the relationship between sequence and structural motifs and employs accurate FFT based sampling. Results are shown for a set of domain-motif interactions. It is important to note that this set was derived from the generic diverse peptide-protein benchmark set PeptiDB v2  Grants AF 1527292 and DBI 1458509, US Israel Binational Science Foundation Grant 2009418,2015207, and European Research Council (ERCstG 310873).Conflict of Interest: none declared.Supplementary DataClick here for additional data file.


We report here interesting synergistic effects of proline and sorbitol, two well-known chemical chaperones, in the inhibition of fibrillation of two proteins, insulin and lysozyme. A combination of many biophysical techniques has been used to understand the structural morphology and modes of interaction of the chaperones with the proteins during fibrillation. Both the chaperones establish stronger polar interactions in the elongation and saturation stages of fibrillation compared to that in the native stage. However, when presented as a mixture, we also see contribution of hydrophobic interactions. Thus, a co-operative adjustment of polar and hydrophobic interactions between the chaperones and the protein surface seems to drive the synergistic effects in the fibrillation process. In insulin, this synergy is quantitatively similar in all the stages of the fibrillation process. These observations would have significant implications for understanding protein folding concepts, in general, and for designing combination therapies against protein fibrillation, in particular. A variety of proteins have the ability to self-assemble and form fibrillar aggregates. Protein aggregation and accumulation of aggregated proteins are responsible for various diseases which have been collectively named as protein conformational disorders (PCDs) –5. PCDs Chemical chaperones are small molecules which are known to have positive influence on protein folding and stability . They doWe have selected two osmolytes, proline and sorbitol to quantitatively investigate the influence of chemical chaperones on inhibition of protein fibrillation. Proline is a multifunctional amino acid and well known chemical chaperone which is reported to protect citrate synthase against thermo-denaturation, and also stimulates renaturation of citrate synthase after urea denaturation . In addiBacillus licheniformis it is engaged in refolding of guanidine hydrochloride denatured trehalose-6-phosphatehydrolase  compared to that when proline and sorbitol are present individually  is an ultrasensitive technique for thermodynamic characterization of intermolecular interactions. ITC measures the heat released (exothermic reaction) or absorbed (endothermic reaction) by the stepwise addition of ligand to the solution containing the molecule under study. For a binding reaction at equilibrium, standard enthalpic and entropic changes (ΔH° and ΔS° respectively) contribute to the Gibbs free energy of binding (ΔG°) and are expressed by following equation:The binding enthalpy (ΔH°) reflects the change in enthalpy resulting from the formation of various noncovalent interactions such as hydrogen bonds, electrostatic interactions, vanderWaals interactions and hydrophobic interactions at the binding interface. The enthalpy change is a global property and a result of formation and disruption of many individual interactions  which in-1 of favourable enthalpy  than with the native protein. The relationship of polar interactions with the protein is established based on the basis of observed exothermic effects which are more than that with the native state. But in spite of large number of hydroxyl groups present, sorbitol is not able to establish such interactions with the protein at the elongation stage.-1 to -(23.97±0.03) kJmol-1]. On the other hand, sorbitol interacts with protein fibrils in an endothermic manner, though in the mixture, the interaction becomes exothermic.Similar trend is observed in the interaction of the two osmolytes with mature fibrils. The extent of polar interaction with proline is highest as seen from the exothermicity in the mixture [ΔH values ranging from -(25.11±0.03) kJmolIt was seen from ThT fluorescence data  that pro-1] for native stage and becomes more and more exothermic at elongation [-(15.14±0.23) to -(15.79±0.09) kJmol-1] and saturation stages [-(21.53±0.06) to -(23.25±0.08) kJmol-1] respectively. The observed values of enthalpy of interaction are less exothermic compared to those of calculated values at all stages of fibrillation. This decrease in exothermicity can be a consequence of involvement of hydrophobic interactions which contribute towards endothermicity. It is well known that hydrophobic interactions are the main driving force for aggregation. Thus, it is clear from the above data that the mixture of osmolytes has ability to interfere in both electrostatic as well as hydrophobic interactions that may be causing protein self-association leading to fibrillation and the observed synergy between the osmolytes is an outcome of a co-operative effect of one osmolyte on the other, with regard to their interactions with the protein.ITC data of lysozyme could not be analysed because of high heat of dilution of guanidium hydrochloride which masks actual heat of interaction of lysozyme with the osmolytes. The extent of synergy in case of insulin aggregation can be understood by comparing the experimental value of enthalpy of interactions in mixture of osmolytes with that of the sum total of the individual enthalpy values. OS1, OS2, and P represent first osmolyte, second osmolyte and protein respectively. We have also calculated here the limiting standard enthalpy of interaction  h and (1.5±0.1) h, respectively. Proline and sorbitol suppress the fibrillation of proteins, and the action of the osmolytes is synergistic. The values of limiting standard enthalpies of interaction of osmolytes and insulin obtained from ITC suggest prevention of β-sheet stacking and assembly by establishing polar and hydrophobic interactions between the osmolytes and the protein chains. The interaction of osmolytes with the proteins is observed to be protein specific. The synergy observed between the two chosen osmolytes can be representative and can be expected to happen with many others. This has implications for combination therapy against protein fibrillation and diseases, in general.S1 FigControl experiments showing the fluorescence properties of ThT in absence and presence of proline, sorbitol and their mixture in (A) buffer at pH 2.0 and (B) buffer containing 4M GuHCl at pH 7.0.(TIF)Click here for additional data file.S2 FigKinetics of (A) bovine pancreatic insulin, (B) hen egg white lysozyme fibrillation monitored by the binding of ThT with amyloid fibrils.(TIF)Click here for additional data file.


Structural modeling of protein-protein interactions produces a large number of putative configurations of the protein complexes. Identification of the near-native models among them is a serious challenge. Publicly available results of biomedical research may provide constraints on the binding mode, which can be essential for the docking. Our text-mining (TM) tool, which extracts binding site residues from the PubMed abstracts, was successfully applied to protein docking . Still, many extracted residues were not relevant to the docking.We present an extension of the TM tool, which utilizes natural language processing (NLP) for analyzing the context of the residue occurrence. The procedure was tested using generic and specialized dictionaries. The results showed that the keyword dictionaries designed for identification of protein interactions are not adequate for the TM prediction of the binding mode. However, our dictionary designed to distinguish keywords relevant to the protein binding sites led to considerable improvement in the TM performance. We investigated the utility of several methods of context analysis, based on dissection of the sentence parse trees. The machine learning-based NLP filtered the pool of the mined residues significantly more efficiently than the rule-based NLP. Constraints generated by NLP were tested in docking of unbound proteins from the DOCKGROUND X-ray benchmark set 4. The output of the global low-resolution docking scan was post-processed, separately, by constraints from the basic TM, constraints re-ranked by NLP, and the reference constraints. The quality of a match was assessed by the interface root-mean-square deviation. The results showed significant improvement of the docking output when using the constraints generated by the advanced TM with NLP.The basic TM procedure for extracting protein-protein binding site residues from the PubMed abstracts was significantly advanced by the deep parsing  in purging of the initial pool of the extracted residues. Benchmarking showed a substantial increase of the docking success rate based on the constraints generated by the advanced TM with NLP.The online version of this article (10.1186/s12859-018-2079-4) contains supplementary material, which is available to authorized users. Protein-protein interactions (PPI) play a key role in various biological processes. An adequate characterization of the molecular mechanisms of these processes requires 3D structures of the protein-protein complexes. Due to the limitations of the experimental techniques, most structures have to be modeled by either free or template-based docking . Both dohttps://www.ncbi.nlm.nih.gov/ pubmed) are becoming increasingly important, employing Natural Language Processing (NLP) algorithms to purge non-relevant information from the initial pool of extracted knowledge. TM + NLP techniques are widely used in biological text mining [In recent years, the number of biomedical publications, including PPI-relevant fields, has been growing fast . Thus, at mining –18, partt mining –34 and ft mining , 36.Recently, we developed a basic TM tool that extracts information on protein binding site residues from the PubMed abstracts. The docking success rate significantly increased when the mined residues were used as constraints . Howeverockground resource (http://dockground.compbio.ku.edu) [http://www.ncbi.nlm.nih. gov/books/NBK25501) requiring that either the names of both proteins (AND-query) or the name of one protein in a complex (OR-query) are present in the abstract. The text of the retrieved abstracts is then processed for the residue names. The structures of the individual proteins are used to filter the pool of the extracted residues by: (i) correspondence of the name and the number of the extracted residues to those in the Protein Data Bank (PDB) file, and (ii) presence of the extracted residue on the surface of the protein. Several NLP-based approaches  were further applied for additional filtering of the extracted residues from the abstracts retrieved by the OR-queries. Performance of the TM protocol for a particular PPI, for which N residue-containing abstracts were retrieved, is evaluated asi for this PPI, not filtered out by a specific algorithm . It is convenient to compare the performance of two algorithms for residue filtering in terms ofPTM value yielded by algorithms X1 and X2, respectively. The N(0) and N(1) values capture the general shape of the PTM distribution. Thus, the effectiveness of an algorithm can be judged by its ability to reduce N(0)  and increase N(1) . In this study, advanced residue filtering algorithms are applied to the pool of residues extracted by the OR-queries with the basic residue filtering, thus X2 will hereafter refer to this algorithm. The negative values of ΔN(0) and the positive values of ΔN(1) indicate successful purging of irrelevant residues from the mined abstracts.The TM procedure was tested on 579 protein-protein complexes (bound X-ray structures purged at 30% sequence identity level) from the D.ku.edu) . The bas.ku.edu)  Fig. 1)ockgroundhttp://wordnet.princeton.edu), were downloaded from http://search.cpan.org. The score thresholds for the residue filtering were set as 20, 0.2, and 0.11, for the Lesk, Lin and Path scores, respectively.Generic keywords semantically closest to PPI-specific concept keywords (see Results) were found using Perl module QueryData.pm. The other Perl modules lesk.pm, lin.pm and path.pm were used to calculate similarity scores introduced by Lesk , 40, LinThe keywords relevant to the PPI binding site (PPI + ive words), and the keywords that may represent the fact of interaction only (PPI-ive words) Table  were selhttp://nlp.stanford.edu/software/index.shtml) downloaded from http://search.cpan.org. The score of a residue in the sentence was calculated asi and PPI-ive word j in that sentence, respectively. Distances were calculated by edge counting in the parse tree. An example of a parse tree of residue-containing sentence with two interface residues having score 0.7 is shown in Additional file The parse tree of a sentence was built by the Perl module of the Stanford parser , 47  of the residue-containing sentence and the keyword scores of the context sentences (see above). In addition, the scores accounting for the presence of protein names in the sentenceThe features vector for the SVM model was constructed from the  kernels –50. The P, recall R, accuracy A, and F-score [The SVM performance was evaluated in usual terms of precision  F-score .5\documementclass2pt{minimmentclass2pt{minimockground unbound benchmark set 4. The set consists of the unbound crystallographically determined protein structures and corresponding co-crystallized complexes (bound structures). Binary combinations of OR and AND queries were generated [NR is the number of abstracts, mentioning residue R, ai = 1, if abstract i was retrieved by the OR-query only, and ai = 2, if the abstract was retrieved by the AND-query. For each protein, the top five residues were used as constraints in GRAMM docking [f = 1) and the high confidence (f = 10) constraints, while alleviating the effect of possible residue overrepresentation in published abstracts .TM constraints were incorporated in the docking protocol and the docking success rates assessed by benchmarking. Basic TM tool  with OR-enerated . The orienerated . The con docking . The conFor the NLP score, the confidence ranking scheme was modified such that the range is preserved between 1 and 10 and the AND-query residues are given higher precedence than the OR-query residues. The NLP was used for re-ranking within each category asα - Cα distance. The top three residue-residue pairs were used in docking with the highest confidence score 10, to determine the maximum possible success rate for the protein set.The residues at the co-crystallized interface were used as reference. Such residues were determined by 6 Å atom-atom distance across the interface. The reference residue pairs were ranked according to the CThe simplest approach to examining the context of a residue mentioned in the abstracts would be to access the semantic similarity of words (token) in the residue-containing sentence to a generic but at the same time PPI-relevant concept. For the purpose of this study, such concept was chosen to be “binding site” as the one describing the physical contact between the two entities (proteins). We designated the words “touch” and “site” as the most semantically similar words relevant to this concept (binding site) to be used in WordNet , 45 gen. Lin’s sN(0) < 0 in Table N(1) in Table Next, we tested applicability of the 7 specialized dictionaries Table  to filteAll tested dictionaries were designed for the mining information on the existence of interaction. Thus, we also tested our own dictionary, designed specifically to distinguish keywords relevant and irrelevant to the protein-protein binding sites (see Methods). Despite the small amount of PPI-relevant words in the dictionary, the filtering of the mined residues based on this dictionary led to considerable improvement in the TM performance  will be analyzed for finding new PPI + ive and PPI-ive keywords.In the dictionary look-up approach all residues in the sentence were treated either as interface or non-interface ones. The parse tree  of a sentence enables treating residues in the sentence differently depending on a local grammatical structure. Also, two adjacent words in a sentence can be far apart on the parse tree, and vice versa (distant words in a sentence can be close on the parse tree). This mitigates fluctuations in distances between keywords in “raw” sentences, caused by peculiarities in author’s writing style (some authors favor writing short concise sentences whereas others prefer long convoluted sentences). We adopted a simple approach based on the proximity of mined residue(s) to the PPI + ive and PPI-ive keywords Table  on the pSX > 0.25 . In the former algorithm, a mined residue is treated as interface residues if its SX > 0.25 and a PPI + ive keyword was spotted in the context sentences. The latter algorithm requires a more complicated approach as there is no clear distinction between the context-sentence scores for interface and non-interface residues. Thus, classification of the residues was performed by an SVM model with the optimal parameters (see Methods).The main message of a sentence can propagate through the article text comprising several sentences around the master sentence (context) and therefore it would be logical to include context information in the residue filtering as well. However, there is no clear understanding how far away the message can spread, especially in such dense text as an abstract. Thus, we treated as context only sentences immediately preceding and following the residue-containing sentence. These sentences usually do not contain residues. Thus, we included context information either by simple spotting PPI + ive keywords in these sentences (Method 2) or by calculating TM = 0.20). SVM model has filtered out all four non-interface residues, elevating TM performance to PTM = 1.00  used for the training of the SVM model. Despite a significant drop in the coverage, the results on the reduced set . The set consists of 395 pairs of separately resolved unbound protein structures and their co-crystallized complexes. Each unbound complex was docked by GRAMM three times, using (1) constraints from the basic TM, (2) constraints re-ranked by NLP, and (3) the reference constraints. The output of the global low-resolution docking scan consisted of 20,000 matches, with no post-processing . The matches were scored by the sum of the f values , calculated between the interface of the docked unbound ligand and the corresponding atoms of the unbound ligand superimposed on the bound ligand in the co-crystallized complex. Success was defined as at least one model with i-RMSD ≤5 Å in top 10 predictions. The results  and irrelevant (non-interface) residues. Efficient filtering of irrelevant residues can be done only using a narrowly specialized dictionary, which comprises words relevant to PPI binding mode (binding site), combined with interpretation of the context in which residue was mentioned. Interestingly, the size of such specialized dictionary is not a critical factor for the protocol efficiency. We tested several methods of context analysis, based on dissection of the sentence parse trees. The best efficiency was achieved using machine-learning approaches for examining residue-containing and surrounding sentences (as opposed to the rule-based methods). Docking benchmarking showed a significant increase of the success rate with constraints generated by the advanced TM with NLP.Additional file 1:Supporting information for the main manuscript, including Additional file


Characterization of protein interaction domains is crucial for understanding protein functions. Here we combine cross-linking mass spectrometry (XL-MS) with deletion analysis to accurately locate minimal protein interaction domains. As a proof of concept, we investigated in detail the binding interfaces of two protein assemblies: the complex formed by MICAL3, ELKS and Rab8A, which is involved in exocytosis, and the complex of SLAIN2, CLASP2 and ch-TOG, which controls microtubule dynamics. We found that XL-MS provides valuable information to efficiently guide the design of protein fragments that are essential for protein interaction. However, we also observed a number of cross-links between polypeptide regions that were dispensable for complex formation, especially among intrinsically disordered sequences. Collectively, our results indicate that XL-MS, which renders distance restrains of linked residue pairs, accelerates the characterization of protein binding regions in combination with other biochemical approaches. Many PPIs are mediated by protein domains. These structural units are defined by their topological conformations, forming stable and compact three-dimensional organizations independent of other regions of the protein4. Additionally, PPIs can also involve linear motifs located in intrinsically unstructured protein regions7. Characterization of protein segments directly involved in PPIs allows their targeted disruption, which is crucial for developing a molecular-level understanding of various cellular processes.Proteins are the primary effectors of the cell. They execute a plethora of cellular processes, which depend on protein-protein interactions (PPIs) responsible for formation of stable protein complexes and dynamic interaction networks9. In this approach, a specific gene of interest is cloned into a suitable host for (over)expression of the desired protein segment. Subsequently, affinity purification-based assays such as immunoprecipitations or pull-downs are performed to investigate the interaction between the (over)expressed bait protein and its binding partners. However, generating serial truncated proteins can be time-consuming and labour-intensive, especially when the protein of interest is large and contains many domains10. Furthermore, designing appropriate protein fragments may also be a challenge, as the current approaches, such as predictions from protein structural information in the Protein Data Bank (PDB) or through computational algorithms, often do not render precise results11.For decades, numerous techniques have been introduced to determine protein interaction domains, among which affinity purification combined with deletion analysis (generation of deletion mutants) is one the most popular methods13. In XL-MS experiments, cross-linking reagents, typically containing two reactive groups linked by a space arm, covalently connect two amino acid residues that are close in space. Subsequently, mass spectrometry is used to identify the cross-link and its corresponding linked residues. These experiments provide distance proximities , which can facilitate the characterization of protein interactions as well as their binding interfaces18. Although XL-MS does not provide atomic structural information, which may be a shortcoming comparing to other high-resolution methods, such as X-ray crystallography and cryo-EM, it remains a very attractive approach because of several other advantages. For instance, XL-MS experiments only require a modest amount and purity of proteins, hence readily applicable to various endogenous proteins or protein complexes directly after a small-scale affinity purification. Another advantage of the XL-MS approach is the convenience and promptness, as the experiments can be accomplished in several days. Third, the performance XL-MS is independent of the length and the tertiary structure of proteins, and is therefore beneficial in cases that are challenging for other methods, such as analyses of large proteins and disordered regions.Chemical cross-linking mass spectrometry (XL-MS) is another valuable technique to characterize protein interactions as well as their structural basis at the peptide level19, however, the relationship between cross-linked peptides and their contributions to protein binding has not yet been assessed thoroughly. Here, we focused on two protein complexes of various nature, namely, a complex formed by MICAL3, ELKS and Rab8A, involved in exocytotic vesicle trafficking20, and a complex of CLASP2, SLAIN2, and ch-TOG, important for the regulation of microtubule plus end dynamics21. The two complexes are functionally related: CLASP2 with associated proteins stabilizes microtubule plus ends in the vicinity of the cell cortex near the sites enriched in ELKS and its partners such as LL5β22. ELKS together with MICAL3 and the two Rabs controls the fusion of exocytotic carriers with the plasma membrane23. Together these protein complexes thus participate in the spatial coordination of microtubule organization, secretory vesicle delivery along microtubules to their plus ends and vesicle fusion. Such coordination is important, for example, for regulating the dynamics of focal adhesions at the leading cell edge24. Protein complexes were purified from cell lysates using the biotin (Bio) tag25 and thereafter subjected to XL-MS experiments to determine amino acid residues that are in close proximity. These residue-to-residue connectivities were used to guide the design of protein truncations and deletion mutants, thereby confirming minimal binding domains by affinity purification-based assays  cross-linker. Subsequently, the proteins were enzymatically digested, and the cross-linked peptides were analysed by MS.The first protein complex we investigated is formed by MICAL3, ELKS and Rab8A. The complex functions in constitutive exocytosis and is required for vesicle docking and fusion with the plasma membrane26. To analyse our cross-linking data, we first compared intra-protein cross-links to the available high-resolution structures of proteins and protein domains. MICAL3 consists of 2002 amino acids and contains an N-terminal monooxygenase (MO) domain, followed by a calponin homology (CH) domain, a Lin11, Isl-1 and Mec-3 (LIM) domain and a C-terminal bivalent MICAL/EHBP Rab binding (bMERB) domain27. Out of 57 intra-protein cross-links in MICAL3, nine cross-links could be mapped onto a structure homologue of the N-terminal MO domain  and two cross-links were located within the structure available region of the C-terminal bMERB domain (PDB: 5SZG). While nine cross-links fall below 30 Å (~23.4 Å + 5 Å tolerance for protein flexibility in solution), which is the expected distance range of DSSO, the two cross-links connecting the MO and CH domain of MICAL3 clearly violate the maximal distance restraint   Fig. . Since n30We also identified multiple cross-links connecting the N- and C-terminal parts of MICAL3, in line with the notion that MICAL1 and MICAL3 can be autoinhibited through inter-domain interactions32  were also cross-linked with Rab8A  and TOG-helical  domains using Phyre234 and mapped the three intra-protein cross-links within these two regions onto the homology models  defined previously21. We reason that the dispersed cross-linking pattern may likely due to the intrinsic disorder of SLAIN2, the sequence of which is rich in basic residues and has been predicted previously as largely unstructured21. The cross-linking results of SLAIN2 indicate XL-MS is prone to present cross-links that are unrelated to direct binding for intrinsically disordered proteins.The cross-linked lysine residues of SLAIN2, however, span the entire sequence of this protein Fig. , which iWe designed five truncation constructs of CLASP2, covering two or three cross-linked lysine residues  and performed affinity purification to characterize the interaction domains of SLAIN2 and CLASP2 Fig. . We obse37, has also been used to direct mutagenesis to disrupt the surface interactions in several cases40. However, the question of how accurate cross-linked residues and neighbouring regions reflect the minimal binding site of the interaction has not been investigated in detail. Here, we examined this issue using XL-MS in combination with deletion analysis in two illustrative examples. In the complex formed of MICAL3, Rab8A and ELKS, cross-linked sites fitted reasonably well with the interaction interfaces determined by biochemical assays, whereas in the analysis of the interaction interfaces between SLAIN2, CLASP2 and ch-TOG, the match between the protein fragments that are required for protein binding and the cross-linking results was less good. Despite some discrepancy, cross-linking sites provided valuable guidance for designing truncation and deletion mutants to narrow the interaction domains. However, the exact sites of interaction needed to be further validated by biochemical methods, as cross-links may likely be boarder than the minimal protein interaction regions.XL-MS, which has been widely applied to facilitate structural modelling of proteins/complexes and docking of interaction interfaces33.A nice example of XL-MS guided mapping is provided by identifying a short amino acid stretch of ELKS (residues 865–880) essential for the interaction with MICAL3, and the identification of the minimal ELKS-interacting domain in MICAL3. Similarly, in our recent study describing the interaction between MICAL3 and the centralspindlin component MKLP1, XL-MS data also helped us to rapidly generate small deletions in MKLP1 to disrupt its binding to MICAL3, allowing functional assessment of the interaction27. Interestingly, our mapping showed that Rab8A interacts with MICAL3 through the C-terminal half of the bMERB domain of MICAL3 (MICAL3-C8), while the entire bMERB domain (MICAL3-C3) of MICAL3 was needed for binding to MKLP1, in this was reflected in the distinct cross-linking patterns in these protein complexes , and the chromokinesin Xkid43, indicating that CLASP likely engages with its partners in independent protein complexes to stabilise microtubules in different contexts. These data will be useful for dissecting the detailed mechanisms underlying the functions of the studied complexes.In the other example analysed here, we showed that SLAIN2 binds to the C-terminal helical domains of CLASP2, similar to most of the other CLASP binding partners, such as CLIP-170 and CLIP-115In summary, we show XL-MS facilitates the characterization of minimal protein interaction domains. XL-MS studies can be performed under physiological conditions with modest amounts of material, and in some cases, as exemplified by identification of cross-links between SLAIN2 and ch-TOG, applicable to proteins at endogenous expression levels. This is clearly a major advantage in comparison to methods that require highly pure and large amount of proteins. Another advantage of XL-MS, comparing to computational means for binding site prediction, is that XL-MS does not require any prior structural information of proteins or protein homologues, which is an essential element for meaningful predictions in many circumstances. However, the predictive power of XL-MS depends on the structural properties of proteins: for the protein complexes analysed here, the method clearly performed much better with folded entities than intrinsically unstructured polypeptides. Taken together, with the knowledge of the advantages and limitations of the methodology, XL-MS is likely to become an important addition to the routine methods of PPI characterization.22, as well as GFP-MKLP133. MICAL3 truncation mutants were derived from BioGFP-MICAL3 using a PCR-based strategy and subcloned into mCherry- and BioGFP-expressing vectors. ELKS truncation and deletion mutants were obtained from GFP-ELKS using a PCR-based strategy in combination with Gibson assembly.The following constructs were used in the study: protein-biotin ligase BirA, BioGFP, BFP-Rab8A, mCherry-Rab6, BioGFP- and mCherry-ELKS, BioGFP- and mCherry-MICAL3-WT, BioGFP-SLAIN2 and GFP-CLASP220. Secondary antibodies were purchased from commercial sources: IRDye 800CW/680LT goat anti-rabbit, and goat anti-mouse (Li-Cor Biosciences).The following primary antibodies were obtained from commercial sources: mouse-anti-mCherry (Clontech), rabbit-anti-GFP , mouse-anti-GFP , rabbit-anti-Rab8 (D22D8) , and rabbit anti-ELKS . Polyclonal rabbit-anti-MICAL3 antibody were custom raised and has been used previouslyHEK293T cells were purchased from ATCC and passaged in 1:1 mixture of DMEM and Ham’s F10 medium (Lonza) supplemented with 10% fetal calf serum and 1% Penicillin-Streptomycin solution (Sigma). Cell lines were routinely checked for mycoplasma contamination using LT07-518 Mycoalert assay (Lonza). HEK293T cells were transfected with either a BioGFP-fusion construct (pull down bait) together with a plasmid encoding the biotin ligase BirA or an mCherry-, mStrawberry-, GFP- or BFP- fusion construct (pull down prey) for 24 hr using Polyethylenimine (Polysciences). Transfected cells were lysed in the lysis buffer ) and cell lysates were cleared by centrifugation at 13,200 rpm for 20 minutes. Dynabeads® M-280 streptavidin (Invitrogen) were pre-treated in blocking buffer  for 1 hour at 4 °C prior to pull down assays. For pull down assays, lysates of cells expressing a BioGFP- and of cell expressing the prey protein, were mixed and incubated with the beads at 4 °C for 2 hours to allow formation of the protein complexes. Beads were washed four times in ice-cold washing buffer  using a magnet (Invitrogen), the proteins were eluted by adding 2 × SDS-PAGE sample buffer and heating at 95 °C for 5 min. For successive Western Blot analysis, the samples were equally loaded onto either 8% or 10% SDS-PAGE gels, and run on 120 V in a Mini-PROTEAN® cassette (Bio-Rad). The samples were subsequently transferred to nitrocellulose membranes using a semi-dry blotting setup (Bio-Rad) and Bjerrum Schafer-Nielsen transfer buffer . Blots were blocked with 2% Bovine Serum Albumin in PBS/0.05% Tween-20 for 1 hour at room temperature and incubated with primary antibodies at 4 °C overnight. Blots were washed three times with 0.05% Tween 20 in PBS for 5 minutes at room temperature and incubated with secondary IRDye 680LT and IRDye 800CW antibodies (LI-COR Biosciences) for 1 hour at room temperature. Subsequently, blots were washed three times before imaging on an Odyssey Infrared Imaging system (LI-COR Biosciences). For cross-linking experiments, HEK293T cells were co-transfected with a BioGFP-fusion construct together with a plasmid encoding BirA and prey construct/constructs for 24 hr using Polyethylenimine (Polysciences). Transfected cells were lysed as described above and the extracts were incubated with pre-treated Dynabeads® M-280 streptavidin (Invitrogen) at 4 °C for 2 hours. Beads were washed four times in ice-cold washing buffer.45. The cross-linking reaction was carried out for 45 min at room temperature and quenched with 20 mM Tris-HCl (pH 7.8) for 30 min at room temperature. Subsequently, the on-beads cross-linked proteins were denatured with 2 mM urea, reduced with 4 mM dithiothreitol at 56 °C for 30 min and alkylated with 8 mM iodoacetamide at room temperature for 30 min in the dark. Proteins were digested using trypsin at an enzyme-to-protein ratio of 1:20 (w/w) at 37 °C for 2 hr. The supernatant was removed from the beads and further digested at 37 °C overnight. Protein digests were desalted using Sep-Pak C18 cartridges (Waters), dried and stored at −20 °C for further use. The desalted digests were fractionated by strong cation exchange chromatography (SCX) using an Agilent 1200 system (Agilent Technologies). Zorbax BioSCX-Series II column  was used. SCX fractions were collected, dried and later SCX fractions which predominantly contain + 4 and higher charged species were subjected to LC/MS analysis.Protein/complexes from streptavidin pull-downs were further washed two times in cross-linking buffer  and cross-linked on beads using 1 mM DSSO cross-linker45 or CID-MS2-MS3-ETD-MS2 acquisition strategies46. In both strategies, peptides of charge states 4–10 were selected for MS2 acquisitions. MS scan range was set to 375–1575 m/z and resolution was set to 60,000. MS2 scans were performed with a resolution of 30,000 and an AGC target of 5 × 104. The precursor isolation width was 1.6 m/z and the maximum injection time was 100 ms. The CID MS2 normalized collision energy was set to 25%. Calibrated charge dependent ETD parameters were enabled. MS3 scans were performed in the ion trap with CID collision energy of 35%; AGC target of 2 × 104 and MS2 isolation window of 3 m/z. The maximum injection time was set to 100 ms.Peptides and cross-linked peptides were analysed using an ultra HPLC Agilent 1200 system (Agilent Technologies) coupled on-line to an Orbitrap Fusion mass spectrometer (Thermo Fisher Scientific). Reversed-phase separation was performed using a 50 cm analytical column  with a 90 min gradient. Samples were analysed using either CID-ETD-MS247 and 1% FDR was used to report cross-link results. Furthermore, the search was also performed using the full sequences of the proteins that include the tags. Additional cross-links are presented in Supplementary Table and Supplementary Figure Raw data files were converted to *.mgf files using Proteome Discoverer 1.4 software (Thermo Fisher Scientific) with deconvolution option provided by add-on node MS2-Spectrum Processor . XlinkX v2.0 algorithm was used for cross-link identification. The following settings were used: MS ion mass tolerance: 10 ppm; MS2 ion mass tolerance: 20 ppm; MS3 ion mass tolerance, 0.6 Da; fixed modification: Cys carbamidomethyl; variable modification: Met oxidation; allowed number of mis-cleavage: 3. Database comprises the target sequences of each protein assembly concatenated with the same number of decoy sequences. FDR is calculated based on a target-decoy strategySupplementary informationDataset 1


Top-down proteomics (TDP) analyzes intact proteins without digestion and provides high quality data to identify and characterize toxiforms. Denaturing top-down proteomics is the most disseminated subarea of TDP, which performs qualitative and quantitative analyzes of proteoforms up to ~30 kDa in high-throughput and automated fashion. On the other hand, native top-down proteomics provides access to information on large proteins (> 50 kDA) and protein interactions preserving non-covalent bonds and physiological complex stoichiometry. The use of native and denaturing top-down venomics introduced novel and useful techniques to toxinology, allowing an unprecedented characterization of venom proteins and protein complexes at the toxiform level. The collected data contribute to a deep understanding of venom natural history, open new possibilities to study the toxin evolution, and help in the development of better biotherapeutics.The protein composition of animal venoms is usually determined by peptide-centric proteomics approaches (bottom-up proteomics). However, this technique cannot, in most cases, distinguish among toxin proteoforms, herein called  Its comoxiforms , 7. Protoxiforms . In the Different proteomics approaches reveal the protein content of any venom. The most common and the gold standard method used nowadays is bottom-up proteomics (BUP) . In all Focused on peptides, sometimes it is difficult for BUP strategies to infer the proteins present in a sample as well as to provide a biological interpretation of the data, especially when performing analysis of venom or toxic secretions. In these cases, the same peptide is often present in multiple different toxiforms. Such shared peptides, in most cases, lead to ambiguities in determining the identity of toxins , a method capable of measuring intact protein masses (without enzymatic digestion) and their fragment ions by MS, can provide the toxin information not reached with BUP techniques, as the identification and quantification of toxiforms and toxin complexes  is the most disseminated subarea of TDP. In this approach, at least once, a non-native condition – e.g. a denaturing substance  – is used and/or a physical method  that disrupts protein interactions and quaternary conformations. Usually, proteins are extracted in buffers containing strong detergents, chaotropic substances, and/or reducing agents before pre-fractionation using a denaturing method. Additionally, protein precipitation steps are also required to make the sample compatible with the next analyses. Then, fractions are submitted to LC-MS/MS, in which separation is performed by reversed-phase chromatography using organic solvents at low pH, making possible the identification of proteoforms and complex subunits present in the sample , 18.dTDP has expanded fast in the last years and is reaching maturity for the analysis of proteins up to ~30 kDa, being capable to routinely perform qualitative and quantitative high-throughput analyses of intricate biological matrices in different proteomics laboratories worldwide , 19–21. Proteome dynamic range is generally vast, especially in venom samples in which it can reach up to four orders of magnitude . ConcomiVarious techniques for protein fractionation have been used to separate intact proteoforms before MS; some of these methods are well known in biochemistry like reversed-phase liquid chromatography (RPLC), capillary isoelectric focusing (CIEF), size-exclusion chromatography (SEC), and capillary zone electrophoresis (CZE) –28. Neve1) and, especially for fragmentation spectrum (MS2), to correctly discriminate fragment ions that will be used for precise protein identification. Besides resolution, high mass accuracy, high sensitivity, and high speed are also important. Hybrid instruments that have time of flight (ToF), Fourier transform ion cyclotron resonance (FT-ICR), or FT-orbitraps as main mass analyzers achieve these aims; the last two are the most used in dTDP [MS instruments used for TDP need to have high-resolving power, typically >50,000, to determine intact proteoform masses (MS in dTDP –36.Orbitrap instruments are becoming the workhorses in dTDP because they are more cost effective and present promising hybrid architecture coupled to distinct fragmentation methods. Collision induced dissociation (CID) and high-energy collisional dissociation (HCD) are the classical fragmentation methods used in TDP studies . HoweverHowever, in spite of all these new capabilities, old problems limit the wide applicability of dTDP. Under denaturing conditions and using electrospray ionization (ESI), intact proteoforms show charge-state polydispersity (wider charge state envelopes). Additionally, the average number of protein charge states increases with the length of polypeptide chain causing ion signals split into several channels reducing the signal-to-noise ratio. In parallel, chemical noise from solvents and other substances used in sample handling, incomplete ion desolvation during ionization process, and presence of multiple PTMs in the same protein can frustrate the detection of proteoforms >30 kDa .2 2 (Q9DF33) and two toxiforms of weak toxin DE-1 (P01412), both from the veom of Ophiophagus hannah that allowed correct proteoform identification and characterization [Venom – as a very complex mixture of proteins, mostly under 30 kDa depending on the venom source – requires high speed, high resolution and sensitivity to distinguish similar toxiforms. Moreover, only with high coverage of toxin fragmentation, it is possible to identify SNPs and PTMs of toxiforms not present in databases. Figure rization .Fig. 2Fr1 and MS2 data. The complex data obtained need to be deconvoluted to simplify the protein identification process and the softwares TRASH and MS-Deconv are commonly used [With the increase in data collection, it is necessary to use software platforms to perform fast and confident automated processing of high resolution MSnly used , 44. Pronly used , 46. Othnly used –51.p-score is calculated for each proteoform identification, representing the probability that a random sequence could account for the matching ions [ProSight PC (Thermo Scientific) is one of the most used tool that performs three distinct types of search: (1) absolute mass, similar to a PSM search with large tolerance window used for identification of proteoforms with PTMs; (2) biomarker search against all possible protein fragments within the database, similar to a BUP “non-enzymatic” search and ideal to identify protein cleavage; and (3) sequence tag search performing identification of proteins based on de novo sequencing from the fragmentation data, which is indicated for identification of proteins not included in a database . In all ing ions .2 fragmentation pattern and high sequence coverage, including the modified regions, are always required.Software improvements made feasible high-throughput automated identification and characterization of several thousand proteoforms with high-confidence , 21, 53.Tityus serrulatus (Brazilian yellow scorpion). They obtained 1449 sequence tags of at least five amino acids from 73 proteins in total, by TDP approach, unraveling the role played by proteolysis in the molecular diversity of scorpion toxins [Apis dorsata bee [Verano-Braga et al., in 2013 , coined n toxins . In the sata bee .Ophiophagus hannah (king cobra) venom. A total of 15 intact toxins were manually identified by coupling LC-MS/MS analysis, intact mass values of reduced and non-reduced proteins, and BUP. Similar workflow based in locus specificity was applied to study the venom of Vipera anatolica (Anatolian meadow viper), Dendroaspis angusticeps (East African green mamba) and D. polylepis (black mamba) [A first experimental attempt to apply dTDP to the study of snake venoms was made by Petras et al.  analyzink mamba) , 58.2 identification was performed on the venom of O. hannah by Melani et al. [The first high-throughput proteoform-centric dTDP study totally based in automated MSi et al. . They api et al. .A key distinction between locus-centric versus toxiform-centric studies is that while the first simply seeks to identify a specific protein product present within the sample, proteoform analysis attempts to locate all sources of molecular variation amongst related toxiforms. Furthermore, unique peptides identified in BUP strategies are enough to assign protein locus, being not necessary TDP studies, which are more expensive and demanding to carry out.34S after protein fractionation and S content of proteins can be absolute measured. In parallel, mass profiling along the chromatographic separation is acquired by other MS, an ESI-Q-ToF, to provide protein molecular weight [Recently, Sanz-Medel’s group  publisher weight , 60.Naja mossambica (Mozambique spitting cobra) it was possible to quantify 27 intact masses of toxins [When this method was applied to the venom of f toxins . Howeverf toxins . Even mof toxins –64. Evenhttp://www.topdownproteomics.org/) has already made available a free repository where a venom database of O. hannah toxiforms is deposited [Venoms from snakes, scorpions, sea anemones, spiders, conus snails, bees, wasps, and other sources are rich in toxins with less than 30 kDa suitable to dTDP. Having in mind the large application of denaturing top-down venomics in the future and the number of identifications and characterizations of new toxiforms, it will be necessary to create new nomenclature rules and a repository site for the toxinology community. The Consortium for Top-down Proteomics , is a growing field , 17, 65.m/z range was used in native MS as a more sensitive and higher resolution alternative [Native MS analyses of protein complexes have been reported since the early 1990s using purified standard proteins and demonstrating that noncovalent interactions could be preserved in the gas phase when spraying aqueous solution at physiological pH –70. Natiernative .1), subunit masses (MS2), and subunit fragmentation (MS3) opening a new possibility in nTDP field [Subunit ejection in the gas phase from homodimer complexes and the origin of asymmetric charge partitioning was only achieved and postulated in the beginning of the 2000s . Late adDP field .Applying the complete complex characterization method, Skinner and colleagues , 74 deveTogether with method advancements in native fractionation and MS data acquisition there is the necessity for new bioinformatics tools for protein complex identification and characterization. A computational database search strategy was created by Neil Kelleher’s group , using aNative fractionation and techniques to determinate protein-protein interactions are being applied to venom studies since classical works with crotoxin to recent studies that coupled SEC and denaturing MS , 78. nTDO. hannah [Native top-down venomics was conceptualized and first applied to interrogate the venom of . hannah . Native . hannah .With many toxins executing their functions as members of protein assemblies, observing biological organization and control at this hierarchical level will provide a more sophisticated view of the molecular composition of large toxiforms and protein-protein/protein-ligand interactions from venom multitoxiforms complexes.Top-down venomics is feasible and being applied in the last years to different venom sources even with some important bottlenecks in the areas of protein fractionation, mass spectrometry and software for data analysis. Future technical advances will make TDP more user-friendly, automated, and cheaper, helping to disseminate the technique throughout the scientific community.Proteoform-centric dTDP is used in venomics studies and will be undoubtedly widely adopted in the toxinology field in the near future to help to answer new and old questions about venom variation, toxiforms and toxin processing/maturation. On the other hand, nTDP is more challenging to perform, demands top-end/customized mass spectrometers, and high-specialized trained personnel to perform experiments. However, it represents the future of top-down venomics because it provides information about large toxins, PTMs, and on protein interactions to unravel the MPCs world.A precise molecular inventory of venom toxins obtained by TDP based in MS/MS techniques will expand our knowledge of the natural diversity of venom toxiforms. This will probably improve the quality and potency of antivenoms, uncover new molecular tools and new potential drugs, as well as provide initial steps needed to understand biological mechanisms the final goal of modern toxinology.


Deciphering complete networks of interactions between proteins is the key to comprehend cellular regulatory mechanisms. A significant effort has been devoted to expanding the coverage of the proteome-wide interaction space at molecular level. Although a growing body of research shows that protein docking can, in principle, be used to predict biologically relevant interactions, the accuracy of the across-proteome identification of interacting partners and the selection of near-native complex structures still need to be improved.E. coli. Finally, we validated our method against the human immune disease pathway.In this study, we developed a new method to discover and model protein interactions employing an exhaustive all-to-all docking strategy. This approach integrates molecular modeling, structural bioinformatics, machine learning, and functional annotation filters in order to provide interaction data for the bottom-up assembly of protein interaction networks. Encouragingly, the success rates for dimer modeling is 57.5 and 48.7% when experimental and computer-generated monomer structures are employed, respectively. Further, our protocol correctly identifies 81% of protein-protein interactions at the expense of only 19% false positive rate. As a proof of concept, 61,913 protein-protein interactions were confidently predicted and modeled for the proteome of Protein docking supported by evolutionary restraints and machine learning can be used to reliably identify and model biologically relevant protein assemblies at the proteome scale. Moreover, the accuracy of the identification of protein-protein interactions is improved by considering only those protein pairs co-localized in the same cellular compartment and involved in the same biological process. The modeling protocol described in this communication can be applied to detect protein-protein interactions in other organisms and pathways as well as to construct dimer structures and estimate the confidence of protein interactions experimentally identified with high-throughput techniques.The online version of this article (doi:10.1186/s12859-017-1675-z) contains supplementary material, which is available to authorized users. Protein-protein interactions (PPIs) are ubiquitous and play crucial roles in all biological processes within and between cells by mediating signaling pathways in cellular networks and controlling intracellular communication . Since cOn the other hand, interaction details, which can only be obtained from three-dimensional structures, are crucial to fully comprehend interaction mechanisms at the atomic level. Unfortunately, despite ongoing efforts in structural genomics projects to determine complex structures, structural biology is lagging behind in the current trends of high-throughput methods. While the repertoire of monomeric protein structures solved by X-ray crystallography and NMR spectroscopy is increasing exponentially, the structural space of interacting proteins is still far from complete. In fact, there is an increasing gap between the number of identified interactions and the number of 3D structures of these associations. Thus, it is imperative to develop and continuously improve computational techniques to accurately identify interacting proteins and the corresponding complex structures.A number of computational approaches have been developed to discover and model new interactions at a system level. Modeling complex structures can be accomplished using two distinct types of techniques, template-free and template-based. The former methods, also known as protein docking, construct a complex model by assembling the monomeric structures of target proteins through a conformational search followed by the selection of high scoring binding orientations. In contrast, template-based approaches build complex structures by mapping monomeric targets to experimentally solved template complexes often followed by the refinement of the initial structural framework. Both methods have advantages and disadvantages. Template-based approaches can construct dimeric models directly from target sequences, therefore, monomer structures may not be required. Further, these techniques select templates based on sequence , 8, sequIn contrast, template-free methods are, in principle, applicable to those protein targets whose monomer structures are either solved experimentally or can be generated with homology modeling. These techniques do not require the structures of related complexes to model the association between targets proteins. Consequently, template-free approaches provide a higher coverage in large-scale applications focusing on the construction and analysis of PPI networks. Although template-free modeling is often applied to a pair of proteins known to interact with one another, several studies have successfully employed the exhaustive rigid-body protein docking and post-docking analysis to predict PPIs and PPI networks –19. For Another study attempted to predict the protein-protein interaction network of the bacterial chemotaxis signaling pathway using an all-to-all docking approach . Here, tEscherichia coli. Interaction data generated for E. coli is primed for experimental validation and further computational analyses. In addition, we validated our method against the human immune disease pathway. Encouragingly, our results demonstrate that protein docking can be used not only to identify near-native complexes but also to predict interaction partners. Overall, this study shows that combining computational modeling, structural bioinformatics, machine learning, and function annotation provides a powerful methodology for the bottom-up assembly of protein-protein interaction networks.In that regard, we developed a new approach to discover and model PPIs across proteomes employing an exhaustive all-to-all docking strategy. This pipeline comprises six major steps including protein threading and homology modelling, the prediction of binding interfaces, a rigid body docking, the flexible refinement and scoring of the modeled interfaces, and a series of function annotation filters. Our approach was carefully benchmarked on a large and representative dataset of experimental structures and computer-generated models of target proteins. In order to demonstrate its utility in large-scale projects, we modeled dimer structures and predicted PPIs across the proteome of http://www.brylinski.org/content/efindsiteppi-datasets), which was previously compiled to evaluate the accuracy of interface residue prediction and the re-ranking of docked models [The pipeline to model PPIs is benchmarked on the BM1905 dataset  [E. coli dataset consists of 2,300 proteins forming 6,341 interactions. DIP provides the sequences of interacting proteins, therefore, we constructed monomer structures with Modeller [eThread [The developed pipeline to predict PPI networks is validated using ns (DIP)  in MarchModeller  using te[eThread , followeFinally, the protocol to predict and model protein interactions is validated against the human immune disease pathway associated with the Toll-Like Receptor (TLR) signaling cascade. Information on proteins involved in this pathway as well as experimentally detected interactions were obtained from the Reactome database  in June eRankPPI [eRankPPI employs multiple features including residue-level interface probability estimates, protein docking potentials, and energy-based scores. Surface residues in target receptors are annotated with interface probability estimates by eFindSitePPI [eFindSitePPI builds on a strong conservation of the location and geometry of binding sites in evolutionarily related dimers and employs meta-threading, structural alignments, and machine learning to predict interfacial residues for a target protein. The top 10 models selected by eRankPPI are finally subjected to a flexible refinement with FiberDock [For a given pair of protein targets, a collection of docking solutions is generated with the FFT-based rigid body docking program ZDOCK version 3.02 . We use eRankPPI , a recendSitePPI , a struciberDock . FiberDop-values of 10-2, 10-5 and 10-10, respectively. In addition, the quality of dimer models is assessed with iRMSD, a standard evaluation measure in the Critical Assessment of PRedicted Interactions (CAPRI) [The accuracy of dimer models is primarily assessed with iAlign  against  (CAPRI)  and the  (CAPRI) . iRMSD i (CAPRI) .The scoring function to identify biologically relevant assemblies was trained and cross-validated against the HET3519 dataset of experimental hetero-dimers used as positives and a simulated dataset of 14,944 likely non-interacting pairs used as negatives. The negative dataset was constructed by randomly swapping ligands within the HOM14944 dataset. Since HOM14944 proteins share less than 80% sequence identity, this procedure resulted in a random set of hetero-dimers referred to as RND14944. Uniformly choosing random protein pairs excluding experimental interactions produces an unbiased estimate of the distribution of negatives in the prediction of protein-protein interactions . Hence, E. coli proteins were obtained from the EcoCyc database [Positive predictions are further subjected to filtering with Gene Ontology (GO) terms. GO is a hierarchically organized database providing a controlled vocabulary to characterize gene products, divided into three sub-ontologies: cellular component (CC), biological process (BP) and molecular function (MF) . Here, wdatabase  in May 2PPI prediction is assessed using standard evaluation metrics for classification problems:True positive rate:False positive rate:Accuracy:Matthews correlation coefficient:TP (True Positives), FN  and FP  are the number of correctly predicted, under-, and over-predicted PPIs, respectively. TN (True Negatives) is the number of correctly predicted non-interacting partners. The MCC quantifies the strength of the correlation between predicted and actual classes; by heavily penalizing both over- and under-predictions, it provides a convenient assessment measure that balances the sensitivity and specificity.where eThread and Modeller , whereas these connections are largely missed in the random network (red lines). Second, the axes in both hive plots are sorted by the clustering coefficient of individual nodes and the axis scales in Fig. y-axis) forming extensive connections to themselves as well as to low- (x-axis) and high-degree (z-axis) groups. In contrast, extensive connections between all groups are present in the network predicted by the modeling of quaternary structures. These hive plots effectively visualize differences between the predicted and random networks described above.Figure E. coli proteome is assembled by the modeling of interactions between proteins, we discuss a couple of representative examples of the modeled dimer structures. Note that experimentally solved structures are unavailable for these proteins, therefore, the presented molecular assemblies have been constructed solely from the primary sequences of individual monomers. Although monomer models are built on templates whose sequence identity to the target protein is less than 40%, the estimated Global Distance Test (GDT) [eFindSitePPI on fadJ comprises 11 residues, out of which 9 are also found at the interface in the modeled fadJ/fadI complex. Moreover, fadJ has a NAD binding domain according to the Pfam database [eFindSite [eSimDock [Since the PPI network for the st (GDT)  is greatst (GDT) . Even thst (GDT) , no strudatabase . InteresFindSite , but alseSimDock .Fig. 7ExeThread is 0.78 indicating a confident structure model. Next, we predicted the structure of glsA2 homo-dimer as a symmetric complex shown in Fig. eFindSitePPI is that it not only detects interaction sites, but also points out specific molecular interactions that stabilize a putative complex. Molecular interactions predicted by eFindSitePPI for glsA2 include a salt bridge between the side chains of R232 (chain A) and E82 (chain B) as well as aromatic contacts between W252 (chain A) and W252 (chain B), which are found in the top-ranked complex model selected by eRankPPI.The second example is glutaminase 2 (glsA2), an amidohydrolase enzyme responsible for generating glutamate from glutamine, demonstrated to be a self-assembling protein . The GDTE. coli, where our protocol correctly identified 62% of known PPIs.Finally, based on experimental data provided by the Reactome database, we modeled protein complex structures for the human immune disease pathway associated with the TLR signaling cascade. TLRs are sensors of the innate immune system recognizing pathogen-associated molecular patterns , 58. TheIn addition, positive predictions, true positives and false negatives are shown as a binary interaction matrix in Fig. E. coli and within the human immune disease pathway. Our results indicate that protein docking supported by evolutionary restraints and machine learning can be used to reliably identify and model biologically relevant protein assemblies. Furthermore, the accuracy of the identification of interaction partners can greatly be improved by including only those protein pairs co-localized in the same cellular compartment and involved in the same biological process. The proposed method can be applied to detect PPIs in other organisms and pathways as well as to construct structure models and estimate the confidence of interactions experimentally identified with high-throughput techniques. Finally, with the growing volume of structural data, experimentally confirmed protein interactions, and functional annotation, we expect the coverage and accuracy of our approach to increase over time.In this work, we developed a new method combining molecular modeling, structural bioinformatics, machine learning, and functional annotation data to predict PPIs across proteomes. We first comprehensively tested this protocol on representative datasets of experimental structures and computer-generated models of protein dimers and then we applied this methodology to predict PPIs across the proteome of


Most computational approaches for the analysis of omics data in the context of interaction networks have very long running times, provide single or partial, often heuristic, solutions and/or contain user-tuneable parameters.local subnetwork model, LEAN allows exact, parameter-free, efficient and exhaustive identification of local subnetworks that are statistically dysregulated, and directly implicates single genes for follow-up experiments.We introduce local enrichment analysis (LEAN) for the identification of dysregulated subnetworks from genome-wide omics datasets. By substituting the common subnetwork model with a simpler Evaluation on simulated and biological data suggests that LEAN generally detects dysregulated subnetworks better, and reflects biological similarity between experiments more clearly than standard approaches. A strong signal for the local subnetwork around Von Willebrand Factor (VWF), a gene which showed no change on the mRNA level, was identified by LEAN in transcriptome data in the context of the genetic disease Cerebral Cavernous Malformations (CCM). This signal was experimentally found to correspond to an unexpected strong cellular effect on the VWF protein. LEAN can be used to pinpoint statistically significant local subnetworks in any genome-scale dataset.LEANR implementing LEAN is supplied as https://cran.r-project.org).The R-package Bioinformatics online. LEAN is designed to avoid computational and statistical issues through the use of a strongly constrained subnetwork model. The strong constraint permits a combination of fundamental advantages, relative to existing network analysis approaches. In particular, the underlying optimization problem is efficiently solvable, allows to survey all solutions , it has no parameters that have to be tweaked by the user, and LEAN subnetworks of interest imply specific genes for experimental follow-up. We present here a novel network-based approach, termed We evaluated the performance of LEAN by comparing it to previously published subnetwork detection methods on simulated pathways and showed that it is able to extract biologically meaningful common pathways even on a relatively small number of publicly available datasets. An application to a transcriptomic dataset of the response to invalidation of CCM genes in mouse models led to the discovery of the previously unknown involvement of Von Willebrand Factor (VWF) in the pathophysiology of CCM disease.In the following paragraphs, we will introduce the key concepts of the LEAN method. For better readability, detailed descriptions of how presented results were obtained are relegated to the Results and P-values) with large-scale interaction networks. It is based on a local subnetwork model, which assumes that higher-order biological activity can be detected by aggregating signals from a single gene and its direct network neighbors  subnetwork model, in terms of computational complexity, and the assessment of statistical significance. While the number of subnetworks is typically exponential in the number of genes, networks contain only a single local subnetwork per gene. The identification of optimal subnetworks is computationally NP-hard  for some or all genes and an interaction network. In many applications, P-values originate from a statistical test for differential expression, such as limma . Analysis is performed using the given interaction network restricted to genes for which an input P-value has been calculated based on the transcriptomic data. A local subnetworkcenter geneLEAN is based on two ingredients: A list of measures of statistical significance . To compute gES, for each position P-value list, we first calculate the probability P-values, at least k of the ip are lower or equal to kp using the cumulative distribution function of the binomial distribution:P-value list of gunnormalized enrichment score ES is then defined as:enrichment P-valuegES to a background distribution of m from the set of input P-values to determine the background distribution of P-values. For each candidate subnetwork with a significant LEAN P-value, our implementation of LEAN returns its central gene along with the above mentioned intermediate scores and additional information on the candidate subnetwork. For each candidate subnetwork For use in this study, we employed the murine STRING interaction network . STRING P-values to the genes contained in these subnetworks  in the low FPR range  and tumor necrosis factor alpha (TNFα) in comparison to appropriate controls. The remaining two datasets respectively compare basal gene expression between two tissue types and a common reference tissue, murine cerebellum. The characteristics of the selected datasets are summarized in limma gene-by-gene analysis of differential expression, as well as LEAN , a condition characterized by vascular malformations of the central nervous system that lead to cerebral hemorrhages. Familial CCM (about 20% of CCM patients) occurs as a condition with autosomal dominant transmission mode caused by loss-of-function mutations in one of the 3 CCM genes: CCM1/KRIT1, CCM2/Malcavernin/OSM and CCM3/PDCD10 . PrevioTo investigate the mechanisms underlying the development of CCM lesions, we performed microarray analyses  and Von Willebrand Factor (VWF), respectively, were the only ones detected in all three of the CCM invalidation experiments. Applying the approach on a medium-confidence version of the STRING database . Secondly, the interpretation of predicted subnetworks is complicated by the fact that most subnetwork methods do not indicate statistical significance of a detection. Moreover, used subnetwork scores are oftentimes not comparable between different experiments. This additional degree of uncertainty reduces the practical utility of such methods. Thirdly, many approaches have limited practical value due to additionally required input data, non-obvious choices of input parameters, dependency on commercial software libraries and/or impracticably long running times  statistics ensures sensitivity even in the case of very few significant P-values across a wide range of parameters.The diversity of evidence represented in functional interaction databases poses a challenge for any subnetwork method. With rapidly advancing technology, the number of known functional interactions across multiple contexts increases, making it unlikely that all known functional interactions of any given protein are involved in the interpretation of any given set of experimental data. This, together with experimental false-positives, can statistically be interpreted as noise that can be expected to dilute the statistical significance of the few relevant interactors. Just as GSEA, LEAN guards against this problem through a statistic that emphasizes the role of the most significant neighborhood P-value of the corresponding local subnetworks. A good example of this behavior is VWF, whose local subnetwork covers genes involved in angiogenesis, hemopoiesis and blood coagulation. When analyzed as a set of known pathways, the transcriptomic signals from these pathways each fail individually to meet statistical significance, but the combination of their signal in LEAN makes the joint signal detectable under all three CCM invalidations.A further interesting property of LEAN is its increased detection sensitivity for genes that participate in multiple dysregulated pathways, since signals from all such pathways will contribute to the LEAN The local subnetwork model is clearly a very restricted representation of biological pathways. This fact, however, does not appear to restrict LEAN’s capability to detect important biological signals. LEAN outperformed a reference method using the common unconstrained subnetwork model on simulated pathways which had a significantly larger average graph radius  than the radius of 1 of the local subnetwork model. As expected, we detected a negative correlation between graph radius and LEAN performance in a more detailed subnetwork simulation study see . LEAN diin vivo in our CCM mouse model. Indeed, while a punctate VWF staining was detected in cerebral and retinal vessels from control animals as well as in normal vessels of CCM2-ablated animals, an accumulation of ultra-large VWF (UL-VWF) multimers was observed on the surface of iCCM2 lesions. Observation of such ultra-large vWF multimers in vivo is very unusual since they are proteolyzed very rapidly in normal conditions.LEAN analysis, applied to CCM transcriptomic data, detected two consistently deregulated local subnetworks in all three CCM groups. One of these local subnetworks was centered on VWF, which was not shown to be deregulated itself at the transcription level using gene-by-gene analysis. The hypothesis of a higher-level dysfunction of the subnetwork around VWF was validated The biological question raised by our study is how this VWF dysfunction relates to the CCM pathogenesis. Our first hypothesis is that VWF strings observed at the surface of the CCM lesions are a consequence of an endothelial injury occurring early on during the lesional process. Indeed, it is important to note that CCM transcriptomic analysis, used as inputs for LEAN, was performed on vessels prior to CCM lesion development.in vivo under several pathological conditions. VWF strings were detected in human malignant melanoma tissues anchored at the microvessel surface of the tumor, promoting cancer progression  properties , proteomics (‘hotspots of proteomic activity’) or integrated datasets comprising multiple data sources. We note that LEAN also applies to incomplete data, as typical in the case of shotgun proteomics: Network nodes without information can just be ignored in the computation of the LEAN statistics.We introduced here LEAN, an efficient, exact, exhaustive and parameter-free method that complements single-gene analysis and circumvents common problems in gene set enrichment and network-based analysis through a restricted local subnetwork model. In our evaluation on simulated data, LEAN–despite its restricted subnetwork model–performs better than most other tested methods. Applied to the transcriptome of murine models for the genetic disease CCM, LEAN predicted the involvement of VWF, which had neither been implicated in the pathophysiology of the disease, nor shown detectable deregulation on the mRNA level. Experimental evaluation of VWF protein in vivo confirmed abnormal VWF conformation and localization in the mouse model of CCM disease.Supplementary DataClick here for additional data file.


Identifying protein functional sites (PFSs) and, particularly, the physicochemical interactions at these sites is critical to understanding protein functions and the biochemical reactions involved. Several knowledge-based methods have been developed for the prediction of PFSs; however, accurate methods for predicting the physicochemical interactions associated with PFSs are still lacking.fiDPD, which was built using protein domains found in the Protein Data Bank. This method was applied to 13 target proteins from the very recent Critical Assessment of Structure Prediction (CASP10/11), and our calculations gave a Matthews correlation coefficient (MCC) value of 0.66 for PFS prediction and an 80% recall in the prediction of the associated physicochemical interactions.In this paper, we present a sequence-based method for the prediction of physicochemical interactions at PFSs. The method is based on a functional site and physicochemical interaction-annotated domain profile database, called http://202.119.249.49.Our results show that, in addition to the PFSs, the physical interactions at these sites are also conserved in the evolution of proteins. This work provides a valuable sequence-based tool for rational drug design and side-effect assessment. The method is freely available and can be accessed at  Most proteins perform biological functions via interactions with their partners, such as small molecules or ligands, DNA/RNA, and other proteins, forming instantaneous or permanent complex structures. Of particular importance is that only a few pivotal amino acids on a protein’s surface, usually called protein functional sites (PFSs), play key roles in determining these interactions. Thus, understanding protein functions depends upon accurate predictions of PFSs. However, PFSs alone do not reveal the details of their physicochemical interactions, which is indispensable information for understanding protein biochemical reactions. Together with PFS prediction, accurate protein-ligand interaction (PLI) prediction opens up a new dimension in correctly annotating protein function and thus provides valuable information for rational drug design and drug side-effect assessment –3. To daIn the last few decades, many computational methods have emerged to identify PFSs from protein structures and sequences . Most sefDPD, and then assigns the types of interactions most likely to appear at the predicted sites. In this study, we derived a functional site- and interaction-annotated domain profile database, called fiDPD, which plays the primary role in the prediction. A profile hidden Markov model of the HMMER program was used in the prediction to search a module member of the database for a given protein. We applied the fiDPD method to 10 target proteins of CASP10 [In this paper, we develop a new method for predicting physical interactions occurring on functional sites based on the amino acid sequences of given proteins. This sequence-based method first predicts PFSs from a functional site-annotated domain profile database, or f CASP10  and CASPf CASP10  and founfiDPD. We first introduced the fDPD as a list of representative profile modules built by sorting out structure-and-sequence similar protein domains in the SCOP databases [fDPD profile modules and thus to build the fiDPD.Figure atabases . Next, Phttp://scop.berkeley.edu/references/ver=2.05, which includes 214,547 domain entries extracted from 75,226 protein structures in the PDB. In SCOP, these domain structures are arranged in a hierarchical 7-level system—Class (cl), Fold (cf), Superfamily (sf), Family (fa), Protein Domain (dm), Species (sp), and PDB code identity (px)—according to their sequence, function and structure similarity. Specifically, those domains listed in a given domain entry (dm) presumably share the same class, fold, superfamily and protein family but might differ in species and PDB code entry. Theoretically, PFSs are more likely to be conserved when they share both higher structural and sequential similarity, and this assumption forms the basis for our algorithm of fiDPD in the prediction of PFSs and PLIs. Using a profile hidden Markov model of the HMMER program, the MSA of all the domains within the same dm entry gives a single representative profile module. In this way, 12,527 representative profile modules were created for all the dm entries, forming the basis of fDPD and fiDPD.We started with a modified classification of protein domain structures collected in the SCOP database , 51. In fDPD, it is important for protein domains within the same dm entry to be structurally and sequentially close to one another. However, a quick calculation reveals that the Cα root-mean-square-distance (RMSD) can be as large as 12 Å for many domain structures listed in the same dm entry. This result indicates that there are many domains listed in the same dm entry of SCOPe 2.05 that have quite different structures, which makes the profile modules of fDPD less representative of member proteins within the dm entry. To reduce the difference, we divided the domains within a dm entry into a few smaller groups or subgroups so that selected domains within the same subgroup would have mutual Cα-RMSD < 7 Å and a mutual sequence similarity > 10 . Thus, fDPD, sequences of protein domains in a subgroup were extracted and aligned using the MSA program MUSCLE [hmmbuild module of the HMMER program . A profS was assigned to each position in an fDPD profile module to mark its propensity of being a functional site. To this end, we first mapped known functional sites of member proteins within the same subgroup to the profile module according to the MSA  were calculated between PFSs (ASite) and ligands (ALigand). Finally, a few types of bonding and nonbonding interactions for each ASite were determined based on the pairwise distances and the biochemical properties of involved amino acids.To annotate the profile modules with PLIs, atomic interaction patterns between the protein and ligand were initially determined based on their 3D protein-ligand complex structures. Specifically, the atomic 3D coordinates of amino acids listed in PDB SITE sections and those of ligand molecules were filtered out from the PDB files; then, a series of atomic distances  and have either a parallel or perpendicular orientation , 63. Herd between a nonhydrogen atom of protein functional site and a nonhydrogen atom of ligands satisfies the following inequality:A) is the Van der Walls radius of atom A and no covalent bond, coordination bond, hydrogen bond, electrostatic force or π-stacking interaction is found between them. A similar definition of the Van der Waals interaction was also used by Kurgan and colleagues in their study of protein-small ligand interaction patterns [A Van der Waals interaction is formed when the distance patterns  and by Mpatterns . The atopatterns . Each fuR(A) is the radius of atom A. For metal-ion ligands, this condition also defines coordinate bonds between metal ions and PFSs. Usually, in coordinate bonds, the shared electrons are present in atoms with higher electronegativity in a functional site. We denoted NCOV as the total number of covalent bonds involving atoms in the functional site and NCOO as the total number of coordinate bonds involving atoms in that site.Usually, nonbonded forces dominate interactions between a ligand and its target protein; however, irreversible covalent bonds are also found in PLIs when a tight and steady connection between the ligand and receptor is essential to the biological function, such as in the rhodopsin system . A covalV = . The interaction vectors of all member proteins were summed in different pivotal sites of the profile module according to the MSA of the studied subgroup. As a result, each fDPD profile module was annotated with interaction vectors V on hypothetical functional sites, thus forming the fiDPD.We characterized a PLI between a PFS and the ligands with a 7-dimensional interaction vector fiDPD is essentially a list of profile module entries annotated with domain functional sites and PLIs. In fiDPD, two steps are required to predict the hypothetical functional sites and involved PLIs for a given inquiry protein: 1) identifying profile modules in fiDPD that match the query sequence best and 2) interpreting pivotal functional sites and associated PLIs of the matched profile modules as a prediction of PFSs and PLIs for the query protein based on certain statistical evaluations.fiDPD scans the query sequence against all its module entries using the SCAN module of the HMMER program [− 5. Each alignment , we defined a scoring function Fi for the ith residue of the query protein as its propensity to be a functional site:j and i′ stands for the position of the profile module that matches the ith residue of the query protein. Residues with a high-valued F-scoring function will be predicted as hypothetical functional sites.In the first step,  program . The scaj in Eq. ) is assiF-valued sites for a query protein is to simply choose a certain number (n) of top-valued residues, called n-top selection. This method has been used for enzyme catalytic site prediction [F-scores were smaller than a cutoff percentage M% of the maximum F-value Fmax; then, for the remaining residues, the top T% were predicted as hypothetical functional sites of the query protein. Usually, this selection strategy tends to give a greater prediction function for larger proteins. We used this selection strategy to predict PFSs in the remainder of this paper. The server is freely available and can be accessed at http://202.119.249.49. For clarity, F-scores are renormalized to a 1–100 range for predicted sites.One way to determine high-ediction  since exediction . Anotherediction , 34. In Ii = {NCOVi, NCOOi, NHBAi, NHBDi, NPIi, NELEi, NVDWi} for the ith residue of the query protein following Eq.  and Recall = TP/(TP + FN), where the true positives (TPs) are the predicted residues listed as functional sites in the dataset, the false positives (FPs) are the predicted sites not listed in the dataset, and the false negatives (FNs) are the functional sites listed in the dataset but missed by the method. Another relevant quantity is the true negative (TN), which stands for the correctly predicted nonbinding/nonfunctional site residues. In our calculations, the statistics did not take account of the “no-hit” predictions. The overall precision is the sum of all the TPs divided by the total number of predicted residues, and the overall recall is the sum of all the TPs divided by the total number of listed functional sites in the dataset. The precision-recall curve was found to be slightly dependent on the cutoff percentage M% and T% in the selection method. The MCC  was usedThe predicted PLIs were compared with those directly derived from 3D protein-ligand complex structures, and precision and recall values were obtained to qualify PLI predictions.fiDPD scanned the T0737 sequence against the database and found 4 profile module entries, all from the Apolipoprotein family with a structure of a four-helical up-and-down bundle. The 4 entries include an automated-match-domain profile built from 10 sequences from Arabidopsis thaliana, a second automated-match-domain profile built from 4 sequences from Rattus norvegicus, an augmenter of liver regeneration domain profile built from 13 sequences from Rattus norvegicus, and a thiol-oxidase Erv2p domain profile built from 6 sequences from Saccharomyces cerevisiae. The scanning E-value ranges from 2 × 10− 8 to 1 × 10− 19, indicating that the query sequence only has moderate similarity with the annotated sequences in the database. A total of 56 annotated pivotal sites in the 4 fiDPD profile modules were then collected and sorted according to their functional site scoring functions. When mapping to the query sequence, 12 functional sites were then automatically identified, resulting in a 92% prediction precision and 57% recall. We also examined those functional sites that fiDPD failed to identify and found that they are located in a different C-terminal domain than the four-helical up-and-down bundle domain.The 292aa mimivirus sulfhydryl oxidase R596 is target T0737 of CASP10, whose structure was later determined at 2.21 Å  was comparable with that of LIBRA, especially when known sites of the target PDB structures were not used.We compared the performance of ds LIBRA  , from which we developed a sequence-based method for predicting both PFSs and PLIs. Our method is based on the assumption that proteins that share similar structure and sequence tend to have similar functional sites located on the same positions on a protein’s surface. A profile module entry in fiDPD is representative of a bunch of annotated domain structures that share high sequence and structure similarity. The fiDPD method first identifies profile modules in the database and then, as a prediction, maps the annotated pivotal sites and associated interactions of the module(s) to the residues of the query protein.In this paper, we present a new functional site- and physicochemical interaction-annotated domain profile database , ligand-specific models, such as zinc-binding sites or RNA-binding sites, should endow fiDPD with the corresponding capability to predict catalytic sites, zinc-binding sites or RNA-binding sites. Annotations of fiDPD modules using other resources, such as dynamic simulations, FDPA calculations [fiDPD predictions that involve the protein dynamics and drug activity in PLIs. Second, considering that the classification of binding sites plays a key role in drug discovery and design, it would be interesting to use the clustering sites [fiDPD prediction suffers from the sampling limitation of annotations of known proteins. This sampling problem might be partially solved with large-scale protein sequencing efforts and worldwide structural genomics projects.ulations , pocket ulations , drug-taulations , etc., sng sites , 80 inst