Drosophila melanogaster, there exists a pressing need for specialised tools, which can seamlessly collect, integrate and analyse these data. Here we describe a database coupled with a mining tool for protein-protein interactions (DAPPER), developed as a rich resource for studying multi-protein complexes in Drosophila melanogaster.The identification of interaction networks between proteins and complexes holds the promise of offering novel insights into the molecular mechanisms that regulate many biological processes. With increasing volumes of such datasets, especially in model organisms such as Drosophila tissues and cultured cells. The web access to DAPPER is provided via an accelerated version of BioMart software enabling data-mining through customised querying and output formats. The protein-protein interaction dataset is annotated with FlyBase identifiers, and further linked to the Ensembl database using BioMart’s data-federation model, thereby enabling complex multi-dataset queries. DAPPER is open source, with all its contents and source code are freely available.This proteomics database is compiled through mass spectrometric analyses of many protein complexes affinity purified from easy-to-navigate and extensible platform for real-time integration of diverse resources containing new and existing protein-protein interaction datasets of Drosophila melanogaster.DAPPER offers an The online version of this article (doi:10.1186/s13040-015-0063-3) contains supplementary material, which is available to authorized users. All the software and data contents are freely accessible through the web portal. Users can upload their own data and integrate with existing datasets through coordinating with DAPPER team. For sensitive/unpublished datasets, users are also welcome to download all of the DAPPER software as well as datasets on their local machine and integrate their datasets locally.Proteins control cellular events in living organisms by catalysing biochemical reactions, transporting molecules, providing scaffolds for distinct pathways, and receiving and transmitting signals from the environment. The identification of protein interaction networks can offers insights into the molecular mechanisms that regulate many biological processes. Proteomics allows large-scale analysis of protein-protein interactions in an efficient and sensitive way. Targeted and high throughput approaches now enable the identification of protein complexes , 2, entiDrosophila cultured cells or Drosophila embryos for analysis by mass spectrometry was published previously [Drosophila melanogaster protein database obtained from FlyBase (www.flybase.org). Importantly, the last step of the analysis was the download of the data generated in each experiment using the “Export search results” function available on the Mascot Search Results web page (in the “Format As” section). The settings for the “Export search results” web page were as follows: Export format: “XML”; Significance threshold: “p < 0.05”; Ions score cut-off: “30”; Threshold type: “Identity”; Max. Number of hits: “AUTO”; Protein scoring: “MudPIT”; Include sub-set protein hits: “0”; Preferred taxonomy: “All entries”; and with checked boxes for the following parameters: Header, Decoy, Modification deltas, Search parameters, Format parameters, Score, Description, Mass (Da), Number of queries matched, Percent coverage, Experimental Mr (Da), Experimental charge, Calculated Mr (Da), Mass error (Da), Number of missed cleavages, Score, Expectation value, Sequence, Variable modifications, Query title. The XML-type files, which were generated during the download step, were then uploaded to DAPPER via the upload web page and further analysed using DAPPER-specific tools.The detailed methodological description of the affinity purification of protein complexes from eviously , 9. For eviously , 9, witheviously . The rawDrosophila cell cycle regulation, we accumulated a large volume of proteomics data. Using these datasets, we created a database and a data-mining resource. This resource not only facilitates unified storage for lists of proteins identified during AP-MS experiments, but also allows cross comparison of individual datasets, and extraction of information difficult to mine otherwise. The DAPPER web interface (MartView) for querying protein-protein interaction datasets is available at: http://dapper.gen.cam.ac.uk/biomart/martview. The database is also made available through the BioMart Central Portal [Drosophila melanogaster database  [www.biomart.org). Therefore, DAPPER contents are freely available to all the users of the aforementioned analytical platforms as well. DAPPER offers built-in integrative mining of Ensembl Drosophila melanogaster database annotations. A user query is split into DAPPER-specific attributes and Ensembl-specific attributes. Both databases are mined using MySQL queries independently, and results are integrated on-the-fly using Drosophila CG Identifiers. The data merging is performed in batches [While analyzing protein networks involved in l Portal . DAPPER l Portal , 13; sysl Portal . The con, BDGP5) . DAPPER , BDGP5) , Galaxy , BDGP5) , Taverna, BDGP5)  and Cyto, BDGP5)  users caPresently, DAPPER contains data from 36 different cell cycle-related bait proteins  of the protein of interest and then clicking the “Search” button . The query retrieves a list of pre-selected (default) attributes that are the properties of the protein encoded by CG10722, including the gene name(s) of its baits and FlyBase identifiers . The accession number for the Fzy protein, CG4274, is entered in the Filter Bait accession number , a protein involved in the spindle assembly checkpoint that was shown to directly bind Fzy and also to localise to kinetochores when the spindle assembly checkpoint is active [CG11451), a kinetochore component involved in the recruitment of the SAC machinery via direct interaction. So far there has been no evidence of the direct interaction between Fzy and Spc105R. Thus, this result suggests a potential new kinetochore loading pathway for Fzy: Spc105R might recruit Fzy onto the kinetochore via the SAC. For additional example, please see Additional file  term in Ensembl from the entire mass spectrometric database. In the following example, the user identifies known components of the “kinetochore” (a proteinous cellular structure formed in the centromeric region of chromosomes) amongst all the proteins co-purified with a specific bait protein  [www.flybase.org) [ad hoc analytical tools. In contrast, DAPPER is extremely flexible and extendable. The open source software and database readily enables integration of new mass spectrometry datasets with existing ones; either in-house private or public datasets. Furthermore, DAPPER exploits BioMart’s user-friendly interface enabling powerful queries. This makes DAPPER unique compared to other experimentally validated protein-interaction databases of Drosophila melanogaster.DAPPER shares some features with other databases already available online. For example, The idb.org)  also allase.org) . HoweverThe data deposited in DAPPER originated from studies targeting cell cycle-relevant proteins and protein complexes and therefore the database is particularly useful for researchers interested in cell cycle regulation. As demonstrated in Query 3, DAPPER can facilitate the characterisation of unknown regulatory cascades of the proteins of interest. However, the contents of DAPPER are increasingly growing beyond cell cycle interactions, with various other bait such as chromatin-related proteins and associated interactions , phosphoad-hoc database, DAPPER is also an analytically unique method of mining and integrating previously published datasets alongside new experiments. In future, we intend to extend DAPPER to human protein-protein interactions and federate these with Ensembl Homo sapiens database.DAPPER is a collection of a large number of proteomic datasets never previously released. Beyond an Project name: DAPPERhttp://dapper.gen.cam.ac.uk/Project home page: Operating systems: Windows, Unix/Linux, Mac OSXProgramming languages: Perl, Java script, HTMLOther requirements: noneLicense: GNU Lesser General Public License (LGPL) v3Any restrictions to use by non-academics: none


Due to limited computational resources, the protein-protein docking approach has been developed under the assumption of rigid docking, in which one of the two protein partners remains rigid during the protein associations and water contribution is ignored or implicitly presented. Despite obtaining a number of acceptable complex predictions, it seems to-date that most initial rigid docking algorithms still find it difficult or even fail to discriminate successfully the correct predictions from the other incorrect or false positive ones. To improve the rigid docking results, re-ranking is one of the effective methods that help re-locate the correct predictions in top high ranks, discriminating them from the other incorrect ones.Protein-protein docking is an In this paper, we propose a new re-ranking technique using a new energy-based scoring function, namely IFACEwat - a combined Interface Atomic Contact Energy (IFACE) and water effect. The IFACEwat aims to further improve the discrimination of the near-native structures of the initial rigid docking algorithm ZDOCK3.0.2. Unlike other re-ranking techniques, the IFACEwat explicitly implements interfacial water into the protein interfaces to account for the water-mediated contacts during the protein interactions.2Dock, the IFACEwat performed equivalently well or even better for several Antigen/Antibody complexes.Our results showed that the IFACEwat increased both the numbers of the near-native structures and improved their ranks as compared to the initial rigid docking ZDOCK3.0.2. In fact, the IFACEwat achieved a success rate of 83.8% for Antigen/Antibody complexes, which is 10% better than ZDOCK3.0.2. As compared to another re-ranking technique ZRANK, the IFACEwat obtains success rates of 92.3% (8% better) and 90% (5% better) respectively for medium and difficult cases. When comparing with the latest published re-ranking method FWith the inclusion of interfacial water, the IFACEwat improves mostly results of the initial rigid docking, especially for Antigen/Antibody complexes. The improvement is achieved by explicitly taking into account the contribution of water during the protein interactions, which was ignored or not fully presented by the initial rigid docking and other re-ranking techniques. In addition, the IFACEwat maintains sufficient computational efficiency of the initial docking algorithm, yet improves the ranks as well as the number of the near native structures found. As our implementation so far targeted to improve the results of ZDOCK3.0.2, and particularly for the Antigen/Antibody complexes, it is expected in the near future that more implementations will be conducted to be applicable for other initial rigid docking algorithms. UnderstDue to the compromise of protein flexibility against limited computational resources, most current protein docking algorithms are driven under the assumption of rigid docking, i.e. one of the protein partners remains rigid during the complex associations -11. HencAlthough the rigid docking has successfully predicted formations of many protein complexes, it often fails if the proteins undergo conformational changes (e.g. Antigen/Antibody complexes) or their interactions are influenced by the solvent . In factRe-ranking technique used in protein docking is an effective approach to discriminating the correct predictions from the others -17 by re2Dock also shows better performances by locating more correct predictions in higher ranks  (Antigen/Antibody - type easy), the first hit was ranked at rank 126 by F2Dock, but ranked at rank 2 by the IFACEwat. However, for the [PDB:1BGX] case above F2Dock did not obtain any hit  of the initial docking ZDOCK, the water contribution via the energies of protein-water dispersion and water-mediated Hydrogen bonds fulfills the overall picture of water-facilitating interactions between the protein partners, particularly in the Antigen/Antibody complexes.hit to locate the near native structures of protein complexes in as high ranks as possible (as describe above). Together with the inclusion of the interfacial water, the IFACEwat improves not only the number of hits found but also the ranks of the first hit, which represents the best complex predicted and the nearest to the corresponding original protein complex .Our results of the re-ranking show that the IFACEwat also improves the number of near native structures and their ranks for several protein complexes that do not contain interfacial water Table . In factinterface helps improve significantly the rank of the first hit.In addition to highlighting the contributions of water in the interfaces, the IFACEwat accounts for the interactions occurring at the complex interfaces by including the free energy change as the last potential term (Equation 1). Re-ranking observations of some complex structures that do not contain water indicated that the contribution of the free energy change ΔGinterface term, the energies of side chain- and backbone-involved hydrogen bonds contributed noticeable portions in the overall value of the ΔGinterface and helped determine the influence of side chain flexibility in the interfaces. Except for the complex [PDB:4CPA] (Enzyme/Inhibitor) in Table interface were -76.04 kcal/mol and -178.17 kcal/mol, respectively. Their break-down of the hydrogen bond-involving energies of -2.805 kcal/mol and -5.712 kcal/mol, respectively, indicated the sufficient contributions of the hydrogen bonds in the protein interactions. However, those energies were ignored by the initial docking algorithm ZDOCK.Among the components that were integrated in this ΔGinterface = -348.73 kcal/mol) and [PDB:1KXQ] , the IFACEwat located the first hit at rank 1 while ZDOCK could not find any hit. Therefore, it was suggested that including the consideration of the flexibility of protein side chains during the protein associations improved the discrimination of good binding complexes from the others, thereby improving the rank of the first hit or the best near native structure of the protein complexes.Similarly, for some water-containing complex cases, the first hit of which was ranked top-1 by the IFACEwat, the free energies of the complex interfaces also obtained significantly low values (as in the more negative the more favorable). Particularly, in 2 cases of Antigen/Antibody complexes, i.e. [PDB:1BGX] , each of which reflects an independent influence of each potential onto the overall f score (Equation 1). As discussed more in detail in this section, the free energy change (ΔGinterface) contributes the most among the four potential terms, especially in Antigen/Antibody complexes (i.e. 4 = 6.2w). The free energy change (ΔGinterface) is used to evaluate the interactions occurring at the complex interfaces, therefore determining the more favorable bound complex conformations .The IFACEwat scoring function includes various potential terms of the complex-binding score and the protein interactions at the interfaces (i.e. protein-protein and protein-water interactions). The contributions of these terms are evaluated given a set of weights . Resulting observations show that the contribution of the free energy change (ΔGinterface) plays the most important role among the potential terms. When 4 = 0w, the protein-protein interactions that occurred at the interfaces were mostly ignored and the side chain flexibility was limited (in experiment #4), consequently leading to the noticeable drop of the total number of hits found  and the free energy change (ΔGinterface) were disabled in experiment #6, the results of which significantly became worse. In the experiment #6, numbers of hits obtained for each complex type were 4.87% (A), ~50% (AB), ~30% (E), and 18.7% (O) as compared to the results of experiment #0. Similarly, results of the experiment #8 reinforced the important role of the free energy change (ΔGinterface) in the IFACEwat scoring function, particularly for the Antigen/Antibody complexes. Decrease of the number of hits therefore implies the increases of the false positives when the unfavorable complex conformations are not detected.Further, results of both the experiments (#4 and #6) with 1 = 0 win the experiment #1, except for type A (41.5%), most of the complexes obtained only around >70% (nearly 30% drops) of the number of near native structures as compared to the experiment #0 results. Comparisons of the two experiments #1 (with 1 = 0w) and #4 (with 4 = 0w) again revealed the different influences of both shape complementarity and free energy change on protein complexes of different physicochemical properties. Although the shape complementarity surprisingly affected much on the complexes type A (i.e. more than 50% drop) as shown in experiment #1, ignoring the contribution of the free energy change (ΔGinterface) resulted in more decrease of hits (i.e. more than 82% drop shown in experiment #4). A similar decreasing trend was observed in type AB in experiments #1 and #4 as well as in the experiments #7 and #8. On the other hand, the shape complementarity showed more predominant role in determining the number of hits in the Enzyme/Inhibitor (type E) complexes.It was observed in Table 2 = 0 w(experiment #2), especially if there existed water molecules at the complex interfaces, this might cause the decreases in the number of hits in type AB, but not in type A.It was also observed in the Antigen/Antibody complexes that the effect of water molecules was clearer on the complexes of type AB than on the complexes of type A. As discussed above, interactions of the Antigen with the bound-conformation of the Antibody might result in more spacious interfaces where the space could be occupied by the water molecules, thereby increasing the effect of water onto the protein interactions at the complex interfaces of type AB. Hence, when 1, w2, w3w, and 4 wof the proposed scoring function (Equation 1) differently according to the different complex types.We performed various experiments to investigate the dependency of different potential terms on physicochemical properties of the protein complexes (data not shown). It was indicated that interactions between the Enzymes and the Inhibitors were driven mostly by hydrophilic and planar residues and that methods based on the Atomic Contact Energy did not likely work well in predicting interactions of Antigen/Antibody complexes. Because the dataset used in this work contained different types of protein complexes, i.e. Antigen/Antibody (A), Antigen/bound-Antibody (AB), Enzyme/Inhibitor (E), and Others (O), it was expected that contributions of the four potential terms in the Equation 1 (see Methods) would be diverse. Therefore, we estimated the weights After a number of iterations  to estimate the weights using Monte Carlo method, the sets of weights  obtained for each complex type are  for type A,  for type AB,  for type E and  for type O.w4 is set negative to maintain the positive f scores (Equation 1). The free energy change ΔGinterface represents the interactions of the protein partners at the interfaces. It is considered as a complementary potential terms to either (1) the ScoreIFACE, which favors the shape complementarity of the two molecules, or (2) the complex flexibility due to the trade-off of the rigid docking. The more negative ΔGinterface will favor the more stable interactions (ΔG < 0) and also reflects the difficulty level that drives the two molecules to interact with each other, e.g. 4 = -6.2 wfor type A/AB as compared to 4 = -1.6 wfor type E/O.Among the four weights evaluating the interactions of the protein complexes, 4 wbetween the two types, the weight of the first term 1 walso indicates the susceptibility to shape complementarity of type E/O (1 = 1.6 or 2.0w) over the other type A/AB (1 = 0.96w). The Enzyme-Inhibitor interactions are characterized as geometrical matching or shape complement whereas the Antigen and Antibody interactions are known as "induced fit" and require conformational changes during the associations. These properties however could not be totally interpreted using rigid docking although some "soft" docking techniques were applied .2=0.95w) than in type A (2=0.002w). Structural observations reveal that steric constraints of the Antibody conformations between the unbound (A) and bound (AB) might lead to the difference. Interactions of the Antigen with the Antibody bound-conformation (AB) might have formed more spacious interfaces where there is room for the presence of water. In fact for the AB structures, interactions of the Antibody and its substrate might re-orient the overall conformational packing and therefore expose more hydrophilic regions on its surface; consequently forming hydrophilic cavities when interacting with the Antigen. Increase of the hydrophilicity in those cavities might facilitate occupation of one or more water molecules, substantially increasing the water effect onto the interfacial free energy change of these AB complexes.While there are no significant differences of weight values between type E and O (in both of which the shape complementarity was more predominant), noticeable difference of weights is observed between type A and AB cases of Antigen/Antibody complexes. Especially, the Lennard-Jones repulsive energy contributes more dominantly in type AB  by the IFACEwat  model in its energy-based function, it was shown that the IFACEwat performed equivalently with or even better than the GB-rerank F2Dock, especially for the Antigen/Antibody complexes. Our results showed that the improvement of those cases was facilitated by taking into account some extent of protein side chain flexibility when the proteins interacted, especially for those of difficult cases that required induced conformational fit during the protein associations.The IFACEwat achieved a success rate of 83.8% for Antigen/Antibody complexes, which was 10% better than ZDOCK3.0.2. As compared to another re-ranking technique ZRANK, the IFACEwat obtained success rates of 92.3% (8% better) and 90% (5% better) respectively for medium and difficult cases. When comparing with the current re-ranking method in FAlthough the IFACEwat achieved a high success rate of 83.3% for Antigen/Antibody complex, 60% for Enzyme/Inhibitor, and more than 90% for medium and difficult cases, the method was restricted to improve particularly the result of the rigid docking algorithm ZDOCK3.0.2 and especially for the Antigen/Antibody complexes. In the near future, we will be further developing the algorithm to apply for other shape complementary-based docking algorithms to achieve better robustness.In this project, we extracted a dataset from the latest Zlab protein-protein benchmark 4.0  and retrAs we were interested in the interfaces of one-receptor versus one-ligand complexes, we only selected the complexes that contained monomer ligands from the benchmark 4.0 and included them in the dataset, which yielded a total of 159 cases. The dataset contains 113 easy cases , 26 medium, and 20 difficult cases  of the dataset included water. Among the difficult cases, 16 out of 20 cases contained at least 6 water molecules buried in the protein interfaces. Hence, it was expected that taking into account the interfacial water molecules and water-mediated interactions would contribute significantly to the prediction of the protein-protein associations.We proposed a new energy-based scoring function to re-rank the results of the initial rigid docking algorithm ZDOCK3.0.2. The scoring function contains a linear combination of potential scores and derived energies that are involved in the protein-protein and protein-water interactions at the complex interfaces (Equation 1).IFACE is derived from the combination of shape complementarity, electrostatics, and desolvation (SC+ELEC+DEIFACE using the interface Atomic Contact Energy IFACE) employed in the initial rigid docking algorithm of ZDOCK3.0.2. The ELJ_repulsive and Ewater-mediated-Hbond are Lennard-Jones repulsive and water-mediated Hydrogen bond energies respectively to represent the protein-water interactions. Finally the ΔGinterface is the free energy change of the interface representing the interactions between the protein partners. The four parameters w1, w2, w3, and w4 are separate weights of the corresponding potentials. Flowchart of the whole process is shown in Figure in which the ScoreIFACE = w5SC + w6ELEC + w7DEIFACE, in which 5 = 0.01, w6 = 0.06w, and 7 = 1 w[ZDOCK3.0.2 is the rigid docking algorithm to optimize functions of shape complementarity (SC), electrostatics (ELEC), and desolvation (DE) as linear combination of correlations using FFT algorithm . The ZDOd w7 = 1 . For theo rotational sampling, which yielded 3,600 predictions. We selected top 2,000 predictions for the further implementation. In this work, we set the searching exhaustively to cover the entire binding regions of the ligand interfaces, which involved only the interacting residues with respect to the receptor. The purpose of doing this was to minimize the chances of getting false positives which were the wrong predictions but obtained high ranks by the ZDOCK3.0.2.The searching space was obtained from 15IFACE was obtained based on correlations of two grid-based discrete functions of the ligand and the receptor. In order to simplify the calculation to achieve better computational efficiency, instead of following the FFT-based calculations, we chose to estimate the sum of all the energy terms linearly as shown in the Equation 1 and weight them separately. We therefore estimated firstly the ScoreIFACE before heading to other energy implementations.Observations of the FFT-based docking algorithms indicated that the ScoreLJ_repulsive) and the water-mediated Hydrogen bonds at the interfaces (Ewater-mediated-Hbond). Initially, we extracted the complex interfaces and subsequently followed a process of adding water molecules. There was no water molecule found in the interfaces of the predicted complexes by ZDOCK3.0.2 because no water was so far involved in the previous dockings of the protein complexes.The water effect and contacts are represented by the energies of the protein-water dispersion  to stabilize the water positions with respect to the whole structure of the protein interface. Initially, 2500 steps of steepest descent were performed and followed by another 500 steps of conjugate gradient minimization. During the minimization, no restraint was applied and random initial velocities were used to maintain the robustness. The minimized water-containing structures of the protein interfaces were then subject to the next process of energy calculations.After adding water into each of the extracted interfaces of the protein complexes, we conducted the structural minimization using AMBER10 (force field LJ_repulsive), and derived it from the linear van der Waals repulsive potential:In the presence of the interfacial water, there exist repulsive forces between the water molecules (oxygen atom) and any other atoms in a distance of 3.6Å (which is the maximum distance of the equilibrium inter-nuclear separation between oxygen and any atom). In this work, we presented contribution of the force to the energy changes of the protein interfaces by using the Lennard-Jones repulsive energy  is the inter-nuclear distance, and σ is the sum of van der Waals radii of water 1.4Å) and the interacting atom  includes van der Waals interactions of protein atoms, hydrogen bond energies involved in side chain-backbone, side chain-side chain, backbone-backbone, and a potential of side-chain and backbone torsions. Further in this calculation, we included the solvation energy to complement the desolvation term (DE in the ScoreIFACE). The calculation of this ΔGinterface term was performed using PyRosetta [The last energy term in the proposed scoring function (Equation 1) represents interactions of protein partners at the interfaces. In order to take into account the flexibility of the interfaces and the trade-off of the rigid docking, we included all the interactions that involved side-chains. Derived from the solvated rotamers by Jiang et al. , the freyRosetta , the Ros1, w2, w3w, and 4 wrepresenting different contributions of the four potential terms. Optimal values of these weights were obtained by performing Monte Carlo experiments for the whole dataset of 159 protein complexes which were clustered into four types of complexes, i.e. Antigen/Antibody (A), Antigen/bound-Antibody (AB), Enzyme/Inhibitor (E), and Others (O). The weights were estimated during the recurring experiments until the maximal numbers of hits converged. In this project, hits were defined as the near native structures, RMS of which was within 2.5Å from the crystalized structures (RMS ≤ 2.5Å).The proposed scoring function to re-rank the protein complexes (Equation 1) contains four separate weights IFACE: interface Atomic Contact Energy,A/AB: Antigen/Antibody complex ,E: Enzyme/Inhibitor complex, O: other complex types,RMS: root mean square deviation,SC: shape complementarity, ELEC: electrostatics, DE: desolvation,LJ_repulsive: Lennard-Jones repulsive energy, Ewater-mediated-Hbond: water-mediated hydrogen bond energy,Einterface: free energy change at the protein interface.ΔGThe authors declare that they have no competing interests.CTTS and TDN carried out the experiments. CTTS, TDN, JZ, and CKK wrote the manuscript. All authors read and approved the final manuscript.Overall results of ZDOCK, ZRANK, and the IFACEwat in terms of numbers of near-native structures (hits) found and the rank of the first hit. Cases which are in bold indicate that IFACEwat method performs equivalently with or better than both of the others.Click here for fileBest RMS of conformations in the top 10, 100, and 1000 ranks found by the IFACEwat against ZDOCK (left) and ZRANK (right). Each dot represents a protein complex case according to difficulty level: easy (black), medium (red), and difficult (blue).Click here for file2Dock-GB rerankRe-rankings results that are equivalent with or better than the F. To maintain a rational assessment, results between each of the methods and ZDOCK3.0.2 separately are used as the intermediate comparison since all the 3 methods are applied for the same dataset of protein complexes (the benchmark 4.0) with 15o rotational sampling. Results of F2Dock are done by Chowdhury et al. Filename: AdditionalFile-3.pdfClick here for fileet al. Angle and distance constraints to define a water-mediated Hydrogen bond according to the solvated rotamer library by Jiang d is the distance between the water (W) oxygen and polar (acceptor A/donor D) atoms. H and AB are Hydrogen and Acceptor-Base atoms respectively.Click here for file


The modular architecture of protein-protein interaction (PPI) networks is evident in diverse species with a wide range of complexity. However, the molecular components that lead to the evolution of modularity in PPI networks have not been clearly identified. Here, we show that weak domain-linear motif interactions (DLIs) are more likely to connect different biological modules than strong domain-domain interactions (DDIs). This molecular division of labor is essential for the evolution of modularity in the complex PPI networks of diverse eukaryotic species. In particular, DLIs may compensate for the reduction in module boundaries that originate from increased connections between different modules in complex PPI networks. In addition, we show that the identification of biological modules can be greatly improved by including molecular characteristics of protein interactions. Our findings suggest that transient interactions have played a unique role in shaping the architecture and modularity of biological networks over the course of evolution. Modular architecture is important for the evolution of cellular systems. Modular rearrangements facilitate functional innovations and modular insulations provide robustness to perturbations. However, molecular-level understanding of the mechanisms underlying modular network evolution is currently not well understood. Here we show that strong domain-domain interactions (DDIs) and weak domain-linear motif interactions (DLIs) made different contributions to the evolution of the modular architecture of PPI networks. Especially, DLIs mediate between-module interactions, and that their relative abundance has dramatically increased in metazoan species. Linear motifs have been identified as evolutionary interaction switches since subtle amino acid changes can cause the short sequences in linear motifs to appear and disappear. Our results suggest that subtle changes in linear motifs have contributed to the rewiring of functional modules and, consequently, to functional innovations in metazoan species. Biological modules have played an important role in the evolution of cellular systems. After all, it is a group of genes, rather than a single gene, that cooperatively carries out cellular functions and determines phenotypic consequences Current approaches to identifying modules in protein-protein interaction (PPI) networks often fail to consider the molecular components of connections. Hence, they cannot explain the molecular characteristics underpinning the evolution of network modules. Instead, they often rely on network topology, describing the organization of protein interactions Social network studies have shown that network architecture and evolution are closely related to interaction strength 3–106 fold stronger affinities than DLIs. Domains are globular structures of long peptides with defined binding or catalytic activities, whereas linear motifs are short peptides composed of specific sequence patterns that bind to other domains. Due to structural differences in the interacting components, DDIs tend to be characterized by large, strong interfaces between two globular domains, whereas DLIs are typically composed of small, weak interfaces between short peptides. In addition, domains and linear motifs have evolved in distinct manners. Domains are often conserved over a wide evolutionary range, evolving in a divergent manner The physical characteristics of protein interactions are largely determined by their interface structures, which in general are classified into two groups: domain-domain interactions (DDIs) and domain-linear motif interactions (DLIs) In this study, we investigated the role of DLIs and DDIs in biological modules and found that DLIs are more likely to connect proteins between different biological modules, whereas DDIs tend to connect proteins within the same biological modules, including functional groups, protein complexes, and subcellular localizations. Furthermore, evolutionary analysis of PPI networks revealed that an expansion of DLIs in complex organisms has contributed to an increase in modularity, which may compensate for the cost of network complexity during evolution. We also demonstrated that module identification could be improved by utilizing DLI/DDI information. Indeed, interaction strength represents a unique biological aspect of network modules, one not incorporated by topology information alone. Our study suggests that inclusion of the physical characteristics of protein interactions will improve our understanding of the architecture and evolution of PPI networks.We classified human PPIs into DDIs and DLIs to investigate the relationship between interaction strength and the modular architecture of networks . BrieflyWe found that the quality of linear motifs increased during DLI classification steps. Because linear motifs have high rate of false positives n = 816) matched the reference DDIs, whereas only 1.0% of the assigned DLIs (n = 10) were included in the reference DDI set. By contrast, 52.6% of the assigned DLIs (n = 92) matched the reference DLIs, whereas only 1.7% of the assigned DDIs (n = 3) were included in the reference DLI set. This also validates our approach to a classification of PPIs into DDIs and DLIs.We further compared assigned DDIs and DLIs to reference sets in which the interfaces of human PPIs were identified directly from 3D structures or the literature see . We founp = 1.0×10−323).We found that DDIs and DLIs have distinct roles in organizing the modular architecture of the human PPI network. DDIs tend to link proteins within the same topological clusters, whereas DLIs are more likely to connect different topological clusters in the network . To quanp = 1.0×10−5 for DDIs; p = 1.5×10−3 for DLIs). This was further confirmed based on the conservation of motifs constituting DLIs. We changed DLI datasets by varying motif conservation scores and measured average clustering coefficients. We found that the average clustering coefficients of DLIs were lower than that of DDIs, regardless of their motif conservation scores . This confirms that DLIs generally have lower clustering coefficient, which is not restricted to several prevalent motifs.Because of the degeneracy in regular expressions, certain motifs could stochastically occur in many proteins. Therefore, we removed DLIs with low information content and reanalyzed the dataset. We confirmed that clustering coefficients of DLIs were lower than that of DDIs when we removed motifs with higher probability to be found by chance. DLIs showed lower clustering coefficient compared to DDIs even after removed 89 motifs with probability over 10−5 . MoreoveWe next compared the role of DLIs and DDIs in various biological modules. Because biological modules are groups of proteins with tight functional relationships We found that DLIs were enriched in protein interactions connecting different functional groups, whereas DDIs were enriched in interactions connecting proteins within the same functional group . FunctioWe found that DLIs were enriched in between-complex interactions, whereas DDIs were enriched in within-complex interactions . For exaWe found that DLIs were enriched in protein interactions across different subcellular localizations, whereas DDIs were enriched in protein interactions within subcellular localizations . For exaWe confirmed that DDIs are biased toward within-module interactions regardless of they are mediated by same or different domains. One might ask that the observed bias of DDIs toward within-module interactions emerged from similar functions of identical domains. To test this question, we divided DDIs into two groups, homo- or hetero-DDIs. Any DDIs mediated by one or more pairs of same domains were classified as homo-DDIs and the rest of them were classified as hetero-DDIs based on their Pfam ID. We found that both homo- and hetero-DDIs are biased toward within-module interactions for functional groups, protein complexes, and subcellular localizations . This int-test, p = 2.4×10−43). As expected, we found that the increases of linear motifs and DLI domains are more significant than that of DDI domains . We again tested whether the decrease of PPIM is due to any evolutionary association from same domains and found that PPIM decreased for both homo- and hetero-DDIs  were grouped into the same module . We also found that DDIs and DLIs in SPPI network are enriched in within- and between-module interactions, respectively , release 9 CS is the confidence score for the pair domain i and domain j, k indicates the prediction method, W is a precalculated weight factor for a specific prediction method, and I is an indicator of the prediction result  = 1 if the method k gives a positive prediction for the pair domain i and domain j; kI = 0 otherwise). The weight factor assigned each prediction method was equal to its precision:0CS). To select a reliable 0CS, we investigated the 1F score of prediction results, increasing 0CS from 0 to 1.20 in 0.01 increments , we obtained 6,911 interacting domain-domain pairs predicted using various computational approaches. In total, this procedure gave us 16,527 interacting domain-domain pairs from both 3D structures and predictions. To avoid any bias in biological modules, we excluded prediction methods that exploited functional similarity.We classified a PPI as a DDI if two partner proteins had one or more interacting domain-domain pairs. Data on human protein domains were obtained from the Protein Family Database (Pfam), release 27.0 crements . The F1 We classified a PPI as a DLI if two partner proteins had one or more interacting domain-linear motif pairs. We identified linear motifs in human proteins using regular expressions that represent motifs ELM instances, experimentally validated motifs in ELM database, were downloaded June 12, 2014. Among them, we found 695 positive and 12 negative motifs presented in the network. Because the number of negative motifs were too small to assess quantitatively, we also generated 10,000 random sets comprising 695 motifs of random selection for each and compared them to the positive set.iμ and iσ are mean and standard deviation of CSV, respectively, of  residues including residue i itself. We used Shannon's entropy of each column in aligned ortholog sequences as CSV:i denotes each column, α is an amino acid presented in a column, and P(α) is the frequency of the amino acid α in a column. Orthologs were obtained from Inparanoid database and only 100% confidence orthologs were used We assessed the conservation of a motif using relative local conservation score (RLC) for each comprised residue and took their average for the motif We collected reference sets of human DDIs and DLIs whose status could be directly ascertained from 3D structures and literatures. Although 3did, iPfam and ELM databases provided experimentally confirmed DDIs and DLIs, only part of them might be interactions found in human proteins. Therefore, we chose reference DDIs from 3did and iPfam, if two protein constructs in the experiment were derived from human sequences by tracking species information from Protein Data Bank C, between two nodes, i and j, was measured as follows i and j; g is the order of cycles, i.e. the number of nodes included in each cyclic structure. Here, we set g = 4. We generated 10,000 permutations of DDIs and DLIs to obtain empirical p-values for the clustering coefficients. We permuted domains and linear motifs preserving their number in each protein and reassigned DDIs and DLIs.Edge clustering coefficient measures the ratio of observed cyclic structures over possible cyclic structures around two connected nodes. Specifically edge clustering coefficient, By definition, biological modules in PPI networks are groups of proteins that have tight functional relationships For protein complexes, we used the Mammalian Protein Complexes (CORUM) database We used the consensus localization prediction (ConLoc) method To investigate the role of DLIs and DDIs in biological modules, we classified PPIs as within-module or between-module interactions. PPIs were considered within-module interactions if the interacting proteins had identical module memberships. Conversely, PPIs were considered between-module interactions if the interacting proteins had no common module membership. However, there were PPIs that met neither of these criteria (dubbed “overlapping interactions” in n11), between-module DDIs (n12), within-module DLIs (n21), and within-module DDIs (n22). Enrichment was calculated as the observed number of interactions over the expected number of interactions for a specific association. For the observed number of xyn, the expected number was calculated as n11+n12)×(n11+n21)/(n11+n12+n21+n22), i.e., the number of between-module interactions multiplied by the fraction of DLIs among the annotated proteins. We also determined if the level of enrichment was significant by calculating the p-value from Fisher's exact test. An analysis of MF terms for modules sized 80–160 proteins is shown in Next, we further characterized the association of DLIs and DDIs with between and within-module interactions. We constructed a 2×2 contingency table with four types of interactions: between-module DLIs . The resulting modules were provided in We identified DLI/DDI-informed modules based on a procedure similar to the one used to identify conventional PPI modules; however, it weighted DDIs and DLIs differently i, j is the protein pair and X is the set of biological annotations. Module quality was calculated as the average similarity of protein pairs. Fold increase in module quality was measured by comparing module quality to the average similarity of all protein pairs in the network. The p-value comparing module quality between the DLI/DDI-identified modules and conventional PPI modules was calculated using the Kolmogorov-Smirnov test. We also investigated the effect size of employing DLI/DDI information upon module quality using Cohen's d, designated e in We assessed module quality by measuring how similar proteins within the same module were. The similarity of each protein pair was calculated as the Jaccard index of biological annotations:Figure S1Edge clustering coefficient of DLIs with various conservation scores. DLIs were ordered by the conservation of comprising motifs and divided into 10 groups. Numbers in parentheses show the number of DLIs in each group.(TIF)Click here for additional data file.Figure S2Clustering coefficients of DLIs according to the probability of motif regular expression. (a) Clustering coefficients of DLIs with varying probability cutoffs were compared with that of DDIs. (b) Each dot represents a motif with unique regular expression having its probability to be found by chance on x-axis, while average clustering coefficient of interactions on y-axis. The probability of motif regular expression was calculated as the product of the amino acid probability in each position of a motif (TIF)Click here for additional data file.Figure S3Examples of DLIs and DDIs in interactions between and within biological modules for (a) functional groups, (b) protein complexes, and (c) subcellular localizations.(TIF)Click here for additional data file.Figure S4p-value was taken comparing 45 nonmetazoan and 53 metazoan species by t-test.Number of domains and linear motifs mediating DDIs and DLIs in the PPI networks of eukaryotes. Average number per protein was calculated for each species and (TIF)Click here for additional data file.Figure S5Enrichment of DLIs and DDIs in between and within-module interactions. Non-overlapping GO terms were used as functional groups. Module size represents the number of proteins in a given GO term.(TIF)Click here for additional data file.Figure S6Enrichment homo-DDIs and DLIs in between and within-module interactions.(TIF)Click here for additional data file.Figure S7Enrichment hetero-DDIs and DLIs in between and within-module interactions.(TIF)Click here for additional data file.Figure S8a) The number of between and within-module interactions in eukaryotic species among protein complexes. Values are shown for nine representative eukaryotic species. (b) Average proportion of between and within-module interactions in nonmetazoan and metazoan species.Increase in between-module interactions in metazoan PPI networks among protein complexes. ((TIF)Click here for additional data file.Figure S9e, and the p-value are shown, stratified by module size.Comparison of module quality between DLI/DDI-identified modules and conventional PPI-identified modules for different functional groups. For both molecular function and biological process, the effect size, (TIF)Click here for additional data file.Figure S10Edge clustering coefficient of DDIs and DLIs in SPPI network.(TIF)Click here for additional data file.Figure S111F score for the prediction of interacting domain-domain pairs in relation to the cut-off value, 0CS. The 1F score was calculated for positive predictions, which were domain-domain pairs with a confidence score, CS, greater than the 0CS.(TIF)Click here for additional data file.Figure S12Schematic illustrating how PPIs were categorized as between or within-module interactions.(TIF)Click here for additional data file.Table S1DDI and DLI-assigned human PPI network.(XLSX)Click here for additional data file.Table S2Number of DDIs and DLIs as within- or between-module interactions.(XLSX)Click here for additional data file.Table S3Fraction of DLIs with different conservation in between- or within-module interactions.(XLSX)Click here for additional data file.Table S4Enrichment of homo- and hetero-DDIs in interactions between and within biological modules.(XLSX)Click here for additional data file.Table S5Number of interactions in orthologous PPI networks of eukaryotic species.(XLSX)Click here for additional data file.Table S6Number of DDIs and DLIs as within- or between-module interactions in SPPI network.(XLSX)Click here for additional data file.Table S7Module membership of proteins for PPI modules and DLI/DDI modules.(XLSX)Click here for additional data file.


Protein complexes formed by non-covalent interaction among proteins play important roles in cellular functions. Computational and purification methods have been used to identify many protein complexes and their cellular functions. However, their roles in terms of causing disease have not been well discovered yet. There exist only a few studies for the identification of disease-associated protein complexes. However, they mostly utilize complicated heterogeneous networks which are constructed based on an out-of-date database of phenotype similarity network collected from literature. In addition, they only apply for diseases for which tissue-specific data exist.In this study, we propose a method to identify novel disease-protein complex associations. First, we introduce a framework to construct functional similarity protein complex networks where two protein complexes are functionally connected by either shared protein elements, shared annotating GO terms or based on protein interactions between elements in each protein complex. Second, we propose a simple but effective neighborhood-based algorithm, which yields a local similarity measure, to rank disease candidate protein complexes.Comparing the predictive performance of our proposed algorithm with that of two state-of-the-art network propagation algorithms including one we used in our previous study, we found that it performed statistically significantly better than that of these two algorithms for all the constructed functional similarity protein complex networks. In addition, it ran about 32 times faster than these two algorithms. Moreover, our proposed method always achieved high performance in terms of AUC values irrespective of the ways to construct the functional similarity protein complex networks and the used algorithms. The performance of our method was also higher than that reported in some existing methods which were based on complicated heterogeneous networks. Finally, we also tested our method with prostate cancer and selected the top 100 highly ranked candidate protein complexes. Interestingly, 69 of them were evidenced since at least one of their protein elements are known to be associated with prostate cancer.Our proposed method, including the framework to construct functional similarity protein complex networks and the neighborhood-based algorithm on these networks, could be used for identification of novel disease-protein complex associations.The online version of this article (doi:10.1186/s13015-015-0044-6) contains supplementary material, which is available to authorized users. Protein complexes are formed by non-covalent interactions among proteins and have specific biological functions. These protein complexes and their cellular functions have been concurrently identified by a number of methods based on protein interaction networks -3 and afIt is shown that interactions among proteins forming protein complexes do not only provide a better understanding of cellular functions, but also improve our understanding about human diseases -9. A numIn this study, we propose a novel method to identify novel disease-protein complex associations. First, we presented a framework to construct functional similarity protein complex networks where each node is a protein complex and two protein complexes are functionally connected if they either share protein elements, share annotating gene ontology (GO)  term or First, we obtained 1,704 human protein complexes from a database of mammalian organisms protein complex CORUM  .Based on this definition and the set of collected human protein complexes, we constructed a functional similarity protein complex network including 1,579 nodes and 16,981 interactions . More specifically, we defined the interaction based on a measure of gene functional similarity introduced in . The AUC values were calculated for each disease phenotype on each individual functional similarity protein complex network. Then, the performance of each algorithm was averaged over those disease phenotypes for each individual functional similarity protein complex network. Figure , 0.9. ThAnother advance of NBH compared to RWR and PRINCE is that NBH is free of parameters, whereas the performance of RWR and PRINCE is controlled by back-probability and trade-off parameters, respectively. Indeed, Figure SharedProteinComNet than that in SharedGOTermComNet and SharedPPIComNet , and PRINCE was shown less as a negative trend in SharedProteinComNet than that in SharedGOTermComNet and SharedPPIComNet . Considering the network density of SharedProteinComNet was much lower than that in SharedGOTermComNet and SharedPPIComNet , this observation indicated that disease protein complexes are more connected in SharedGOTermComNet and SharedPPIComNet. However, networks with very high densities such as SharedGOTermComNet and SharedPPIComNet, may contain unreliable functional interactions that can reduce the performance of the methods. Indeed, Figure SharedProteinComNet was better than that on SharedGOTermComNet and SharedPPIComNet  and decreased when the threshold increased. However, for RWR  of interaction weight to extract different functional similarity protein complex networks of which interactions having weight no less than t. Particularly, for In addition to comparison of prediction performance of the proposed algorithm with RWR and PRINCE, we here show its advance in running time. To this end, we ran each method for 282 disease phenotypes, where their known disease associated protein complexes were used as source nodes. For RWR and PRINCE, we also varied back-probability and trade-off parameters in a range of  and took the average of running time over this range. The final running time for each disease phenotype was averaged over the set of 282 disease phenotypes. Table To our knowledge, only a few studies have directly been proposed for identification of disease-associated protein complexes -22. In wSharedProteinComNet. After applying NBH to rank all candidates, we selected 100 highly ranked candidate protein complexes. By searching associations between 219 genes coding proteins involved in those selected protein complexes with prostate cancer on GeneRIF [ftp://ftp.ncbi.nih.gov/gene/GeneRIF/generifs_basic.gz), we found 28 of them reported to be associated with prostate cancer  is a complex disease and there were 22 genes associated with it as published in OMIM . Followi76807 is  GeneRIF  .To make the results for identification of novel prostate cancer-associated protein complexes more convincing, we additionally randomly selected 200 sets of 100 protein complexes among protein complexes in the same functional similarity protein complex network. Then, we repeated the same procedure as we did for the 100 highly ranked candidate protein complexes to find novel prostate cancer-associated protein complexes for each set. The result showed that, on average, about 28 protein complexes in each set were enriched with genes which are associated with Prostate cancer (See in Additional file Protein complexes play important roles in cellular functions; however, their roles in causing disease have not been paid enough attention because there have been a few studies directly focusing on identifying disease-protein complex associations. In this study, we have proposed a novel method for identification of novel disease associated protein complexes. Comparing to our previous study , which u


The flexibility of individual proteins aids their evolutionary recruitment into complexes with increasing numbers of distinct subunits. The intrinsic flexibility of proteins allows them to undergo large conformational fluctuations in solution or upon interaction with other molecules. Proteins also commonly assemble into complexes with diverse quaternary structure arrangements. Here we investigate how the flexibility of individual protein chains influences the assembly and evolution of protein complexes. We find that flexibility appears to be particularly conducive to the formation of heterologous  intersubunit interfaces. This leads to a strong association between subunit flexibility and homomeric complexes with cyclic and asymmetric quaternary structure topologies. Similarly, we also observe that the more nonhomologous subunits that assemble together within a complex, the more flexible those subunits tend to be. Importantly, these findings suggest that subunit flexibility should be closely related to the evolutionary history of a complex. We confirm this by showing that evolutionarily more recent subunits are generally more flexible than evolutionarily older subunits. Finally, we investigate the very different explorations of quaternary structure space that have occurred in different evolutionary lineages. In particular, the increased flexibility of eukaryotic proteins appears to enable the assembly of heteromeric complexes with more unique components. Proteins often interact with other proteins and assemble into complexes. Here we show that the flexibility of individual proteins is important for their recruitment to complexes, as it facilitates the formation of asymmetric interfaces between different subunits. The role of flexibility becomes increasingly important as a greater number of distinct proteins are packed together within a single complex: the more distinct subunits, the more flexible those subunits need to be. A consequence of this is that, when a protein complex gains a new subunit during evolution, the newer subunit will tend to be more flexible than the older subunits. This suggests that we may be able to partially reconstruct the evolutionary history of a protein complex by considering the flexibility of its subunits. We also find that the types of protein complexes an organism forms are closely related to the flexibility of its proteins, with eukaryotic species, and particularly animals, using their increased flexibility to assemble complexes involving more distinct components. The assembly of proteins into protein complexes is ubiquitous within the cell The intrinsic flexibility of proteins is intimately related to their assembly into complexes. For example, flexibility is often crucial for binding—either for facilitating the structural changes that are induced upon binding or for allowing the intrinsic fluctuations within the unbound state that enable a conformational selection binding mechanism The structures of a huge number of protein complexes are now available. Although many structure-based methods are available for characterizing protein flexibility and dynamics, we are primarily interested in the intrinsic flexibility of monomers before they assemble into a complex. Because there are no unbound-state structures available for most individual proteins observed as subunits of protein complexes, it has previously been difficult to characterize their flexibility. Algorithms for predicting intrinsic disorder from protein sequences can provide some useful information, and have revealed a significant tendency for the subunits of large multiprotein complexes to be disordered in isolation Arel), is highly predictive of various flexibility measures, including those calculated from protein structures and those derived directly from experimental measurements Arel and independent measures of flexibility is as strong or stronger than the correlation of those different flexibility measures with each other. Arel also shows a strong correspondence with the extent of conformational changes that occur upon complex assembly We recently introduced a simple method for predicting the intrinsic flexibility of proteins from their structures. This method relies on the fact that the folding of a protein from its unfolded state is driven primarily by the intramolecular burial of surface area Arel is a simple ratio describing how much solvent-accessible surface area a protein is exposing compared to what we expect for a typical folded, monomeric, crystallizable protein of the same molecular weight. Roughly speaking, Arel values of 0.8–0.9 are observed for the most compact, rigid proteins, whereas Arel values greater than 1.2 are seen for highly flexible proteins that undergo large conformational changes upon binding Arel involves major simplifications, it is important to emphasize that its use as a measure of flexibility arises from fundamental energetic principles—it is not merely a probe of globularity. In fact, some proteins are highly efficient at burying enough intramolecular surface area to become quite rigid, while retaining fairly extended overall conformations. As discussed previously, by assuming constant energy per unit of surface area buried, Arel can be directly related to the difference in conformational entropy with respect to an idealized folded state Although Arel is calculated for the bound subunits of protein complexes , there is a very strong correlation between the Arel values of bound subunits and those same proteins in their unbound states There is another major benefit for our purposes here: when Arel values ArelArel values of bound subunits and the fraction of residues predicted to be disordered  interface between the two subunits. Such isologous interfaces are extremely common, which has been suggested to be due to their inherent energetic favourability (1) Twofold dimeric complexes, represented by the Cn (n>2) symmetry groups, possesses higher order rotational symmetry, with the subunits forming closed rings via heterologous  interfaces. Note that although the C2 complexes do have twofold rotational symmetry, here we will only refer to complexes with at least threefold symmetry as cyclic, due to their distinct interface properties.(2) Cyclic complexes, represented by the Dn (n>1) symmetry groups, can be thought of as a doubling of the other topologies through the addition of a new twofold rotational axis . All dihedral complexes therefore have isologous interfaces corresponding to this twofold axis. Dihedral complexes with at least six subunits usually  have a mixture of both isologous and heterologous interfaces. Dihedral complexes appear to be particularly conducive to facilitating allosteric regulation, as the isologous interfaces associated with the twofold axis provide a simple way to transmit conformational changes between subunits (3) Dihedral complexes, represented by the C1, can be formed in various ways but are characterized by the existence of different subunits in nonequivalent positions  Asymmetric complexes, represented by the trivial symmetry group Arel, of homomeric subunits from these different groups. Most strikingly, we observe a highly significant tendency for the subunits of cyclic and asymmetric complexes to be more flexible than those forming twofold dimeric or dihedral topologies. Much weaker trends are observed if sequence-based intrinsic disorder predictions are used instead of Arel  or cyclic (Cn (n>2)) intermediates. Interestingly, complexes with dimeric intermediates are nearly three times as abundant as those with cyclic intermediates, consistent with the finding that isologous interfaces are generally more ancient We considered those homomers with both isologous and heterologous interfaces that therefore have at least two possible evolutionary pathways. These were split into those predicted to have evolved via either twofold dimeric . In other words, those complexes in which a heterologous interface is the largest will tend to have more flexible subunits, further demonstrating the relationship between subunit flexibility and heterologous interface formation. This also reveals a fascinating connection between subunit flexibility and evolutionary history: just as the evolution of a complex is related to the sizes of its interfaces, it is also reflected in the flexibility of its subunits.We also observe a significant tendency for subunits that assemble via cyclic intermediates to be more flexible than those that assemble via dimeric intermediates  complexes  to have higher Arel values than those from D2  complexes. However, the Arel values from the two groups of dimeric precursors are nearly identical (1.086 for Dn (n>2), 1.088 for D2, p = 0.5, Wilcoxon rank-sum test), suggesting that flexibility at the level of dimeric subcomplexes is not as closely related to quaternary structure as is monomer flexibility.Finally, it is interesting to specifically consider those dihedral complexes predicted to have evolved via dimeric intermediates. If we consider each dimeric precursor together as an individual “subunit,” we can calculate an Although homomeric interfaces between identical chains can either be isologous or heterologous, heteromeric interactions between dissimilar subunits are inherently heterologous. Therefore, just as flexibility appears to facilitate the packing of heterologous homomeric interfaces, flexibility might also promote the formation of heterologous interfaces in heteromers.Arel  have formed part of a similar complex in the last common ancestor.Combining these two approaches, we considered each human (or closely related) protein complex from the perspective of different species of varying evolutionary relatedness to humans. Proteins for which an ortholog could be identified in a given species were considered to be the “putative older subunits.” In contrast, proteins for which no ortholog or homolog could be detected in that species, or any other species of similar or greater evolutionary divergence from humans, were considered to be the “putative newer subunits.” An example of a complex in which two subunits could be confidently assigned as having different evolutionary ages is shown in In Arel = 1.213 versus 1.082, p = 6×10−6, Wilcoxon signed-rank test). Similarly, in the large majority of complexes (48/61), the newer subunit(s) are more flexible than the older subunit(s) .We can also combine the observations made for different species into a nonredundant set of 61 complexes where both older and newer subunits can be identified. In this case, the newer subunits are also far more flexible than the older subunits . It has also been observed that evolutionarily newer proteins are generally shorter than older proteins Arel = 1.221 versus 1.115, p = 0.007, N = 24).Although many subunits from protein complexes of known structure are truncated forms of full proteins , a strong tendency for newer subunits to be more flexible is still observed when only full-length or nearly full-length proteins are considered . This is consistent with the previous observation that heteromers are enriched in vertebrates relative to unicellular organisms The observation that subunits gained later in evolution tend to be more flexible raises interesting questions about proteome and interactome evolution. Specifically, it suggests that the average flexibility of proteins in an organism might increase over the course of evolution as new proteins are acquired and the number of protein complex interactions increases. Therefore, it is interesting to first consider how quaternary structure varies in evolution, by comparing the proportion of homomeric and heteromeric complexes in bacteria, archaea, and eukaryotes . InteresNext, to explore the evolutionary relationship between flexibility and quaternary structure, we grouped complexes by their species of origin and plotted the number of nonhomologous subunits per complex against the mean subunit flexibility . There iArabidopsis thaliana and the protozoan Plasmodium falciparum. This is quite interesting given that these two species are the most evolutionary divergent eukaryotes, relative to the more closely related yeast and metazoans A. thaliana) are considered together, they have more nonhomologous subunits per complex (1.172) than observed in any of the prokayotes, but very low subunit flexibility (mean Arel of 1.067). From this limited evidence, it is difficult to tell whether these results reflect genuine evolutionary differences. However, this does hint that some of this divergence may have occurred in the fungi/metazoa lineage.There are two eukaryotes that cluster with the prokaryotes: the plant Bos taurus, in particular, has more than any other species. A possible explanation for this is that many of these large multiprotein complexes are likely to have been natively purified from bovine tissues. Thus, the complexes tend to contain more of the biologically relevant subunits present in vivo, whereas complexes from other organisms are more likely to have been recombinantly produced. Interestingly, we note that Saccharomyces cerevesiae also has a relatively large number of nonhomologous subunits per complex, as does Escherichia coli when compared to other prokaryotes. These organisms are often used for protein production and so their complexes may also be more likely to have been natively purified. These results highlight the interesting  point that protein complexes in vivo are likely to have a much greater tendency to contain more distinct subunits than has generally been observed crystallographically.The eukaryotic species have a much greater spread in nonhomologous subunits per complex. Arel, we also looked at the relationship between predicted intrinsic disorder and protein–protein interactions. Previous observations have shown a strong tendency for proteins with more interaction partners to possess a greater fraction of intrinsically disordered residues As a complement to this structure-based analysis of flexibility using In this study, we have demonstrated a close association between intrinsic subunit flexibility and the assembly of protein complexes. The origin of this is simple: because flexibility is largely controlled by how little surface area a protein buries intramolecularly This work also extends our understanding of protein evolution, as it shows how the evolutionary history of a protein complex can be directly related to the flexibility of its subunits. This suggests that flexibility could potentially be quite useful in the reconstruction of protein complex evolutionary histories. To some extent, our results suggest that the eukaryotic increase in flexibility may have been driven by the evolution of protein complexes with more components. In addition, it is possible that some of the increased flexibility in eukaryotic subunits may be reflective of a greater propensity to form multiple nonconcurrent interactions, as has been seen for intrinsic disorder This new knowledge of the relationship between quaternary structure topology and flexibility could aid the prediction of protein complex topologies from limited information. For example, if some knowledge of intrinsic flexibility is available , this could be used to help assess the relative likelihoods of different quaternary structure arrangements. Similarly, just as flexibility appears to facilitate quaternary structure evolution, it might also prove important for engineering multiprotein assemblies, if the principles of flexibility and interactions can be harnessed to enable the packing of heterologous interfaces.in vivo, as these proteins will usually be protected by chaperones prior to assembly. Ultimately, more studies will be required to quantify the extent of in vivo flexibility and disorder, and to further disentangle the functional importance of unbound-state properties from the conformational requirements of bound subunits.In the present study, we have interpreted our results as showing that intrinsic flexibility facilitates the assembly and evolution of quaternary structure. However, it is possible that, rather than flexibility being required for assembly, it can to an extent be thought of as arising from the physical requirements of the bound state. That is, the packing of multiple, different-shaped subunits within a single complex may necessitate flexibility. Any protein that could form sufficient intersubunit interactions might be inherently flexible in its unbound state due to a lack of intramolecular contacts. A related issue has recently been discussed by Janin and Sternberg, who suggested that many intrinsically disordered proteins are simply “proteins waiting for a partner” db_id assignment. Additionally, protein complexes annotated as having quaternary structure assignment errors Biological units of protein crystal structures (<5 Å resolution) were taken from the Protein Data Bank on 2012-08-08, considering chains ≥40 residues. We filtered out backbone-only models and structures containing nucleic acids or >10% nonwater heteroatoms. Heteromers formed by subunit cleavage were also removed by identifying nonidentical chains from the same complex having the same Arel values were calculated according to Arel = As/4.44M0.77, where As is the solvent-accessible surface area and M is the molecular mass, as in Arel values of the dimeric precursors of dihedral complexes were calculated in the same way, except the total solvent-accessible surface area of each dimer was calculated, and the masses of the two subunits were summed. Complexes with two possible assembly pathways were identified as those symmetric homomers with at least six subunits having both heterologous and isologous interfaces >800 Å2. Homomeric interfaces were identified as being isologous if the correlation between the residue-specific buried surface area for each subunit in an interacting pair was >0.7.Solvent-accessible surface areas and interface sizes were calculated with AREAIMOL. Secondary structure was calculated for each protein chain with STRIDE Protein complexes in which all unique chains share >50% sequence identity were clustered. In addition, to avoid highly similar complexes that vary only slightly in their subunit composition, heteromeric complexes sharing at least four unique chains were clustered. From each cluster, only the complex with the most amino acid residues (ignoring subunit repeats) was selected for the nonredundant set used in this study . However, we note that this sequence-redundancy filtering is not perfect, as proteins can share sequence identity significantly lower than 50%, yet still be quite similar structurally. Therefore, we also created a stricter nonredundant set of protein complexes that are nonhomologous at the structural level by only considering only complexes with unique SUPERFAMILY domain assignments . The main structural analyses from blastp search against all human proteins in Ensembl was performed for each protein chain. All chains with >70% sequence identity to a human protein were considered. Orthologs of these proteins were then identified in a variety of different species with Ensembl Compara To map human genes against protein structures, a To identify the “putative newer subunits” that were likely not present in an ancestral species, we also considered homologs at the level of domain architecture. This allows us to identify more divergent proteins that might have possibly been playing a similar subunit role in an ancestral complex. Importantly, we do not use this information to say that an ancestral subunit was present, but instead to say that an ancestral subunit might possibly have been present. Using SUPERFAMILY genome-scale domain assignments To generate nonredundant sets of protein complexes having both putative older subunits and putative newer subunits, we only considered a single complex mapping to a given pair of old and new human genes. Similar filtering was performed when the sets of different species were combined. All the sets of putative older and newer subunits are provided in Figure S1Arel values of bound subunits from protein complexes are predictive of intrinsic flexibility in the unbound state. (A) Comparison between Arel values of monomeric proteins, Arel(free), and those same proteins  bound as subunits within homomeric or heteromeric complexes, Arel(bound). In total, 288 homomer and 387 heteromer pairs were identified from the nonredundant dataset used in this study  and Arel(free) is 0.9% (mean absolute difference of 2.6%) for homomers and 0.7% (mean absolute difference of 3.0%) for heteromers, suggesting that there is a very slight tendency for Arel(bound) to overestimate Arel(free). These values are consistent with a recent study showing that the accessible surface area of interface residues in the bound state are on average 3.3% higher than in the unbound state Arel value, but the free state is stabilized by the same intermolecular interactions being formed intramolecularly. Given the overall high correlations and the rarity of outliers observed here, and the fact that domain swapping is only observed in ∼5% of protein families Arel value. Error bars represent SEM. The overall correlation (r) between Arel and intrinsic disorder is 0.313 . For those subunits for which a corresponding monomer structure does exist (sequence identity >50%), the correlation is much lower .(TIFF)Click here for additional data file.Figure S2Arel distributions for subunits from different groups of protein complexes. Boxplots are generated in R using standard settings. The y-axes are plotted logarithmically. Nonoverlapping notches can be used as a rough indicator of statistically significant differences between two groups. (A) Subunits of homomers from different symmetry groups, as in p values for the differences between groups are shown calculated with the Wilcoxon rank-sum test. (B) Subunits from heteromers with different numbers of nonhomologous subunits, as in Boxplot representations of (TIFF)Click here for additional data file.Figure S3Arel as a measure of intrinsic flexibility. Comparison of the percentage of residues predicted to be intrinsically disordered for subunits from (A–B) homomeric complexes from different symmetry groups Click here for additional data file.Figure S4Arel, to the number of subunits in homomers from different symmetry groups. The overall correlations (r) between Arel and number of subunits are 0.115 for cyclics (p = 0.0002), 0.056 for dihedrals (p = 0.03), and 0.092 for asymmetrics (p = 0.07). Thus, there appears to be a very slight but significant tendency for larger homomers to have more flexible subunits. Error bars represent SEM.Subunit flexibility is largely independent of the number of subunits in a homomeric complex. Comparison of subunit flexibility, as measured by (TIFF)Click here for additional data file.Figure S5Interface size is related to symmetry but does not explain the observed flexibility trends. Comparison of interface sizes for homomeric subunits in different symmetry groups: (A) mean interface area per subunit; (B) mean relative interface area per subunit . Error bars represent SEM. The trends here show essentially no correspondence with the flexibility results in (TIFF)Click here for additional data file.Figure S6Arel values for human (or closely related) subunits whose most ancient orthologs are of varying evolutionary ages. Error bars represent SEM. There is no clear tendency for newer subunits to be more flexible , suggesting that our results cannot be explained by a general tendency for newer proteins to be more flexible. Full species names and the different evolutionary groups are provided in Arel and plotted the mean sequence identities of Ensembl Compara orthologs from different species. This shows that, for the most part, sequence conservation is fairly constant with respect to Arel, although there is some tendency for the most flexible human subunits to be less conserved, particularly when compared to yeast.The observation that evolutionarily more recent subunits are more flexible does not arise from a general tendency for increased flexibility in newer proteins. Although we observed a strong trend for the evolutionarily more recent subunits of protein complexes to be more flexible, it is possible that this could to some extent reflect a general tendency for evolutionarily more recent proteins to be more flexible. This could also arise if more flexible proteins tend to evolve at a faster rate, thus making them less likely to be detected as orthologs. We have addressed this in two ways: (A) comparison of (TIFF)Click here for additional data file.Figure S7Arel and the number of nonhomologous subunits in three ways . (C) The yellow bars represent the “intrafamily normalized” Arel values, in which all variation should be due to differences between different types of domains. In these, the Arel value of each subunit has been replaced with the mean Arel value for all subunits with the same domain architecture. Thus we can see that nearly all of the trend in (A) can be explained by differences between evolutionarily unrelated proteins, strongly suggesting that the scheme in The correspondence between subunit flexibility and the number of nonhomologous subunits per complex is not due to existing subunits evolving to become more flexible. The correspondence between subunit flexibility and the number of nonhomologous subunits per complex could possibly be explained if the existing  subunits of a complex can evolve to become more flexible as new, more rigid subunits are added. To test this, we grouped subunits by their SUPERFAMILY domain architecture. We considered only those groups where evolutionarily related proteins participate in different complexes that have different numbers of nonhomologous subunits. We then plot the relationship between (TIFF)Click here for additional data file.Figure S8M. musculus and T. maritima appear to deviate, although this is likely due to the limited size of the dataset, including the fact that no complexes with >3 nonhomologous subunits are present for these species.The association between flexibility and the number of nonhomologous subunits per complex is preserved across different species. This plot is essentially the same as (TIFF)Click here for additional data file.Figure S9Increasing intrinsic disorder is associated with a greater number of interaction partners across different species. Comparison of the percentage of residues predicted to be intrinsically disordered for proteins grouped by their number of experimentally identified interaction partners. Experimental protein–protein interactions were taken from STRING v9.0 (TIFF)Click here for additional data file.Table S1Controlling for structural factors when comparing the flexibilities of subunits from different groups of protein complexes. This table provides the raw values for the main results in (XLSX)Click here for additional data file.Table S2Pairwise flexibility comparison between putative older and putative newer subunits of protein complexes with respect to all species used in this analysis. These values are the same as used in (XLSX)Click here for additional data file.Table S3Comparison of the mean subunit flexibility and number of nonhomologous subunits per complex from different species. These are the same values used in (XLSX)Click here for additional data file.Table S4Homomeric and heteromeric protein complexes used in this study.(XLSX)Click here for additional data file.Table S5Properties of protein complex subunits.(XLSX)Click here for additional data file.Table S6Putative older and newer subunits identified from each species, along with the combined set of nonredundant complexes that have both older and newer subunits. Also included here are the results of the analyses including only full-length or nearly full-length PDB chains, and only complexes in which the newer subunits are longer than the older subunits. The highest sequence identity between a human gene and its ortholog in Ensembl Compara is provided for each older subunit.(XLSX)Click here for additional data file.Table S7Arel, interfamily normalized Arel, and intrafamily normalized Arel values for subunits from different domain families. These are the values used for the analysis in (XLSX)Click here for additional data file.


Identification of protein complexes can help us get a better understanding of cellular mechanism. With the increasing availability of large-scale protein-protein interaction (PPI) data, numerous computational approaches have been proposed to detect complexes from the PPI networks. However, most of the current approaches do not consider overlaps among complexes or functional annotation information of individual proteins. Therefore, they might not be able to reflect the biological reality faithfully or make full use of the available domain-specific knowledge.In this paper, we develop a Generative Model with Functional and Topological Properties (GMFTP) to describe the generative processes of the PPI network and the functional profile. The model provides a working mechanism for capturing the interaction structures and the functional patterns of proteins. By combining the functional and topological properties, we formulate the problem of identifying protein complexes as that of detecting a group of proteins which frequently interact with each other in the PPI network and have similar annotation patterns in the functional profile. Using the idea of link communities, our method naturally deals with overlaps among complexes. The benefits brought by the functional properties are demonstrated by real data analysis. The results evaluated using four criteria with respect to two gold standards show that GMFTP has a competitive performance over the state-of-the-art approaches. The effectiveness of detecting overlapping complexes is also demonstrated by analyzing the topological and functional features of multi- and mono-group proteins.http://mail.sysu.edu.cn/home/stsddq@mail.sysu.edu.cn/dai/others/GMFTP.zip.Based on the results obtained in this study, GMFTP presents to be a powerful approach for the identification of overlapping protein complexes using both the PPI network and the functional profile. The software can be downloaded from Detecting protein complexes, which is crucial for elucidating the structural and functional architecture of cells, has attracted a lot of attention in recent years. Well-known experimental methods such as tandem affinity purification with mass spectrometry and protDue to the development of high-throughput techniques, a large number of physical protein-protein interactions (PPI) have been generated and accumulated, which paves the way for establishing or reconstructing the PPI networks,5. Two pProteins are often involved in more than one complex to serve different functions,18; for In order to reduce the negative effect brought by the spurious interactions, several researchers have tried to incorporate functional information into complex detection process. These approaches can be mainly classified into two categories, preprocess-based-25 and pAs an alternative, we couple the functional profile with the network topology to detect overlapping protein complexes. To this end, we resort to probabilistic models which have been applied to analyze PPI networks,21,35-37N proteins. Each protein i has an annotation profile of fixed length C, Fi = T ∈ {0,1}C, where Fic = 1 if protein i is associated with function c, Fic = 0 otherwise, and C is the total number of functions considered. For convenience, we denote F = T = [Fic] ∈ {0,1}N×C as the functional profiles for all proteins. The PPI network is represented as an adjacency matrix A = [Aij] ∈ {0,1}N×N, where Aij = 1 if proteins i and j are connected, Aij = 0 otherwise. We assume that there are K complexes. In the typical model-based clustering setting, the value of K is initially unknown and needs to be predetermined. Here we assume that the value is given first and address how to set it at the end of this section.Before introducing our model, we introduce some notations first. We consider the functional and topological properties of Fic and the interaction Aij as follows. In a similar manner to that of as the protein-complex affinity matrix and Ψ = [ψkc] as the complex-function preference matrix.GMFTP generates both the annotation  that of,39, a nounctions, for a gθik and ψkc, if protein i obtains higher affinity score θik and complex k obtains higher preference score ψkc, protein i is more likely to be associated with function c, and vice versa. Then θikψkc can be assumed as the likelihood that protein i is associated with function c in terms of complex k. Taking into account all the K complexes, we can assumei is associated with function c. Then the association Fic between protein i and function c is independently generated by a Bernoulli distribution with success rateσ(x) = 1 - exp(-x) is a function which maps the input argument from  and optimal PR scores when λ∈  for both the two gold standards  and the two gold standards (CYC2008 and SGD). For GMFTP, we concentrate on the results of two cases . For ClusterONE, we use the results of the two versions (weighted and unweighted) of networks. A protein is regarded as a multi-group protein if it belongs to more than one predicted (or reference) complex, and it is a mono-group protein if it belongs only to one predicted (or reference) complex. Overall, the multi-group proteins recovered by our model significantly  overlap with those of the other approaches and the gold standards ) times and update of Ψ takes O(NKC) times. Therefore, the total time cost of GMFTP is O(KNT(N + C)), where T is the number of iterations. Given that the real-world PPI networks and functional profiles are extremely sparse, the overall cost can be reduced to O(KT(N + C + E + R)), where E is the number of interactions and R is the number of functional associations  and (12). Each update of Θ takes 1 and .Click here for fileFunctional enrichment of the detected protein complexes. We provide the functional enrichment analysis results of the complexes predicted by GMFTP, Linkcomm and SR-MCL using only the PPI network with respect to the three individual subontology  in this section.Click here for fileSupplementary tables for the analysis of multifunctional proteins detection. This file include supplementary tables which describe the general properties of multi-group proteins detected by various approaches, the statistical results of the complexes predicted by GMFTP using the PPI network and the total GO annotation, and P-value of Wilcoxon test of populations of topological and functional features of mono- and multi-grouped proteins detected by GMFTP.Click here for file