The translation of personal genomics to precision medicine depends on the accurate interpretation of the multitude of genetic variants observed for each individual. However, even when genetic variants are predicted to modify a protein, their functional implications may be unclear. Many diseases are caused by genetic variants affecting important protein features, such as enzyme active sites or interaction interfaces. The scientific community has catalogued millions of genetic variants in genomic databases and thousands of protein structures in the Protein Data Bank. Mapping mutations onto three-dimensional (3D) structures enables atomic-level analyses of protein positions that may be important for the stability or formation of interactions; these may explain the effect of mutations and in some cases even open a path for targeted drug development. To accelerate progress in the integration of these data types, we held a two-day Gene Variation to 3D (GVto3D) workshop to report on the latest advances and to discuss unmet needs. The overarching goal of the workshop was to address the question: what can be done together as a community to advance the integration of genetic variants and 3D protein structures that could not be done by a single investigator or laboratory? Here we describe the workshop outcomes, review the state of the field, and propose the development of a framework with which to promote progress in this arena. The framework will include a set of standard formats, common ontologies, a common application programming interface to enable interoperation of the resources, and a Tool Registry to make it easy to find and apply the tools to specific analysis problems. Interoperability will enable integration of diverse data sources and tools and collaborative development of variant effect prediction methods.The online version of this article (doi:10.1186/s13073-017-0509-y) contains supplementary material, which is available to authorized users. Recent progress in DNA-sequencing technologies has ushered in an era of rapid and cost-effective genome sequencing, enabling clinical applications  and the To assess the potential pathogenicity of genetic variants, singly or in combinations, it is useful to assess their frequency in control or general populations, as already mentioned. Public databases are burgeoning with information about genetic variants in humans and in many model organisms. Resources such as dbSNP , dbVar , COSMIC VHL, which are proximal to the binding site of VHL for its ubiquitination target HIF1A [The biomedical research community is fortunate to have access to such a wealth of information, but its sheer size and disparate nature are also daunting. In addition to public databases, hundreds of DNA- and RNA-sequencing experiments are revealing manifold genetic variants and mutations each year, and an increasing number of these can be linked to protein structure. For example, protein structure analysis of a novel variant in the ubiquitin-protein ligase TRIM11, observed in individuals affected with inflammatory bowel disease, helped determine that the variant is more likely to affect protein–protein interactions rather than protein folding and stability . Functioet HIF1A .Simultaneously, there has been great progress in characterizing the 3D structures of proteins , 20, botHowever, the connections between genetic variations and protein structure are not always easy to find. A few computational tools have begun to emerge  workshop was hosted at the Institute for Systems Biology in Seattle, Washington. The goal of the workshop was to explore the state of the field connecting genetic variation and 3D protein structure, and to bring together some of the key researchers working on interpreting genetic variation data. The workshop consisted of a mixture of talks, discussion sessions, and breakout groups. The program is available at the workshop website . Twenty-After all the presentations and discussion sessions concluded, workshop participants separated into two breakout groups to brainstorm about how the research community as a whole could accelerate progress in the field in ways that individual laboratories could not.Adoption or development of standardized formats for the various major data types .Mechanisms to scale up the information exchange to large-scale queries using big data technologies such as DataFrames  and BigQUse of ontologies to standardize the terminology for the exchange of data and knowledge. These ontologies already mostly exist, and need only be specified as the standard, although some extension may be required.Selection of initial tools that should be part of a pilot phase of the development and initial deployment of the interoperability framework.Development of a tool registry and portal that would serve as a web-accessible resource for finding relevant tools, their inputs and outputs, and also reference data files that can be used to demonstrate and validate the tools and their interoperation.Breakout group 1 discussed existing ontologies, tools, and datasets in the field and considered potential architectures for an integrative framework, focusing on how tools and resources could be made more interoperable to enable more widespread use of the tools and integration of inputs and outputs among the tools. Important aspects that emerged in the discussion include:How to increase the actionability of variants observed in patients. Beyond facilitating access to knowledge on the structural impacts of variants, there is a need for a metric of confidence in the predicted impact. Gene-editing technologies are likely to enhance experimental studies of salient variants.The need to recognize multi-variant interactions within single genes and proteins and mutation effects on protein–protein, protein–nucleic acid, or protein–ligand and drug interactions. Also, annotation of the context in which each variant could have an effect is important. For instance, information on cell types or cellular conditions in which specific interactions or protein complexes are formed, as well as annotation of epistatic relationships with mutations elsewhere in the genome, can help in interpreting a mutation’s influence on the cell.How to improve the interpretation of variants affecting splicing. A proposal was made to create a mechanism for collecting donated RNA-seq data to derive a comprehensive set of splice variants and interpret them in the context of protein structure. It may also be useful to organize data on splice variants by type of alternative splicing , so there would be a benefit from implementing an interoperation framework.How to identify the target audiences. Scientists, tumor boards, clinical geneticists, developers of targeted drugs, patients, and lay people with an interest in genetic testing were all identified as possible audiences.How to improve documentation and outreach. Suggestions included the development of documentation videos and tutorials, and contributing to Wikipedia sections describing the impact of variants on protein structure, building on current experience such as the Protein Standards Initiative  of the HBreakout group 2 discussed unmet needs, ranging from improvements in structural interpretation of splicing variants to more effective dissemination of knowledge to clinical geneticists, tumor panels, and the general public. Salient questions and points that were discussed include:The workshop has already begun to positively impact collaboration and interoperability in the wider research community. For example, an immediate outcome from discussions that occurred during the workshop was that links pointing researchers to the MuPIT resource  were addHere we review methods that use 3D structural information from the PDB to predict the effect of missense mutations; mapping other types of mutations  remains an open challenge. In Table A first set of methods predicts thermodynamic properties related to mutations: (1) change in protein stability , 58–72; A second set of methods , 83–88 pFew prediction methods are purely based on 3D structural information, with the exception of FoldX , which uMost tools to predict the effect of mutations are available online. However, there is a wide variety of input formats and scope of prediction . The majority of the 3D protein structure-based tools take PDB residue numbers of the mutated sites as input  using multiple tools is generally not possible. For instance, it would be useful to run tools that predict multiple effects of mutations simultaneously, such as protein stability and interruption of protein–protein and protein–nucleic acid binding. A further limitation is the input by PDB or UniProt residue position, since SNVs are annotated using genomic coordinates. Mapping between genomic and protein coordinate systems is error prone due to, for example, different genome assembly versions and alternative splicing. Where a mapping from genome to UniProt is possible, SIFTS  and CRAVCurrent tools that predict the effect of missense mutations are based on either protein sequence information, 3D structural information, or both. Tools predict either biophysical changes or effect on phenotype. Those that use 3D structural information and visualization offer additional insights by providing locations of mutations in a 3D context, which is not possible using sequence-based prediction. For example, multiple mutations on a protein can be visualized and potential 3D hotspots can be identified. In the next section, we describe a framework to overcome the large heterogeneity of tools, which limits their usefulness, ease of use, and hinders comparative performance assessments.To facilitate innovation in this field, we recommend the development of a framework of common formats and application programming interfaces (APIs) that enable the many resources available to interoperate more effectively both at the individual variant level and at large scales. We further recommend the development of a portal that can be used to annotate the current state of tools in the field and guide users on how these tools can interoperate and be used to address different research questions. The outline of the recommended GVto3D framework takes its lead both from our wider review of the field as well as from the presentations and discussions that occurred among those members of the research community who attended the workshop; its design incorporates the needs and existing efforts of these researchers.Figure A second component of the portal will be the definition of standard APIs so that information can be sent to and requested from different tools in the same way, thereby reducing software development overheads, which are typically encumbered with different tools using different APIs. It is envisaged that new third-party tools will use the API natively while API adapters will be developed in order to bridge with pre-existing third-party tools. The API enables seamless interoperability between different variant-related tools and also a standard access to multidirectional mapping among genomic, protein sequence, and protein structure coordinates. These mappings will be made available through APIs and as downloadable data files. Mappings will be kept up to date based on the update schedules of the underlying data sources , freeing developers from maintaining and updating copies of these data. Once several similar resources support the standard APIs, the site can be further developed into an aggregation portal, where a query at the portal can be automatically farmed out to multiple resources, and the results collated and returned to the user in a single batch. This framework advances the FAIR principles of findability, accessibility, interoperability, and reusability  for all The use of standard file formats and standardized representations of data enable interoperability of prediction tools, for example, the output from one tool can be passed as input into a second tool, and can thereby simplify the comparison of different methods. The standardized formats are also essential components of a reusable set of integrated tools (software stack), including tools for reading and interpreting data files (file parsers), APIs, and visualization tools. Most of the current tools use a variety of inputs and outputs, placing a large burden on the user to transform data. Standard file formats and uniform APIs will be at the core of future services that will combine and compare different approaches. Various platforms and tools have different schedules and reliability of upgrades; keeping track of versions is important as changes to software may have large effects on the results.The VCF file format , despiteRegarding genome nomeclature, the Human Genome Variation Society, which aims to foster the discovery and characterization of genomic variations, including population distribution and phenotypic associations, has established guidelines and recommendations for the nomenclature of gene variations, and serves as an international standard .Progress in this field depends on global collaboration and the sharing and reuse of tools. APIs provide protocols to enable this collaboration. Tools wrapped in standard APIs present a consistent interface to heterogeneous tools, enhancing interoperability, and shielding the user from changes to the underlying software. As an example, many prediction tools that use 3D protein structural information define the location of mutations at the protein level using either UniProt or PDB coordinates. Mapping genomic coordinates to 3D protein structure is non-trivial and error prone. Robust APIs that can perform this mapping with up-to-date 3D information using both types of protein coordinates can augment existing tools that are based on just linear protein sequence coordinates.Moreover, progress in the prediction of the effect of mutations and use of 3D structural information depend on the availability of well-designed training, test, and validation sets. The tool repository will be a place to share datasets, as well as protocols and references (metadata) for how these datasets were generated. Validation sets, accompanied by well-documented tutorials or vignettes, will include a subset of variants with clearly understood effects that can be used to test the output of available resources. Eventually these can serve as a set of unit tests for the framework itself.The GVto3D workshop held in Seattle in February 2017 represents an important step towards spurring collaboration and advancing progress in proteogenomics research. The disparate nature of current tools and resources and lack of interoperability contribute to slower progress in the field than might otherwise be possible. Development of a community-driven interoperability framework for integrating genetic variation resources and protein structure resources promises further expansion of our understanding of the functional implications of genetic variation. While the use of 3D structural features has enabled the atomic-level exploration of the effects of mutations , the accuracy, scope, and scale of predictions are still limited. The proposed framework will enable pooling of data sources and tools, and collaborative development.However, there will be substantial challenges as we move forward with design of the framework. The first challenge is establishing a durable user base for the framework. One possible approach is to engage a few key laboratories to take the lead as early adopters, and assume that the framework will gain wider community acceptance through their example. We propose a more user-centered design approach, however, which emphasizes an iterative process of engaging multiple communities of practice in conceptualizing, developing, and rolling out the framework; the GVto3D workshop was a first step in this direction. Second, questions of sustainability are also pertinent here, insofar as how such a system will be maintained, and who will be responsible for its ongoing maintenance. Here, we propose an approach that we envision will become self-sustaining through the deployment of open-source technologies in an engaged community. Third, standardization is a key component of any interoperability project, which in this case depends upon work to enhance usage of certain de facto standards, and to establish other standards, including the creation of standard APIs. Working closely with the community of potential framework users, as well as with standard-setting bodies, such as the Global Alliance for Genetics and Health and the Proteomics Standards Initiative of the Human Proteome Organization, will be important for helping these standards gain further traction.Taken together, the user-centered framework we have outlined above—a Tool Registry and a set of standardized formats and common APIs based on deployment of open-source materials—aims to bring the FAIR principles to bear on current and emerging tools while enabling their broader usage across multiple communities of practice. The result promises to be more rapid progress in research that can make use of GVto3D resources and eventual applications to precision medicine, while ensuring that methods and outcomes are findable, accessible, interoperable, and reusable.Benchmark dataset: A curated and well-studied dataset that can be used to evaluate the relative performance of analysis methods and algorithms.File parser: A computer program module that interprets the structure of input data and breaks the input into well-defined parts that can then be used by other parts of the computer program.Rosetta energy terms: Rosetta  estimateSoftware stack: A set of software subsystems or components designed to work together as a platform.Variant Call Format: A standard format of a text file used for storing genome sequence variations relative to a reference genome.Additional file 1: Table S1.shows tools that use three-dimensional structural information from the Protein Data Bank to predict the effect of missense mutations. 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Computational approaches for the structural modeling of PPIs, such as protein–protein docking, are quite needed to complement existing experimental techniques. The reliability of a protein–protein docking method is dependent on the ability of the scoring function to accurately distinguish the near-native binding structures from a huge number of decoys. In this study, we developed HawkRank, a novel scoring function designed for the sampling stage of protein–protein docking by summing the contributions from several energy terms, including van der Waals potentials, electrostatic potentials and desolvation potentials. First, based on the solvation free energies predicted by the Generalized Born model for ~ 800 proteins, a SASA (solvent accessible surface area)-based solvation model was developed, which can give the aqueous solvation free energies for proteins by summing the contributions of 21 atom types. Then, the van der Waals potentials and electrostatic potentials based on the Amber ff14SB force field were computed. Finally, the HawkRank scoring function was derived by determining the most optimal weights for five energy terms based on the training set. Here, MSR (modified success rate), a novel protein–protein scoring quality index, was used to assess the performance of HawkRank and three other popular protein–protein scoring functions, including ZRANK, FireDock and dDFIRE. The results show that HawkRank outperformed the other three scoring functions according to the total number of hits and MSR. HawkRank is available at The online version of this article (10.1186/s13321-017-0254-7) contains supplementary material, which is available to authorized users. Protein–protein interactions (PPIs) are involved in a wide variety of biological processes, such as signal transduction , 2, tranKd) with the scores predicted by the corresponding scoring function, with the highest correlation of only − 0.32. Recently, our group analyzed the prediction results for the 24 targets tested from ROUND14 to ROUND 28 of CAPRI [The ultimate goal of protein–protein docking is the prediction of a near-native structure of the complex from many docking decoys, which generally falls into two stages: sampling and refinement. In the sampling stage, a large number of docking poses are generated and scored by various scoring functions; and in the refinement stage, the top-hit poses (or decoys) given by the first stage are re-scored and re-ranked by more rigorous scoring functions. Apparently, the success of protein–protein docking is, to a large degree, dependent on the ability of the scoring function to score and rank the decoys accurately. So far a large number of scoring functions have been developed, ranging from force field-based scoring functions such as ZRANK and FireDock –13, to kof CAPRI , and we In the past decade, more theoretically rigorous free energy calculation methods, such as Molecular Mechanics/Poisson Boltzmann Surface Area (MM/PBSA) and Molecular Mechanics/Generalized Born Surface Area (MM/GBSA), have been employed to predict binding affinities and identify correct binding structures for protein–protein systems –29. For Although MM/GBSA is of more computational efficiency than other end-state free-energy calculation methods like thermodynamic integration (TI) and free energy perturbation (FEP), it is still much more time-consuming than the commonly used scoring functions in protein–protein docking, such as ZRANK, which treats the desolvation energy term with Atomic Contact Energy (ACE) model . The comHawkRank was developed by combining the weighted van der Waals potentials, electrostatic potentials and desolvation potentials. The workflow of the development of HawkRank is discussed below in details.https://zlab.umassmed.edu/zdock/benchmark.shtml), which is convenience for the training of scoring function. Therefore, we choose ZDOCK benchmark 4.0 for its convenience and the rationality of comparing HawkRank with ZRANK and FireDock. In this study, the 124 complexes were used as the training set to develop HawkRank and the other 52 ones as the test set to validate the actual performance of HawkRank. It should be noted that the benchmark 4.0 only contains binary interactions, so HawkRank is not suitable for the interactions between more than two proteins. Besides, HawkRank is also not suitable for the interactions between protein and peptide.More and more protein–protein complexes have been discovered, researchers classify protein–protein complexes based on various angles. The most common is that classify protein–protein complexes by protein family. The other researchers classify complexes as homo- and hetero-oligomeric complexes, non-obligate and obligate complexes and transient and permanent complexes, by the type of protein–protein interaction . Besidesreduce program (version 3.24) [ZDOCK (version 3.0) was used to generate the decoys for each complex. ZDOCK systematically evaluates a huge number of docked conformations on a grid by using a combination of shape complementarity, electrostatics and statistical potential terms for scoring , and theon 3.24) . The decα atoms of the ligand proteins when the receptors are superposed, was used to assess the global geometric fit between the predicted and native conformations [α atoms of the interfacial residues when the predefined interfacial residues are superposed, was used to measure the geometric fit of the interface regions [ProFit program [Generally, in a protein–protein complex, the smaller protein is defined as the ligand protein and the larger one as the receptor protein. In our study, two types of root mean square deviations (RMSDs) between the predicted structure and the corresponding crystal structure, including ligand RMSD (L_RMSD) and interface RMSD (I_RMSD), were used as the criteria to evaluate the performance of protein–protein docking. L_RMSD, which is calculated over the Crmations . I_RMSD, regions . The int regions . The L_R program , which eThe criteria to evaluate the performance of protein–protein docking for Target 107 in Round 35 of the CAPRI campaign are summarized in Additional file Because of the high computational cost of MM/GBSA, particularly the GB calculation, the use of MM/GBSA as a scoring function to rank thousands to even millions of docked conformations is computationally unaffordable. In order to balance computational cost and prediction accuracy, a novel SASA-based solvation model was developed by fitting the solvation free energies of proteins predicted by GB, as shown in Fig. A protein dataset was established for the GB calculations (referred to as the GB dataset). The 1640 proteins in this dataset were selected from PDB based on the following criteria: (1). the structures are obtained by X-ray experiment, (2). the resolution should be lower than 2 Å, (3) the proteins do not contain any small molecule ligands, (4). the proteins are asymmetric, and (5) the proteins do not contain any modified residue.Besides, for multiple structures whose sequences have at least 30% sequence identity, only a single structure is included in the dataset. Moreover, the protein structures with multiple conformations were eliminated.tleap program in Amber14, and then the partial charges and force field parameters of the ff14SB force field were assigned. Subsequently, all proteins were optimized by 5000 cycles of minimization (2500 cycles of steepest descent and 2500 cycles of conjugate gradient). At last, the electrostatic solvation free energy for each protein was computed by using the modified GB model developed by Onufriev and colleagues (referred to as GBOBC1) [The electrostatic/polar solvation free energies for the proteins in the GB dataset were computed by using the GB model implemented in Amber14. First, all missing heavy atoms and hydrogens of the proteins were added by using the  GBOBC1) . A valueBased on the study reported by Hou et al. , all atopsolSASA represents the polar component of solvation free energy; i is the IDs  with 50% of the proteins and a test set with the remaining proteins. Least-square fitting was applied to optimize the weights of different atom types and Nres based on the training set, and then the reliability of the developed solvation model was evaluated by the consistency between the predictions given by the GB model and those given by the SASA-based model to the test set.For each protein in the GB dataset, the SASAs were calculated by the NSC function that is an implementation of the DCLM (Double Cubic Lattice Method) variant of the Shrake’s and Rupley’s algorithm for surface calculation . The vanE is the value of potentials and w is the weight of corresponding potentials:The HawkRank scoring function is a linear weighted sum of the van der Waals attractive and repulsive potentials, the electrostatic attractive and repulsive potentials, and the desolvation potentials. The core formula of HawkRank is shown in Equation function , where ΔThe details about the energy terms used in the scoring function are described below. The workflow of the development of HawkRank is shown in Fig. i and j are atoms i and j, rij is the interatomic distance between them, σij is the sum of atomic radii, and εij is the well depth derived from the Amber ff14SB force field [The definition of the van der Waals potentials  was calculated by taking the polar solvation free energy of complex (psolSASA(com)) to subtract the sum of the polar solvation free energies of receptor (psolSASA(rec)) and ligand (psolSASA(lig))  implemented in the R package  was used to determine the optimal set of the weights for the energy terms used in HawkRank. We set the maximum (3.0) and minimum (0.0) value for the weight of the van der Waals attractive potentials, electrostatic attractive potentials, electrostatic repulsive potentials and the polar desolvation potentials. However, we set the maximum (0.001) and minimum (0.0) value for the weight of the van der Waals repulsive potentials, on account of inherent defect in the equation function to calculate the van der Waals repulsive potentials and we want to reduce the impact of the van der Waals repulsive potentials on the whole scoring function. Besides, the population size was set as 200, the number of iterations was set as 1500 and the number of chromosomes that are kept into the next generation was about 20% of the population size.The capability of HawkRank to recognize the near-native poses from the decoys was compared with those of two popular force field-based scoring functions used in protein–protein docking, ZRANK  and FireZRANK is a force field-based scoring function that is a linear combination of atom-based potentials, including electrostatics, van der Waals, and desolvation potentials. Pairwise Atomic Contact Energy (ACE) model  is used FireDock is a method for the refinement and rescoring of rigid-body docking solutions. The function of FireDock includes ACE, softened van der Waals interactions, electrostatic interactions and internal energy. Moreover, hydrogen and disulfide bonds, π-stacking and aliphatic interactions are also considered . ComparedDFIRE is an all-atom statistical and knowledge-based energy function. Each polar atom is treated as a dipole with a direction. The orientation of the dipole is defined by the bond vectors that connect the polar atoms with other heavy atoms, and the function of dDFIRE is extracted from protein structures based on the distance between two atoms and the three angles involved in dipole–dipole interactions . BesidesN predictions, has been widely used to evaluate the performance of a given scoring function [It is well known that how to quantitatively evaluate the performance of a scoring function is quite essential. Traditionally, success rate (SR), the proportion of total cases with at least one hit in the top function . Howeverxi represents a protein–protein complex, size(X) is the total number of the studied systems in a benchmark, HIT equals to the total number of the hits for a protein–protein complex, ranki is the rank number of a hit, topj is the reciprocal of rankj, Y is the integrated value to evaluate the performance of the scoring function for multiple protein–protein complexes, and F is the value to evaluate the performance of the scoring function for a specific protein–protein complex.In order to evaluate the ranking performance of a scoring function on the whole benchmark in a reasonable way, MSR (modified success rate) proposed by us (Equation function r = 0.96). The plot of the polar solvation free energies predicted by the SASA-based model versus those predicted by the GB model is shown in Fig. r = 0.93, Fig. wi) for the 21 atom types derived by fitting the GB polar solvation free energies for the training set are listed in Table w0 and the constant term determined by fitting are − 10.51 and 59.78, respectively.On the whole, the SASA-based solvation model  for the 46 cases in the test set. As shown in Fig. N is larger than 150 while ZRANK’s SR is higher than 50% when N is larger than 100. When N is larger than 600, HawkRank’s SR value equals to or exceeds ZRANK’s SR value. According to SR, the performances of HawkRank and ZRANK are comparative. However, HawkRank’s SR values are always higher than those of dDFIRE and FireDock.Based on the training set, the optimal weights for the five types of potentials were determined by GA as follows: 0.850 for the van der Waals attractive potentials, 0.0005 for the van der Waals repulsive potentials, 1.887 for the electrostatic attractive potentials, 1.853 for the electrostatic repulsive potentials, and 0.9303 for the polar desolvation potentials. Then, the trained HawkRank scoring function was used to score the decoys for the test set. Among the 46 cases in the test set, the correct binding structures for 2 cases  are the best hits, those for 6 cases  can be found in the top 5 ranked predictions and those for 13 cases can be found in the top 20 ranked predictions. Then, we calculated the SR values  were calculated for each scoring function. Overall, as shown in Fig. N is small than 160, HawkRank shows comparative performance with ZRANK and dDFIRE, but when N becomes higher than 160 it performs better. With N increases, the Y values of HawkRank increase much faster than those of the other three scoring functions. Therefore the HawkRank scoring function can identify more hits with better ranking in the top N = 160–20,000 than the other three scoring functions. Besides, the performance of Firedock performs apparently worse than the other three scoring functions  between I/L_RMSDs and each energy term given by HawkRank, and the numbers of the cases in four intervals of r are also displayed in Fig. Three examples  with rI/rL > 0.4 and three examples  with F (N = 500) values for the 46 tested cases with the hits found in the top 10,000 decoys are listed in Additional file F values in ascending order. F = 0 means that the scoring function does not find any hit in the top 500 predictions. For dDFIRE, ZRANK, FireDock and HawkRank, the numbers of the cases with F = 0 are 16, 8, 12 and 10, respectively. For 1XU1, 1ZHH, 2B4 J and 2OOR, the four scoring functions cannot find any hit in their top 500 predictions. For 1GXD, 1JZD, 1US7 and 2FJU, more than 60 hits docked by ZDOCK, but HawkRank performs poorly on them.Although HawkRank has better ranking capability than ZRANK, FireDock and dDFIRE, it still has some limitations. The The COCOMAPS online service  was usedThe arithmetic speed of HawkRank is closely related to the size of the protein–protein complex. The speed of HawkRank is fast enough to meet the requirements for scoring a large number of decoys in the sampling stage of protein–protein docking. The scoring of a complex with 500 residues needs less than 0.3 s on a core (Intel Xeon CPU E5-2692 v2 @2.20 GHz) with Linux operating system.In this study, we developed a new scoring function named HawkRank by combining polar desolvation potentials, van der Waal potentials and electrostatic potentials. HawkRank introduces a fast and effective way to calculate the desolvation potentials based on a SASA-based solvation model. Compared with ZRANK, FireDock and dDFIRE, HawkRank shows better ranking capabilities to the 46 cases in test set. Besides, the scores predicted by HawkRank have higher correlations with L/I_RMSDs than those predicted by ZRANK, FireDock and dDFIRE, suggesting that the HawkRank scoring gives a better funnel-shaped energy landscape than the other three scoring functions. Although its prediction accuracy still needs to be improved for some protein–protein complexes with small interface areas, HawkRank is efficient to meet current requirements for scoring a large number of decoys in the sampling stage of protein–protein docking. In the light of the above assessment and the conclusion in our previous study that MM/GBSA rescoring has good capability to distinguish the correct protein–protein binding structures from the decoys, it would suggest to be an efficient protocol of using HawkRank followed by the MM/GBSA rescoring to improve the predictions of protein–protein docking.


Identifying complexes from PPI networks has become a key problem to elucidate protein functions and identify signal and biological processes in a cell. Proteins binding as complexes are important roles of life activity. Accurate determination of complexes in PPI networks is crucial for understanding principles of cellular organization.We propose a novel method to identify complexes on PPI networks, based on different co-expression information. First, we use Markov Cluster Algorithm with an edge-weighting scheme to calculate complexes on PPI networks. Then, we propose some significant features, such as graph information and gene expression analysis, to filter and modify complexes predicted by Markov Cluster Algorithm. To evaluate our method, we test on two experimental yeast PPI networks.Sn values of 0.3774 and 0.3453. Comparing to existing methods, our method improves Precision value by at least 0.1752, F-Measure value by at least 0.0448, Sn value by at least 0.0771. Experiments show that our method achieves better results than some state-of-the-art methods for identifying complexes on PPI networks, with the prediction quality improved in terms of evaluation criteria.On DIP network, our method has Precision and F-Measure values of 0.6004 and 0.5528. On MIPS network, our method has F-Measure and  Detecting of protein complexes from PPI networks is a key problem to elucidate protein functions and identify biochemical, signal and biological processes in a cell. Like other biological molecules, most proteins do not work in isolation; they cooperate with other proteins to perform a particular biological function. These complexes are molecular aggregations of two or more proteins assembled by PPIs . AccuratRestrictedNeighborhoodSearchClustering (RNSC) algorithm [ClusterONE method [ClusterONE is the ability to not just find overlapping clusters, but also clusters that may be contained in another cluster.In past several years, a large number of technologies have been developed for the large-scale analysis of complex detection from PPI networks –13. Heurlgorithm  deploys E method , detectsClustEval framework [One of the most widely used graph clustering algorithm is Markov Cluster Algorithm (MCL) , which iramework , such asramework , Spectraramework , Transitramework , fanny [ramework , but ourClustering−basedonMaximalCliques (CMC) [Complete enumeration algorithms aim to enumerate all possible subgraphs in G with density exceeding a specified threshold. Spirin and Mirny  proposedes (CMC)  method gLeung  developeAbove methods only focus on static PPI networks. In reality, PPI networks in a cell are not static but dynamic –43. The Existing methods constructed PPI networks, based on gene expression variance of each protein , 50. SegSn value by at least 0.0771.We propose a novel method to identify complexes on PPI networks. First, we design an edge-weighting MCL method to calculate complexes on PPI networks. Second, we propose a novel co-expression analysis method to evaluate predicted complexes, based on differential co-expression information. To evaluate our method, we test on two experimental yeast PPI networks. On DIP network, our method has Precision and F-Measure values of 0.6004 and 0.5528. On MIPS network, our method has F-Measure values of 0.3774. Comparing to existing methods, our method improves Precision value by at least 0.1752, F-Measure value by at least 0.0448, We propose a novel method to identify protein complexes on PPI networks. First, we use Markov Cluster Algorithm with an edge-weighting scheme to calculate complexes on PPI networks. Second, we design a novel co-expression analysis method to measure each protein complex, based on differential co-expression information. Figure G=, where vi∈V represents a protein and ∈E denotes that protein vi interacts with protein vj.A PPI network is formulated as an undirected graph G, N(vi) denotes all neighbors of vi in the PPI network. Let A be a |V|×|V| adjacency matrix, and A denotes the confidence weight of edge , defined as follows. N(vi)∩N(vj)| is the intersection of neighbors between N(vi) and N(vj), and |N(vi)∪N(vj)| is the union of neighbors between N(vi) and N(vj).Given a graph M indicates the probability of transitions via a random walk, and M represents the probability of a transition from vi to vj, defined as follows. n is the number of all vertices in the graph, and each column of M sum up to 1.Since two vertices having a larger proportion of common neighbors, one vertex can move to another vertex with great probability. A canonical flow matrix M, iterating until convergence. In addition, Prune is performed at the end of each iteration, in order to remove entries with very small values.Markov Cluster Algorithm proposed by Stijn van Dongen , is an iMexp=M×M. We calculate Mexp on the basis of M, and then assign the obtained matrix Mexp to M.The operation of Expand is simply expressed as M using parameter r, and re-normalizes elements in each column that sum up to 1. Then, we assign the obtained matrix Minf to M. The operation of Inflate, named by Minf, is expressed as follows. n is the number of all vertices in the graph, and r is the parameter of Inflate, by default r=2,.The operation of Inflate raises each entry in In the iterative process, there are some entries with very small values, and let these entries to be zero. This operation can make convergence faster, and keep the key part of aggregation information.Lj={k|M>0} to represent a collection of vertices in column j with values greater than zero; in other words, it is a collection of vertices that flow to vertex vj. We calculate the average value of all elements in Lj as follows. We use j of M as follows. w is a parameter to adjust the threshold value, by default w=1.And also, we calculate the threshold to filter entries with small values in column thd(j) in column j of M, filled with zero. After the operation, M must be re-normalized, and elements in each column of M sum up to 1.We remove entries with very small values less than M. We assign all vertices flowing to the same vertex as belonging to one cluster.After lots of iterations, we find that most vertices flow to one vertex, and there exists one non-zero entry per column in the flow matrix We propose some significant features, such as graph information and gene expression, to filter and modify complexes predicted by Markov Cluster Algorithm.n-length shortest path as n-connection.The direct connection (edge) in the PPI network, denotes that one protein interacts with another protein. We not only use the interaction information, but also consider indirect connection with a n-connection between vi and vj, we can define Connect=1; else, Connect=0. Moreover, PathNum denotes the total number of n-length shortest paths from vm to vn.If there exists a vk and a complex C, we calculate the ratio of n-connection proteins in C from vk, defined as follows. Given a protein n-length shortest paths for n-connection proteins in C from vk, defined as follows. Also, we calculate the ratio of total Here, we calculate these features of 2-connection and 3-connection.C to describe intensive degree of n-connection proteins, defined as follows. We can use the density of complex vk is added into complex C, a new complex C′ can be formed. We calculate the density difference between these two complexes, as follows. C′={C,vk}.When a protein Here, we also calculate these features of 2-connection and 3-connection.Ti(l) represents the expression value of protein vi at the time point l.Gene expression data could reflect features of proteins under various conditions in a biological process , 51. It eIF3 complex, and Fig. On 36 intervals,Fig. Eco of proteins vi and vj, defined as follows. m is the number of expression intervals.Two proteins have a similar degree of expression at the same time interval leading to a high co-expression value. If the gene expression data of proteins are not similar, their co-expression value is low. Therefore, we calculate the co-expression value C, we can measure the co-expression value, as follows. For a complex vk and a complex C is defined as follows. And, the co-expression value between one protein Eco(avg). If Eco>Eco(avg), we set Co=1, else, Co=0.We calculate average co-expression value of all pairs of proteins in the PPI network, defined as C, as follows. We calculate the ratio of co-expression protein pairs in a given complex vk is added into complex C, a new complex C′ can be formed. We calculate the co-expression difference between these two complexes, as follows. C′={C,vk}.When a protein vk, we calculate the number of co-expression proteins in a complex C, as follows. For protein vk in a complex C, as follows. Also, we calculate the ratio of co-expression proteins for protein Den, Eco(C), Cotadio(C). We reserve complexes with high qualities, discard complexes with low values, and modify median complexes. Algorithm 1 shows the overall algorithm of filtering method.We set two thresholds, a lower bound and a higher bound for each of three features, to filtering predicted complexes: ConnectRatio, PathRatio, DenDiff, Eco, CoDiff, CoProNum, CoProRatio. We delete proteins in a complex with low qualities, and add neighbor proteins with high values into the complex. Algorithm 2 shows the overall algorithm of modifying method.We use a linear function of seven features to determine how to modify a given complex: Experiments show that our method achieves better results than some state-of-the-art methods for identifying protein complexes on PPI networks, with the prediction quality improved in terms of many evaluation criteria.Our method is applied on two experimental yeast PPI networks. One is retrieved from the Database of Interacting Proteins (DIP) , which wCYC2008 [All predicted complexes are compared with the benchmark data, referred to as CYC2008 . There aGSE3431 [We analyze gene expression data GSE3431  downloadAt present, there are two popular measurements for evaluating the performance of complexes detection method, from many literatures , 56.Sn), Positive Predictive Value (PPV) and geometric Accuracy (Acc) have recently been proposed to evaluate the quality of protein complex prediction [n benchmark complexes and m predicted clusters, let as Ti,j denote the number of common proteins between the i-th benchmark complex and the j-th predicted cluster. Then, Sn, PPV and Acc are defined as follows. In addition, Sensitivity  indicates the tradeoff between Sn and PPV. It is obtained by computing the geometrical mean of them.Generally, O is used to assess how effectively a predicted complex Cp matches a benchmark complex Cb [Cp| is the number of proteins in the predicted complex, and |Cb| is the number of proteins in the benchmark complex. If a predicted complex Cp that has no common proteins with a benchmark complex Cb, then O=0.The overlapping score mplex Cb , definedP be the set of complexes predicted by computational methods and B be the set of benchmark complexes in the PPI network. Then, the number of complexes in P at least matching one real complex is denoted by Ncp=|{Cp|Cp∈P,∃Cb∈B,O≥ω}|, while the counterpart number in B can be denoted by Ncb=|{Cb|Cb∈B,∃Cp∈P,O≥ω}|, by default ω=0.2.Usually, a predicted complex and a benchmark complex are considered as a match if their overlapping score is no less than a threshold value . Let P bNcp and Ncb, Precision and Recall can be defined as follows. Based on above definitions of And, F-measure is their harmonic mean, defined as follows. Vector(C) to filtering predicted complexes. Based on co-expression value as Eco(C), CoRatio(C) as well as graph information as Den, the preliminary complex clustering result will be determined to be either reserved, removed or further modified. Two thresholds including a upper bond and a lower bond are set for each of the three features. The Tables We consider three features Eco(C) can be set to 50; that is, our method removes complexes with low co-expression values, and Ncb and Recall decrease slightly, but Precision increases a lot. The maximum value of Eco(C) can be set to 80; that is, our method reserves complexes with high co-expression values, and Precision and F-Measure increase slightly. Moreover, the threshold of CoRatio(C) can be set to , and the threshold of Den can be set to .In addition, the optimized parameter thresholds are obtained from the three tables.The minimum value of We use a linear function of seven features to evaluate the probability of a protein adding into a complex, as follows. w={0.01,0.02,0.01,0.24,0.36,0.03,0.33}. Gene Expression information as CoProRatio,CoDiff, Eco contribute the most.We discuss the effectiveness of our linear function, by using some randomly generated positive and negative samples to regression. First, we randomly select a protein in the network, and choose the size of generated complex. Then, we randomly choose another protein from the collection of neighbors. Repeat this step until producing a complex. Finally, we calculate the neighboring collection of this generated complex. If adding a neighboring protein makes new complex better than old one, we assign it is positive; else, it is negative. Similarly, we traverse all proteins in this complex. If deleting a internal protein makes new complex better than old one, we assign it is positive; else, it is negative. We use 1894 positive samples and 9309 negative samples to produce the optimal parameters, as CYC2008, we filter some complexes with less than three proteins, keeping 236 complexes with average size of 6.68 proteins. For each complex, we randomly put in a protein from PPI network to form a new complex. When randomly delete a protein, the probability of correct deleting is For P for adding neighboring proteins. When randomly add a protein, the probability of correct adding is P>0.5, and accuracy of our adding method is 0.3333 if P>0.8. The analysis of adding method is shown in Table On DIP network, we generate 1507 complexes and use We compared the performance of our method with three existing methods, such as COACH, ClusterONE and MCL. COACH is a novel core-attachment method to detect complexes with two stages . It deteOn DIP network, results by our method and three existing methods are shown in Table Sn value by at least 0.0771. Although our method did not achieve the best recall value, as we can see from the table, method with high recall values like DPClus and RRW, unavoidably have a particular poor precision, which indicates the high recall is based on counting into a overall large number of clusters and hence the precision is weakened. Comparatively, our method remains a relatively high recall value and achieves the best precision revealing the overall efficiency of our model.On MIPS network, results by our method and three existing methods are shown in Table http://www.comp.nus.edu.sg/~lixl/, and a fast and free implementationof ClusterONE is available at http://www.paccanarolab.org/cluster-one/. Also, we compare our method to many other complex detection methods in Table An available COACH system is downloaded from Our experiments are conducted on a PC with Intel(R) Xeon(R) CPU E5-1620 of 3.7 GHz and 12.0 GB RAM. Here, we compare the running time of different methods on the PPI network with 4930 nodes and 17201 edges. Our method completes complex detection within 736 s, as shown in Table We propose a novel method to identify complexes on PPI networks. First, we design an edge-weighting MCL method to calculate complexes on PPI networks. Second, we propose some significant features, such as graph information and gene expression, to filter and modify complexes predicted by Markov Cluster Algorithm.Experiments show that our method achieves better results than some state-of-the-art methods for identifying complexes on PPI networks. To evaluate our method, we test on two experimental yeast PPI networks. On DIP network, our method has Precision and F-Measure values of 0.6004 and 0.5528, improves by at least 0.1752 and 0.1853. On MIPS network, our method has F-Measure value of 0.3774, improves by at least 0.0448.


Therefore, the bulk of information on protein interactions deposited in databases and peer-reviewed published literature is constantly growing. Multiple databases interfaced from user-friendly web tools recently emerged to facilitate the task of protein interaction data retrieval and data integration. Nevertheless, as we evidence in this report, despite the current efforts towards data integration, the quality of the information on protein interactions retrieved by in silico approaches is frequently incomplete and may even list false interactions. Here we point to some obstacles precluding confident data integration, with special emphasis on protein interactions, which include gene acronym redundancies and protein synonyms. Three human proteins  and three different web-based data search engines focused on protein interaction data retrieval  were used to explain the potential occurrence of undesired errors that should be considered by researchers in the field. We demonstrate that, despite the recent initiatives towards data standardization, manual curation of protein interaction networks based on literature searches are still required to remove potential false positives. A three-step workflow consisting of: (i) data retrieval from multiple databases, (ii) peer-reviewed literature searches, and (iii) data curation and integration, is proposed as the best strategy to gather updated information on protein interactions. Finally, this strategy was applied to compile bona fide information on human DREAM protein interactome, which constitutes liable training datasets that can be used to improve computational predictions. Understanding protein interaction networks and their dynamic changes is a major challenge in modern biology. Currently, several experimental and  In 1996, it was estimated that the human genome consists of 50,000–100,000 genes ,3, the cversus weak or transient, predicted computational interactions without experimental validation may be considered, etc.). The size and dynamics of the human interactome is currently unknown. As displayed in Although there is hardly a consensus on the number and validity of protein interactions, there is ample agreement on the idea that our understanding of the human interactome is in its early stages. Deciphering the interacting network of each individual protein and the conditions altering the interactome may be crucial to understanding their function and thus requires the integration of data available gathered from different sources including: (i) experimental data, (ii) prediction algorithms, (iii) database searches, and (iv) literature searches.Regarding experimental data, three main experimental strategies allow for large-scale screening of protein networks, including yeast-two-hybrid (Y2H) experiments, co-immunoprecipitation or affinity purification, followed by mass spectrometry and protein arrays ,12. The i.e., bona fide list of protein interactions) to test and to improve their predictions. A number of prediction algorithms and computational methods currently coexist and can be used to infer the occurrence of protein–protein interactions ,17,18,19Salmonella host interactome [Salmonella proteins with human and mouse proteins, both acting as host for this bacteria. The authors stated that only six of these interactions were retrievable from databases and concluded that literature searches represented an essential step. In this sense, we strongly support that manual curation of protein-protein interactions based on literature searches is a requisite [A range of databases currently allows the retrieval of information on protein interactions, including predictions of interactions and even modeling the pathways involved (reviewed in ). In thieractome  comparedequisite . Indeed,comma separated values (CSV)   and is aad hoc for the retrieval of the updated list of human proteins. The list of proteins considered in this report is provided as a comma separated values (CSV) (The consensus amino acid sequence of human proteins was retrieved from the Uniprot protein knowledgebase/Swiss-Prot . The stres (CSV)  and can Protein networks and list of interacting proteins were carried out using PSICQUIC , DASMI  and BIPSThe official gene acronyms and the corresponding synonyms accepted for each human gene were retrieved from UniProtKB (May 2013). Redundant symbols within gene names and synonyms were retrieved for each UniProtKB entry using case-sensitive comparison of all human symbols. A text file where each line represents alternative symbols used for a specific gene was prepared. For each line, redundant gene symbols were removed. That is, each line contains a non-redundant list of alternative symbols for a specific gene. From this per gene entry non-redundant list, a new redundant list of symbols was prepared where each line contains one symbol. The frequency of each symbol in this redundant list corresponds to the number of times a symbol refers to different genes and can conveniently be calculated using the “table” command in the R statistical programming language.in silico searches may lead to errors. To exemplify this, we first retrieved for human choline kinase  interactors using three web interfaces able to retrieve and integrate data from multiple source databases: PSICQUIC [In this report, we demonstrate that searching for protein–protein interactions using PSICQUIC , DASMI [PSICQUIC  and BIPSPSICQUIC . In all PSICQUIC ).The SwiPSICQUIC  and sevei.e., CK). This was already detailed in a recent publication [The example above clearly evidences that database queries may lead to errors, thereby allowing us to hypothesize that the source for such errors is the lack of consensus in the gene acronyms and protein abbreviations used to refer to human proteins. This hypothesis is based on the observation that casein kinases and choline kinase may be abbreviated in the scientific literature using the same abbreviation  . NeverthSeveral databases may be used for proteomic research and their selection mainly depends on the quality of protein annotation and on objective pursued. In this regard, the Swiss-Prot protein database is widely considered as the “gold standard” for proteomic approaches because it contains high-quality and manually annotated data, as well as the sequence of the proteins and access to protein-protein interaction data. Remarkable efforts were carried out in the past and others are under way towards the convergence, integration and standardization of the bulk of the data available in different protein databases. For instance, in 2011, the Swiss-Prot database incorporated the information of the human International Protein Index database (further information in this link ), a dataFor that reason, every protein included in the Swiss-Prot database is characterized by at least one protein accession and one protein entry that are exclusively attributed to a known protein product. Proteins characterized by more than one accession may be included in Swiss-Prot. For example, uromodulin  was associated in the past with four different Swiss-Prot accessions . Currently, only the first accession is accepted (P07911), but previous accession numbers still appear in the literature, contributing to the confusion. Regarding protein descriptions and continuing with the same example, uromodulin is also frequently referred to as the Tamm-Horsfall protein. The latter term is frequently used in medical literature for historical reasons because it was first purified from the urine of healthy individuals by Tamm and Horsfall ,42. It iThe trend towards data simplification and integration is evident, and the number of initiatives aiming overall integration of -omic(s) data is under way, but it is still not sufficient. As an example, a recent web-based application termed KUPKB (Kidney and Urinary Pathway Knowledge Base) offers access to data from multiple -omic approaches . To reitAccordingly, in order to promote automated data integration, the lists of proteins identified by proteomic experimental approaches should include primary accessions and/or entries exactly in the form they appear in the Swiss-Prot database and, if possible, the corresponding gene acronym as recommended by the HUGO Gene Nomenclature Committee. Similarly, the use of recommended protein names exactly as they appear in the Swiss-Prot database should be promoted, since this could significantly simplify database searches whilst reducing ambiguity at the same time.in silico database searches typically fail to provide information on protein interaction changes as a response to modifications in the experimental conditions. To exemplify this, we used human calsenilin , a protein of interest in our lab that was originally identified as calsenilin—a Ca2+-binding protein belonging to the family of neuronal calcium sensor proteins [2+-dependent gene silencer DREAM (downstream regulatory element antagonist modulator) [+ channel, Ca+2 binding protein and transcriptional repressor, respectively) and interacts with different subsets of molecules—including the interaction with DNA in the nucleus [+2 ions that provoke structural changes. Thus, it is foreseeable that the DREAM interactome significantly varies depending on sub-cellular localization or the presence/absence of Ca+2. It is important to underline that none of the databases tested—including 22 databases focused on protein-protein interactions available at [in vitro and in vivo models used in the experiments, human gene acronyms (including synonyms), UniprotKB/Swiss-Prot accessions and entries, description, techniques used for detection of the interactions. Further information influencing DREAM interactions is also included, such as the potential effect of Ca+2 and post-translational modifications.The retrieval of the overall list of interactors for a specific protein of interest is essential to discerning novel from previous findings and also serves to identify the pathways involved. In the sections above we quoted some problems affecting the number and quality of interactors retrieved by searching in multiple databases and the need to complete and curate that information by comparing the list of interactors with published literature. However, the interactome of a given protein of interest is not static and adapts to changes in the environmental conditions. Thus, understanding the dynamics or protein interaction networks is crucial to unraveling the role and the regulation of proteins under different cellular conditions . Here, wproteins . Shortlydulator)  and, latdulator) . The thr nucleus . At strulable at  or integlable at , DASMI [lable at  and BIPSlable at —offered lable at ). Thus, in silico sources is an error-prone task and, thus, requires manual data curation. The study of protein interactomes and the pathways involved is a major objective currently pursued. The overall understanding of protein networks requires gathering and merging information from multiple sources  but compilation of protein interactions still constitutes a major current bottleneck. Different methodologies currently enable the generation of large bulks of data, and a plethora of databases are continuously fed with novel data. It is important to highlight that despite the rapid advances towards data integration and standardization, integration of interaction data from Here, we pointed to two sources of ambiguities that promote the accumulation of errors: gene acronym redundancies and protein description synonyms. We take advantage of several proteins of interest in our group to exemplify the occurrence of such ambiguities and to evidence that arbitrary ways to name human genes and proteins undermine fully automated and reliable data integration.We emphasize that gene acronym redundancies constitutes a frequent event. A significant number of gene acronyms suffer some degree of redundancy, which probably constitutes the most significant hurdle towards data integration. In parallel, at the protein level, it is obvious that different authors refer to the same proteins using different acronyms arbitrarily chosen. These two sources of ambiguities lead to errors, especially when using bioinformatics and data-mining algorithms for the retrieval of protein interactions. Initiatives such as the Human Proteome Organization Proteomics Standards Initiative (HUPO PSI-MI) focused on the need for data format standardization and proposed the incorporation of controlled vocabularies to facilitate data exchange and integration . In our opinion, this could be the best way to foster data integration while ameliorating the tedious task of manually reviewing, extracting data and filtering out errors based on published data. This rationale is supported by the fact that once published, it is hardly possible to include changes, including designation and abbreviation of genes and proteins. In our opinion, our proposal is certainly feasible, since a list of unambiguous gene acronyms and protein accessions are already available from the HUGO Gene Nomenclature Committee (HGNC) and UniprotKB/Swiss-Prot). A clear and concise specification of the source of gene description and abbreviation may facilitate future data integration strategies, including the identification of protein interactions. Therefore, we propose that the source of genes and/or proteins used and their corresponding abbreviations should be clearly quoted in publications, including sentences such as: “Human gene names, description and abbreviations used in this report follow the recommendations from the HUGO Gene Nomenclature Committee (HGNC),” or “Human protein names, description and abbreviations follow the recommendations from the UniprotKB/Swiss-Prot database.”


Evolutionary information stored in multiple sequence alignments (MSAs) has been used to identify the interaction interface of protein complexes, by measuring either co-conservation or co-mutation of amino acid residues across the interface. Recently, maximum entropy related correlated mutation measures (CMMs) such as direct information, decoupling direct from indirect interactions, have been developed to identify residue pairs interacting across the protein complex interface. These studies have focussed on carefully selected protein complexes with large, good-quality MSAs. In this work, we study protein complexes with a more typical MSA consisting of fewer than 400 sequences, using a set of 79 intramolecular protein complexes. Using a maximum entropy based CMM at the residue level, we develop an interface level CMM score to be used in re-ranking docking decoys. We demonstrate that our interface level CMM score compares favourably to the complementarity trace score, an evolutionary information-based score measuring co-conservation, when combined with the number of interface residues, a knowledge-based potential and the variability score of individual amino acid sites. We also demonstrate, that, since co-mutation and co-complementarity in the MSA contain orthogonal information, the best prediction performance using evolutionary information can be achieved by combining the co-mutation information of the CMM with co-conservation information of a complementarity trace score, predicting a near-native structure as the top prediction for 41% of the dataset. The method presented is not restricted to small MSAs, and will likely improve interface prediction also for complexes with large and good-quality MSAs. Proteins work together as functional units known as protein complexes to perform the majority of cellular functions, and the analysis of protein-protein interactions forms an essential part of the “systems biology” enterprise. Proteins have to evolve in parallel with their interacting partners, to maintain the functional repertoire of protein complexes. This evolution can be traced by analysing the amino acid sequences of proteins, through the means of multiple sequence alignments (MSAs). Amino acid residues of proteins within protein families exhibit correlations, thus maintaining protein structure and interactions. For example, even when the sequence identity falls to 25%, protein structures typically change by no more than 2 Å . The evoAmino acid sequence conservation analysis studies –10 locatdirect information (DI) 41]. For β-sheet contact prediction of proteins [In this work, we used a maximum entropy based CMM that avoids falsely high correlations due to indirect coupling. This CMM is derived as the maximum entropy based CMM we previously developed to improve proteins .N1 and N2, with a concatenated MSA containing M sequences of length N = N1 + N2. We defined fi(Ai), i = 1, …, N as the frequency distribution of the amino acid Ai at position i of the MSA, and fij, i, j = 1, …, N as the joint frequency distribution of amino acids Ai and Aj at positions i and j (i and j might or might not be on the same protein). Instead of simply looking at the frequency distributions in the MSA of the protein complex, we considered the MSA to be our observation of the underlying concatenated-sequence probability distribution P(A) of the entire families of the complexed proteins, including observed and unobserved sequences A = . We required that our model match the single and pairwise amino acid frequency distributions, Pi(Ai) = fi(Ai) and Pij = fij, where Pi(.) was the marginal distribution at position i and Pij was the joint marginal distribution at positions i and j. Any higher-order moments are impractical to consider, due to the sparsity of the data. We considered a protein complex with proteins of length S(.) was the entropy of a probability distribution, and eij and hi were the Lagrange multipliers of the constrained optimisation. To perform the optimisation, we used contrastive divergence [eij can be viewed as pair-interaction energies, and hi as local fields [P(A), the first order moments Pi(Ai) matched the single amino acid frequencies fi(Ai) by construction. The second order moments correspond to the direct correlations via the interaction energies. Our residue-level CMM between residues i and j was defined asFollowing the maximum entropy principle, we derived the model distribution that was least biased. The least biased distribution is the one with the highest entropy , and varvergence , 49, 50,l fields . In our ormation , 23.For protein interactions, only the joint distributions of amino acid pairs across the interface are of direct interest. However, note that to correctly model the indirect coupling between these amino acid pairs, the model also has to include all pairwise amino acid interactions within the individual proteins.D, is a measure of the interaction strength between residues i and j, and high D values suggest co-evolution. Based on the residue level CMM, we have developed an interface level CMM score, suitable for ranking the 10,000 decoys generated by FTDock.In the previous section, we described how residue level CMM scores were derived from the concatenated MSA of the complexed proteins. Our CMM, i were defined asdij, the distance between residues i and j, was the smallest distance between any heavy atom of residue i to any heavy atom of residue j. We definedi, or all surface residues on the protein for non-surface residues. For example, in First of all, we defined the surface residues to be the amino acids whose relative surface accessibility  needed to be standardised before being used. For this standardisation we took the sample mean μi and standard deviation σi of the set We defined the interface-level raw CMM score astructure  and prottructure . In this∈I} . We symmSCMM score into a combined scoring function which includes structural as well as evolutionary information, in a similar manner to previous studies [S, was chosen to be the logit function of the likelihood of the decoy being a near native structure, taking a positive value for decoys that are more likely to be native than non-native. We defined the combined scoring function asS’s were scores described below and the w’s were weights to be optimised. SN was the number of interface residues of the docking decoy. SRP was a knowledge based potential, the Residue Pair potential (RP) score [Sent was the entropy score, the average of the entropy in the MSA for the individual interface residues (−∑aP(a) ln P(a)) weighted by the number of residue interactions across the interface. SCT was the Complementarity Trace (CT) score, quantifying the proportion of interacting interface residue pairs for which complementarity was preserved in the MSA, adapted from [SCMM is the Correlated Mutation Measure (CMM) score described in the previous section.We have integrated the  studies , 52. TheP) score , as implP) score . Sent wated from . All resThe following forms of the scoring function were considered in this work:tion set .We first investigated if the residue level standardised CMM scores could be used as predictors of inter-protein residue contacts, for our data set with small auto-generated MSAs. The effective number of sequences in our data set was typically much smaller than in other studies , 37, as To quantify the amount of information the CMM scores contain about the native interactions, we calculated the distribution function of the residue-level standardised CMM scores for the natively interacting and all inter-protein surface-residue pairs. We found that although both the native and all inter-protein residue-level CMM scores had a heavy tail in the positive direction, the distribution function of the residue-level standardised CMM scores had a significantly larger probability density in the positive domain for native interface residue contacts than for all inter-protein surface residue pairs . These rS, S and S, 6 more complexes (7.6% of the dataset) had a near-native prediction in the top 1 predictions than without the SCMM score, indicating that even for protein complexes without a large, good-quality MSA, the CMM scores can be used to improve interface prediction accuracy.Next, we evaluated the effect of our interface-level CMM scores on the protein-protein interaction interface prediction accuracy. We used the raw interface-level CMM scores , the surSrawCMM) , to rankD scores over surface neighbours, the (native or non-native) strong residue-level D scores were diluted. On the one hand, for protein complexes with few strong natively interacting residue-level D scores, the surface-neighbour averaging resulted in poorer interface prediction. On the other hand, for some protein complexes, where a few non-native residue-level D scores obliterated the low but consistently more positive native residue-level D scores (see previous section), this averaging increased the signal-to-noise ratio. Using the less noisy SCMM then resulted in a more marked improvement when incorporated into a combined score. These results are in agreement with previous observations that evolutionary information contributions from surface residues close to the interface might well play a role in the assembly of protein complexes [We next focussed on the effect of surface-neighbour averaging of the CMM scores. Although the interface-level raw CMM score was a somewhat better predictor in itself than the surface-neighbour-averaged interface-level CMM score, incorporating it into the combined score gave less improvement on the prediction. When examining the list of protein complexes with near-native decoys in the top 20 predictions, out of the 9 and 6 proteins for the raw and surface-neighbour-averaged CMM scores, there was only 1 protein complex for which both CMM scores had a near-native prediction . The reaomplexes , 56–58.Next, we considered what evolutionary information could be extracted from the MSA. The first order moments of the MSA describe the distributions of individual amino acid sites, and their variability is measured by the entropy score. The second moments contain the pairwise amino acid site distributions, which can be modelled by looking at co-mutations or co-conservation. By definition, the CMM models co-mutating residue pairs, and it does not interrogate the co-conservation of pairs of amino acid residues across the interface. Co-conservation information, on the other hand, does not measure co-mutations, although some co-mutation information may be recovered by collapsing the 20 amino acid residues into groups of residues, as in the SCOTCH complementarity trace (CT) score . There hR = −0.02), there was a significant correlation between the entropy and CT score predictions (Pearson’s R = 0.60). These results reflected the theoretical considerations about the information stored in the MSA measured by the three scores. On the one hand, we found that for protein complexes with plenty of strongly co-mutating residue pairs, where the CMM signal was strong, the entropy score was a weak predictor, and the CT score, using an amino acid alphabet reduced to 4 polarity groups, ignored the more subtle changes in co-mutations. Mintseris et al. [We compared the best near-native predictions of the individual CT, CMM and entropy scores . There ws et al.  had founS, there was a smaller improvement in the prediction accuracy. In the top 1 prediction, the combined score including the CT score predicted 1 fewer protein complexes correctly than without the CT score, while the combined score including the CMM score predicted 6 more protein complexes correctly in the top 1 predictions. Incorporating both the CMM and the CT scores into a combined score provides the best prediction of near-native complexes, as, compared to the score with neither the CMM nor the CT scores, it gave a near-native prediction for 8 more protein complexes (10% of the dataset) in the top prediction . In conWe demonstrate that the interface level CMM score presented here improves the interface prediction, and it does that by by adding orthogonal information. First of all, the maximum entropy-based CMM measures the direct correlation of co-mutating amino acid residue pairs across the interface, decoupled from indirect correlations. Secondly, by performing a surface-neighbour averaging in the calculation of interface level patch scores, amino acid residues neighbouring the interface, but not directly involved in interactions in the rigid complex can also contribute their direct correlation information to the interface residues of the other protein.Sent, SCT) and co-mutation (SCMM) prediction scores based on the first and second-order moments of the MSA. We find that the co-conservation scores of the first- and second-order moments were correlated, and from the second-order moments, overall a combined score using the CMM ) performed better than the CT score ) for the studied dataset. We also find that a combined score including both the CMM and complementarity trace scores ) had the best predictive performance, as co-mutation and co-conservation represent complementary information. We note that in other datasets with more amino acid conservation at the interface, the co-conservation score may have stronger prediction power than co-mutation, as is suggested by our findings that different protein complexes were predicted correctly by the co-conservation and co-mutation based combined scores. However, the complementarity of information captured by the co-conservation and co-mutation scores would still hold. Our results demonstrate that the best strategy for protein-protein interaction interface prediction is to combine co-mutation and co-conservation in a joint scoring function. We suggest that our results are not limited to protein complexes with small MSAs, and a joint scoring function would improve protein-protein interaction interface prediction for complexes with larger, better-quality MSAs.We have investigated the evolutionary information stored in the MSA, by comparing co-conservation (S1 Text(PDF)Click here for additional data file.S1 FigThe effective number of sequences as a function of the average length of the proteins in the complexes. Blue dots show the data set used in , red dot(PDF)Click here for additional data file.S2 FigThe interaction scores plotted against the interface level RMSD, for the D1A5KC1_D1A5KC2 complex. Vertical dashed line shows the near-nativeness threshold (RMSD<3Å). The scoring functions used are the same as in (PDF)Click here for additional data file.S3 FigThe interaction scores plotted against the interface level RMSD, for the D1AY0A2_D1AY0A3 complex. Vertical dashed line shows the near-nativeness threshold (RMSD<3Å). The scoring functions used are the same as in (PDF)Click here for additional data file.S4 FigThe interaction scores plotted against the interface level RMSD, for the D1DTWB1_D1DTWB2 complex. Vertical dashed line shows the near-nativeness threshold (RMSD<3Å). The scoring functions used are the same as in (PDF)Click here for additional data file.S1 TableThe list of protein complexes in the data set, with the number of amino acid residues in the complex (N), the number of sequences in the MSA (M), the top near native complex prediction for the different scoring functions, and the test set the protein complexes belonged to.(PDF)Click here for additional data file.S2 TableGREMLIN and EVCOMPLEX predictions for the protein complexes in the data set. For each complex, the number of sequences in the MSA relative to the number of residues in the complex (M/N) and the rank of the top predicted native contact for both methods are shown. In the M/N columns, ‘—’ means no predictions due to not enough sequences (GREMLIN) or failed concatenation (EVCOMPLEX).(PDF)Click here for additional data file.


Direct coupling analysis (DCA) is a powerful statistical inference tool used to study protein evolution. It was introduced to predict protein folds and protein-protein interactions, and has also been applied to the prediction of entire interactomes. Here, we have used it to analyze three proteins of the iron-sulfur biogenesis machine, an essential metabolic pathway conserved in all organisms. We show that DCA can correctly reproduce structural features of the CyaY/frataxin family (a protein involved in the human disease Friedreich's ataxia) despite being based on the relatively small number of sequences allowed by its genomic distribution. This result gives us confidence in the method. Its application to the iron-sulfur cluster scaffold protein IscU, which has been suggested to function both as an ordered and a disordered form, allows us to distinguish evolutionary traces of the structured species, suggesting that, if present in the cell, the disordered form has not left evolutionary imprinting. We observe instead, for the first time, direct indications of how the protein can dimerize head-to-head and bind 4Fe4S clusters. Analysis of the alternative scaffold protein IscA provides strong support to a coordination of the cluster by a dimeric form rather than a tetramer, as previously suggested. Our analysis also suggests the presence in solution of a mixture of monomeric and dimeric species, and guides us to the prevalent one. Finally, we used DCA to analyze interactions between some of these proteins, and discuss the potentials and limitations of the method. Protein sequences determine the folds of proteins and what interactions they may form with their partners. The logic connecting residue–residue contacts to evolutionary correlation is very simple: residues in contact cannot evolve independently. If one residue gets larger, the other needs to be smaller in a concerted and not necessarily pairwise way. Charges must be compensated in the same way. Stabilizing/destabilizing amino acid substitutions need to be compensated by substitution of other interacting positions to retain function. In principle, one can thus use a comparative analysis of the primary sequences of proteins as a powerful way to predict their structures and interactions. This idea has been an “elusive Holy Grail” for decades , isc , and suf  operons. Amongst these, the most universal is the isc operon, whose gene products have direct orthologs in eukaryotes. Malfunction in FeS cluster assembly has direct effects on health  clusters, which are essential prosthetic groups that provide electrons in reduction/oxidation reactions and/or stabilize protein folds. This biosynthesis is a complex process involving specialized machinery that mediates the recruitment of sulfur and free iron from the cellular environment, catalyzes the synthesis, and delivers the newly formed clusters to acceptor proteins. In bacteria, the systems able to perform these tasks belong to the isc machine are IscS (or Nfs1 in eukaryotes) and IscU (Isu)  Figure . However) Figure  but it i) Figure . Two reg) Figure , acts as) Figure . Puzzlin) Figure . IscA isWe found that DCA is able to describe the proteins considered in great detail. We used frataxin, which is monomeric and globular, to benchmark the method. We questioned whether evolution could tell us if IscU is folded or a mixture of ordered and disordered species; we also addressed the question of how the IscU dimer, which undoubtedly must exist to allow coordination of the cluster, assembles; finally, we wondered if DCA could give us indications of which of the reported structures of IscA is more representative of the protein in solution.We observed no trace of the D state of IscU whereas the S state is clearly represented in the contact co-evolution. Instead we observed structural evidence which hints at a head-to-head dimerization of IscU. This is in agreement with what is required by cluster coordination. We also found that not all the IscA structures in the PDB database match the conserved contacts, which suggests that the coordination of the FeS cluster was likely misattributed. Finally, we were able to predict successfully interactions between IscU and the functional partner IscS, whereas contacts predicted for CyaY do not match our current knowledge. These observations are likely to reflect the possibilities but also the limitations of DCA.The major sequence divergence within the CyaY/frataxin family is in the non-conserved and mainly unstructured N-terminus  from the Uniprot database, constructed from a seed made of the 196 CyaY entries of the Swiss-Prot database. We then built a multiple sequence alignment (MSA) containing 3,459 sequences, defining 109 consensus residue positions where 1,102 sequences were from eukaryotes and 2,326 from bacteria. The number of retrieved sequences is relatively small for a successful application of DCA but reflects the absence of frataxin in several species , which is also where the structures differ most. Contacts between the N-terminus and the ß2-ß3-α2 region are conserved, in support of a structured state in the α1 region . The fold of each protomer consists of a β-sandwich of a mixed twisted four-stranded β-sheet, β4-β5-β2-β3, packed against a three-stranded β1-β6-β7 sheet. The protomers could form a dimer or two possible tetramers or dimer of dimers 2 structure is similar to the 1R95 A tetramer. Also, this structure lacks a defined C-terminus but the authors modeled it based on stereochemical parameters. The authors concluded that the cysteines of the dimer would be unable to coordinate the FeS cluster and that tetramer formation is necessary to stabilize coordination  are involved in cluster coordination, whereas Cys35 would remain idle. The only fully resolved holo IscA is from T. elongatus (1X0G)  which exchange their central domain forming a long intertwined β-sheet ] . These proteins are all part of the A-type carrier (ATC) family and should have overlapping functions. Structurally, SufA (2D2A) and IscA  have similar contact maps except for two regions, which account for contacts within the C-terminus and between the C-terminus and residues 30–40 , most of the sequences belong either to the IscA or to the ErpA subfamilies but comprise also SufA and the eukaryotic paralogs IscA1/IscA2  is bent toward the C-terminus and stabilized by steric hindrance from the swapped central twisted β-sheets. In this structure, cluster coordination is asymmetric and achieved by Cys37 and Cys101 of the α protomer and Cys103 of the β protomer. The evolutionary trace of contacts between the C-terminus (residues 98–112) and the loop between residues 33–41  Figure . The 68%In the case of interactions between frataxin and IscU, IPA identified three contacts with very high acceptance frequencies (>94%) Figure  and poteDCA is a powerful method, by now shown to be robust and reliable as long as a sufficiently high number of independent protein sequences are available    for each of the studied protein families were constructed using the following protocol: We first gathered all sequences from Uniprot with gene names corresponding to the canonical members of the families . We then aligned the sequences of each seed using MAFFT  Katoh, . The resi and Jij of the generalized Potts model to the sequences in a MSA according to Equation 1:ij were computed as the Frobenius norm of the local coupling matrices Jij of the Potts model. The modifications introduced in Feinauer et al.  interacting protein families, a concatenated MSAs consisting of interacting sequences in families A and B must first be built. The main challenge rises when multiple paralogs of proteins A and B are present in an organism. In this case, it is not straightforward to match correctly interacting paralogs for all organisms. To build matched MSAs of two interacting protein families (denoted A and B), we used the Iterative Paralog Matching (IPA) strategy  denote the number of paralogs of class A (resp. B) in a given organism. This means that for each organism, all sequences belonging to the family with less paralogs in the current organism will be matched to a single paralog of the opposite class.Note that the random seed is discarded after the first iteration, therefore at iteration 1, the MSA will contain NIPA times with different initial random sequence pairings. For each of these NIPA MSA, we performed DCA with the Pseudo-Likelihood method described above to perform contact prediction. The strongest inter-protein contacts were recorded for all iterations. To select the strongest co-evolving inter-protein contacts for a given iteration, we used a selection criterion introduced in Hopf et al. . Min stands for the minimum over all inter-protein residue pairs. Note that the minimum is only taken over the inter-protein scores SijInter. This normalized score (Equation 3) partially removes the dependency of the scores on the length of the protein and on the depth of the alignments. As discussed in Ovchinnikov et al. . Contacts being accepted more often across several IPA runs should reflect more robustness and higher statistical significance.To obtain an estimate of the robustness of inter-protein DCA predictions, we ranked all possible inter-protein contacts by the normalized number of times they were accepted in the NIPA = 200 iterations for the IscU-IscS system, and NIPA = 300 for the frataxin-IscU and frataxin-IscS systems. The inter-protein structural contact maps where built with the same contact threshold as intra-protein maps, i.e., inter-protein pairs of residues were considered to be in contact if at least one pair of heavy-atoms between the two were less than 8.5 Å apart.We used Ni and j is defined as the minimal number of contacts in the structural contact map needed to join these two residues. DCA-predicted contacts which are native contacts in the structural map therefore have an SP of one. This analysis helps to highlight the mediation of contacts, and is a natural measure of the topological propagation of contact information  analysis introduced in (Malinverni et al., MF did the experiments, analyzed the data and wrote a first draft. DM did the experiments and analyzed the data. PD analyzed the data. AP analyzed the data and wrote the final version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.