Protein–protein interactions are fundamental to many biological processes. Experimental screens have identified tens of thousands of interactions, and structural biology has provided detailed functional insight for select 3D protein complexes. An alternative rich source of information about protein interactions is the evolutionary sequence record. Building on earlier work, we show that analysis of correlated evolutionary sequence changes across proteins identifies residues that are close in space with sufficient accuracy to determine the three-dimensional structure of the protein complexes. We evaluate prediction performance in blinded tests on 76 complexes of known 3D structure, predict protein–protein contacts in 32 complexes of unknown structure, and demonstrate how evolutionary couplings can be used to distinguish between interacting and non-interacting protein pairs in a large complex. With the current growth of sequences, we expect that the method can be generalized to genome-wide elucidation of protein–protein interaction networks and used for interaction predictions at residue resolution.DOI:http://dx.doi.org/10.7554/eLife.03430.001 DNA is often referred to as the ‘blueprint of life’, as this molecule contains the instructions that are required to build a living organism from a single cell. But these instructions largely play out through the proteins that DNA encodes; and most proteins do not work alone. Instead they come together in different combinations, or complexes, and a single protein may participate in many complexes with different activities.Proteins are so small that it is difficult to get clear information about what they look like. Visualizing protein complexes is even harder. Most protein–protein interactions remain poorly understood, even in the best-studied organisms such as humans, yeast, and bacteria.Proteins are made from smaller molecules, called amino acids, strung together one after the other. The order in which different amino acids are arranged in a protein determines the protein’s shape and ultimately its function. Like DNA, protein sequences can change over time. Sometimes, the sequence of one protein changes in a way that prevents it binding to another protein. If these two proteins must work together for an organism to survive, the second protein will often develop a compensating change that allows the protein–protein complex to reform.Identifying pairs of changes in the sequences of pairs of proteins suggests that the two proteins interact and gives some information about how the proteins fit together. Different species can have copies of the same proteins that have slightly different sequences. Since the DNA sequences from many different organisms are already known, there are now many opportunities to find sites in pairs of proteins that have evolved together, or co-evolved, over time.To find sites that seem to have co-evolved, Hopf et al. used a computer program based on an approach from statistical physics to look at pairs of proteins that were already known to form complexes. Co-evolving sites were found in over 300 pairs of proteins; including 76 where the structure of the complex was already known. When sites that were predicted to be co-evolving were then mapped to these known complex structures, the co-evolving sites were remarkably close to the true protein–protein contacts. This indicates that the information from the co-evolved sequences is sufficient to show how two proteins fit together.Hopf et al. then turned their attention to 82 pairs of proteins that were thought to interact, but where a structure was unavailable. For 32 of these pairs, structures of the entire complex could be predicted, showing how the two proteins might interact. Furthermore, when other researchers subsequently worked out the structure of one of these complexes, the prediction was a good match to the solved complex structure.The machinery of life is largely made up of proteins, which must interact in ever-changing but precise ways. The new methods developed by Hopf et al. provide a new way to discover and investigate the details of these interactions.DOI:http://dx.doi.org/10.7554/eLife.03430.002 Escherichia coli, respectively  structure determination of protein complexes which has been significantly extended by homology-inferred 3D models  Hart et. Howeverectively . With thectively , with foectively . Howeverectively , and subectively . Others ectively . These pectively . More reectively , and thiectively . In prinbetween proteins. To assess this hypothesis, we built an evaluation set based on all known binary protein interactions in E. coli that have 3D structures of the complex as recently summarized (inter-protein EC pairs (inter-ECs) and sufficient to calculate accurate 3D models of the complexes in the docked subset (Since the recent demonstration of the use of evolutionary couplings (ECs) between residues to determine the 3D structure of individual proteins , includimmarized . We deved subset . This apWe first investigated whether co-evolving residues between proteins are close in three dimensions by assessing blinded predictions of residue co-evolution against experimentally determined 3D complex structures. We follow this evaluation by then predicting co-evolved residue pairs of interacting proteins that have no known complex structure.To compute co-evolution across proteins, individual protein sequences must be paired up with each other that are presumed to interact, or being tested to see if they interact. Without this condition, proteins could be paired together that do not in fact interact with each other and therefore detection of co-evolution would be compromised. Given that the evolutionary couplings method depends on large numbers of diverse sequences , some asE. coli  has only 3 predicted inter-protein residue pairs above the score threshold, but in fact has 5 additional correct pairs with EVcomplex scores slightly below the threshold of 0.8 which cluster with the 3 high-scoring contacts on the monomers, indicating that they are also correct.Some of the high confidence inter-protein ECs in the test set are not close in 3D space when compared to their known 3D structures. These false positives may be a result of assumptions in the method that are not always correct. This includes (1) the assumption that the interaction between paired proteins is conserved across species and across paralogs, and (2) that truly co-evolved residues across proteins are indeed always close in 3D, which is not always the case. In addition, the complexes may also exist in alternative conformations that have not necessarily all been captured yet by crystal or NMR structures, for instance in the case of the large conformational changes of the BtuCDF complex .To test whether the computed inter-protein ECs are sufficient for obtaining accurate 3D structures of the whole complex, we selected 15 diverse examples (with 5 or more inter-protein residue contacts) for docking  with HADThe top 10 inter-EC pairs between MetI and MetN are accurate to within 8 Å in the MetNI complex (PDB: 3tui ), resultThe ATP synthase ε and γ subunit complex provides a challenge to our approach, since the ε subunit can take different positions relative to the γ subunit, executing the auto-inhibition of the enzyme by dramatic conformational changes . In a reinter-protein evolutionary couplings scores are available in ‘Supplementary data’). 32 of these have high EVcomplex scores with at least one predicted contact  in order to copy damaged DNA (2) has at least two contrasting conformations where the N-terminal arm is placed on opposite sides of the dimer in one conformation or in close proximity in the alternative (The UmuD and UmuC complex is induced in the stress/SOS response facilitating the cleavage of UmuD to UmuD’ (between C24 and G25) to form UmuD’aged DNA . The truernative . For 6/7ernative . AlthougWhilst this manuscript was in review, the 3D structure of the previously unsolved biofilm toxin/antitoxin DinJ–YafQ complex was published (PDB: 3mlo ), showinE. coli ATP synthase complex as a test case. The ATP synthase structure is of wide biological interest (reviewed in E. coli ATP synthase) and transformed the ECs into EVcomplex scores for all inter-protein residue pairs , EVcomplex is able to provide information for about 1/10Determining the three-dimensional models of complexes from the predicted contacts was successful in many of the tested instances. Using minimal computing resources and a small number of inter-EC-derived contacts, low interface positional RMSDs relative to experimental structures can be achieved. However, a significant number of proteins exist as homomultimers within larger complexes. To determine models of these complexes one must deconvolute homomultimeric inter-ECs from the intra-protein signal, which is an important technical challenge for future work.The analysis of subunit interactions in ATP synthase in this work is a ‘proof of principle’ study showing that methods such as EVcomplex can determine which proteins interact with each other at the same time as specific residue pair couplings across the proteins . UndersThe approximately scale-free EVcomplex score is a heuristic based on the distribution of raw EC scores from the statistical model, their dependence on sequence alignment depth and the length of the concatenated sequences. The score provides a simple way of accounting for these dependencies such that a uniform threshold, say 0.8, can be used for any protein pair with the expectation of reasonably accurate predictions. Since cutoff thresholds can be useful but overly sharp, we recommend investigating predicted contacts below the threshold used in this work, especially where there is independent biological knowledge to validate the predictions.The work presented here is in anticipation of a genome-wide exploration and, as a proof of principle, shows the accurate prediction of inter-protein contacts in many cases and their utility for the computation of 3D structures across diverse complex interfaces. As with single protein (intra-EC) predictions, evolutionarily conserved conformational flexibility and oligomerization can result in more than one set of contacts that must be de-convoluted. Can evolutionary information help to predict the details and extent for each complex? A key challenge will be the development of algorithms that can disentangle evolutionary signals caused by alternative conformations of single complexes, alternative conformations of homologous complexes, and effectively deal with false positive signals. Taken together, these issues highlight fruitful areas for future development of evolutionary coupling methods.Despite conditions for the successful de novo calculation of co-evolved residues, the method described here may accelerate the exploration of the protein–protein interaction world and the determination of protein complexes on a genome-wide scale at residue level resolution. The use of co-evolutionary analysis in computational models to determine protein specificity and promiscuity, co-evolutionary dynamics and functional drift will open up exciting future research questions.E. coli including yeast two-hybrid experiments, literature-curated interactions and 3D complex structures in the PDB (E. coli genome obtained from the UniProt database. The resulting list of pairs (∼350) was then filtered for pseudo-homomultimeric complexes based on the identification of Pfam domains in the interacting proteins (330). All remaining complexes with a known 3D structure  using jackhmmer  to searcIn order to calculate co-evolved residues across different proteins, the interacting pairs of sequences in each species need to be matched. Here, we assume that proteins in close proximity on the genome, e.g., on the same operon, are more likely to interact, as in the methods used previously matching histidine kinase and response regulator interacting pairs  ‘Supple for mappInter- and intra-ECs were calculated on the alignment of concatenated sequences using a global probability model of sequence co-evolution, adapted from the method for single proteins  using a inter, we therefore calculate a raw reliability score  most pairs of positions in an alignment are not coupled, i.e., have an EC score close to zero, and tend to be distant in the 3D structure; (2) the background distribution of EC scores between non-coupled positions is approximately symmetric around a zero mean; and (3) higher-scoring positive score outliers capture 3D proximity more accurately than lower-scoring outliers See also . The widNeff is the effective number of sequences in the alignment after redundancy reduction, and L  is the length of the concatenated alignment. Previous work on single proteins has shown that the method requires a sufficient number of sequences in the alignment to be statistically meaningful. We thus filter for sequence sufficiency requiring Neff/L > 0.3 . As a negative control, each protein complex was also docked using center of mass restraints alone (ab initio docking mode of HADDOCK) . 500/100/100 models generated for each of the 3 steps, respectively. All other parameters were left as the default values in the HADDOCK protocol. Each protein complex was run using predicted ECs as unambiguous distance restraints on the Cα atoms (dHADDOCK)  and in telec) and van der Waals (Evdw) energies complemented by an empirical desolvation energy term (Edesolv) (Each of the generated models is scored using a weighted sum of electrostatic (EEdesolv) . The dishttp://www.bioinf.org.uk/software/profit/). The interface is defined as the set of all residues that contain any atom <6 Å away from any atom of the complex partner. For the AtpE–AtpG complex, we excluded the 2 C-terminal helices of AtpE as these helices are mobile and take many different positions relative to other ATP synthase subunits Supplementary data 1: Concatenated alignments for complexes predicted in this workSupplementary data 2: Genome distance distribution of concatenated sequences per alignmentSupplementary data 3: EVcomplex predictions for evaluation and de novo setSupplementary data 4: Docking input files and top 10 predicted models for evaluation setSupplementary data 5: ATP synthase predictions, ATP synthase subunit a modelSupplementary data 6: UmuC model review process). Similarly, the author response typically shows only responses to the major concerns raised by the reviewers.eLife posts the editorial decision letter and author response on a selection of the published articles . An edited version of the letter sent to the authors after peer review is shown, indicating the substantive concerns or comments; minor concerns are not usually shown. Reviewers have the opportunity to discuss the decision before the letter is sent , working with a member of our Board of Reviewing Editors, and 3 reviewers.Thank you for sending your work entitled “Sequence co-evolution gives 3D contacts and structures of protein complexes”““ for consideration at The editors and the other reviewers discussed their comments before we reached this decision, and the Reviewing editor has assembled the following comments to help you prepare a revised submission.eLife. Nevertheless, it is the consensus opinion of the reviewers that the present manuscript is potentially suitable for publication in eLife as well, provided that all of the comments raised by the reviewers can be addressed satisfactorily.This manuscript describes an approach to predict 3D residue-residue contacts at protein interfaces from an analysis of multiple sequence alignments. The described computational approach  appears to be quite successful and is an important contribution that is of interest to a broad audience. The analyses, results, and conclusions are similar to work conducted during the same period by Baker & coworkers, published recently in We have appended the comments from the three reviewers below. Although many individual points are raised, you will see that the principal concern of the reviewers is that the manuscript falls short of providing the reader with sufficient analysis to judge the validity of the conclusions. It should be possible for you to revise the manuscript to address these concerns without much in the way of new calculations, so we hope that it should be relatively straightforward to deal with these issues.In addition, since the Baker paper came out recently, it should be given appropriate credit in a revised version. We recommend removing the word “new” from the manuscript for this reason, and instead simply state what was done.Reviewer 1:1) The Abstract claims that the authors' approach can discriminate between interacting and non-interacting proteins. However, as far as I can see, the rest of the paper concerns only calculating the interface residues of proteins which are known to interact. No evidence is presented to support the claim to be able to calculate non-interactors. The authors' claim should be corrected accordingly.2) The authors claim that the co-evolutionary approach has not previously been applied to prediction of protein-protein interfaces. This is incorrect, as the authors seem to be unaware of the previous work of Raphael Guerois' group, which seems rather surprising. The authors should acknowledge the prior work of Faure et al (2012) and Andreani et al. (2013), and they should modify their claim to novelty accordingly. It would also be appropriate to cite properly the prior work of the Baker group  in the same paragraph (the authors currently mention this work only as a “note added during submission”), and the recent review by Andreani and Guerois (2014).3) In several places the authors say that their approach exploits the fact that interacting proteins are often coded close to each other in a genome. Why is this assumption necessary? According to the description given in Methods, any pair of sequences which are presumed to interact could be concatenated and then used in the authors method. Please clarify.4) If I understand the authors’ method correctly, they first concatenate the multiple sequences of the presumed interactors, and they then use their previous approach for detecting interacting pairs of columns in the multiple alignment. In this case, when concatenating the alignments for protein “A” with those of protein “B”, the “intra-EC” pairs would appear as A-A and B-B pairs, while the “inter-EC” pairs would come from A-B or B-A interactions. My question is then, are there any observable differences in the inter and intra scores? One might expect that the conservation scores would be lower for inter interactions than intra interactions, as an interface might show more “plasticity” than the core of a domain. It would be very interesting if the authors could comment on this, preferably supported by numerical results.5) It is incorrect for the authors to refer to their own data set as a “benchmark”. Suggestion: remove all references to the term “benchmark”. However, if the authors make all their data available in a convenient way on-line, it could be proposed as a new “benchmark”.6) It is wrong to claim that “Experimental evidence... agrees with EVcomplex predictions” (!). Please rewrite this to say that your predictions agree with the experimental evidence. But, if this is your claim, please also support it in some way. What is the experimental evidence? How to you calculate and quantify “agrees with”?7) Same point in the main text. “The resulting model is consistent with the topologies from cross-linking”. How do you quantify consistent? RMSD from the earlier models? Please provide supporting details.Reviewer 2:My main comments concern the way the data in the current manuscript are analyzed and presented. While the main Figures / 1) Please support the statement “Benchmark calculations here ... indicate that the number of sequences in the alignment is critical...” with a Figure showing overall performance versus # of sequences (2) Show a main Figure comparing predicted contacts against residue distances in the 30 known crystal structures of complexes that had a sufficient number of sequences (3) “... these benchmarks show ... and demonstrate the criteria needed for successful prediction of unknown interactions”. To support this statement, it would be useful to have a main Figure / Table for each important criterion, analyzed over an entire benchmark set (as for the number of sequences in point 1).4) Please add metrics to support statements that ECs are “completely consistent” with crosslinking data, or that a coupling “coincides with experimental evidence”. The comparison Table in Supplementary Figure 6 could be presented in more quantitative terms in the main text .5) What are the possible reasons for false-positive co-variation that does not correspond to contact?6) The manuscript deals entirely with pairwise co-evolution, and seems to neglect the possibility of higher-order complexity in the sequence pattern.Reviewer 3:The manuscript presents an upbeat and optimistic view of the success of the method, with a relatively small amount of critical discussion and little if any investigation of what one can learn from cases in which it does not perform as expected.  Likewise, a set of methods is presented that is relatively particular, and the reason why these specific methods were chosen over other possibilities is not provided. Moreover, the manuscript deals entirely with pairwise co-evolution, and seems to neglect the possibility of higher-order complexity in the sequence pattern. The manuscript could be substantially strengthened by adding analyses and insights in these issues. Thank you for your comments and forwarded reviews of our paper. The revised paper addresses the questions from the reviewers. As you point out, the principal request of the reviewers was to see more quantitative analysis to judge the validity of the conclusions. This is addressed by detailed additions to the paper including:1) A quantitative analysis of the accuracy with summary statistics (2) The use of the EVcomplex scores for inter-protein ECs that allow comparison across proteins (3) A fuller analysis and clearer explanation of how the scores are used to distinguish interactions from non-interactions with a proof of principle exercise of all ATP synthase subunits against all others , resulting in 86% accuracy.4) Detailed analysis of agreement of cross-linking experiments and predictions for a –subunit ECs and a- to b- subunit inter-ECs, The results are essentially the same as in the original submission and we have now provided the reader with comprehensive access to the numerical results, including sequence alignments, list of all contact predictions, precision of predicted contacts with respect to known 3D structures for all complexes, docked models for 15 complexes,Reviewer 1:1) The Abstract claims that the authors' approach can discriminate between interacting and non-interacting proteins. However, as far as I can see, the rest of the paper concerns only calculating the interface residues of proteins which are known to interact. No evidence is presented to support the claim to be able to calculate non-interactors. The authors' claim should be corrected accordingly.We have updated the manuscript to clarify the way in which the method addresses discrimination between interacting and non-interacting proteins, and give the specific example of all against all in the ATP synthase complex. We suggest that the correct identification of interacting and non-interacting protein subunits of the ATP synthase complex could serve as a 'proof of principle' that scores derived from evolutionary couplings can identify interactions (in addition to the identification of the interacting residues between protein pairs). Since identification of the interactions of the 8 subunits of ATP synthase is the only example we report, we have been careful to talk about this as a proof of principle that may be generalized with further work  The authors claim that the co-evolutionary approach has not previously been applied to prediction of protein-protein interfaces. This is incorrect, as the authors seem to be unaware of the previous work of Raphael Guerois' group, which seems rather surprising. The authors should acknowledge the prior work of Faure et al (2012) and Andreani et al. (2013), and they should modify their claim to novelty accordingly.It would also be appropriate to cite properly the prior work of the Baker group  in the same paragraph (the authors currently mention this work only as a “note added during submission”), and the recent review by Andreani and Guerois (2014).We have now referred to the body of work by the Guerois group in the Introduction. We apologize for the oversight of this very interesting work. We also added more complete referencing to the work of the Valencia group; see response to reviewer 3. The Guerois group has used evolutionary information to improve their machine learnt statistical potentials. Our goal is to ask if we can we identify co-evolving residues  across proteins in order to ask whether these co-evolving pairs identify close residues, (1) whether they can be used to determine complexes in 3D and (2) whether we can use this to say if two proteins interact. Hence the approaches we present and those of the Guerois group are nicely synergistic and we hope to work with them to bring these methods together in the future.3) In several places the authors say that their approach exploits the fact that interacting proteins are often coded close to each other in a genome. Why is this assumption necessary? According to the description given in Methods, any pair of sequences which are presumed to interact could be concatenated and then used in the authors method. Please clarify.Thanks for making it clear that we did not explain this sufficiently well. We have now clarified this in Results, Discussion, and in the Materials and methods.E. coli, there are not yet enough sequences available to allow the use of just one pair per genome, as the complex may not necessarily occur in all bacteria. In these cases, for the time-being, our approach relies on using paralogous interactions to get sufficient sequences for instance histidine kinase; response regulators, transporters and their ATPases. Since we therefore use several paralogous interactions per complex in each genome, the algorithm requires that the partner needs to be correctly paired, something that we do not necessarily know a priori, and for which we use genome proximity as a proxy (as others have done in references 22-25). Clearly this problem will secede for many interactions in the next few years as many thousand more bacterial genomes are sequenced, but the problem is an interesting one that we think will have to be thought about in relation to eukaryotic complexes, too.To summarize: The accuracy of the co-evolutionary analysis relies on the concatenation of the protein pair representing a true interaction. This means that the contact prediction across proteins will only be as good as the input alignment pairs. When there is only one protein family member of both interacting partners in each species, one is guaranteed to get the correct alignment, assuming that it is a real complex in all organisms. Hence, subunits of ATP synthase that have only one copy in the genome, would not require this condition, and one could have simply concatenated the one pair per genome. However, for most protein interactions in 4) If I understand the authors’ method correctly, they first concatenate the multiple sequences of the presumed interactors, and they then use their previous approach for detecting interacting pairs of columns in the multiple alignment. In this case, when concatenating the alignments for protein “A” with those of protein “B”, the “intra-EC” pairs would appear as A-A and B-B pairs, while the “inter-EC” pairs would come from A-B or B-A interactions. My question is then, are there any observable differences in the inter and intra scores? One might expect that the conservation scores would be lower for inter interactions than intra interactions, as an interface might show more “plasticity” than the core of a domain. It would be very interesting if the authors could comment on this, preferably supported by numerical results.The reviewer makes a good point and one that we have now highlighted more clearly in the text. The inter-EC scores within each complex are almost universally lower than the intra–EC scores as would be expected, intra-protein scores to assess the predicted inter-contacts. We chose to develop a scoring method independent that did not rely on the existence of experimentally determined 3D structures of the monomers. The EV complex score implicitly considers where the inter-EC scores lie in the whole distribution instead, hence excluding those inter-ECs that lie within the noise of the whole distribution, see Materials and Methods  It is incorrect for the authors to refer to their own data set as a "benchmark". Suggestion: remove all references to the term "benchmark". However, if the authors make all their data available in a convenient way on-line, it could be proposed as a new "benchmark".We have removed the references to the set of known 3D interactions as ‘benchmark’ and proposed this as a set the community could use in the future, as the reviewer suggests .6) It is wrong to claim that "Experimental evidence... agrees with EVcomplex predictions" (!). Please rewrite this to say that your predictions agree with the experimental evidence. But, if this is your claim, please also support it in some way. What is the experimental evidence? How to you calculate and quantify "agrees with"?We have removed this sentence, and reworded this to reflect the fact we are using the experimental data to support the EC predictions and not the other way around.7) Same point in the main text. "The resulting model is consistent with the topologies from cross-linking". How do you quantify consistent? RMSD from the earlier models? Please provide supporting details.2 total, see previous work and work of others for why this number is chosen.To our knowledge there are no experimentally determined atomic resolution structures of the a-subunit of ATP synthase. A 3D model of the a-subunit is from 1999 (PDB ID: 1c17) and was computed using five helical–helical interactions that were inferred from second suppressor mutation experiments, and then imposed as distance restraints for TMH2-5 [1], revealing a four helical bundle (with no information for TMH1). Later, zero-length cross-linking experiments by the Fillingame group [2] identified contacting residues from all pairs of helical combinations of TM2-TM5 (6), supporting the earlier 4 helical bundle topology. 7 of the 8 cross-linked pairs from Schwem et al., are either exactly the same pair (L120-I246) or adjacent to many pairs in the top L intra a-subunit evolutionary couplings (ECs)*, see Information from the cross-linking was not used to construct the model and no presumed membrane topology was used as input. The consistency we report is based on the correspondence between the helical-helical contacts from the experiments and evolutionary couplings together with the resulting contacts in the computed model. These can be examined in detail in the supplementary material. We have clarified this part of the analysis and provided more detail.Reviewer 2:[…] I feel the paper could be substantially improved if the following points were addressed:1) Please support the statement "Benchmark calculations here ... indicate that the number of sequences in the alignment is critical..." with a Figure showing overall performance versus # of sequences (shows dependency of accuracy on relative rank for only two examples).2) Show a main Figure comparing predicted contacts against residue distances in the 30 known crystal structures of complexes that had a sufficient number of sequences . This type of analysis could also help to support the statement "For the top ranked benchmark complexes, the majority of the top 5 ECs is correct to within 8A..." with a more quantitative analysis .3) "... these benchmarks show ... and demonstrate the criteria needed for successful prediction of unknown interactions". To support this statement, it would be useful to have a main Figure / Table for each important criterion, analyzed over an entire benchmark set (as for the number of sequences in point 1).The claims in the paper are now supported by more explicit quantitative analysis, reported in the main figures, the text and supplementary material. Specifically we show a score that normalizes the ECs for the number of sequences and length of protein. This score allows a clear comparison of EC pairs across complexes so that one threshold can be applied across all complexes and precision can be measured for each complex and summarized overall. This material is provided in 4) Please add metrics to support statements that ECs are "completely consistent" with crosslinking data, or that a coupling "coincides with experimental evidence". The comparison Table in Supplementary Figure 6 could be presented in more quantitative terms in the main text .th predicted EC with no cross-link support is between a66K and b68a. This EC is most likely a false positive as residue 68 in the b-subunit may be too far from the membrane surface to be plausible. Taking the body of work together from the experimental side [3-8], there are a number of specific regions of the a-subunit that are in contact with one or both of the b-monomers, and all of these regions are represented in the top evolutionary couplings; namely the first cytoplasmic loop, aTMH2, upper portion, aTMH3 and the periplasmic end of aTMH5, all of which are represented in the EVcomplex pairs  predicted inter-protein contacts between subunit-a and subunit-b are in the neighborhood of residues identified in cross-linking studies, where neighborhood is defined as within 6/3 residues on the chain of the cross-linked residue . The a23ex pairs .These clarifications are reflected in the main text and See reply to Reviewer 1 and 5) What are the possible reasons for false-positive co-variation that does not correspond to contact?There are two categories of false positives:1) There is co-evolution between residues that are not close in a 3D structure.i) This could be functional coupling – e.g. cases of information transmission through membrane proteins, allosteric signaling and indeed we have seen this anecdotally in a number of very interesting cases. The challenge is to distinguish this from other false positives.ii) The current crystal structures do not capture all the conformations of the protein(s). This is very clear in examples such as alternating-access membrane transporters that alter their conformations substantially2) The assumptions used for the input sequence alignments are wrongi) The interactions in the alignments are not homologous despite sequence similarity of the component parts.ii) The sequence diversity is insufficient to capture realistic parameters in the global model.Deconvolution of these different causes of false positives is a critical part of future work. We have made these points clearer in the Results and the Discussion.6) The manuscript deals entirely with pairwise co-evolution, and seems to neglect the possibility of higher-order complexity in the sequence pattern.E. coli interactions (Discussion) and see response to Reviewer 1 – comment 3. It will be interesting to see what the extent of this higher order dependence might be when enough sequences become available.There could well be higher order correlations that the current method is unable to determine at this stage. The number of sequences needed for a global model such as this to determine accurate higher order covariation would be at least an order of magnitude higher than the requirement for the current method. Indeed there are insufficient sequences for many of the currently known Reviewer 3:The manuscript presents an upbeat and optimistic view of the success of the method, with a relatively small amount of critical discussion and little if any investigation of what one can learn from cases in which it does not perform as expected.We have updated the manuscript to include far more quantitative analysis, see replies to Reviewer 2 comments 1-4 above. We have also balanced the enthusiasm with a more specific description of the current limitations, that include sequence availability but also some that require algorithmic development (see Discussion). Critical discussion on the classification of false positives is now more clearly organized in the manuscript; see response to Reviewer 2, comment 5. Likewise, a set of methods is presented that is relatively particular, and the reason why these specific methods were chosen over other possibilities is not provided.There is a large body of work that has implemented other methods, both force field–based docking and other sequence based methods to explore co-variation e.g. mutual information and phylogeny to detect co-variation of residues across proteins. We have now added more discussion and references to this oeuvre in the Introduction, including new references to the body of work by the Valencia group that used correlations in multiple sequence alignments [9-10], and those suggested by Reviewer 1. Many other methods have addressed somewhat different issues, for instance, identification of interacting proteins (not residue level information), identification of residue patches rather than specific interactions, concentration on docking with hybrid approaches for known interactions or were relatively less successful (e.g. older work). For this reason it is hard to make direct comparisons. The unique aspect to this work is that it can resolve the question of interacting partners together with residue level information if the sequence diversity and space is sufficient. The EVcomplex approach, along with that of the recent work by the Baker group [11] also does this from sequences alone. Since experimentally determined structures may be a somewhat biased set of all protein interactions, our method has the advantage of not learning on known complex structures.We look forward to the combination of the co-evolution approach together with existing computational and experimental approaches to accelerate the field.Moreover, the manuscript deals entirely with pairwise co-evolution, and seems to neglect the possibility of higher-order complexity in the sequence pattern. The manuscript could be substantially strengthened by adding analyses and insights in these issues.See response to Reviewer 2, comment 6.ReferencesNature402, 263-268, doi:10.1038/46224 (1999).1 Rastogi, V. K. & Girvin, M. E. Structural changes linked to proton translocation by subunit c of the ATP synthase. The Journal of biological chemistry281, 37861-37867, doi:10.1074/jbc.M607453200 (2006).2 Schwem, B. E. & Fillingame, R. H. Cross-linking between helices within subunit a of Escherichia coli ATP synthase defines the transmembrane packing of a four-helix bundle. J Biol Chem277, 27288-27293, doi:10.1074/jbc.M202118200 (2002).3 Long, J. C., DeLeon-Rangel, J. & Vik, S. B. Characterization of the first cytoplasmic loop of subunit a of the Escherichia coli ATP synthase by surface labeling, cross-linking, and mutagenesis. Biochimica et biophysica acta, doi:10.1016/j.bbabio.2014.03.005 (2014).4 Fillingame, R. H. & Steed, P. R. Half channels mediating H transport and the mechanism of gating in the F sector of Escherichia coli FF ATP synthase. FEBS Lett587, 892-897, doi:10.1016/j.febslet.2013.02.012 (2013).5 DeLeon-Rangel, J., Ishmukhametov, R. R., Jiang, W., Fillingame, R. H. & Vik, S. B. Interactions between subunits a and b in the rotary ATP synthase as determined by cross-linking. FEBS Lett555, 29-34 (2003).6 Fillingame, R. H., Angevine, C. M. & Dmitriev, O. Y. Mechanics of coupling proton movements to c-ring rotation in ATP synthase. et al. Individual interactions of the b subunits within the stator of the Escherichia coli ATP synthase. The Journal of biological chemistry288, 24465-24479, doi:10.1074/jbc.M113.465633 (2013).7 Brandt, K. Biochemistry39, 3486-3490 (2000).8 McLachlin, D. T. & Dunn, S. D. Disulfide linkage of the b and delta subunits does not affect the function of the Escherichia coli ATP synthase. Journal of molecular biology271, 511-523, doi:10.1006/jmbi.1997.1198 (1997).9 Pazos, F., Helmer-Citterich, M., Ausiello, G. & Valencia, A. Correlated mutations contain information about protein-protein interaction. Proteins47, 219-227 (2002).10 Pazos, F. & Valencia, A. In silico two-hybrid system for the selection of physically interacting protein pairs. eLife3, e02030, doi:10.7554/eLife.02030 (2014).11 Ovchinnikov, S., Kamisetty, H. & Baker, D. Robust and accurate prediction of residue-residue interactions across protein interfaces using evolutionary information.


MS, and its associated techniques, has become accepted as a method for the study of protein–ligand interactions, not only for covalent complexes, where the use of MS is well established, but also, and significantly for protein–ligand interactions, for noncovalent assemblies. In this review, we employ a broad definition of a ligand to encompass protein subunits, drug molecules, oligonucleotides, carbohydrates, and lipids. Under the appropriate conditions, MS can reveal the composition, heterogeneity and dynamics of these protein–ligand interactions, and in some cases their structural arrangements and binding affinities. Herein, we highlight MS approaches for studying protein–ligand complexes, including those containing integral membrane subunits, and showcase examples from recent literature. Specifically, we tabulate the myriad of methodologies, including hydrogen exchange, proteomics, hydroxyl radical footprinting, intact complexes, and crosslinking, which, when combined with MS, provide insights into conformational changes and subtle modifications in response to ligand‐binding interactions.Proteins undergo dynamic interactions with carbohydrates, lipids and nucleotides to form catalytic cores, fine‐tuned for different cellular actions. The study of dynamic interactions between proteins and their cognate ligands is therefore fundamental to the understanding of biological systems. During the last two decades  ABCATP‐binding cassetteAPaffinity purificationATPaseATP synthasecATPasechloroplast ATP synthaseCCScollision cross‐sectionCIDcollision‐induced dissociationDSPC1,2‐distearoyl‐sn‐glycero‐3‐phosphocholineEMelectron microscopyGPCRG‐protein‐coupled receptorHX‐MShydrogen exchange monitored with MSIMion mobilityKDdissociation constantLC‐MS/MSliquid chromatography‐coupled MSOmpFouter membrane porin FP‐gpP‐glycoproteinPOPA1‐palmitoyl‐2‐oleoyl‐sn‐glycero‐3‐phosphateTTXtentoxinU‐snRNPuridine‐rich small nuclear ribonucleoprotein particleMS is well established as an analytical technique with a mass range encompassing single atoms through to complex polymers of several million daltons. Within this mass range from tens to several million daltons, many interesting biological assemblies reside. During the last two decades, MS has become indispensible for the study of proteins and their assemblies. Many of these developments emanated from the discovery of two ‘soft’ ionisation techniques ESI . WhereasIn line with developments in proteomics, MS is also gaining in importance in structural biology  when it is in complex with a small protein of < 10 kDa than to resolve a similar‐sized ligand within a megadalton ribosome complex. Moreover, recent discoveries of new ways of transporting membrane proteins into the gas phase from detergent micelles bring their own challenges. Resolving small molecules within the context of the vast excess of detergent molecules that surround the membrane complex and subsequently distinguishing detergent molecules from small‐molecule ligands is particularly challenging.Here, we give an overview of MS techniques that are commonly applied to study protein–ligand interactions. Specifically, we include proteomics studies, hydrogen–deuterium exchange, hydroxyl radical footprinting, chemical crosslinking, and MS of intact complexes Table . We discProteomics at its inception was defined as the study of the proteome of a cell or an organism under a set of controlled conditions. Today, proteomics not only involves relatively straightforward protein identification, but also, increasingly, simultaneous quantification and identification of post‐translational modifications. Moreover, and pertinent to this review, proteomics has also been used to study protein complexes in terms of their composition, subunit stoichiometry, and interactions  images to define the composition of particles by semiquantitative peptide/spectral counting e.g. . Of partDistinguishing specific from nonspecific binding proteins has long been problematic when large protein interaction networks are defined. Although interactions can be identified readily, following AP coupled with MS, relative quantification is needed to distinguish between specific and nonspecific binders. Furthermore, quantitative AP coupled with MS allows the monitoring of dynamic and transient interactions in large protein assemblies. This was used to good effect in studies of chromatin, wherein specific protein binders were defined and assembled into complexes Accordingly, MS‐based proteomics is capable of identifying interaction partners in protein assemblies, and, in conjunction with MS of intact complexes, of confirming subunit stoichiometries or of unambiguously identifying specific interaction partners.2O) leads to the exchange of solvent‐accessible amide protons for deuterium. Although many different factors affect hydrogen exchange, including temperature, pH, solvent accessibility, and hydrogen bonding , photolysis of peroxide, or radiolysis of water. Fenton chemistry is a simple and inexpensive technique, but does require longer reaction times than photolysis or radiolysis approaches. In contrast, laser‐induced dissociation of hydrogen peroxide and high‐flux X‐rays from synchrotron sources generate hydroxyl radicals on microsecond to millisecond timescales, allowing for kinetic studies e.g. . The hyd2+ ‐dependent structural changes of gelsolin Hydroxyl radical footprinting experiments typically include first the exposure of the protein complex, and then digestion into peptides and analysis by MS. Unmodified and modified peptides are usually quantified according to extracted ion current values, and the rate of modification can be calculated from the dose–response curve plotted for each peptide as a function of exposure time Chemical crosslinking is a straightforward approach for studying the three‐dimensional structure of proteins and protein complexes. Functional groups of neighbouring proteins are covalently linked by the use of bifunctional crosslinking reagents, and MS of the crosslinked peptides, after digestion of the proteins, identifies the crosslinked peptides. The length of the crosslinker defines a distance constraint between crosslinked amino acids that allows deductions to be made concerning the three‐dimensional arrangement of proteins in noncovalent assemblies  can be applied to study protein–protein interactions. The underlying principles involve insertion of photoreactive groups into proteins by absorption of UV light, without causing damage to the protein itself, while generating the reactive crosslinking group. Most of these photolabile precursors are azides, diazirines, diazo compounds, and benzophenones. Upon UV irradiation, these precursors react nonspecifically with CH and NH groups of the protein backbone to form covalent, stable bonds that are suitable for detection by MS , which, in turn, can be related to the conformation of the macromolecular assembly. One of the earliest IM‐MS studies showed that the native structure of a ring‐shaped protein complex could be preserved in the gas phase of the mass spectrometer, and that the CCS measured with IM‐MS was in accord with that calculated from the crystal structure The gold standard for determining high‐resolution structures of proteins and their complexes is undoubtedly X‐ray crystallography and, for small proteins that are soluble at high concentrations, NMR spectroscopy. However, these techniques are dependent on relatively high purity and abundance, which is not always possible for protein assemblies isolated directly from cells without overexpression or from tissue or organisms. Integration of data from a number of methods is often required to obtain structural models of protein–ligand complexes from these sources. Integrative modelling approaches are therefore required to optimise the modelling process  and sulfoquinovosyl diacylglycerol . By detection of the membrane ring with bound lipids as an intact complex and the use of standard lipids, the protein/lipid stoichiometry was determined in different species. This information was then used in a docking approach to place lipids within the membrane ring  ing Fig. . The cavNew opportunities are provided by recent discoveries of lipid binding to protein complexes within nanodiscs and micelles. Nanodiscs have been studied by the use of hydrogen–deuterium exchange and CID, which revealed the conformation of the membrane structural proteins and the cohort of lipids within nanodiscs, respectively 1‐ATPase, isolated directly from spinach, revealed subcomplexes of the F1 moiety containing two and three bound ATP molecules. Using a previously published assignment strategy Nucleotide binding can be studied similarly to lipid binding, but the results have to be interpreted with care, owing to the ability of nucleotides to undergo nonspecific addition to basic proteins. Careful data analysis has to be applied to distinguish specific from nonspecific binding Quantitative proteomics has recently been applied to distinguish specific protein binding from background binding to RNA motifs. For this purpose, bait and control RNA sequences, which either contained or did not contain the specific RNA motif, were incubated with light (unlabelled) and heavy (labelled with stable isotopes) cell populations, by use of a technique known as stable isotope labelling by amino acids in cell culture. Background protein binding occurred to an equal extent in both samples, whereas proteins that bind specifically showed enrichment in the pulldown experiments with the bait RNA Peptide binding to proteins or their complexes is of particular importance, for several reasons. First, peptides represent an important class of neurotransmitters, and thus regulate specific functions in the nervous system; and second, synthetic peptides can be used to study protein–protein interaction interfaces in detail. The latter have been used frequently to probe specific binding (binding motifs), structural dynamics, or cellular mechanisms.2+‐dependent Munc13–calmodulin interactions 2+ after irradiation with UV light. Covalent complexes were then characterised with SDS/PAGE and MS to identify Munc13‐binding sites. This strategy was further combined with chemical crosslinking to gain insights into peptide orientation. Information from both methods was used in modelling approaches to obtain structural models of the complexes that were identified.Bringing together photoaffinity labelling and chemical crosslinking led to an elegant study of CaPeptide binding also formed a key component of a recent study of the passage of colicin through the pore of the trimeric outer membrane porin F (OmpF) A combination of MS of intact protein complexes and quantitative proteomics is often used to define the stoichiometry of assemblies, e.g. the CRISPR interference CSM complex with eight different subunits Similarly, determining the copy number of L7/L12 proteins in the stalk complex of the ribosome was defined either with native MS of the intact stalk complex 1 head of intact F1‐cATPase purified from spinach leaves was studied with MS. Differences in the complexes present in solution were observed  binds and specifically inhibits some chloroplast ATPases (cATPases) ved Fig. . Withoutxes Fig. . AccordiStudying drug binding to the ABC transporter P‐gp is complicated by the background of detergent and lipids that constantly pass through the pump It is clear from content of this review that no one MS approach is sufficient in isolation for all cases, but, rather, a combination of MS‐associated methods is needed for each application. It could be argued that changes in subunit interactions of the cATPase were revealed entirely by chemical crosslinking. However, the mechanism linking these changes to nucleotide binding was only determined by studying the extent of nucleotide binding in the intact complex. Similarly, the intact subunit stoichiometry of the CSM complex remained ambiguous following MS of the intact complex and subcomplexes generated in solution. When the results were studied in conjunction with quantitative proteomics, however, it was possible to define a unique subunit stoichiometry and to produce a model compatible with the EM density and IM CCS In this review, we have shown how MS with its diverse techniques and applications is being applied to gain insights into the structure and dynamics of protein–ligand complexes. The implementation of molecular modelling with the combination of these various approaches proved to be essential in the majority of the studies presented here. It is undoubtedly the case that continued developments in instrumentation, methodology and data analysis will enhance opportunities and widen the range of available structural information. Particularly exciting is the accessibility of highly dynamic and heterogeneous complexes that are often beyond the scope of more established structural biology approaches. Overall, we believe that, whereas the ability of MS to contribute to structural models of protein–ligand interactions are emerging, the full potential of MS in drug discovery has yet to be realised.


Protein-protein interactions (PPI) are involved in virtually every cellular process and thus represent an attractive target for therapeutic interventions. A significant number of protein interactions are frequently formed between globular domains and short linear peptide motifs (DMI). Targeting these DMIs has proven challenging and classical approaches to inhibiting such interactions with small molecules have had limited success. However, recent new approaches have led to the discovery of potent inhibitors, some of them, such as Obatoclax, ABT-199, AEG-40826 and SAH-p53-8 are likely to become approved drugs. These novel inhibitors belong to a wide range of different molecule classes, ranging from small molecules to peptidomimetics and biologicals. This article reviews the main reasons for limited success in targeting PPIs, discusses how successful approaches overcome these obstacles to discovery promising inhibitors for human protein double minute 2 (HDM2), B-cell lymphoma 2 (Bcl-2), X-linked inhibitor of apoptosis protein (XIAP), and provides a summary of the promising approaches currently in development that indicate the future potential of PPI inhibitors in drug discovery. Proteins form the basic machinery of cells, and the precise interactions between them, known as Protein-Protein Interactions (PPIs), are fundamental for appropriate execution of all cellular mechanisms. At a high level, we can differentiate two types of interactions: one involving more stable interactions that establish macromolecular complexes, the other involving transient interactions, usually between proteins that mediate signalling pathways and regulatory process .The former group of PPI are usually mediated by reciprocal recognition interfaces at the protein surface – domain-domain interactions (DDI) – while the latter involves domain binding to a continuous binding epitope, or domain-motif interaction (DMI). Generally, a short segment or an unstructured region of the target protein contains the recognition motif . These mThis mechanism of recognition is very common in cellular processes, evidenced by the large number of recognition domains encoded by the human genome , 3–5. ThDMIs have therefore been an attractive group of new drug targets, because their fine modulation would allow for numerous desirable therapeutic effects , 23–26. In this review, we describe in detail the challenges of targeting DMI interactions. Following this we review successful approaches and discuss how they overcame the challenges of targeting DMI. We present specific cases, categorized by the nature of the inhibitor . We do not aim to set out the detailed pros and cons of these two categories here, as there are many insightful articles that do this elsewhere , 34. FinAlthough there is little doubt that small-molecules can interfere with PPIs, there are currently only a limited number of published examples of molecules capable to inhibit DMIs. This limited success is mainly due to the following factors.As is mentioned above, the majority of DMIs are involved in signalling, with moderate binding affinities. This is important for precise control of the transmitted signals, but makes their capture difficult, in particular in high-throughput screens, where the majority of our data stems from. This, together with the complexity of signal pathways, makes identification of all the partners of critical proteins in a key cellular process a challenging goal. While recent methodologies have helped to provide increasing information, many such interactions remain undetected and thus can’t be inhibited for therapeutic effect , 36.Even if a particular interaction or a pathway is identified, the transient nature and moderate affinity of many DMIs often leads to a lack of structural information, which in turn makes it difficult design molecules that mimic the natural interfaces , 37–43. DMI interfaces are modular, and present in a wide number of proteins, cellular localizations and contexts. This means that proteins involved in protein-protein interactions can often be ‘promiscuous’ binders – targeting several proteins using the same motif , 44. TheTypical protein-protein interaction interfaces tend to be large, flat and mainly hydrophobic, where punctual electrostatic interactions are key for the binding , 6, 28. In general these geometric and physiochemical properties are incompatible with the classic small molecules that satisfy Lipinski’s rule of five, with good pharmacokinetics properties. This is shown empirically given the low ratio of success by high-throughput screening in identifying compounds , 27, 29.A common scenario is that a lead shows promising binding affinity, but is not active in cell-based or in vivo assays. One example is the inhibitor for the transcription factor HIF-1 PAS domain . In ordeClassic drug development works with small, chemically manufactured active molecules. These molecules have a wide range of desirable properties for drug discovery. For instance, they are relative easy to synthetize and manipulate, and in general they have a good cellular uptake. However, these molecules tend to bind better with smaller and deeper grooves than the DMI interfaces . TherefoThis has been accomplished by increasing the complexity of drug molecules, in order to fit the properties and sizes of the DMI interfaces. At the same time, growth and refinement of the lead was carried out with a complete structural description of the natural binding motif. Precise identification of the motif, and of hot spots involved in the interaction, were critical to maximize specificity and affinity while keeping the compound size reasonable.In many cases there is little information available on the targets, and High Throughput Screening (HTS) is a more effective approach. However, as we mentioned, HTS has shown limited success against PPIs due to a bias of scaffolds in the compound libraries , 23. NotThe tumor-supressor gene p53 induces cellular apoptosis in response to DNA damage, avoiding possible tumorigenesis. Although many human cancers have a mutation or deletion in p53, in a significant proportaion of cancers the function of p53 is inactivated by a deregulated expression of the onco-protein, HDM2 , promoting tumorigenesis and poor response to cancer therapy . TherefoA successful alternative to HTS are fragment-based drug discovery strategies. These methodologies are based on identifying small chemicals, which may bind weakly at different spots on the target interface, and then combining them to produce a single lead with higher affinity and specificity. These approaches allow the construction of larger, more complex compounds, more likely to block specifically DMI interfaces. For instance, using Fragment-based drug discovery a potent inhibitor for B-cell lymphoma 2 (Bcl-2) has been discovered.The Bcl-2 family proteins are important regulators of the cellular apoptosis mechanism. Aberrations in this decision mechanism can enable cancer cells to evade death . For insL inhibit apoptosis by binding a 16 residues α-helical portion of the pro-apoptotic protein Bcl-2 antagonist/Killer (BAK) or a 26 residue α -helix portion of Bcl-2 antagonist of Cell Death (BAD). This structural information was completed by identification of the hot-spots at the interface through alanine mutational scanning . The classic two-hybrid system can be altered to link cell growth to the disruption of a complex rather than the complex formation, a method called reverse two-hybrid system (RTHS). In parallel, cells are transformed with an extra vector that encodes for a peptide of a combinatorial library and the necessary proteins to perform the intracellular synthesis of cyclic peptides (SICLOPPS) [, 118. ThPeptides and peptidomimetics – and even proteins – present a completely new set of challenges to solve, but there are proposed solutions with promising preliminary results. Probably the largest challenge for employing biologics as inhibitors of intracellular interactions is cellular uptake.Recent discovery of potent therapeutic molecules, which did not reach the clinic due to poor delivery and low bioavailability, has made the delivery of such molecules a key issue in therapeutic development. A wide range of different strategies are being explored to achieve this, as such, lipid-derived compounds (pepducins and liposiomes) , 120, poThe initial research of a drug, often occurring in academia, generates data to develop a hypothesis that the inhibition or activation of a protein or pathway will result in a therapeutic effect in a disease state. The outcome of this activity is the selection of a target, which may require further validation prior to progression into the lead discovery phase in order to justify a drug discovery effort.The complexity of PPI networks make it difficult however, to identify clear targets, even using high-throughput methods such as yeast two-hybrid (Y2H) or affinity-purification mass spectrometry (AP/MS). While other methodologies, like peptide arrays, split-protein systems , 135, anIvarsson and coworkers use custom oligonucleotide arrays to construct defined phage display libraries comprising the entire human and viral C-terminuses found in Swissprot. Oligonucleotides encoding the c-terminal heptapeptide sequences were printed on microarray slides, PCR amplified, and cloned into a phagemid designed for the display of peptides fused to the C-terminus of the M13 major coat protein p8. The libraries were used in binding selection with PDZ domains and the selected pools were analysing by next-generation sequencing on the illuminia platform. This approach allowed them to screen several orders of magnitude larger than peptide arrays, avoid the bias inherent in random exploration, and scan natural interactions. Using this approach they identified known and novel human and viral ligands, and validated candidates in vivo and in vitro .Discovery and subsequent refinement of PPI inhibitors with strong affinity has proven to be a challenging, though not impossible, quest. A number of inhibitors were discovered by close examination of the interactions and precise identification of DMI hot-spots. Likewise, the adaptation of techniques used to investigate specific characteristics of PPIs has been critical to the successful identification of new inhibitors.Several inhibitors for DMI are currently in the late stages of clinical trial and more are expected to follow. Furthermore, inhibitors that failed during late stages of clinical trials, such as Nutlin and Cilengitide, have a second chance to be used in combination therapies –140.at libitum, and develop therapeutic solutions to individual pathologies.New approaches and new targets are currently emerging, and new developing technologies of the post-genomic era may yield more advanced methodologies for PPI inhibition. In the coming decades we may plausibly reach the capability to disrupt PPI networks and modulate signalling pathways


The detection of associations between protein complexes and human inherited diseases is of great importance in understanding mechanisms of diseases. Dysfunctions of a protein complex are usually defined by its member disturbance and consequently result in certain diseases. Although individual disease proteins have been widely predicted, computational methods are still absent for systematically investigating disease-related protein complexes.We propose a method, MAXCOM, for the prioritization of candidate protein complexes. MAXCOM performs a maximum information flow algorithm to optimize relationships between a query disease and candidate protein complexes through a heterogeneous network that is constructed by combining protein-protein interactions and disease phenotypic similarities. Cross-validation experiments on 539 protein complexes show that MAXCOM can rank 382 (70.87%) protein complexes at the top against protein complexes constructed at random. Permutation experiments further confirm that MAXCOM is robust to the network structure and parameters involved. We further analyze protein complexes ranked among top ten for breast cancer and demonstrate that the SWI/SNF complex is potentially associated with breast cancer.MAXCOM is an effective method for the discovery of disease-related protein complexes based on network optimization. The high performance and robustness of this approach can facilitate not only pathologic studies of diseases, but also the design of drugs targeting on multiple proteins. Protein complexes are essential cellular functional units in which several proteins work as parts of assemblies. The functionality of a protein complex is based on interactions of its member proteins that are typically densely connected in a protein-protein interaction (PPI) network, reflecting the modular organization of the network. In pathogenic conditions, dysfunctions of complex members usually affect the entire function of the complex -3. AlthoProtein complexes have been experimentally and computationally proved to be associated with amounts of diseases. For example, different mutations in SWI/SNF chromatin remodelling complex were reported to cause Coffin-Siris syndrome ,15, NicoTo this aim, we propose in this paper a computational method, MAXCOM, to prioritize candidate protein complexes. To optimize the relationship between a query disease and a protein complex, the maximum information flow (MIF) between them is calculated through a heterogeneous network that is constructed by using protein-protein interactions and disease phenotypic similarities. MAXCOM then prioritizes all candidate complexes by ranking the MIFs of them. We test, in a cross-validation setting, the utility of MAXCOM in prioritizing protein complex with at least one known gene. Results show that MAXCOM can recall higher proportion of complexes at top one against large randomly constructed negative controls. We also demonstrate the power of MAXCOM by studying the associations of breast cancer and SWI/SNF complex. We believe that our method and predictions provide a useful platform for initially investigating how protein complexes link their actions to development and homeostasis of human diseases.The prioritization of protein complexes is modelled as an optimization problem, in which the objective is to find the maximum information flow between a query disease and a candidate complex through a heterogeneous network. MAXCOM takes several steps to prioritize all candidate complexes to a query disease Figure . First, The heterogeneous network is composed of disease phenotypic similarities, disease-protein associations and protein-protein interactions. The phenotypic similarities were downloaded from the literature , includie noises , and thue noises . FinallyTo optimize the relationship of a query disease and a complex, we modelled it as the MIF from the query disease node to the sink through all member proteins of the complex Figure . Here thFor the heterogeneous network where the information flow i-th complex is defined as j-th edge from j-th protein to the sink node. We use the HR_PR algorithm [Second, the MIF from the query to lgorithm  to solveLeave-one-out cross-validation experiments are adopted to assess the capability of MAXCOM in identifying protein complexes that are associated with human diseases. For this purpose, we define a protein complex to be associated with a disease if at least one member protein of the complex has been annotated as associated with the disease. After mapping on 5,080 diseases and 9,998 proteins, a total of 539 disease-related protein complexes are collected from the CORUM database (released in February 2013) . In eachThree criteria are used to quantify the performances of MAXCOM. First, if a positive control complex is ranked at the top in a validation run, it is considered as a successful prediction. We calculate the top ranked ratio (TOP) as the number of all successful predictions divided by all validation runs. Second, we calculate the average rank of all positive controls and normalize it by the lengths of ranking lists to obtain a mean rank ratio (MRR). Third, given a threshold of the relative rank, we calculate the sensitivity (true positive rate) as the fraction of test protein complexes ranked above the threshold and the specificity (true negative rate) as the fraction of control protein complexes ranked below the threshold. A rank receiver operating characteristic curve (ROC) is then drawn by varying the threshold value from 0 to 1, and the area under this curve (AUC) is calculated. Obviously, larger TOP and AUC, as well as smaller MRR indicate higher performance.To examine how well MAXCOM prioritizes candidate protein complexes, we assessed its capability of uncovering 539 protein complexes with known disease proteins by using the leave-one-out cross-validation experiments. For each of these protein complexes, we first generated 99 randomly constructed complexes as negative controls. By counting the number of test protein complexes with different ranking positions, we observed that 382 of all 539 test cases are ranked top one, achieving a TOP value of 70.87%. The mean rank ratio (MRR) was only 8.69% and a total of 412 test cases were ranked in top 5, suggesting a faster accumulation of top rankings Figure . The areTo simulate the real case in disease studies that user may want pinpoint known complexes for further biological validations, we performed a cross-validation on all 539 disease-related complexes. With a complex selected as positive control, the left 538 complexes were taken as negative controls. In this critical version, MAXCOM also exhibited a faster accumulation of top rankings Figure . For exaThe robustness of MAXCOM in operating potential noise in biological networks is of great important because much noise is widely observed in existing biological data ,43. The Second, we randomly added 10% edges of the heterogeneous network. At this case, MAXCOM achieved a TOP of 70.5%, an MRR of 9.83% and an AUC of 90.16% on random control protein complexes. The decreases in these same validation experiments were as small as 0.37% for TOP, 1.14% for MRR and 1.17% for AUC. On real control protein complexes, MAXCOM achieved a TOP of 12.8%, an MRR of 38.56% and an AUC of 82.02%. The decreases in these same validation experiments were as small as 2.23% for TOP, 0.85% for MRR and 2.23% for AUC Figure . These tThird, validation experiments were also performed by shuffling edges in the heterogeneous network but fixing the degree distribution . For this permutated network, the AUC scores were both reduced by approximately 50% on both control sets, while the result for the random control protein complexes was slightly higher as 57.34% Figure . This vaWe also introduced a parameter OM Table . If no aThe parameter To demonstrate MAXCOM's ability in predicting novel disease-related complexes, we performed a case study of breast cancer OMIM 114480), one of the most commonly occurring cancers. We systematically examined the top ten complexes that were prioritized through 539 candidates Table . There w80, one oWe then analyzed, in detail, the PBAF complex (SWI/SNF complex) since it did not include known disease proteins of breast cancer according to OMIM database  and was listed at last in our ten analyzed complexes. SWI/SNF complex was a multi-subunit chromatin-remodelling complex which mobilizes nucleosomes and remodel chromatin, playing key roles in control of lineage specification, gene expression and repression, metastasis, epigenetic tumor suppression. We found numerous literatures reported that SWI/SNF complex was associated a variety of cancers, including breast cancer. As inactivating mutations in several SWI/SNF subunits had recently been identified at a high frequency in a variety of cancers, a widespread role in tumour suppression had been proposed to SWI/SNF complex ,47,48. AWith the explosion of large-scale "omics" data, computational methods of integrating these complex heterogeneous data can provide a more thorough and systemic analysis for characterizing disease related factors. Here we have proposed a network-based strategy to prioritize candidate protein complexes by integrating disease phenotypic similarities and protein-protein interactions. As analyzed in validation results, MAXCOM is useful in tracing relationships of diseases and complexes through the heterogeneous network. Compared with early works for prioritizing individual disease proteins ,29,30, oAlthough MAXCOM is proved useful, some methodological improvements may be necessary in further research. An important extension is how to describe the tissue specificity. Since different cells have specific cellular functions such as regulation and expression , splicinFinally, MAXCOM could potentially be applied to find combinatorial protein targets and then help design network drugs. Here a disease is considered as the perturbations of the complex intracellular and intercellular network that links tissue and organ systems . The abiThe authors declare that they have no competing interests.RJ provided guidance and planning for the project. YC produced the program and wrote the manuscript, particularly producing the results section. YC, TJ and SZ contributed in preparing data and analysis of the results. All authors read and approved the final manuscript.


Interprotein contact prediction using multiple sequence alignments (MSAs) is a useful approach to help detect protein–protein interfaces. Different computational methods have been developed in recent years as an approximation to solve this problem. However, as there are discrepancies in the results provided by them, there is still no consensus on which is the best performing methodology. To address this problem, I-COMS (interprotein COrrelated Mutations Server) is presented. I-COMS allows to estimate covariation between residues of different proteins by four different covariation methods. It provides a graphical and interactive output that helps compare results obtained using different methods. I-COMS automatically builds the required MSA for the calculation and produces a rich visualization of either intraprotein and/or interprotein covariating positions in a circos representation. Furthermore, comparison between any two methods is available as well as the overlap between any or all four methodologies. In addition, as a complementary source of information, a matrix visualization of the corresponding scores is made available and the density plot distribution of the inter, intra and inter+intra scores are calculated. Finally, all the results can be downloaded  for comparison and visualization and/or for further analysis. Recent work has demonstrated the accuracy of coevolution-based protein contact prediction using several approaches as mutual information (MI) –3, direcet al.  to ensure that both proteins belong to the same organism, a minimal condition for a putative interaction.A visualization of either intraprotein and/or interprotein results is provided in a circos representation. Additionally comparison between any pair of methods is available as well as the overlap between all four methods. As a complementary source of information, a matrix visualization of scores is available and the density plot distribution of inter, intra and inter+intra scores is shown. Finally, all the results can be downloaded  to further analysis, comparison and visualization.http://I-COMS.leloir.org.ar/.This website is free and open to all users and there is no login requirement. URL: et al. (2008)  are calculated between pairs of columns in the MSA as described in  using thet al. 08 (20) wmfDCA is a statistical inference framework used to infer direct coevolutionary couplings among residue pairs in MSAs. mfDCA, in opposite to original mfDCA, provides the computational power to apply this tool in a high throughput manner to a number of protein and domain families.PSICOV introduces the use of sparse inverse covariance estimation to the problem of protein contact prediction. The method builds on work which had previously demonstrated corrections for phylogenetic and entropic correlation noise and allows accurate discrimination of direct from indirectly coupled mutation correlations in the MSA. PSICOV and mfDCA scores are computed using freecontact tool , which ihttps://bitbucket.org/soedinglab/ccmpred) is a C implementation of a Markov Random Field PLM for learning protein residue–residue contacts as made popular by Ekeberg (10) and Balakrishnan  and the I-COMS built MSAs. The performance of each method on both types of alignments, measured as the areas under the ROC curve for interprotein and intraprotein contact prediction as well as the extent to which the alignments are similar in terms of retrieved Uniprots and taxIDs, is provided as supplementary material . In summary, GREMLIN's automatic procedure for alignment construction may provide a more cared set of interacting proteins only when they are prokaryotic, while I-COMS procedure favors a simpler but wider applicable approach using a procedure already tested for similar purposes , the one with the lowest expected value for that query is used. In the case that the expected value is equal, the one with the higher bit-score is chosen (more likely orthologous to its query). The same methodology was used to build pairs of MSAs of orthologous proteins to calculate coevolution by Ochoa and Pazos 2010) . Then, t010 . Thepurposes . We consThe web server was developed on Ubuntu 12.04 linux OS and is running on an Apache 2 server and PHP 5.3 as server-side scripting language. The server pipeline was written in perl 5 and interacts with scripts written in python 2.7, Java 7 and C/C++. This is mostly due to the different natures of the code used by each algorithm. The interactive graphical output in the client-side was written in Javascript. Its execution relies entirely on the web browser and was tested on the latest versions of Firefox and Chrome.The MSAs can be generated by the server or loaded by the user. The input for automatic MSA construction are (i) the UniProt primary accession numbers or (ii) a PDB file (iii) the protein sequences in one letter code, of the two or more (up to six) proteins to be analyzed.When loading each protein, the server will display the Pfam domains in order to choose whether to work with a specific domain or with the whole length protein. These sequences are used as seed to build the MSAs.The server allows loading two or more (up to six) protein families MSA in fasta format. Those MSA should meet some requirements: they must have the same number of sequences, and sequences at the same position in each MSA must belong to the same genome or species (same taxID).The first sequence of each MSA is assumed to be the reference sequence; all results shown are based on these sequences. In all cases MSAs were gap trimmed to remove positions with gaps in a reference sequence. In addition sequences covering <50% of the reference sequence length are removed.The construction of these alignments may be difficult and time consuming to inexperienced users; therefore we offer a tool to build them automatically.The user can choose which methods to run, including corrected MI, mfDCA, CCMpred and PSICOV. All methods are set with default parameters, but by opening the ‘advanced options’ menu, the parameters for each method can be customized by the user.The server limitations are: six proteins at a time; 2000 residues are allowed as the sum of all proteins length and there is no limit on the number of protein sequences in the MSAs.The server builds an MSA for each individual protein (if not provided by the user) and also a concatenated (paired) MSAs for every combination of two MSAs.After calculation, the number of columns, the number and clusters of sequences at the selected identity value of each alignment is informed. The number of clusters is computed using Hobohm-1 algorithm . Note thA circos representation of the covariation scores of each of the selected methods and protein pairs are displayed.The circos representation is a rich piece of information: the outer circle shows the residue type and number of the reference sequence. The next circle shows the position conservation  of eachWhen moving on top of each line, the covarying positions are displayed in the upper left corner.If more than one method is run, two circos panels can be displayed at once. The user can load the circos for each method in the left and right panels Figure . A novelHence, the user is always in control of the visualization. As an example, the top 100 intraprotein links, the top 100 interprotein links or the top 100 intra+interprotein links could be observed. In the example in Figure This information is useful in order to take decisions about how well ranked are the interprotein covariation scores  when compared to the intraprotein score of each of the single proteins. For this same reason, the server informs the score density plot of each method where the distribution of the interprotein and intraprotein scores can be observed .A foremost feature is that the overlap of the methods can be analyzed. In that section the user can visualize the overlap between any methods including between the four methods together. In the same fashion as with one method, either the intraprotein, interprotein or both types of links can be observed.At last, a covariation matrix for each of the methods can be displayed. The square box in the miniature on top of the panel is a navigator panel, it shows which part of the matrix is visualized and permits easy navigation through the entire matrix. Pair covariation scores are shown as points in the matrix colored upon the score rank  . The matrix is symmetric and only the upper triangular matrix is displayed.Finally, both the circos and the matrix images can be downloaded as Portable Network Graphics, Scalable Vector Graphics or Portable Document Format files.Escherichia coli ATP synthase complex  and Ubiquitin (UBC_HUMAN) in the supplementary file. Rab proteins are small GTPases of the Ras oncoprotein family involved in the regulation of intracellular membrane traffic in mammalian cells and Ubiquitin is a regulatory protein that makes post-translational modifications that affects the proteins fate in many ways. The structure of each individual protein is known, there is evidence of interaction but no complex structural information is available .Covariation analysis between proteins is an active field of work. Several computational tools have been developed in the last years. However, comparison of the results between methods is not an easy task. The web server presented here is aimed to help computing and comparing covariation between proteins using three relevant methods. To make this task easier, we focus in showing the results in a graphical and interactive way, therefore users can dynamically explore data without any additional software other than a web browser.The server goal is to provide an environment for methods comparison. Results might be used to prioritize protein positions in a mutation analysis based on their inter and intra (if part of a complex) coevolutionary role. It might also provide a guide for docking experiments and to gain insights into protein–protein interaction when a structure of the complex is not available.This web server generates intuitive and informative graphical representations for this type of data. For different user needs, we allow users to download raw covariation data and alignments for further analysis. Also, we provide a form to ask for new features or propose new methods, which allows any user to suggest new ways of representing results or analysis to be included in the server.Supplementary Data are available at NAR Online.


Proteins interact with a variety of other molecules such as nucleic acids, small molecules and other proteins inside the cell. Structure-determination of protein-protein complexes is challenging due to several reasons such as the large molecular weights of these macromolecular complexes, their dynamic nature, difficulty in purification and sample preparation. Computational docking permits an early understanding of the feasibility and mode of protein-protein interactions. However, docking algorithms propose a number of solutions and it is a challenging task to select the native or near native pose(s) from this pool. DockScore is an objective scoring scheme that can be used to rank protein-protein docked poses. It considers several interface parameters, namely, surface area, evolutionary conservation, hydrophobicity, short contacts and spatial clustering at the interface for scoring.We have implemented DockScore in form of a webserver for its use by the scientific community. DockScore webserver can be employed, subsequent to docking, to perform scoring of the docked solutions, starting from multiple poses as inputs. The results, on scores and ranks for all the poses, can be downloaded as a csv file and graphical view of the interface of best ranking poses is possible.http://caps.ncbs.res.in/dockscore/.The webserver for DockScore is made freely available for the scientific community at: The online version of this article (doi:10.1186/s12859-015-0572-6) contains supplementary material, which is available to authorized users. Protein-protein docking is the computational method to study protein-protein interactions, based on electrostatics, shape and geometric complementarities [Proteins in the cell rarely act in isolation and in fact, are known to interact with other biomolecules like DNA, RNA, other proteins, small molecules etc. . Studyinc. [etc. ,3) and ac. [etc. . Proteintarities -8. Dockitarities ; ZDOCK, tarities ; ClusProtarities ; GRAMM-Xtarities ; FRODOCKtarities  and HADDtarities ). These tarities .It is possible to limit the search space by guiding the docking around certain residues based on evolutionary or biochemical data. However, in the absence of such an information or even from a set of docking decoys, selection of the best docked pose becomes a difficult task. In these cases, one can analyze the interfaces which are proposed by the docking program. We had recently proposed a scoring scheme, named DockScore, to re-rank the docked poses and identify the native or near-native poses from the pool . DockScoWe had assessed the performance of DockScore on 30 protein-protein complexes and CAPRI targets and compared the performance of our scoring scheme with two other methods namely dDFIRE  and FireThere are several scoring programs available as a downloadable package -21 in orfor e.g. transcription factors) or RNA-binding in nature. The presence of positively charged residues at the interface is penalized to minimize the overlap of protein-protein interaction site with that of DNA-binding region.The webserver presented here employs the scoring scheme called DockScore to perform the ranking of the docked poses. It utilizes the parameters of the interface formed upon interaction of the two given protein chains. These interface parameters are surface area, conserved residues, hydrophobicity, short contacts and spatial clustering. There is an additional parameter, which is based on the presence of positively charged residues at the interface. This can be employed selectively and especially when the interacting protein chains are DNA-binding  Each ofSurface Area: It is computed using NACCESS  by S. J. Hubbard, J. M. Thornton).in-house program MOTIFS [Conservation of residues: The individual protein chains are used as queries to perform PSI-BLAST  in orderm MOTIFS , where pInter-chain short contacts: Our in-house program CoilCheck  is emploβ-Cβ distance cut-off of 14 Å were considered as spatially clustered residues.Spatial Clustering: The pairwise distances between the interface residues were computed between the two chains and the residues with a CHydrophobic residues: We ranked those docked poses with high numbers of hydrophobic residues  at the interface with a high score, as protein-protein interfaces are known to be rich in such residues ,29,30.The parameters, which are used for the scoring, are explained briefly Figure  and for Zipped file containing the docked poses in PDB format with coordinates of both the interacting chainsPDB coordinates and chain ID of both the protein chains used to perform dockingFor performing scoring, the following files should be supplied as an input to the server Figure :Figure 2The computation is not initiated if the files are not in PDB format and coordinates for both the interacting chains are not provided by the user.Scores for each of the parameters individuallyAverage scoreAfter performing scoring, the following scores are reported:3.Normalized weighted scoreAll parameters are assigned an equal weight of 1 and the average is reported for each docked pose.4.Z-score for the normalized weighted scoreThe weights derived were further normalized by the total sum of weights and normalized weighted score was calculated.For each pose, a Z-score is calculated to assign a significance of normalized weighted score. We have tested this on our test dataset (30 cases) and note Z-score >1.5 is discriminatory to identify the native (or near-native) pose . The interface residues from the two interacting chains are highlighted in different colors . The interface residues from the two interacting chains are highlighted in different colors.For an example output, it takes 70 seconds to rank 100 poses (7 sec/docked pose), for a homodimer with 73 residues on the webserver with 8 cores; Intel(R) Xeon(R) CPU E5620 @ 2.40GHz and 5Gb RAM to finish the computation and obtain the results. For all the docked poses, a link is provided to view the interface residues and the results for each of the parameter , which was a success while testing DockScore, we sampled 1000 docked poses to see if DockScore is still able to rank the native pose as a top-ranking pose out of a pool of 1000 docked poses. We find that the performance of DockScore is not reduced due to enhanced sampling : Platform independentProgramming language: Perl, Java, JavaScriptOther requirements: Java plug-in for the respective browserLicense: Free for academic useAny restrictions to use by non-academics: Free for academic purposes. For commercial use please contact the corresponding authorThe software driving the webserver can be made available upon request for academic use.