Proteins adapt to environmental conditions by changing their shape and motions. Characterising protein conformational dynamics is increasingly recognised as necessary to understand how proteins function. Given a conformational ensemble, computational tools are needed to extract in a systematic way pertinent and comprehensive biological information.communication blocks and communicating segment pairs, and evaluates the connections and communication strengths between them. We show the utility and capabilities of COMMA by applying it to three archetypal proteins, namely protein A, the tyrosine kinase KIT and the tumour suppressor p53.Here, we present a method, Communication Mapping (COMMA), to decipher the dynamical architecture of a protein. The method first extracts residue-based dynamic properties from all-atom molecular dynamics simulations. Then, it integrates them in a graph theoretic framework, where it identifies groups of residues or protein regions that mediate short- and long-range communication. COMMA introduces original concepts to contrast the different roles played by these regions, namely www.lcqb.upmc.fr/COMMA.Our method permits to compare in a direct way the dynamical behaviour either of proteins with different characteristics or of the same protein in different conditions. It is useful to identify residues playing a key role in protein allosteric regulation and to explain the effects of deleterious mutations in a mechanistic way. COMMA is a fully automated tool with broad applicability. It is freely available to the community at The online version of this article (doi:10.1186/s12859-015-0855-y) contains supplementary material, which is available to authorized users. Protein conformational dynamics are directly linked to protein functions , 2. TheyThe representation of a protein as a graph unravels more easily and readily its properties at the atomic or residue level. Typically, each node of the graph represents one residue of the protein and the edges represent non-covalent interactions that stabilise the protein three-dimensional structure , 9. Infoin silico mutagenesis [Communication between residues results in allosteric coupling, i.e. the propagation of a perturbation signal between distinct sites, possibly located far away in the sequence and structure of the protein, that modulates the function of the protein. Experimental evidence have demonstrated that protein residues communicate either through stable non-covalent interactions  or via cagenesis . MONETA The present work builds up on these previous efforts to propose a systematic dissection of protein architectures from a dynamical perspective. We provide Communication Mapping (COMMA), a method for analysing molecular dynamics-based communication in proteins and for mapping this information onto protein three-dimensional structures. COMMA introduces new measures and new algorithms, with respect to MONETA, to dissect a protein’s architecture building blocks. It integrates different types of structural and dynamical information in a unified graph representing the protein. It detects communication blocks and communicating segments pairs from this graph, which are new concepts representing groups of residues or protein regions that mediate short- and long-range communication. COMMA allows to compare in a very straightforward way the conformational dynamics of different proteins or different states of the same protein. It provides mechanistic insights on the effects of deleterious mutations on the stability and internal dynamics of proteins by pinpointing residues playing key roles in the propagation of these effects. COMMA is fully automated and is intended for large-scale application. It only requires an ensemble of protein conformations as input. Importantly, we have implemented an automated procedure to set all parameters depending on the properties of the protein analysed. Here, we have applied COMMA on three case studies to illustrate its capabilities.a. It analyses the conformational ensemble and extracts five residue-based dynamic properties: local dynamical correlations, minimum distances, communication propensities, non-covalent interaction strengths and secondary structures (box 1).b. These properties are used to group residues into (i) independent cliques and (ii) communication pathways (boxes 2–3). Independent cliques are clusters of residues that display concerted atomic fluctuations while communication pathways are non-covalent chains of residues that move together (see below).c. The information obtained from the independent cliques and the communication pathways is integrated in a graph, called Protein Communication Network (PCN) (box 4).d. Connected components are extracted from this graph to define protein communication blocks (box 5).e. The communication pathways that link different secondary structure elements are used to define communicating segment pairs and measure the strength of the interaction (box 6).The workflow of the COMMA method is depicted on Fig. COMMA allows to visualise communication blocks and communicating segment pairs by mapping them onto the protein average conformation.COMMA defines several measures that reflect the dynamic properties of the query protein. These measures are computed from each input set of conformations. Four measures are defined for pairs of residues and provide 4 distinct matrices. A fifth measure, which is new compared to MONETA, evaluates the likeliness of a residue to belong to a secondary structure.Local dynamical correlations Principal Component Analysis (PCA) is used to describe the atomic fluctuations of a protein through eigenvectors or modes. These modes are linear combinations of degrees of freedom. Starting from n PCA modes, describing the protein’s essential dynamics , we apply a statistical technique called Local Feature Analysis (LFA) [CorrLFA between residues i an j as: is (LFA) . LFA comd is the -coordinate index of each C α atom in a residue and Ψr is the PCA rth eigenvector. The CorrLFA matrix is characterised by sparse correlation patterns  belonging to residues i and j respectively, averaged over the set of conformations.Communication propensities We evaluate the communication propensity CP of residues i and j as the variance of the inter-residue distance [distance : (2)\dodij is the distance between the C α atoms of residues i and j and CP are indicative of efficient signal transmission between residues i and j.where Non-covalent interaction strengths We consider as non-covalent interactions hydrogen(H)-bonds and hydrophobic contacts, detected using the HBPLUS algorithm [i) maximum distances of 3.9Å for D-A and 2.5Å for H-A, (ii) minimum value of 90° for D-H-A, H-A-AA and D-A-AA angles, where AA is the acceptor antecedent. Hydrophobic contacts are identified with an inter-atomic distance lower than 3.9Å. The detected non-covalent interactions are then classified as backbone-backbone, backbone-side chain and side chain-side chain. For a given interaction type, an interaction strength matrix INT is computed, where each entry  describes the percentage of conformations in which at least one non-covalent interaction is formed between some pair of atoms  in residues i and j.lgorithm . H-bondsSecondary structures Secondary structures are defined from the backbone torsion angles of the protein by using the DSSP algorithm [pα, pβ and pturn are computed for each residue. They reflect the percentage of conformations in which the residue is in a α-helix, a β-sheet or a turn, respectively. The secondary structure type that has the highest persistence value is assigned to the residue.lgorithm . Three pBy combining the measures described above, COMMA identifies groups of residues that mediate communication across the protein structure, namely independent cliques and communications pathways. The computation is performed on each input set of conformations. These components are similar to the independent dynamic segments and communication pathways identified by MONETA. What is new in COMMA is the automated set up of pertinent values for the parameters depending on the system studied see .CorrLFA matrix is characterised by dense correlation patterns around every seed identified by LFA analysis. COMMA defines independent cliques as protein regions that correspond to these patterns. Each seed is extended into an independent clique S of residues by means of an extension algorithm that progressively adds residues in such a way that: (i) have a minimum distance smaller than 3.7Å and (ii) display concerted atomic fluctuations, indicated by high local dynamical correlations, that is the mean correlation value computed over S must be higher than a threshold [It can happen that two seeds detected by LFA are very close in the sequence (distant by less than 6 residues). In that case, only the seed with the highest fluctuations is retained. The hreshold : (3)\doS is made by the starting seed. We obtain k≤n independent cliques, where n is the initial number of seeds. Notice that the algorithm identifying the independent cliques uses information coming from the local dynamical correlation and the minimum distance matrices.The set up of elow see . The exti and j are considered to communicate efficiently if their communication propensity is below a threshold, CP≤CPcut. They form stable non-covalent interaction(s) if their interaction strength is higher than a threshold, INT≥INTcut. The set up of the parameters CPcut and INTcut is explained below (see i) are not adjacent in the sequence, (ii) form stable non-covalent interaction(s) and (iii) communicate efficiently. We ask that all residues in a path communicate efficiently with each other by transitivity. Notice that the algorithm identifying the pathway-based edges uses the communication propensity and the interaction strength matrices, and also the secondary structure information, that plays a role for the set up of CPcut  is a coloured graph defined by nodes N that correspond to the residues of the protein and edges E that connect dynamically correlated residues. Two types of edges are constructed: Clique-based edges: two vertices representing residues i and j are connected by a clique-based edge if they belong to the same independent clique and if Pathway-based edges: two vertices representing residues i and j are connected by a pathway-based edge if they are consecutive in some communication pathway.Independent cliques and communication pathways are used to construct a Protein Communication Network (PCN) that reflects the way information is transmitted across the protein 3D structure. A PCN communication blocks defined from the PCN, (ii) communicating segment pairs defined from secondary structure elements and communication pathways. These two notions are completely new compared to MONETA.COMMA final outputs consist in dynamics-based decompositions of the query protein 3D structure. Two types of decompositions are produced. The protein is divided into: (MPLcut as defined below (see Connected components in an undirected graph are isolated subgraphs. COMMA extracts connected components from the constructed PCN by using depth-first search (DFS) and defines protein communication blocks. Different types of communication blocks are defined, namely clique-based blocks and pathway-based blocks. Clique-based blocks are directly extracted by considering all clique-based edges. Different kinds of pathway-based blocks are defined, either by considering all but very short (≤3 residues) pathways, or by considering pathways longer than a fixed number of residues. An interesting threshold is given by elow see .A,B) of SSEs: (i) the proportion PRAB (resp. PRBA) of residues from A (resp. B) that are linked by at least a communication path to some residue from B (resp. A), (ii) the number of pairs of residues  of A and B that are consecutive in a communication path, ContAB. The residues of A and B that are linked by at least a communication path constitute a communicating segment pair. The communication strength between the two segments defined from A and B is calculated as: COMMA detects pairs of protein segments that are part of secondary structure elements (SSEs) and that are linked by communication pathways. A SSE is constituted by residues (at least three) that adopt the same secondary structure type. First the algorithm identifies all SSEs contained in the protein structure. Then, it computes, for each pair  as: red from . IntuitiN is the total number of residues. CPcut is chosen such that the proportion pss of MCP values are lower than CPcut <CPcut are considered to communicate efficiently.where cut Fig. b. Any twINTcut We define a threshold value INTcut to filter out non-covalent interactions that are not relevant. For this, an adjacency graph is constructed from the INT matrix by considering different cutoff values, ranging from 0.25 to 1, by increments of 0.05, and the size of the largest connected component is computed  the B domain of staphylococcal protein A , a highly stable protein, (ii) the DNA-binding domain of the human tumour suppressor protein p53 , a highly flexible protein, (iii) the cytoplasmic region of the receptor tyrosine kinase KIT , an allosterically regulated protein.We applied the COMMA method to three archetypal proteins:  in position 816 into a valine (V) using MODELLER 9v7 [i) hydrogen atoms were added, (ii) Na + or Cl − counter-ions were added to neutralise the systems charge, (iii) the solute was hydrated with a cuboid box of explicit TIP3P water molecules with a buffering distance up to 10Å. The environment of the histidines was manually checked and they were consequently protonated with a hydrogen at the ε nitrogen. The details of structure preparation and solvent models are given in Additional file The 3D coordinates for the studied proteins were retrieved from the Protein Data Bank (PDB) . All cryd stable , 36. ConLLER 9v7 . All modLLER 9v7 , with thi) 10,000 steps of minimisation of the water molecules keeping protein atoms fixed, (ii) 10,000 steps of minimisation keeping only protein backbone fixed to allow protein side chains to relax, (iii) 10,000 steps of minimisation without any constraint on the system. Heating of the system to the target temperature of 310 K was performed at constant volume using the Berendsen thermostat [Cα atoms with a force constant of 10 kcal/mol/Å2. Thereafter, the system was equilibrated for 100 ps at constant volume (NVT) and for further 100 ps using a Langevin piston (NPT) [The systems were minimised, thermalised and equilibrated using the SANDER module of AMBER 12. The following minimisation procedure was applied: (ermostat  and whilon (NPT)  to maintptraj module of AMBER 12.For every protein, 2 replicates of 50 ns, with different initial velocities, were performed in the NPT ensemble using the PMEMD module of AMBER 12. The temperature was kept at 310 K and pressure at 1 bar using the Langevin piston coupling algorithm. The SHAKE algorithm was used to freeze bonds involving hydrogen atoms, allowing for an integration time step of 2.0 fs. The Particle Mesh Ewald method (PME)  was emplα atoms root mean square deviation (RMSD) from the equilibrated structure, the stability of secondary structures and the radius of gyration were recorded along each 50-ns MD simulation replicate  a set of reference conformations are identified, (ii) all MD conformations from the trajectory are clustered into corresponding reference groups. Each reference conformation is first picked up randomly and the conformations distant by less than an arbitrary cutoff r are binned with it. Then the trajectory is split in two halves and conformations from each half are grouped based on their RMSD from each reference conformation. If the simulation has converged, then each reference cluster should be populated equally from both halves of the trajectory.To evaluate the convergence of the dynamic properties extracted by COMMA, a convergence analysis  was applα atoms and the cutoff r was empirically chosen so as to get a reasonable number of representative MD conformations, typically between 2 and 7. To reduce the bias resulting from the random choices of the references, the process was repeated 5 times for each analyzed trajectory. The convergence quality of each simulation was measured using a convergence criterion c defined as [The RMSD was computed on the C fined as : (6)\doc is comprised between 0 and 1; a value of 1 corresponds to an optimal convergence. All trajectories show good to very good convergence, with values of c ranging between 0.6 and 0.9 . The convergence criterion KIT is a receptor tyrosine kinase of type III implicated in signalling pathways crucial for cell growth, differentiation and survival –46. The 33KIT communication blocks can be classified according to the structural and dynamical information used to identify them. In the wild type Fig. 3. These ba) inclusion, e.g. block H is included in block I, (b) overlap, e.g. blocks D and I share some residues in common, (c) contact, e.g. some residues from blocks B and I are adjacent in the sequence, (d) interaction, e.g. some residues in blocks A and C form a stable H-bond or hydrophobic contact. We observed that two blocks that share residues or contact each other  are also connected by non-covalent interactions (type d).Different types of connections are established between blocks Fig. a–b, nameThe architecture of KIT is composed of a core of long-range communicating residues forming block H, that represents more than one third of the protein Table . This co3The communication blocks identified by COMMA in wild-type and mutated KIT were compared. The pairs of blocks from the two proteins that are constituted in large part by the same residues were identified Table . Overall33The definition of KIT communication blocks provided by COMMA can be compared with the definition of KIT regulatory regions reported in the literature –54. BlocCOMMA results were compared to those obtained with MONETA 2.0 Fig. c. MONETA33MONETA previously permitted to put in evidence a crucial communication pathway in wild-type KIT that links the A-loop and the JMS through residue D792 from the catalytic loop . The patThis analysis illustrates how COMMA can help dissect a protein 3D structure from a dynamical perspective and characterise the effect of a deleterious mutation on the structural dynamics of a protein. The information provided by COMMA was found in agreement with the previous findings on KIT allosteric communication. It further allows a more systematic assessment of the differences between two proteins or two states of the same protein and permits to pinpoint with high precision regions or residues instrumental in the establishment or alteration of the protein communication.Staphylococcus aureus is a small α-helical protein. It comprises 60 residues arranged in three helices, namely H1 (residues 10-19), H2 (residues 25-37) and H3 (residues 42-56), linked by two turns, namely T1 (residues 20-24) and T2 (residues 38-41). The fast-folding kinetics of protein A have been extensively characterised through experiments and computer simulations [i) the isolated H3 has a higher stability and helical content compared to the two other helices, (ii) H2 and H3 form a stable or marginally stable intermediate, (iii) H1 is docked in the rate limiting step.The B domain of protein A (BdpA) from α-helices formed by residues 5-18, 25-37 and 39-55 and two turns formed by residues 2-4 and 56-59. We focus here on the three α-helices, which match well the experimentally-defined helices H1, H2 and H3. Three pairs of communicating segments were identified between H1/H2, H1/H3 and H2/H3 . For this, we performed 2 replicates of 50-ns MD simulations, starting from an average nuclear magnetic resonance (NMR) structure see . By anal/H3 Fig.  for thes/H3 Fig. . Moreoveβ-sheets, characteristic of the immunoglobulin-like β-sandwich fold  and also cross them. The edges linking different β-strands reflect well the interactions that stabilise the two β-sheets of the protein. Filtering out pathways smaller than 6 residues yields a communication block twice as small  communication pathways identified in p53 yielded one communication block Fig. b, in redues commuβ-sheets) are stabilised by H-bonds formed between backbone atoms . We analysed the impact of disregarding information from these interactions on p53 DBD communication. Only interactions involving side chain atoms were retained to construct communication pathways and the corresponding communication blocks were extracted  of the p53 DBD in the protein’s dynamical architecture. Specifically, the residues constituting the first β-sheet communicate at shorter range than those constituting the second β-sheet. Furthermore, it showed the preponderant role of backbone-backbone interactions in establishing communication between the two β-sheets. These results illustrate how COMMA can be employed to contrast different protein regions from a dynamical point of view and to investigate the molecular determinants of protein communication at a precise level.This analysis unveiled the hierarchical roles played by the different structural units : Platform independentProgramming language(s): C++ and Python 2.7External tools: PyMol Other requirements: MDTraj, Eigen and Numpy python packagesAdditional file 1Supplementary Materials.


Thus, we hypothesize that analysis of the dynamics of the complex can reveal details about the nature of the interaction, and in particular whether it is obligatory, i.e., persists throughout the entire lifetime of the proteins, or not. Indeed, normal mode analysis, using the Gaussian network model, shows that for the most part obligatory and non-obligatory complexes differ in their decomposition into dynamic domains, i.e., the mobile elements of the protein complex. The dynamic domains of obligatory complexes often mix segments from the interacting chains, and the hinges between them do not overlap with the interface between the chains. In contrast, in non-obligatory complexes the interface often hinges between dynamic domains, held together through few anchor residues on one side of the interface that interact with their counterpart grooves in the other end. In automatic analysis, 117 of 139 obligatory (84.2%) and 203 of 246 non-obligatory (82.5%) complexes are correctly classified by our method: DynaFace. We further use DynaFace to predict obligatory and non-obligatory interactions among a set of 300 putative protein complexes. DynaFace is available at:  http://safir.prc.boun.edu.tr/dynaface.Protein-protein interactions mediate, in essence, all inter- and intra-cellular processes. Thus, understanding their molecular mechanism is of utmost importance. Here we focus on one mechanistic aspect: differentiation between obligatory interactions, which persist throughout the entire lifetime of the protein complex, and non-obligatory, which do not. For proper function, a protein complex should facilitate transduction between the interacting proteins. Therefore the complex’s dynamics should reveal whether it is obligatory or non-obligatory. Indeed, normal mode analysis shows that the dynamic domains of obligatory complexes often mix segments from the interacting chains. In contrast, in non-obligatory complexes the inter-chain interface often hinges between dynamic domains, held together through few anchor residues. An automated methodology based on these observations correctly classifies over 80% of the interfaces in a test set. We use it also to predict obligatory and non-obligatory interactions among putative protein complexes. DynaFace, a web-server implementation of the methodology, is available at:  PLOS Computational Biology Methods paper”“This is a Inter-protein interactions mediate a wide range of cellular and biochemical processes , 2. The Protein chains engaged in an obligatory complex are found only in association with their partner chains and bind throughout their functional lifetime, for example, because they are unstable on their own . The mosNon-obligatory interactions could be further classified as transient or permanent, depending on their lifetimes, providing a kinetic dimension of the association. In addition, changes in pH, ionic strength or concentrations of the interacting chains can shift the dynamic equilibrium towards or away from association. Thus, transient complexes can be further classified as “weak” or “strong”, which are generally found in their unbound and bound states, respectively .Obligatory interfaces compared to non-obligatory interfaces are larger, flatter and more evolutionarily conserved , 15. ResPrevious studies have focused mostly in the analysis and prediction of protein surfaces that mediate protein-protein interactions rather than in differentiation between the interaction types. Evolutionary conservation profiles of the amino acid positions have appeared as a valuable source of information for the success of sequence-based methods in binding site predictions –20. StruThe prediction of protein interaction sites and/or interaction type based on a single property is challenging . The exiThe NOXclass web-server  is uniquTo facilitate biological functionality, protein-protein association should involve transduction, e.g., of signal. Thus, it is anticipated that protein complexes that differ in their functionalities would manifest different dynamic behavior. In other words, the dynamic infrastructure underlying non-obligatory and obligatory interactions should be in compliance with the required functional motion of the respective interface types. Indeed, a recent analysis of proteins with multiple conformations in PDB showed that, on average, transient associations involve smaller conformational changes than permanent associations . Here agThe dataset used here is a compilation of two available datasets of protein-protein interface types , 31. BotAdditionally, a dataset of predicted structural models  is used c), are assumed to be connected by harmonic springs. The potential function for a protein structure of N residues in the elastic network description is given asΔR refers to the fluctuation R vector of each residue at its alpha carbon position. Γ is the Kirchhoff matrix defined asij is the distance between alpha carbon atoms i and j. rc is taken as 7 Å. The diagonal Lambda_ii term is the degree of a node and a measure of the local packing density around a given residue.GNM , 45 is tΔRi and ΔRj, of residues i and j forms the covariance matrix given asU is an orthogonal matrix whose columns ui are the eigenvectors of the Kirchhoff matrix and Λ is a diagonal matrix whose elements λi represent the eigenvalues, kB is the Boltzmann constant, and T is the absolute temperature in The correlation between equilibrium position fluctuations, The covariance matrix  of a modThe conformational transitions of proteins are intimately related to their function. With the premise of the link between the dynamic infrastructure provided by the interacting chains and the interface type, we propose a dynamic measure based on GNM through the analysis of obligatory versus non-obligatory complex structures in the dataset: The dynamics are often dominated by the two slowest modes, yet further refined by the next slow modes; dynamic domains capture the global connectivity similarity for obligatory versus non-obligatory complex structures.Given a protein complex structure, the DynaFace web-server builds the covariance matrix and uses it to classify the inter-protein interfaces as obligatory vs. non-obligatory. Various combinations of different sets of modes are used in order to best exploit the dynamic correlation patterns. For the most part, the two slowest modes capture the pattern of the dynamic domains with respect to the interaction type. The ten slowest modes contribution is significant, but higher modes lead to fine-tuning. As a result, DynaFace uses the ten slowest modes  to increase the prediction accuracy. The addition of the all-modes terms increase the performance in comparison to using only the slowest 10 modes, as well as in comparison to using the 10 slowest plus modes 11 and higher.subunit refers to the interacting partner unit; a single chain or more than one chain. A decision outcome D is calculated with respect to an obligatory interaction as followsobligatory; otherwise it is non-obligatory. The threshold value for each attribute is given in The underlying scoring function of DynaFace basically reflects the pattern of the cross-correlation map. The following seven attributes are calculated: The average of all (A), negative (N) and positive (P) correlations between residue fluctuations of two interacting subunits in the average ten slowest (s) and all (a) modes of motion, and the number of associating regions between two subunits in all modes (AR), defined in the following paragraph. For each attribute, a knowledge-based decision parameter (threshold) is set based on the corresponding correlation values of 139 obligatory and 246 non-obligatory complex structures in the whole dataset; see Associating regions  in all modes with the anchor residue across the interface.The PDB includes many interfaces, some obligatory, some non-obligatory, and some artifacts due to crystal-packing. There are methods to filter out crystal-packing interfaces, which work at least to some extent. We offer a complementary method that can differentiate between obligatory and non-obligatory interfaces based on the dynamics of the structural complex. For completeness, and because some crystal-packing artifacts might ‘sneak in’, we also check whether they confuse our method. The results show that DynaFace might mistakenly assign crystal-packing artifacts to be non-obligatory interfaces. We also use our methodology on a dataset of predicted complex structures as well as their corresponding template structures  to suggeEquilibrium residue fluctuations define dynamic domains, where the cooperative modes of motion are the main determinants. The collective dynamics facilitates biological function(s), and correlated motions within the structure could reveal functional predispositions. Thus, the expression of the dynamic couplings across the inter-subunit interface could reveal whether the complex is obligatory or non-obligatory. To examine this we start with two exemplary dimeric complexes. The first is the obligatory homodimer 1QU7 (Cytoplasmic domain of a serine chemotaxis receptor) , and theCross-correlations of residue fluctuations in the two slowest modes for these dimers are presented in The cytoplasmic domain of the serine chemotaxis receptor of Escherichia coli, is a homodimeric complex with two protein chains of 227 amino acids, marked as A and B . Each suAccording to the slowest mode, the dimeric structure has four hinges, two in each subunit; A: HIS328/LEU329, A: THR450/ARG451, B: HIS328/LEU329, and B: ARG451/VAL452 . The hinThe complex between subtilisin BPN'  with its streptomyces inhibitor  is an example for non-obligatory interaction . The slThe second slowest mode with hinge sites at SER24/ASN25, VAL150/ALA151 of the enzyme (chain E) and HIS43/PRO44, PRO72/MET73, ASN99/GLU100 of the inhibitor (chain I) also separates the structure into two dynamic domains . This moa, Na, As) suggest that this protein is non-obligatory, as it really is.The two slowest modes dominate the average dynamic behavior of all modes , which dThe examples above suggest that it could be possible to discriminate between obligatory (1QU7) and non-obligatory (2SIC) interactions based on their dynamic footprints . To examThe results, summarized in As a reference we examine the success rates of NOXclass  on the sA similar comparison has been made with PiType. PiType, which uses Uniprot IDs, makes use of already existing interaction networks in its database, and most of the cases in our dataset are not included in their database. We thus made a partial comparison on 62 proteins  that exist both in our dataset and PiType’s. DynaFace successfully reproduced annotation of 85.4% (82.3% of obligatory and 86.7% of non-obligatory complexes) of the complexes, while PiType reproduced only 80.6% (70.6% of obligatory and 84.4% of non-obligatory complexes) of these complexes, respectively.Due to the close packing of protein molecules in crystals, some interactions observed in X-ray crystal structures are non-biological. PDB entries are thus known to contain artifacts of crystallization that do not have any biological relevance . It is o2 or more, where the monomeric state was confirmed by biochemical or biophysical studies [Because a reliable gold-standard of non-biological interfaces is not available, we compiled one based on previous studies. The starting point was a dataset of 63 large crystal packing interfaces with buried surface area of 800 Å studies . DynaFac studies , PITA [5 studies , PISA [5 studies  and PInS studies  and NOXc studies . The DIM studies  was exclTo be on the safe side, we filtered the dataset and applied a particularly restrictive criterion, keeping only 13 crystal-packing interfaces with consensus prediction  and we attribute it to parametric error in the twilight zone between obligatory and non-obligatory interfaces.The NMR structure of the transforming growth factor beta 1  was useAs a sample case, the underlying dynamics for the structural model P09529_P01137 is presented in Protein-protein complexes are involved, in essence, in all intra- and inter-cellular processes, and the nature of the inter-protein interface determines mechanistic aspects of the interaction. The interfaces have been evolutionarily-designed to enable transduction between the proteins, suggesting that analysis of the dynamics of the complex can reveal the nature of the interaction. The global dynamics of protein complexes should be particularly crucial for function. Following this hypothesis, DynaFace exploits the dynamics of protein complexes in order to detect obligatory vs. non-obligatory interactions among the subunits. The global perspective of interactions across the subunits interface is described mainly by the dynamics of the structural complex, which is not easily accessible by studying only local sequence and structural properties. The dynamic domains, the motions of sub-structural units and how they cooperate with respect to the interacting chains, i.e. the dynamic infrastructure provided by the interacting chains, overall captures the global connectivity similarity for the obligatory and non-obligatory complex structures. To this end, the dynamics of protein complexes in terms of the motions of their dynamic domains and how they are dynamically coupled for their function is of importance for design and function modification of proteins. It is important to note that overall, the interaction spectrum is continuous and does not readily land itself to any discrete classifications. Thus, it is not surprising that DynaFace predictions are imperfect.DynaFace could readily be embedded in other tools for predicting interface type. Such tools often use local characteristics, which are complementary to the global features used in DynaFace. Indeed, an approach that combines both local and global features would provide a means to discover further and more novel aspects of biological processes.S1 FigThe results obtained using the dataset of (TIF)Click here for additional data file.S2 FigΔRi ⋅ ΔRj〉 for the average over the two slowest modes (A), the ten slow modes (B), and all modes (C) of motion for an obligatory interface, 1QU7 [The upper panels show the correlations 〈ce, 1QU7 . The lowce, 1QU7 .(TIF)Click here for additional data file.S3 FigThe anchor residues MAT70-VAL74 on the inhibitor (chain I) are shown as solid spheres, and the groove residues GLY100, SER125, THR220 on the enzyme (chain E) are shown as doted spheres. The figure was produced using PyMOL .(TIF)Click here for additional data file.S4 FigRi ⋅ ΔRj〉, of mode 1 (A) and mode 2 (B) color-coded for negative (blue) and positive (red) correlations. The boundaries between the subunits are marked in white. The bottom panels show projection of the correlations on the 3D-structure: C- mode 1 and D- mode 2. The subunits are shown on the PyMOL [The top panels show the matrices of correlations between residue fluctuations, 〈Δhe PyMOL  figures (TIF)Click here for additional data file.S1 TableThe original references as well as the references used to update the interaction type are given. DynaFace predictions are also included.(DOCX)Click here for additional data file.S2 Table“Model ID”–the PrePPI index number of the predicted dimer model. “Probability”- the PrePPI prediction likelihood: high, low, very low. “Template ID”–the PDB accession number of the template PrePPI used to model the dimer. “Template Chain IDs”–the chains PrePPI used to model the structure. “Model”–DynaFace prediction of the interface type of the model structure: obligatory vs. non-obligatory. “Template”–DynaFace prediction of the interface of the template.(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 TableThe values of the attributes for two examples are presented, where attributes that exceed the threshold are highlighted in bold. PDB IDs 1QU7 and 2SIC are predicted to be obligatory and non-obligatory, respectively, as they should.(DOCX)Click here for additional data file.S5 Table(DOCX)Click here for additional data file.S6 Table(DOCX)Click here for additional data file.S7 Table(DOCX)Click here for additional data file.S8 Table(DOCX)Click here for additional data file.S1 Method(DOCX)Click here for additional data file.


Protein complexes are major forms of protein-protein interactions and implement essential biological functions. The subunit interface in a protein complex is related to its thermostability. Though the roles of interface properties in thermal adaptation have been investigated for protein complexes, the relationship between the interface size and the expression level of the subunits remains unknown. In the present work, we studied this relationship and found a positive correlation in thermophiles rather than mesophiles. Moreover, we found that the protein interaction strength in complexes is not only temperature-dependent but also abundance-dependent. The underlying mechanism for the observed correlation was explored by simulating the evolution of protein interface stability, which highlights the avoidance of misinteraction. Our findings make more complete the picture of the mechanisms for protein complex thermal adaptation and provide new insights into the principles of protein-protein interactions. Proteins are essential cellular components and the major executors for biological functions. Thousands of biochemical reactions in a cell are carried out by protein molecules and their interactions. A large part of proteins function in the form of complexes and protein complexes have quaternary structures maintained mainly by weak interactions such as hydrogen bonds, salt bridges, Van der Waals and hydrophobic forces3Due to the fact that proteins are products of expressed genes, gene expression level determines protein abundance. In a previous work, it was found that highly expressed proteins may be more thermostable than lowly expressed ones due to their unique sequence compositions6310The structures for the protein complexes in two thermophilic bacteria and three mesophilic bacteria (model organisms) were collected from the PDB database and their corresponding genes were identified and downloaded from the NCBI RefSeq databaseaa), interface size measured by NACCESS area (Anaccess), expression level measured by CAI value and expression level taken from GEO database for the protein complexes were listed in The basic information for the two thermophilic bacteria and three mesophilic bacteria (model organisms) was presented in aa and the NACCESS interface area Anaccess and the two measures for expression level: CAI and GEO).The expression levels of the studied protein complexes were measured at transcription and translation level by GEO experimental data and the calculated CAI values, respectively. The protein chains with top 30% expression levels were considered as (relatively) highly expressed and the other 70% lowly expressed. We also checked other partition schemes, for example, 20% vs. 80%, 40% vs. 60%, and half-half, and the results are qualitatively similar (data not shown). Then the average interface sizes for the highly and lowly expressed proteins were compared and it was found that the highly expressed proteins have larger interfaces than lowly expressed proteins , consistaa-CAI combination  for E. coli (E. coli versus about 100 ones in other organisms (aa-CAI and Anaccess-CAI) in mesophilic organisms BSS and PAE, the interfaces of highly expressed protein complexes are even smaller than those of the lowly expressed ones  and lowly (the other 70%) expressed protein complexes measured by GEO expression data. We found that the highly expressed protein complexes in thermophilic organisms have significantly greater  average binding energy than lowly expressed ones , suggestWe simulated the evolution of protein interface stability by using lattice models for the high temperature (350K) and normal temperature (310K) conditions, respectively . The evoIndeed, the multimerization of proteins in living cells proceeds in an environment of thermal noise. To maintain the robustness of protein interaction, there should be an energy gap between the native and the mis-interacted conformations of protein complexes and this energy gap could be optimized by evolution. As shown in We have studied the interface size in the protein complexes from thermophiles and mesophiles and the relationship between the interface size and the protein expression level. It was found that highly expressed proteins incline to have larger subunit interfaces than lowly expressed ones and this trait is significant for thermophiles and weak or absent for mesophiles  were downloaded as well following the same selection criteria. The downloaded structures were filtered to remove possible errors in the PDB files. The basic information for the finally used dataset was presented in The PDB database was checked recently to get the protein complexes in thermophiles and the structures were selected according to the following criteria: (1) pure protein complexes with chain number ≥2; (2) source from prokaryotes (bacteria or archaea); (3) by X-ray experiment method; (4) resolution better than 2 Å; (5) redundancy removed at sequence identity 90%. After selection, only two thermophilic organisms (Thermotoga maritima MSB8 and Thermus thermophilus HB8) have protein complex numbers larger than 100 and were used in the present analysis. For comparison, the protein complexes from three mesophilic model organisms  was assigned to each protein sequence.The DNA sequences for the genes of the studied organisms were downloaded from the NCBI RefSeq databaseaa); the interface with more than 10 contacting residues  were considered effective and used in the analysis. The number of contacting amino acid pairs was calculated based on the BioPDB model in the Biopython packagenaccess); if the area of the interface is larger than 50 Å2, it was regarded as an effective interface. The average interface size/area for all the effective interfaces in each complex was used in comparison.The interfaces between protein chains in the protein complexes were identified in two ways. One way is by the contacting amino acids: if two residues in two opposite chains have a distance ≤5 Å, they are considered contacting; the interface between two chains in a complex is comprised by the contacting residues; the size of the interface is defined in this case as the number of the contacting residues (NThe protein expression levels were measured at two layers: the transcription level and the translation level. The transcription levels for the studied genes were taken from Gene Expression Omnibus (GEO) database22https://www.ebi.ac.uk/pdbe/pisa/) to count the numbers of these two types of bonds in each interface and compared the average bond number over all effective interfaces in protein complexes between highly and lowly expressed proteins.The interface between two chains in a protein complex has its special contacting amino acid pairs. We calculated the binding energy and chemical bonds of an interface based on the amino acid contacting pattern. Each contacting amino acid pair has a particular binding energy which is measured by the MJ matrixThe evolution of protein interface stability was simulated by using lattice models. In the simulation, 100 virtual cells were generated as a population for each generation during the evolution and within each cell 60 proteins were constructed by using a 3*3*3 lattice model. Random amino acid mutations and genetic drifts were considered during the evolution process. The conformations of interacting proteins pairs were generated for each cell in each generation and the interacting interface of a protein pair were represented by 9 contacting residue pairs on the two sides of two lattices in a particular orientation. The binding energy between two proteins (a protein pair) was calculated based on contacting potentials between amino acids. The fitness of a cell was defined as the sum of the abundance of interacting proteins within it in each generation. The detailed procedure for the simulation was presented in the How to cite this article: He, Y.-M. and Ma, B.-G. Abundance and Temperature Dependency of Protein-Protein Interaction Revealed by Interface Structure Analysis and Stability Evolution. Sci. Rep.6, 26737; doi: 10.1038/srep26737 (2016).


They have been collected in the PrOnto database, a novel tool which can be of particular use for the identification of moonlighting proteins. The database can be queried via an web-based interface which is freely available at http://tagc.univ-mrs.fr/pronto.Moonlighting proteins are defined by their involvement in multiple, unrelated functions. The computational prediction of such proteins requires a formal method of assessing the similarity of cellular processes, for example, by identifying dissimilar Gene Ontology terms. While many measures of Gene Ontology term similarity exist, most depend on abstract mathematical analyses of the structure of the GO tree and do not necessarily represent the underlying biology. Here, we propose two metrics of GO term  Moonlighting proteins are a subset of multifunctional proteins involved in several, unrelated biological functions. Because of the growing importance of this functional singularity Copley,  for the de facto standard ontology used to formalize gene annotation data. It is organized as three independent directed acyclic graphs (DAGs), one for each of the sub-ontologies Biological Process (BP), Molecular Function (MF), and Cellular Component (CC).The Gene Ontology (GO)  and “positive regulation of apoptotic process” (GO:0043065) share no parent terms apart from the root of the ontology although they are descriptions of tightly linked biological processes. Indeed, TNF is a well known inducer of apoptosis .The structure of the GO DAG means that many GO terms are related, either because they describe related functions or because one term is the child of another. Therefore, proteins annotated to similar GO terms are assumed to perform similar functions and can be categorized as such. This has led to various methods of evaluating the semantic similarity of GO annotations . We use the frequency of co-occurrence of GO term pairs in (i) protein annotations  and in (ii) the annotations of interacting protein pairs  to compute probabilities reflecting biases toward infrequent GO terms associations implying functional dissimilarity .The current version of the PrOnto database contains probabilities for human, mouse, fly, worm and yeast , N is the number of proteins annotated directly to at least two different GO terms, K is the number of proteins annotated to GO1, n is the number of proteins annotated to GO2 and k is the number of proteins annotated to both terms.We express InteractionProbabilities (IPs), N is the number of interactions in the PPI network between proteins annotated directly to at least two different GO terms. K is the number of interactions involving proteins annotated to GO1, n the number of interactions involving proteins annotated to GO2 and k the number of interactions between a protein annotated to GO1 and one annotated to GO2.For N is the size of the event pool. P(X) is the probability of observing as large a co-occurence as X in a set of size N. Therefore, when computing N, only proteins with at least 2 direct annotations  are considered since co-occurence is only relevant for proteins annotated with at least two different GO terms. When calculating cross-ontology probabilities, only proteins with at least one explicit annotation in each ontology of interest are considered. When calculating K, n, and k, all annotations per protein are counted, both direct and inherited. All GO annotations have been included, irrespective of their evidence codes. Indeed, electronic annotations have greatly improved in recent years and their reliability now rivals that of manual annotations  are listed as “Dissimilar” and all others as “Not dissimilar.”In both cases, In addition, the Jaccard index and Cohen's kappa have been computed for all pairs.IPs, a high quality interactome was compiled for each target species. Interaction data were retrieved using the PSIQUICK .The final result was high quality interactomes consisting entirely of experimentally verified, direct, binary interaction pairs for each species studied. These networks will be regularly updated with each update of PrOnto. Current interactomes are available on the downloads page of the PrOnto database . The power of the test was calculated using the power.fischer.test function of the statmod R package and 1000 simulations per pair. Jaccard indeces and Cohen's kappa were calculated using a simple awk script. The PrOnto webpage is written using a combination of HTML, PHP, and Javascript, the data are stored in a MySQL database which is queried using a Perl script.The probabilities were calculated using the PrOnto probabilities have been computed from the proteomes and interactomes of several species  to assess the relationships between GO terms of the same sub-ontology as well as between sub-ontologies.Dissimilar - When the low-tail p < 0.05, the GO terms are very rarely associated among protein annotations and are therefore qualified as “dissimilar” by PrOnto. For example, the probability of finding a lower co-occurrence than observed for the GO terms “RNA processing” (GO:0006396) and “signaling” (GO:0023052) is very low in human (therefore highly significant), as indicated by their AP, p = 7.5e-33  and “positive regulation of apoptosis” (GO:0043065) is low , clearly indicating that the terms are functionally “similar.” This is explained by the following: as 116 human proteins are annotated to “response to tumor necrosis factor” and 459 to “positive regulation of apoptosis,” 3 are expected to be annotated to both given the human proteome size (17866 annotated proteins), but 25 co-annotated proteins are observed. The same is observed for IP as proteins that respond to TNF signaling will often interact with those that promote apoptosis.NA - Additionally, a NA category exists when no score could be computed. PrOnto produces NA when at least one of the GO terms of the pair under consideration does not annotate any protein in the target species. For instance, PrOnto returns “NA” for the GO terms GO:0030326 (“embryonic limb morphogenesis”) and GO:0048736 (“appendage development”) in yeast. This makes perfect sense biologically speaking since, for obvious reasons, no yeast proteins will be annotated to either of those terms.AP, 84.1–87.1% and IP, 54.2–72.3%). Since the cell is a complex system whose constituent parts are very often interlinked . That they are more numerous in the IPs (0.5–4.9%) shows functions that are rarely carried out by interacting proteins, therefore suggesting that they are performed by different functional modules, known as groups of interacting proteins involved in the same biological process , whereas IPs can offer insights into the links between different cellular processes mediated by protein interactions. In addition their use for the investigation of the links between the functional modules formed by interacting proteins in PPI networks as described above, APs and IPs could also be used to score protein interaction predictions. In some cases, in the absence of experimental data, protein-protein interactions are predicted using bioinformatics approaches. One may then want to assign a confidence score based on real biological data to these predictions. Because PrOnto probabilities are derived from biological knowledge and experimental data, they can be used for this purpose and a lower score can be assigned to predicted interactions between proteins annotated to dissimilar terms.Overall, They can also be used when constructing ontologies from -omics data as recently proposed by Dutkowski et al.  and KramUsing term co-occurence as a proxy for functional similarity has already been done by other methods and tools. For example, the EBI's QuickGO service provides the top 100 most co-occuring terms for the query GO term. However, a way of getting more than those 100 co-occuring terms is not provided, nor is any way of getting terms that do not co-occur (the “dissimilar” terms of PrOnto). As already mentioned, the later can be very useful in studies of protein multifunctionality.http://dragon.bio.purdue.edu/FAM/), unlike PrOnto, are asymmetric, meaning that P(GO1|GO2) ≠ P(GO2|GO1) which is something that should be taken into account when choosing which tool to use. Depending on the analysis, either symmetric or asymmetric probabilities might be preferred.The FAM (Function Association Matrix) which is part of the PFP protein function predictor  provide co-occurrences calculated from the entirety of the UniProt database whereas PrOnto considers a specific species' proteome or interactome. A species-specific measure can indeed be considered an asset depending on the analysis being undertaken since cellular and physiological functions often differ between species, essentially due to tissue-specificity. Term co-occurences are therefore expected to be different across species.Moreover, neither method provides the user with the ability to input a list of terms and obtain a list of probabilities nor the possibility of querying for specific pairs. The web-based interface of PrOnto is designed with that in mind and is a simple and practical way of quantifying GO term functional dissimilarity.Finally, PrOnto is, to our knowledge, the only tool that offers a measure of GO term functional dissimilarity based on a species' interactome. A similar approach was undertaken by Dotan-Cohen et al.  to identfunctional dissimilariy based on two species-dependent metrics of GO term association derived from either the annotations or the interactome data of the species in question. This tool was developed for the specific goal of identifying moonlighting proteins from interactome data. As such, the emphasis has been on the identification of dissimilar functions.We have presented PrOnto, a novel tool for quantifying GO term The current version of PrOnto is using a relatively simple statistical approach which, nevertheless provides robust results (see Chapple et al., 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.


Direct-Coupling Analysis of the co-evolution between inter-protein residue pairs. We use ribosomal and trp operon proteins as test cases: For the small resp. large ribosomal subunit our approach predicts protein-interaction partners at a true-positive rate of 70% resp. 90% within the first 10 predictions, with areas of 0.69 resp. 0.81 under the ROC curves for all predictions. In the trp operon, it assigns the two largest interaction scores to the only two interactions experimentally known. On the level of residue interactions we show that for both the small and the large ribosomal subunit our approach predicts interacting residues in the system with a true positive rate of 60% and 85% in the first 20 predictions. We use artificial data to show that the performance of our approach depends crucially on the size of the joint multiple sequence alignments and analyze how many sequences would be necessary for a perfect prediction if the sequences were sampled from the same model that we use for prediction. Given the performance of our approach on the test data we speculate that it can be used to detect new interactions, especially in the light of the rapid growth of available sequence data.Interaction between proteins is a fundamental mechanism that underlies virtually all biological processes. Many important interactions are conserved across a large variety of species. The need to maintain interaction leads to a high degree of co-evolution between residues in the interface between partner proteins. The inference of protein-protein interaction networks from the rapidly growing sequence databases is one of the most formidable tasks in systems biology today. We propose here a novel approach based on the  Proteins are the major work horses of the cell. Being part of all essential biological processes, they have catalytic, structural, transport, regulatory and many other functions. Few proteins exert their function in isolation. Rather, most proteins take part in concerted physical interactions with other proteins, forming networks of protein-protein interactions (PPI). Unveiling the PPI organization is one of the most formidable tasks in systems biology today. High-throughput experimental technologies, applied for example in large-scale yeast two-hybrid  analysisGiven the fast growth of biological sequence databases, it is tempting to design computational techniques for identifying protein-protein interactions . PromineRecently, a breakthrough has been achieved using genomic sequences for the related problem of inferring residue contacts from sequence data alone. . The so-These models are computationally hard to infer and several approximations have therefore been developed , 17, 18.When applied to two interacting protein families, DCA and related methods are able to detect inter-protein contacts , 20, 21 discriminate interacting from non-interacting pairs of protein families, i.e. to infer PPI networks from sequence information? A positive answer would lever the applicability of these statistical methods from structural biology (residue contact map inference) to systems biology (PPI network inference). An obvious problem in this context is the sparsity of PPI networks, illustrated by the bacterial ribosomal subunits used in the following, cf. N proteins is Here we address an important question: Is the strength of inter-protein residue-residue co-evolution sufficient to It should also be mentioned that the ribosomal structure relies on the existence of ribosomal RNA, which is not included in our analysis. We therefore expect many of the small PPI interfaces to be of little importance for the ribosomal stability and that only large interfaces constrain sequence evolution and thus become detectable by co-evolutionary studies.Ribosomal proteins and their interactions are essential and thus conserved across all bacteria, and it appears reasonable to wonder whether this makes them a specialized example of a protein complex more amenable to co-evolutionary bias. As a second and smaller interaction network, we therefore considered the enzymes of the tryptophan biosynthesis pathway comprising a set of seven proteins in which only two pairs are known to interact , we expN single-protein family alignments, and to extract a pairwise score that measures the co-evolution between the proteins in the alignments. A high co-evolutionary score is then taken as a proxy for interaction. In the spirit of [data generation and matching, the model used for analyzing data and the inference and scoring mechanism.The goal of the present work is to analyze each of the pirit of  we descrN multiple sequence alignments Dp consisting of Mp sequences of length Lp for every protein family p. These alignments are extracted from UniProt [cf. Section A in N, Mp, Lp for ribosomal and trp-operon proteins). For the analysis, it is necessary to concatenate the MSAs of two putative co-evolving protein families. This means to create, for each pair of protein families , a new alignment Dp, p′ of sequence length Lp + Lp′. Each line contains the concatenation of two potentially interacting proteins. More precisely, in the case where families p and p′ actually interact, each line should contain a pair of interacting proteins. The general problem of producing a concatenated alignment out of single MSAs of two protein families is straightforward in two cases only: (i) we have prior knowledge which pairs of sequences represent interaction partners; (ii) no paralogs are present in the considered species . Often, as displayed schematically in genomic distance between sequences using UniProt accession numbers. A better distance between sequences could be defined in terms of their genomic location. Unfortunately, genomic locations are available only in the context of whole genome sequencing projects. The majority of sequences in Uniprot originate from fragments or from incomplete genome sequencing projects. These difficulties lead us to content ourselves with the proxy of accession numbers.The input data is given by  UniProt  using st UniProt  and HMMe UniProt   reduces the number of sequences in the alignments on the average by about 10% for the ribosomal proteins and by about 85% for the proteins of the trp operon  of  length L is modeled by a so-called Potts model, or pairwise Markov Random Field,Jij between residue pairs and position-specific biases hi(xi) of amino-acid usage [Z is the normalization constant of L. The variable xi represents the amino acid found at position i in the sequence and can take as values any of the q = 21 different possible letters in an MSA (gaps are treated as a 21st amino acid). The model parameters are inferred using MSAs of homologous proteins.Within DCA, the probability distribution over amino acid sequences id usage . The numH(x) and H′(x′) are the terms in the exponential in xi is the ith amino acid in sequence x, and jth amino acid in sequence x′. The sum runs over all inter-protein pairs of residue positions. The q × q matrices Jij in this term quantify how strongly sites between the two proteins co-evolve in order to maintain their physicochemical compatibility. The matrix contains a real number for each possible amino acid combination at sites i and j and contributes to the probability in L1 + L2 + .. + LN)2 for N proteins while the number of samples in the concatenated MSA Dp1,….pN becomes smaller because one has to find matching sequences for N proteins simultaneously. This leads us to consider the case N > 2 only for artificial proteins where the total length and sample size are controllable.In the case of two concatenated protein sequence pseudo-likelihood functions. This is an alternative to directly maximizing the likelihood and considerably faster (see Section C in Fij (the average-product corrected Frobenius norm of the matrices Jij) that quantify the amount of co-evolution between sites i and j in the alignments. In order to quantify co-evolution between proteins, we took the Fij corresponding to inter-protein site pairs (i.e. i in x and j in x′) and calculated the mean of the 4 largest. These quantities, a real number for every protein pair, are used to rank protein-protein interaction partners. The number 4 was chosen because it performed well in the small ribosomal subunit, which we used as a test case when designing the algorithm. Subsequent tests on larger systems showed that any number between 1 and 6 performs almost equally well .Following , the parPi has 551 internal contacts. Moreover, each protein interacts with two others in a circular way. The inter-protein contact matrices between Pi and Pi+1 (as well as between P1 and P5) are random binary matrices with a density of 10% of the internal contacts. This models the sparsity of the inter-protein contacts as compared to the intra-protein contacts. A contact map for the artificial complex can be found in Fig E in As the basis for the simulated data we used a fictitious protein complex consisting of 5 proteins. Each protein has a length of 53 residues. The individual contact map of each one is given by the bovine pancreatic trypsin inhibitor (PDB ID 5pti ), which within the single proteins were directly extracted from the Pfam MSA using DCA. For the couplings corresponding to the co-evolution between the proteins, we used a random subset of the internal parameters and used them to couple sites that are in contact according the contact map as defined above. Non-contacting pairs of sites remain uncoupled between artificial proteins. Using this model, a joint MSA D12345 of sequences of length 265 = 5 × 53 was generated using standard MC simulations.In order to define as realistically as possible the coupling parameters of the Potts model used for generating the artificial sequences, we used the Pfam protein family PF00014 of the pancreatic trypsin inhibitor . Note thThe process of defining the contact map, choosing the parameters and generating the sequences is described in Section E of simulated data generated by Monte Carlo (MC) sampling of a Potts model of the form of Materials and Methods.As a first test of our approach, we use The main simplifying assumptions in this context are: (i) We assume intra- and inter-protein co-evolution strengths to be the same. (ii) We assume the distribution of inter-protein residues contacts within the possible contacts to be random. (iii) We assume the sequences to be identically and independently distributed according to our model. This model includes the assumption that non- contacting sites have zero couplings. The number of artificial sequences needed for a good performance of our method should therefore be taken at most as a lower bound for the number of biological sequences needed for a comparable performance.P1, …, P5, each one consisting of 53 residues. In order to simulate co-evolution between the proteins, we generate a joint MSA D12345 for all 5 proteins with a model that contains couplings between inter-protein site pairs. These couplings are modeled in a way to resemble couplings inferred from real proteins . Green links correspond to contact pairs and red links to non-contact pairs. We see that the intra-protein prediction is perfect, whereas a few errors appear for inter-protein predictions in agreement with the results of After having answered the As a more realistic test we apply the method to the bacterial large and small ribosomal subunits . To define contacts and protein interaction partners we used high-resolution crystal structures with PDB-IDs 2z4k (SRU) and 2z4l (LRU) . The conPanels C,E of M between 1468 and 1931). Also the third FP, corresponding to the pair RS4-RS18, is probably due to a small MSA with M = 2064. At the same time, the interaction of RS21 with RS11, which is one of the largest interfaces (199 contacts), is still detected despite the low M = 1729. The same procedure for the LRU (406 protein pairs) performs even better: 9 out of the 10 first PPI predictions are correct .As a distinct test case for our methodology we analyzed the 7 enzymes  that comprise the well characterized tryptophan biosynthesis pathway. In contrast to the ribosomal proteins, these enzymes are only conditionally essential in the absence of environmental tryptophan and their genes are only expressed under deplete tryptophan conditions. In this particular system, only two protein-protein interactions are known and resolved structurally: TrpA-TrpB (PDB-ID 1k7f ) and TrpA global view), these two pairs would be discernible as interesting candidates even if we did not have the 3D structures.After applying our method to all 21 protein pairs we find elevated interaction scores only for TrpA-TrpB and TrpE-TrpG, which are the only known interacting pairs . We ranOur results are corroborated by the finding that all scores measuring the co-evolution between a ribosomal protein and an enzyme from the tryptophan synthesis pathway are small (see the following subsection). No indication for an interaction between the two systems is found, as to be expected from the disjoint functions of the two systems.lowest interaction scores. As it can be seen our scoring produces a false negative just after 420 true negatives. It is interesting to assemble a larger-scale system out of the three systems . To this end, we created all possible pairings between the proteins used in the present study . This leads to a total of 1540 pairs, out of which only 49 pairs are known to interact (which we defined as true positives). We present the findings in A further question is whether it is possible to define a threshold allowing to reliably discriminate between interacting and non interacting pairs in terms of the interaction score. Fig I in The main limitation of the method we present in this paper is arguably the concatenation of the two MSAs. In the test cases we analyzed this step was rendered difficult due to the presence of paralogs in the majority of species. The solution based on the minimization of the genomic distances within the concatenated MSA as outlined in subsection “Data extraction and matching for the ribosomal and trp operon proteins” can be problematic when used naively.An example is the homo-dimerization of OmpR-class Response Regulators . In thisThis suggests that more robust methods than genomic proximity should be developed. This is of course especially true if one is interested in eukaryiotic systems, were we have no evidence that genomic colocalization is an indication for interaction.A second limitation of our method is the apparent inability to actually discern between interacting and non interacting pairs. Everything that the method can do at this point is to state that some interactions are more likely than others and show that this ordering is considerably better than a random one. This is a problem related to all DCA methods as reported for instance in , 17, 18 M = 24,000 sequences in our data) a perfect inference of the artificial PPI network was achieved. MSA for real proteins pairs are typically much smaller. Even for pairs of ribosomal proteins, which exist in all bacterial genomes, only about 1500-3200 sequence pairs could be recovered. This places these data towards the lower detection threshold of PPI. We therefore expect the performance of the presented approach to improve in the near future thanks to the ongoing sequencing efforts (the number of sequence entries in Uniprot [(i) The most obvious explanation is that there are no additional protein-protein interactions beyond those that are known and that no multi-enzyme complex exists for the tryptophan biosynthesis pathway. Alternatively (ii) it seems plausible that there are numerous structural solutions to form a tryptophan biosynthesis complex and that there is no dominant structure from which a co-evolutionary pattern can be observed in the sequence databases. Lastly (iii) it is not out of question that the enzymes of the pathway do not directly form a complex but that they are jointly interacting with an unidentified scaffold component. Of course we cannot exclude that our method is not able to capture other potentially present interactions.To conclude, we have shown that DCA performs well in the systems tested when used to predict protein-protein interaction partners. In the small and large ribosomal subunit our tests resulted in a true positive rate of 70% and 90% in the first 10 predictions (AUC of 0.69 and 0.81) while in the trp operon the two largest interaction scores corresponded to the only two interactions experimentally known (AUC 1). The performance is summarized in  Uniprot  has beenmatching by genomic proximity. This criterion is based on the observation that two sequences are more likely to interact if they are genomically co-localized. Our results have shown that in the case of the ribosomal network better inference results can be obtained by using this matching criterion than by using a random matching or using a conservative matching taking only species with a single sequence in both MSAs into account, cf. From a methodological point of view, one possible algorithmic improvement is creating better MSAs for protein pairs. The vast majority of protein families show genomic amplification within species. This raises the issue of which sequence in one MSA should be matched with which sequence in the other MSA when concatenating the two MSAs, as shown in naive matching strategy can be improved substantially. Even if closeness of sequence pairs on the genome is a good proxy for interaction in some cases, for example if they belong to the same operon, excluding all distal pairs is a very crude criterion. This criterion is known to be erroneous in many cases, for example in the bacterial two component signal transduction system [e.g. to find a matching that maximizes the inter-protein sequence covariation, cf. [We believe that our n system –26. It wion, cf.  for a reS1 TextMultiple Sequence Alignments, Matching procedure, Inference Technique, Ribosomal Protein Interaction Partner Prediction, Artificial Data.(PDF)Click here for additional data file.


The interactions between intrinsically disordered proteins (IDPs) and other proteins rely on changes in flexibility and this is seen as a strong determinant for their function. This has fostered the notion that IDP's bind with low affinity but high specificity. Here we have analyzed available detailed thermodynamic data for protein-protein interactions to put to the test if the thermodynamic profiles of IDP interactions differ from those of other protein-protein interactions. We find that ordered proteins and the disordered ones act as non-identical twins operating by similar principles but where the disordered proteins complexes are on average less stable by 2.5 kcal mol To avoid over-representing a single protein-protein complex we exclusively compared wild-type proteins so that protein specific irregularities will be averaged out. In the cases where a structure of the complex has been determined, we have calculated the interaction surface area using PISA  in the protein data bank ,  compared to ΔH0 and TΔS0 that are found in the ranges from −66.7 to 19.9 kcal mol−1 and from −56.1 to 28.5 kcal mol−1, respectively. The analysis of the thermodynamic parameters shows that the enthalpy (ΔH°) and the entropy (ΔS°) for binding are not significantly different between the two groups of proteins . However, the average entropic contribution (−TΔS°) to the binding free energy for interactions between two ordered proteins is 2.5 ± 1.6 kcal mol−1 smaller (more stabilizing) than for interactions between an ordered and a disordered protein. Within both groups there is a linear correlation between TΔS° and ΔH° , which demonstrates a similar entropy-enthalpy compensation . For the ORD-ORD complexes <ΔG°> = −11.1 ± 0.4 kcal mol−1, and for the ORD-IDP complexes <ΔG°> = −8.5 ± 0.2 kcal mol−1. The difference in <ΔG°> is 2.5 ± 0.4 kcal mol−1, which is primarily accounted for by the difference in TΔS° (vide supra). This number is close to the 2.6 kcal mol−1 recently published from a much smaller dataset based on mutation studies  are exclusively formed between two ordered proteins, and the least stable complexes  are exclusively formed between an ordered and a disordered protein. Among the most stable complexes we find several enzyme: inhibitor complexes, such as the bacterial DNAses in complex with bacterial immunity proteins  , which may alter the energy of binding in vivo cannot be excluded as origin for the displacement either, but again we see no reason why this should not be an even more pronounced effect for the ORD-ORD complexes.One alternative explanation for the lower average stability of IDP-ORD complexes may be purely technical and unrelated to any Based on the data collected, we have reached the—perhaps—counterintuitive conclusion that interfaces formed between globular proteins and IDPs are not overall significantly different from the interfaces between two globular proteins, although the contribution of residues within the binding interface is slightly skewed. We find instead that there is a small but significant difference in the average binding free energy in favor of the ORD-ORD complexes. We suggest that this difference is primarily caused by the loss of conformational free energy upon IDP binding, which affects the off-rate of the complex, although other reasons may exists such as an increased number of binding partners for the IDP.Finally, we would like to add that the present analysis almost exclusively involves binary complexes. It has been suggested that IDPs are particularly well suited as scaffolds for large complexes or as hubs for signaling assemblies. Therefore, we may have missed thermodynamic fingerprints that stand out and reveal IDPs that diverge more from their globular twins than the ones analyzed in the present paper. Allostery in IDP-interactions where more binding sites are in play is an emerging subject (Ferreon et al., The reason, if any, for the evolution of protein intrinsic disorder remains to be disclosed. The present paper hints strongly that the answer does not lie directly in differences in thermodynamic parameters or the energetic principles of ligand binding.All authors contributed equally to the work and wrote the manuscript in collaboration.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.