Such types of networks might provide new reliable characteristics of organism complexity and a better understanding of the evolutionary integration and plasticity of interconnection of genome-transcriptome-proteome functions.We consider the problem of biological complexity via a projection of protein-coding genes of complex organisms onto the functional space of the proteome. The latter can be defined as a set of all functions committed by proteins of an organism. Alternative splicing (AS) allows an organism to generate diverse mature RNA transcripts from a single mRNA strand and thus it could be one of the key mechanisms of increasing of functional complexity of the organism's proteome and a driving force of biological evolution. Thus, the projection of transcription units (TU) and alternative splice-variant (SV) forms onto We use the InterPro and UniProt databases to attribute descriptive features (keywords) to protein sequences. UniProt database includes a controlled and curated vocabulary of specific descriptors or keywords. The keywords have been assigned to a protein sequence via conserved domains or via similarity with annotated sequences. Then we consider the unique combinations of keywords as the protein functional labels (FL), which characterize the biological functions of the given protein and construct the contingency tables and graphs providing the projections of transcription units (TU) and alternative splice-variants (SV) onto all FL of the proteome of a given organism. We constructed SFNs for organisms with different evolutionary history and levels of complexity, and performed detailed statistical parameterization of the networks.The application of the algorithm to organisms with different evolutionary history and level of biological complexity  reveals that the parameters describing SFN correlate with the complexity of a given organism. Using statistical analysis of the links of the functional networks, we propose new features of evolution of protein function acquisition. We reveal a group of genes and corresponding functions, which could be attributed to an early conservative part of the cellular machinery essential for cell viability and survival. We identify and provide characteristics of functional switches in the polyform group of TUs in different organisms. Based on comparison of mouse and human SFNs, a role of alternative splicing as a necessary source of evolution towards more complex organisms is demonstrated.The entire set of FL across many organisms could be used as a draft of the catalogue of the functional space of the proteome world. Information content of genome coding sequences unfolds via functions of proteins. Alternative splicing is one of the ways an organism uses for genome manifestation into its proteome. We consider the problem of projection of genetic information into the functional space of the proteome, where the latter is defined as a set of molecular functions performed by proteins. Not all of the functions of proteins manifest themselves at a level of macroscopic phenotype and therefore the notion of redundancy of proteins could arise. However, this may reflect a failure to provide the correct test for the altered phenotype .An inventory of biological functions of protein is documented in resources such as the FunCat  and partprotein functions which may exist in a living cell/organism. The confluence of the two concepts, namely limited space of sequences/structures and limited space of functions may provide a new way of studying molecular evolution.The protein universe is the set of all proteins of all organisms. Recently all currently known sequences were analyzed in terms of families that have single-domain or multidomain architectures and whether they have a known three-dimensional structure . This anAlternative splicing is a molecular mechanism that produces multiple protein isoforms from a single gene. AS is considered to be a way of proteome diversification to bridge a complexity gap between the relatively low number of genes in a mammalian genome and the variety of functions in a cell. Analysis of possible changes in protein functions introduced by alternative splicing events is also well developed. An abundance of work, such as -16, reviThe investigation of functional diversity of splice variants  is similAn interesting insight into alternative splicing events on the protein sequence level revealed that the size of splicing events follows the power law distribution -19 and tAll of the investigations of proteome function and complexity analysis done to date ,7-14 areRecent reviews of evolution of the protein universe -9 suggesThe FANTOM project  and otheC. elegans (CAEEL) D. melanogaster (DROME), A. thaliana (ARATH), C. intestinalis (CIOIN), T. rubripes (FUGRU), M. musculus (MOUSE), H. sapiens (HUMAN). The information about alternative splicing variants and sequences has been provided by various sources, listed in the Table 12The following organisms have been used for the analysis: The set of TUs can be broken down into two groups according to their ability to produce protein isoforms with identical or different functional assignments. We define them as the monoform and polyform group, respectively. The monoform group includes the majority of TUs. The protein sequences produced by these transcriptional units can differ, but all of them have the same single FL assigned.The number of TUs that produce polyform sequences varies from 0.06 to 15% in different organisms. We estimated the P-values for frequencies of keywords overrepresented in the human and mouse sets; the results are presented in Table Arabidopsis presents an extreme case for the data, in both the statistical and functional results. Figure  distinct structural complexity of the genes and its products.To do this analysis we retrieved from the UniProt database the list of RefSeq Protein IDs corresponding to each keyword. For each Protein ID the corresponding Nucleotide ID was retrieved from UCSC genome database (genome releases hg19 and mm9), along with its exon annotation. Thus, for each RefSeq Protein ID the predicted number of its exons can be obtained. In the proteins annotated by terms G-PROTEIN COUPLED RECEPTOR and RIBOSOMAL PROTEIN and related to monoform TUs, the average numbers of exons per gene are 3.7 and 5.5 for human and 3.8 and 5.7 for mouse, respectively. All these frequency distributions are skewed. The genes with the highest occurrence encode single-exon proteins. The polyform group of genes, represented by the most GO enriched polyform TUs have the keywords NUCLEOTIDE-BINDING, TRANSFERASE and KINASE. In the proteins annotated by these terms, the average numbers of exons per gene are 16.2,13.5,16.5 for human and 14.8, 11.9, 14.7 for mouse, respectively. The frequency distribution of the number of exons in all these cases has gamma-like frequency distribution with maximum nearby average value. These results suggest that differences between the monoform and polyform groups of TUs can be strongly associated withThe function of protein isoforms derived from the same TU could differ significantly. We searched through the polyform group for TUs which may be potential candidates for such functional switches. The Jackard distance between sets of keywords assigned to a given TU was calculated. About 0.8% of the TUs analyzed in human and mouse have the distance equal to zero and present candidates for the functional switches. The method can be applied to genome data to reveal potential functional switches among transcriptional units. There are very interesting cases where the protein function changes dramatically, like PA10101 in human switching from protease activity to oxygen transport.220 FLs are common among all the organisms analyzed. We extracted the top 30 keywords overrepresented in the set of FLs; the diagram is presented in Figure For detailed analysis of the statistical characteristics of protein function – mRNA relations we focus on data sets for two mammalian organisms: mouse and human. The mouse and human transcriptomes have the best annotations of TUs of the data analysed. We used TUs and splice variant information provided by the FANTOM consortium  and FLs defined by our algorithm. We provided an identification of statistics of three transcript-protein functional relations in the mouse and in the human data sets.First, we found 23640 FLs in the mouse proteome and 20929 FLs in the human proteome. For each of these two sets of FLs, we selected 20928 mouse TUs and 18260 human TUs related to at least one FL in the given TU for mouse and for human, respectively. Note, in our analysis, these TUs can represent 3mRNAs translated to known proteins. After that, we identified a model of frequency distribution of the number of occurrences of FLs in a given TU. Figures Secondly, we performed goodness-of-fit analysis of the observed frequency distribution of the number of TUs attributed with a given FL Figures  &3D. Forb=0; )We suggest from the comparison of Tables The polyform group contains alternatively spliced products with altered functions. Whereas the number of functions of proteins increases, the number of polyform TUs follows the trend. The highly demanded functions then become hubs in the SFN and provide connectivity. The network also becomes more stable and less sensitive to removal of such hubs (data not shown). In some cases the domains are similar, but in different combinations, while in others, the domains differ considerably, resulting in diverse functions. TUs with a high level of functional homogeneity may be reusing certain sets of domain combinations, but with domain substitutions in certain positions to provide a small variation in function, for example, substrate or binding specificity. Higher eukaryotes tend to have multi-domain proteins, which provide flexibility in accessing more functions simply by alterations in domain combinations ,18,27. T The connections between functional labels via hub TUs provide insights into the problem of proto-genes. The central cluster of interconnected FLs on the graphical view of functional space transitions can be considered to be a candidate for the set of functions encoded by the ancient proto-genome. The transition from one function to another can be achieved via common sets of exons. The functions could be encoded by ancient genes, sharing a common pool of exons, which keep the relationships to each other undetectable by other methods of analysis. The transitions between different functions can also represent evolutionary plasticity of the genome where the same function can be achieved in different ways. The absence of correlation between amino acids similarity of protein domains and the number of transcriptional units connecting two functional labels also confirms this idea.ftp://fantom.gsc.riken.jp/RTPS as well as DNA sequences for corresponding transcriptional units. Details of the RTPS production pipeline can be found in XX where h,m,c corresponds to FL set – human, mouse or common and XX – unique number.The functional label is We used GeneMerge software  to calcuTo study the relationships between TUs and protein functional space, we constructed basic contingency tables, which show the responses of FL as a function of TU translation. Non-zero elements of the table indicate co-incidence of events belonging to the both sets. Such a table can provide complete statistical characterization within and between events of both data sets. We created the contingency table for both species and then derived FL–TU–FL links from it. The relations have been used for graphs building with the Cytoscape software.We assume that the probability distribution function of structural-functional interconnection events (e.g. the number of distinct FLs associated with a given protein translated in TU) could be modeled as a sum of two distributions:P is the mixture probability distribution function of the interconnection events, X is the random number of events; m=1,2,3,...,J, J=max{m}. 1P is the probability distribution function of “low-complexity” events (belonging to set 1), 0<s<1 is the fraction of events derived by function 1P. 2P is the probability distribution function of occurrences of “high-complexity” events (belonging to set 2).where 1P using the exponential function, standard discrete Pareto function (power law) or the generalized discrete Pareto probability distribution function. We estimated parameters of the functions 1P and2 P and the weight parameter α using algorithm published in [We model ished in .The Generalized Discrete Pareto Probability Function ,19 is usf(m) is the probability that a randomly chosen object occurs m times in the entire sample. The function f involves two unknown parameters, k, and b, where k>0, and b>1;  the normalization factor J, the maximum observed abundance, may be a sample-size dependent quantity J=J(M) [where the y J=J(M) ,19. The We used descriptive statistics and tests provided by SPSS-13 and StatXact-7 software, and goodness of fit analysis and graphical tools from SigmaPlot-8.The authors declare that they have no competing interests.AK developed algorithms, carried out the sequence and network analysis and contributed to the discussion. VK developed statistical analysis algorithms, carried out the statistical calculations and contributed to the discussion. NM participated in the discussion and data analysis and helped to draft the manuscript. AK and VK drafted and revised the manuscript. All authors read and approved the final manuscript.


Protein–DNA interactions are crucial for many cellular processes. Now with the increased availability of structures of protein–DNA complexes, gaining deeper insights into the nature of protein–DNA interactions has become possible. Earlier, investigations have characterized the interface properties by considering pairwise interactions. However, the information communicated along the interfaces is rarely a pairwise phenomenon, and we feel that a global picture can be obtained by considering a protein–DNA complex as a network of noncovalently interacting systems. Furthermore, most of the earlier investigations have been carried out from the protein point of view (protein-centric), and the present network approach aims to combine both the protein-centric and the DNA-centric points of view. Part of the study involves the development of methodology to investigate protein–DNA graphs/networks with the development of key parameters. A network representation provides a holistic view of the interacting surface and has been reported here for the first time. The second part of the study involves the analyses of these graphs in terms of clusters of interacting residues and the identification of highly connected residues (hubs) along the protein–DNA interface. A predominance of deoxyribose–amino acid clusters in β-sheet proteins, distinction of the interface clusters in helix–turn–helix, and the zipper-type proteins would not have been possible by conventional pairwise interaction analysis. Additionally, we propose a potential classification scheme for a set of protein–DNA complexes on the basis of the protein–DNA interface clusters. This provides a general idea of how the proteins interact with the different components of DNA in different complexes. Thus, we believe that the present graph-based method provides a deeper insight into the analysis of the protein–DNA recognition mechanisms by throwing more light on the nature and the specificity of these interactions. The interaction of proteins with DNA is crucial for several cellular processes. Some insights into the mode of interaction can be obtained from the analysis of the complexed structures. Conventional analyses are based on the identification of pairwise interactions. However, a collective representation of the network of interactions and the analyses of such networks provide valuable information, which is not easy to obtain from pairwise analyses. Although the protein structure networks have been described in the literature, this is the first time that a network representation of protein–DNA is described. Construction and analysis of such networks have given valuable information on protein–DNA interactions in terms of network parameters, such as clusters of interacting residues and hubs, which are highly connected residues. Furthermore, the results also represent both the protein- and the DNA-centric viewpoints, because the analysis is carried out on combined networks. The methodology developed here can lead to predictions, such as important residues responsible for stabilizing protein–DNA interactions, and will be of interest to experimentalists. A network of interactions among the macromolecules drives the cell. The protein–DNA interactions orchestrate the high fidelity processes like DNA recombination, DNA replication, and transcription. With the increasing number of high-resolution structures of macromolecular complexes, it is now possible to obtain insights into the atomic details of interactions governing their structural and functional integrity. In the present study, we focus on protein–DNA interactions, which can either be specific or non-specific depending on the functional requirement. Insights into the mechanism of protein–DNA binding and recognition have come from extensive analysis of protein–DNA interfaces As a conceptual turning point, it has been pointed out that most of the information on protein–DNA complexes have been obtained from a “protein-centric” view and new insights are likely to emerge if protein–DNA complexes are investigated from a “protein–DNA-centric” viewpoint The protein–DNA graph is of special interest, since we are dealing with two different types of biopolymers with unique structural and chemical properties. In the case of proteins, the two amino acids are linked by a rigid peptide bond and each amino acid could be unambiguously considered as a node in the protein graph. However, in the case of DNA, the linkage between two nucleotides is through a flexible phosphodiester bond and the nodes can be defined at various levels. For example, a nucleotide as a whole or its individual chemical components such as the phosphate group, deoxyribose and the bases  can be represented as nodes. Such a different representation of nodes has distinct advantages of their own, in interpreting the nature of interaction between the protein and DNA The present graph based analysis of protein–DNA complexes focuses on the following points. Primarily, the interface interactions of protein–DNA complexes have been investigated at a network level. This is achieved by constructing protein–DNA graphs (PDGs) on the basis of the strength of interaction between the nodes and also by performing extensive calibrations to choose the optimal strength of interactions to gain structural insights. Secondly, the clusters and hubs of such interacting amino acids and the nucleotides at the interfaces have been analyzed in a set of protein–DNA complexes.Significant results that are inaccessible by conventional pairwise analysis of the structure or by sequence analysis have been obtained from the present work. These include the identification of spatial networks of interacting residues that are sequentially far apart, the evaluation of a scale of interaction strength along which we can compare and analyze the interaction networks of protein–DNA complexes and the identification of groups of optimally interacting residues which stabilize the structural architecture. Furthermore, we have been able to revisit the classification scheme of DNA binding proteins. Our classification schema, which is based on the concepts of graphs/network, interaction strength, and the type of interaction, is distinct from the classification schemes proposed earlier with a protein-centric point of view. We have compared our results with the other classification schemes ijI  The details of the construction of PDG are presented in the A protein–DNA graph (PDG) is a bipartite graph constructed to represent the interaction between the amino acids of the protein and the nucleotides of the DNA in a protein–DNA complex. A bipartite graph deals with two different node sets and edges are defined across the two node sets. A contact in the bipartite PDG is defined when a side chain of an amino acid interacts with the nucleotide. The interactions of the amino acid with the nucleotide can be considered at different levels: with the phosphate (p), deoxyribose sugar (S) or base (B) components individually, or with the nucleotide as a complete entity. The edges are defined upon quantification of the interaction between the amino acids and the nucleotides with the “Interaction Strength,” ijI evaluated between the nodes is greater than or equal to a user-defined ijI. The user-defined ijI is termed as Minimal Effective Connection (MEC). We have constructed PDGs from protein–DNA complexes using a range of MEC from 0% to 15%. A graph generated with a high MEC is sparse with strongly connected nodes, and the graph generated with a low MEC is dense with weakly connected nodes , which interacts with either the major or the minor groove of the DNA. Unlike the β-sheet groups, the members of this group have diverse structures, albeit a common interaction scheme at the interface. Also, this group contains all the three  clusters. The P-S clusters are significantly less compared to the β-sheet group. Most members of this group have a characteristic P-B cluster located in the β-strand region that interacts with the major groove of the DNA , zipper-type (ZT), zinc coordinating group (ZC), and other α-helix group (OAH) use helices as the major secondary structure to interact with the DNA. HTH and ZT groups have been classified based on their structural motifs, while ZC group is classified according to the presence of coordinated Zinc in the complex. The Other α-Helix group constitutes all the remaining proteins that employ α-helices to interact with DNA. We center our discussion mainly on the well-characterized HTH and ZT groups.Helix turn helix. A HTH motif consists of two helices, which are almost perpendicular to each other, linked by a turn or a loop. One of these helices, the recognition helix, binds to the major groove, interacting with the exposed bases and the other helix is known as the probe helix Zipper-type. Two helices are present in the zipper-type group (basic-zipper family and helix-loop-helix zipper family belong to this group). They consist of a dimerization domain and a DNA binding domain. The dimerization domain is a coiled coil helix with hydrophobic amino acids at the C terminal end. In the N terminal end, the helices diverge to bind to the opposite faces of the major grooves, looking like prongs holding the DNA double helix  is presented below.Nucleosome core particle. In eukaryotic genomes, the packing and assembly of DNA into higher order structures (chromatins) is originated by the initial binding of DNA to histones to form nucleoprotein complexes. They play an important role as a packaging element that determines the accessibility of the DNA to other factors and enzymes of the DNA replication, transcription and repair machineries We have generated P-p, P-S, and P-B graphs for these two histone-DNA complexes (1aoi and 1s32). There is a clear domination of P-p and P-S clusters and a complete absence of P-B clusters. The lack of specific P-B clusters and the predominance of backbone mediated non-specific clusters agree with the fact that the histones interact non-specifically with the DNA through the electrostatic The cluster analysis of these two structures has shown that there are four additional P-p clusters in the clamped structure out of which one is around the linker region. There are five additional P-S clusters and two are present near the linker region . These oThe importance of classifying the DNA binding proteins from the protein–DNA point of view, rather than the protein-centric view is being recognized for developing better protein–DNA recognition code The interface P-p, P-S, and P-B clusters are examined in the protein–DNA complexes. The analysis of these clusters shows that the complexes can either have exclusive P-p, P-S, or P-B clusters or they can contain a mixture of these types of clusters. In cases where more than one type of cluster is observed, we define overlapping (amino acids sharing the same nucleotide) or non-overlapping (amino acids making contact with different nucleotides) clusters. Based on the types of clusters observed, the complexes are classified into seven groups. The complexes containing exclusive P-p, P-S, and P-B clusters are denoted as class 1, 2, and 3, respectively. Mixtures of (P-p+P-S), (P-S+P-B), and (P-p+P-B) clusters are denoted as class 4, 5, and 6, respectively. The complexes containing all the three component clusters  are considered as class 7. A sub-classification based on the presence of overlapping or non-overlapping (P-p+P-S) or (P-S+P-B) or (P-p+P-B) components is made for cases 4 to 7. The details of the classification of the protein–DNA complexes are presented in It should be noted that our classification scheme based on the interaction patterns of amino acids with nucleotide components in PDG does not directly deal with the type of interaction involved . However, indirectly, the P-p cluster is dominated by electrostatic interaction and the P-S clusters are composed of van der Waals interactions along with stacking of aromatic residues with the deoxyribose ring. The P-B graphs are dominated by stacking of amino acids (mostly the planar side chain of Arg) with the bases, H-bonding and also charge mediated interactions.A comparison of the present classification with the structural motif based classification by Luscombe et al Here we have shown that the DBPs classification based on the protein–DNA interface interaction at the molecular level differs significantly from the protein motif based classification, although a little consensus is observed. DBPs have also been classified based on other criteria. For instance, Prabakaran et al The fact that there is only a marginal overlap between different classification schemas underscores the versatilities in protein–DNA recognition mechanism. It may be valuable to use different approaches to obtain complementary information to understand the protein–DNA recognition mechanisms in detail.The present study aims to represent a protein–DNA interface as an undirected bipartite graph based on non-covalent interactions. A quantitative method has been developed to represent the interactions between both DNA and protein as a single, combined graph. Such a representation has facilitated the study of the spatial relationships between the amino acids and the nucleotides at the protein–DNA interface in a holistic way. Thus, protein–DNA interfaces across the spectrum of complexes could be compared at a uniform level, irrespective of the structural and functional differences.In general, we have provided a method of quantifying the interactions of proteins with the components of nucleotides . It is now clear that the combined representation of protein and DNA as PDGs could highlight the intricacies involved in protein–DNA recognition of some families of proteins. For instance, the predominance of protein-deoxyribose (P-S) clusters and hubs has brought out the specificity of the interaction in β-sheet proteins. Such analysis and the group specific features of protein–DNA recognition could be used as a starting point in predicting the DNA binding sites on these proteins. We have also proposed a scheme for classifying the structures based on the nature of the network connectivity present at the protein–DNA interface. Based on comparative analysis, we conclude that different classification schemes could provide complementary information on the nature of protein–DNA interactions.Thus, the analyses performed on a dataset of protein–DNA complexes have highlighted the nature of the clusters and hubs present at the recognition site. These clusters and hubs may not only prove to be valuable in understanding the residues contributing to the stability of the protein–DNA interfaces, but also could be identified as features characteristic for a given group of proteins. The knowledge gained from the study could also provide a platform for further docking and prediction experiments.The protein–DNA complexes with resolution better than 2.5 Å and with protein identity less than 25% were taken from PDB (Version 3.1) i and j, are considered to make a contact if they are within a distance of 4.5 Å and the total number of such contacts (ijn) is evaluated between a pair of nodes i and j. The strength of interaction (ijI) between these nodes i and j is evaluated in a manner similar to that adopted in the case of protein structure graphs ijI is the strength of interaction between the amino acid side chain (i) and the nucleotide (j) of the protein–DNA complex. As the graphs are undirected ijI = jiI. ijn is the number of interactions existing between nodes i and j (contacts within distance of 4.5 Å). iN and jN are the normalization values of the corresponding nodes evaluated as described in the next subsection.The interaction between the amino acids and the nucleotides at the protein–DNA interface is represented as undirected bipartite Protein–DNA Graphs (PDGs). Here, the amino acids comprise one node set and the nucleotides constitute the other node set of the bipartite-PDGs as shown in Here the evaluation of the strength of interaction is restricted only to atom-atom contact and does not explicitly take into account the details such as hydrogen bond, salt bridge interactions etc. Indirectly, this amounts to a measure of packing density at the selected region.ijI is evaluated for all the amino acid-nucleotide (interface) interactions of a given protein–DNA complex. A threshold of ijI is used to connect two nodes in a graph. The threshold ijI representing the minimal atomic connectivity between interacting amino acids and the nucleotides is called as the Minimal Effective Connection (MEC). Thus, an edge between nodes i, j is defined if the ijI evaluated is greater than the user-defined MEC. For instance, a MEC of 6% specified by the user results in the generation of a PDG with nodes connected by the interaction strength ijI≥6%. Here the evaluation of ijI requires the normalization values (maximum number of contacts made by the unit) for corresponding nucleotide and amino acid units. The details of the evaluation of the normalization values are discussed below.Na) and the A, G, T, C nucleotides (Nn) as shown below,ka is the maximum number of non-covalent contacts made by the amino acid, “a,” with other amino acids and nucleotides in the protein–DNA complex “k.” Similarly, Max kn is evaluated for the nucleotide “n” in the structure “k.” “m” is the total number of protein–DNA complexes from the non-redundant dataset.The normalization values are the estimates of the maximum non-covalent contacts an amino acid or a nucleotide can have in protein–DNA complexes. The method of obtaining normalization values for amino acids in proteins was previously given i±1) are ignored. However, in the case of nucleotides, the sequential base contacts (stacking) of a nucleotide (i±1) are taken into account, ignoring only the covalent phosphate and the sugar contacts of the sequential residues. We wish to point out that the normalization values of amino acids obtained here from protein–DNA complexes are not significantly different from those obtained only from protein structures Here it should be noted that, while calculating the amino acid - amino acid contacts, the contacts made by an amino acid side chain with its sequence neighbors , deoxyribose sugar (S), and the base (B). The interactions of an amino acid with all these individual components are characterized by constructing separate interaction graphs of amino acids with all the p, S, and B components of the DNA.NP, NS, and NB) for these dissected components of the nucleotide are also calculated using Equation 3 where, in place of the whole nucleotide, the dissected component of the nucleotide is considered as,kp is the maximum number of non-covalent contacts, made by the phosphate with other amino acid and nucleotide residues in a protein–DNA complex “k.” Similarly, Max kS and Max kB are evaluated for the deoxyribose sugar and the base, respectively, from the complex “k.” “m” is the total number of protein–DNA complexes from the non-redundant dataset.The normalization values  captures the significant interactions that exist between the amino acids and the nucleotides at the protein–DNA interface. In this study, we have focused on the interaction of protein with the components of nucleotides. Thus, we have identified the component clusters, P-p, P-S, and P-B, to capture the interaction of amino acids with the phosphate, deoxyribose and the base of the nucleotides, respectively. In many cases, amino acids interact with more than one component of a nucleotide . In such cases, we have defined the clusters as “overlapping clusters” (which consists of both phosphate and deoxyribose of the same nucleotide). Thus overlapping clusters constitute the interactions of amino acids captured with two or more components of a nucleotide. The details of the interface clusters for DS2 are given in The PDGs are constructed as specified above and represented as binary adjacency matrices at a given MEC. Clusters of interacting nodes are identified from the adjacency matrix using Depth First Search (DFS) algorithm Hubs are highly interacting nodes in a graph. In protein structure graphs, a node was declared as a hub if it was connected to a minimum of four nodes Figure S1Average of the Largest Clusters (as a function of MEC) of all protein-DNA complexes in the dataset. . From the above plot we can see that the sizes of the largest clusters are large at lower MEC (1%–3%) and this region is classified as WMEC. There is a transition in the sizes between MEC 4%–5% (corresponding to OMEC). Beyond this transition zone, the cluster sizes decrease consistently with MEC (SMEC region). Hence we have chosen these values of MEC as cut-offs for the weak, optimal and strong MEC according to the behavior as described above, to analyze different P-p and P-B graphs. Further fine tuning was carried out based on the analysis of specific cases. In this process, we slightly modified the criteria for P-S clusters in which the OMEC was shifted to 4% to 8%. Therefore this plot gives an idea on the basis of binning the MEC  for furt(0.15 MB DOC)Click here for additional data file.Figure S2A bipartite cluster based classification of DNA-binding proteins. P-p clusters are highlighted with yellow color for phosphate and brown for interacting amino acids. P-S clusters are highlighted with green color for deoxyribose sugar and purple color for the interacting amino acids and P-B clusters are highlighted with red color for bases and blue color for the interacting amino acid residues.(4.11 MB EPS)Click here for additional data file.Table S1A comprehensive list of the protein-DNA complexes studied and the general clusters and hubs information.(0.10 MB DOC)Click here for additional data file.Table S2Component clusters in different DNA-binding proteins.(0.18 MB DOC)Click here for additional data file.Table S3Component hubs in DNA-binding proteins.(0.85 MB DOC)Click here for additional data file.Table S4Equivalent clusters in 1aoi (without linker) and 1s32 (with linker).(0.04 MB DOC)Click here for additional data file.Table S5Normalization values evaluated from the updated dataset (DS1).(0.03 MB DOC)Click here for additional data file.


Signal transduction events often involve transient, yet specific, interactions between structurally conserved protein domains and polypeptide sequences in target proteins. The identification and validation of these associating domains is crucial to understand signal transduction pathways that modulate different cellular or developmental processes. Bioinformatics strategies to extract and integrate information from diverse sources have been shown to facilitate the experimental design to understand complex biological events. These methods, primarily based on information from high-throughput experiments, have also led to the identification of new connections thus providing hypothetical models for cellular events. Such models, in turn, provide a framework for directing experimental efforts for validating the predicted molecular rationale for complex cellular processes. In this context, it is envisaged that the rational design of peptides for protein-peptide binding studies could substantially facilitate the experimental strategies to evaluate a predicted interaction. This rational design procedure involves the integration of protein-protein interaction data, gene ontology, physico-chemical calculations, domain-domain interaction data and information on functional sites or critical residues.in vitro biochemical or biophysical studies.Here we describe an integrated approach called "PeptideMine" for the identification of peptides based on specific functional patterns present in the sequence of an interacting protein. This approach based on sequence searches in the interacting sequence space has been developed into a webserver, which can be used for the identification and analysis of peptides, peptide homologues or functional patterns from the interacting sequence space of a protein. To further facilitate experimental validation, the PeptideMine webserver also provides a list of physico-chemical parameters corresponding to the peptide to determine the feasibility of using the peptide for http://caps.ncbs.res.in/peptidemineThe strategy described here involves the integration of data and tools to identify potential interacting partners for a protein and design criteria for peptides based on desired biochemical properties. Alongside the search for interacting protein sequences using three different search programs, the server also provides the biochemical characteristics of candidate peptides to prune peptide sequences based on features that are most suited for a given experiment. The PeptideMine server is available at the URL:  The complexity of biological systems requires efficient integration of data, tools and protocols to extract new information. While high-throughput data integration approaches in bioinformatics have provided new insights into several biological problems, these are often not in a form suited for specific experimental validation. These data nonetheless include additional levels of function associations for proteins and their orthologs alongside enhanced annotation of gene products to aid in the functional characterization of proteins PX[RK] motifs . Cdc28 iSH3 domains are important signalling domains involved in a variety of signal transduction events ,55. In aDrosophila melanogaster 'dsh' is known to be involved in the Wnt signalling pathway in Drosophila. Dsh contains a PDZ domain along with a DEP and DIX domain -X-[FVA]. It is now known that their interaction is more diverse which includes internal protein sequences and lipids [Drosophila and used the signature motif [FYST]-X-[FVA] to search for potential binding sites among the interacting partners of Dsh. The PeptideMine server identified binding sites among the different interacting partners. This example illustrates the combined application of PROSITE pattern search and domain-domain interaction data integrated within the PeptideMine server. Here, a known signature motif was used as a query to identify a putative binding site from the interacting sequence space. As seen by these results, peptide no. 16 mapped on to the LIM domain (Pfam ID: PF00412) and one of its corresponding interacting domain  (obtained from the DOMINE database) is seen to be present in the query sequence. This could be further considered for protein-peptide docking or experimental analysis.X domain . Proteind lipids -61. We uin vivo functional traits can sometimes be difficult to distinguish based on experimental strategies in vitro to identify target peptides. For example, in the case of a PDZ target peptide identified by phage display methods, the sequence of the selected peptide approximated the natural consensus of the PTEN/MMAC protein. A single residue mutation (Lys to Trp) between the natural peptide and the phage display variant led to a high affinity interaction that could be rationalized in structural terms [albeit with some manual intervention required to design peptides with desired characteristics.The interacting proteins identified using PeptideMine also provide a case for the examination of multi-protein associations in a cellular context. Such multi-protein complexes allow for fidelity in signal transduction events while permitting temporal association between specific proteins. In the case of proteins with multiple SH3 or PDZ domains, for example, the PeptideMine strategy can help in the prediction of the components of such cellular complexes using motif-based searches or BLASTP searches in the interacting sequence of proteins with such domains. An important caveat that we emphasize in the PeptideMine approach is that the substrate peptides are chosen 'as is'. Modification, if any, for experimentally suitable chemical properties is at the discretion of the user. This is important as naturally occurring protein-peptide interactions are tuned to their cellular function. Thus high affinity interactions are suggestive of protein association for cellular localization. Low affinity, transient interactions, on the other hand, are more suited for signal transduction events. These al terms . In an eal terms . This apal terms . Our appal terms  and compal terms . Both ofin vitro. PeptideMine is a generic approach to identify biologically relevant putative substrates and peptides using limited information. As the datasets and the approach are not context-biased, the PeptideMine server can be utilised effectively to identify new and potentially interesting interacting partners. This approach scores over other strategies for the identification of interacting peptides as it provides an opportunity to identify peptides from interacting partners based on user-defined residue(s) and length-based criteria along with GO annotations alongside a web-based platform for the quality assessment of peptides. Other parameters, like secondary structure, disordered regions, instability index, charge and amino acid indices, are measured for the peptides identified by PeptideMine. PeptideMine is not trained using any data that may be specific to a particular protein-peptide system. It is thus likely that the PeptideMine method and server would help identify new peptides that can be validated using experimental and computational studies potentially leading to new biological insights. The webserver will be useful for the identification of new peptides to be used in experimental and computational protein-peptide binding studies. The concept of PeptideMine and the server can be specifically used for the identification and analysis of peptides or functional patterns for binding studies, peptide design, peptide-ligand identification, identification of homologous peptides and functional motifs in the interacting sequence space of protein, compilation of peptide libraries for high-throughput screening and protein-peptide docking analysis.The possibility to predict and validate protein-peptide interactions is a crucial step towards identifying the links between different cellular processes. Towards this goal, the rational design of peptides that can best mimic the predicted interactions is essential to ensure experimental validation Drosophila Leucocyte Antigen Related ProteinURL: Uniform Resource Locator; GO: Gene Ontology; PTP: Protein Tyrosine Phosphatase; RPTP: Receptor Protein Tyrosine Phosphatase; CDK: Cyclin Dependent protein Kinases; SH3: Src Homology 3; dsh: dishevelled; DEP: Domain found in Dishevelled, Egl-10, and Pleckstrin; PTEN: phosphatase and tensin homolog; DLAR: Project Name: PeptideMine - A webserver for the design of peptides for protein-peptide binding studies derived from protein-protein interactomes.Project home page: PeptideMine results pages for examples discussed in this manuscript: Operating system(s): Platform independent webserverLicense: Free for academics, Authorization license needed for commercial usage. (Please contact corresponding authors for more details)Any restrictions to use by non-academics: license needed. (Please contact corresponding authors for more details)The authors declare that they have no competing interests.KS contributed to the data integration, server development, programming and compiling the manuscript, LLM, SV and BG were involved in the formulation of the strategy and representative examples, RS contributed to the overall planning of this project and the presentation of the webserver and this manuscript. All authors read and approved the final manuscript.Details about the PeptideMine search parameters and URL to access the PeptideMine results page of experimentally validated peptides.Click here for file


Proteins interacting with each other as a complex play an important role in many molecular processes and functions. Directly detecting protein complexes is still costly, whereas many protein-protein interaction (PPI) maps for model organisms are available owing to the fast development of high-throughput PPI detecting techniques. These binary PPI data provides fundamental and abundant information for inferring new protein complexes. However, PPI data from different experiments do not overlap very much usually. The main reason is that the functions of proteins can activate only on certain environment or stimulus. In a short, PPI is condition-specific. Therefore specifying the conditions on when complexes are present is necessary for a deep understanding of their behaviours. Meanwhile, proteins have various interaction ways and control mechanisms to form different kinds of complexes. Thus the discovery of a certain type of complexes should depend on their own distinct biological or topological characteristics. We do not attempt to find all kinds of complexes by using certain features. Here, we integrate transcription regulation data (TR), gene expression data (GE) and protein-protein interaction data at the systems biology level to discover a special kind of protein complex called conditional co-regulated protein complexes. A conditional co-regulated protein complex has three remarkable features: the coding genes of the member proteins share the same transcription factor (TF), under a certain condition the coding genes express co-ordinately and the member proteins interact mutually as a complex to implement a common biological function.A framework of discovering the conditional co-regulated protein complexes is proposed. Testing on the Yeast data sets under the Cell Cycle, DNA Damage and Dauxic Shift conditions, we identified a total of 29 conditional co-regulated complexes, among which the coding genes in 14 complexes show a strong association with their TFs activity. Based on the close relationship among co-regulation, co-expression and protein-protein interactions in the conditional co-regulated protein complexes, 39 novel TRs were predicted and explained.   This paper was initiated to study conditional co-regulated protein complexes by integrating multiple data sources. Taking into consideration the influence of TFs activity on the protein interactions, we found that the expression coherence of the protein complexes’ coding genes changed in accordance to their TFs’ activity, which implied that the proteins’ interactions also changed in response to the environments. Based on the three features of conditional co-regulated protein complexes, new transcriptional regulation interactions were predicted. Protein complexes perform all kinds of fundamental biological functions in cells. Thus far, there have few reliable techniques to directly detect protein complexes in a large-scale style, whereas binary interaction between two proteins is relatively easy to be detected by experiments such as Yeast Two-Hybrid Y2H) , tandem H , tandeMeanwhile, different types of protein complexes have their own distinct biological ‘pattern’ or ‘topology’ characteristics. Taking the topological feature of complexes as example, some complexes can be modelled as ‘clique’ whose members are densely connected within themselves but sparsely connected with the rest of the network ,18, whilTR), gene expression data (GE) and protein-protein interaction data (PPI) at the level of systems biology. Furthermore, we consider the condition-specific features of the interactions including TR and PPI. Because accurate temporal parameters are not yet available for many protein–protein interactions, a common way to estimate temporal characteristics of protein products is using compilations of GE data [Instead of defining protein complexes only by their topological characteristics, we make use of the three remarkable features of conditional co-regulated protein complexes: (1) the coding genes of the member proteins share the same active transcription factor, (2) the coding genes express co-ordinately and (3) the member proteins mutually interact as a complex to implement a common biological function. In order to study their associations under some given condition, we integrate transcription regulation data  , where EPPI = { | ip, jp ∊ P}. TR interaction data is denoted by TI =  , where ETI = { | itf ∊ T, xj ∊ G}. In particular, for px stands for the coding gene for protein p. Let GE under certain condition, L ⊆ PPI is a conditional protein complex, if and only if it meets the following requirements. Given a for px where p ∊ P ', share the same active tf ∊ T under the given condition,(i) All L is a connected graph,(ii) Score(L) of L is greater than the threshold α and its score degree is consistent with the activity of its TF as least θ conditions in all conditions.(iii) The coherent score α and θ in (iii) are thresholds used to distinguish a conditional co-regulated protein complex from the others.The above three requirements correspond to the three features of co-regulated protein complexes. Parameters Trace-Back algorithm [Identifying the active TFs under given conditions is challenging. A recent research work  adopted lgorithm  to identcore-neighbour strategy to search our target complexes.  The procedure includes two stages: the first one is to search the core part  in the WCCs in a greedy way and the second one is to conduct an extensive search for extra members of the core in the global PPI in a heuristic way. The computational benefit is obvious as compared to ten thousands of edges in a global PPI, the search space in WCCs decreases by orders of magnitude. As constrained by the requirement (iii) in the definition of conditional protein complexes, we also use a coherent scoring threshold α to judge whether a group is a seed. The arc to vertex number of these WCCs varies greatly from several ones to hundreds. It’s infeasible to find out all combinations of the proteins which are linked by hundreds of interactions in the WCC due to the computational cost. However, in practice the member proteins in most known protein complexes do not exceed ten. Therefore, we set two parameters λ and β to limit the minimum and maximum edges to narrow down the search space. We take a greedy method to search all possible groups meeting the two parameters in WCCs, and then we measure their coherent score in the given condition, and finally identify seeds whose score exceeds α and its score degree is consistent with the activity of its TF as least θ conditions.For all target genes of each active TF, their protein products and their protein-protein interactions form a local sub-network in the global PPI network. This sub-network will be decomposed into several weakly connected components (WCCs). Because WCCs disjoint each other, one TF may correspond to more than one WCC. We take a L = . For any iv, jv ∊V, if  e =  ∊ E, we calculate a score of the coherence between iv, jv byAs the coding genes in a co-regulated complex have coherent expression, the change in the coherence degree can indicate different states of the complex function. Taking the scoring methods as used in ,16, a prCorr  is the Pearson Correlation Coefficient between the coding genes of protein i and protein j to reflect their coherence. Different from the formula used in [Where  used in , we do nL, denoted by T(L), is the sum over the scores of all the edges in L:Thus, the coherence score of L. Guo[L with K edges, we randomly choose 10 000 sub-graphs with K edges from the PPI network and compute their score by using formula (2), then calculate the average and standard deviation value of these 10 000 graphs and use formula (3) to standardize the final score for seed L with K edges. After standardization, groups with different number of edges can be compared with the coherence.We note that the coherence score can be influenced by the number of edges in L. Guo and IdekL. Guo have proPPI network, we seek extra proteins of the seeds which interact intensively and co-express with the given seed with a high coherence score. A protein which expresses differently with the seed will make the score decrease; while a protein which expresses consistently with most parts of the seed will increase the score. Therefore, the search process can be converted to optimize the score by adjusting the structure of the sub-graph starting from the seed. Our extensive searching implements a simulated annealing procedure for every seed. The proteins that TR does not indicate the same TF with the seed can be added into the seed by the extensive search. In this situation we can predict a new TR interaction according to the inference model shown in the Figure initialL is corresponding to the seed. The input parameters startT, endT are the initial and ending temperature respectively, and N is the iteration number.In the global TRs data from Luscombe’s work [Trace-Back algorithm to determine active TFs. The distribution of active TFs in the three conditions is shown in Figure The Yeast dataset used in the method evaluation involves three biological conditions: Cell Cycle , DNA Dame’s work , which uλ = 2 and β = 21 to greedily search complex seeds from the all possible parts whose edge number is between λ and β in the WCCs. If two candidates share common 80% proteins, we take the one with a higher coherent score. Because there is no gold-standard threshold for the coherent score to judge which seeds can be in a co-regulated complex, we had to take the top ones in the ranking list to guarantee the prediction accuracy with the parameter α = 1.90.Starting from the active TFs, all WCCs are decomposed according to the structure of the global PPI network. The edge number of the WCCs varies greatly. The maximal edge number of the WWC for the transcription factor YKL112W can reach 293. We set YNL216W and YPR104C regulated the common seed) identified by our method from 21 TFs who’s at least active in one of the three conditions，and all seeds’ coherence degree is consistent with activity of their TF at least θ = 2. In Table YOR028C, YDR451C, YLR183C, YBL021C, YGL013C, YDL020C, YDR207C, YKL112W, YCR065W and YKL109W are perfectly consistent with their TFs’ activity. As the expression coherent levels of the proteins’ coding genes change in accordance to the activities of the TFs in all conditions, we can infer that the functions of their protein complexes also follow the pace with their coding genes and TFs. They are perfect conditional co-regulated protein complex seeds. Take the complex seed L consisting of YDL156W, YAR007C, and YJL115W with 2 edges as example. Their transcription factor is YDR451C. Figure L2edges_random) distribution generated by 10 000 random sampling and the corresponding expression profiles of the complex seed L. This complex seed has T(L)=0.6386, T(L)=0.0605, T(L)=0.3513 and Score(L)=3.18, Score(L)=-0.49, Score(L)=0.25 under the C1, C2 and C3 conditions respectively. Comparing between C2 and C3, T(L)=0.6386 in C1 is significant. The probability of T(L) over 0.63 is 0.07 in the distribution by random sampling as showed in left panel figure L) and Score(L) score are consistent with the activity of their TF.Table In order to validate whether the proteins can form a complex, we identify the corresponding MIPS complexes which contain as many proteins in the predicted complex seeds as possible. 21/29 seeds have over 50% proteins covered by the corresponding MIPS complexes (the coverage genes are shown by bold). However, TFs are not the only factors to determine the behaviours of the coding genes. For example, we found that the complex seeds belonging to TF YBR049C may be influenced by other factors or stimulus, as YBR049C is active in DNA Damaging, but all its complex seeds show low coherent degree in DNA Damaging. We guess it is caused by DNA Damaging. In Table L that consists of YDL156W, YAR007C, and YJL115W with 2 edges. Unfortunately, for the simulated annealing algorithm, there are no choices of parameters that will be good for all problems, and there is no general way to find the best choices for a given problem [After the extensive search, not only the new TRs could be predicted, but also the extra members for the core seeds could be found. First to show whether the simulated annealing algorithm can reach a convergence point and to illustrate the parameter settings, we conducted an experiment to investigate how the score and edge number are changed with the iterations for the example complex seed  problem . In theoTstart=2, Tstart=1 and Tstart=0.05 are shown in Figure Tstart and Tend is big, it will have big acceptance probability for the weak candidates. It could be observed in heading parts of the curve in (a) and (b), whose Score(L) decreased rapidly. When the temperature is cooling down, the weak candidates are likely excluded. However, it could jump out the local optimality to global optimality. In contrast, when Tstart is near to Tend, it’s more possible to reject the weak candidates and easily to reach the local optimal. The parameter N decides the searching times. If N is small, it couldn’t reach all possible searching space. Therefore, N should be set a bit big. Because our framework is based on core-neighbour strategy, the optimal local seeds have been identified and it could set Tstart near to Tend to reject the weak candidates.Three annealing runs starting from different initial annealing temperatures Tstart=1, Tend=0.01, N = 3000. After adding the extra members by stimulated annealing extensive searching, the Score(L) is 15.09 under Cell Cycle and Score(L) is 9.22 under DNA Damaging. If complexes are active under several conditions, we take their overlapping part in the extensive searching results. Figure The seed corresponding to the TF Hsf1 (YGL073W) had the highest score in the cell cycle (11.1) and DNA Damaging (5.75) conditions. As mentioned in the method part, we can use co-expression and protein-protein interactions to infer new transcription regulations. We take it as an example to illustrate how new TRs are discovered based on Hsf1 under the conditions of cell cycle and DNA Damaging. During the extensive search, the parameters are set as follows: We validated our prediction results from three aspects: (1) we retrieved and compared with literature works which predicted the same TRs; (2) we detected the conserved binding motifs from the target genes in the seed and examined whether there were matches in the promoter of the predicted target; (3) we examined whether the function of predicted target genes was consistent with those of target genes in the seed. Of course, the final validation for the prediction result should depend on the biology experiment in the cell cycle condition. We found that the results by ,33 and ,35 suppoHowever, we have not found direct evidence to support that Hsf1 regulates YKL117W. Maybe, it is a good idea to find evidence from binding motifs to support this. There are two significant binding motifs induced by the tool MEME from the upstream 600bp of the coding genes in the seed, which are shown in Figure For other extensive searches, in order to guarantee the accuracy, we only consider those complex seeds whose coherence perfectly consistent with the activity of their transcription factor in three conditions. Table In this paper, we proposed a framework to discover conditional co-regulated protein complexes by integrating TR, GE and PPI data. This kind of protein complexes has three remarkable features: the coding genes of the member proteins share the same transcription factor, under certain condition the coding genes express co-ordinately and the member proteins interact mutually as a complex to implement a common biological function. Comparing to the existing works, one advantage is that our method not only uses the coding genes expression to measure the conditional protein activity but also takes the upstream TF activity into account to study their influence on protein complex. In the experiment, we observed some typical cases in which protein complexes’ coding gene coherent degree is strongly associated with their TF activity under different conditions. Another contribution is that we advanced the procedure of discovering co-regulated protein complex to discover potential unknown transcriptional regulation based on the tight relationships among co-regulation, co-expression and protein interaction. Because our work is based on the integration of several heterogeneous data sources, the result of the work could be influenced by the data quality in several aspects. The first one is the missing value in gene expression profiles. Besides the usual ways to directly assign value zero or the average value of the gene row to them, many other approaches have been proposed such as Singular Value Decomposition (SVD) based method (SVDimpute), weighted K-nearest neighbors (KNNimpute). Brock has examThis study proposed the concept of conditional co-regulated protein complexes and developed a framework to discover them by integrating transcriptional regulation data, gene expression data, and protein-protein data. By linking these three types of data, the coherence change of the conditional co-regulated protein complexes influenced by the activity of TFs was observed, which implied that the functions of the proteins complexes were condition-dependent. We also reported newly inferred transcriptional regulations and validated the result rigorously.The authors declare that there are no competing interests.Luo and Liu initiated the main idea of the paper. Luo conducted all of the programming and computational experiments. Liu and Li supervised the whole work. The writing of the manuscript was conducted by Luo, and revised by Luo, Li and Liu.The protein-protein interaction data is contained in protein protein interaction.mat.Click here for fileTranscriptional regulation data is contained in transcriptional regulation.mat.Click here for fileGene expression data is contained in gene expression.mat.Click here for file


Understanding the mechanisms of protein–protein interaction is a fundamental problem with many practical applications. The fact that different proteins can bind similar partners suggests that convergently evolved binding interfaces are reused in different complexes. A set of protein complexes composed of non-homologous domains interacting with homologous partners at equivalent binding sites was collected in 2006, offering an opportunity to investigate this point. We considered 433 pairs of protein–protein complexes from the ABAC database (AB and AC binary protein complexes sharing a homologous partner A) and analyzed the extent of physico-chemical similarity at the atomic and residue level at the protein–protein interface. Homologous partners of the complexes were superimposed using Multiprot, and similar atoms at the interface were quantified using a five class grouping scheme and a distance cut-off. We found that the number of interfacial atoms with similar properties is systematically lower in the non-homologous proteins than in the homologous ones. We assessed the significance of the similarity by bootstrapping the atomic properties at the interfaces. We found that the similarity of binding sites is very significant between homologous proteins, as expected, but generally insignificant between the non-homologous proteins that bind to homologous partners. Furthermore, evolutionarily conserved residues are not colocalized within the binding sites of non-homologous proteins. We could only identify a limited number of cases of structural mimicry at the interface, suggesting that this property is less generic than previously thought. Our results support the hypothesis that different proteins can interact with similar partners using alternate strategies, but do not support convergent evolution. Interaction between proteins is a fundamental process, generic to most biological pathways. The increasing number of protein–protein complexes with atomic data should help us to understand the major factors that guide protein interactions. In particular, a number of examples are available of similar proteins that interact with proteins that are very different in terms of structure and function. An intuitive hypothesis to explain the ability of these different proteins to recognize the same partner is that they display the same local region for interaction, in other words, they imitate the same binding site. Here, we quantify the similarity between these putatively mimicking binding sites. We show that it is not statistically significant. We confirm this observation on the small sets of evolutionarily conserved residues. Our results suggest that different proteins that bind the same protein do not imitate binding sites, but probably target specific locations or residues at the binding site. Protein-protein interaction is the basis of numerous biological functions, such as immune response, supra-molecular assembly, enzymatic reactions, and many more. Understanding the way proteins interact is thus a fundamental challenge. The collection of all protein-protein interactions, the interactome, is also of great importance for drug discovery A notable element to understand the mechanism of protein-protein interaction is the existence of hot spots, residues that make major contributions to the binding energy, see for example Several groups have addressed the question of the evolutionary conservation of protein-protein binding sites and binding modes. At first found to be insignificant Another important notion we want to introduce here is the existence of promiscuous proteins. Promiscuity, also called multi-functionality or moonlighting, denotes the ability of one protein to perform distinct functions, see reviews The fact that a promiscuous protein can bind to different partners using the same binding site is puzzling, but also of outstanding interest to further understand the mechanisms of protein-protein interactions. Does this observation imply that radically different proteins possess similar binding sites in order to recognize a single promiscuous protein? At first sight, it might seem hopeless to look for similar binding sites on non-homologous proteins that differ in structure, function and ancestry. However, the literature is rich in examples of approaches employing - or searching for - such local similarities between unrelated proteins. This is the case for at least three distinct targets: catalytic sites, ligand binding sites and protein-protein binding sites. In the case of catalytic sites, the well-known example of the catalytic triad pattern, found in diverse serine proteases, has motivated a number of developments In this paper, we analyze a set of protein-protein complexes involving homologous proteins in interaction with different partners. These examples come from an analysis of PDB complexes in terms of SCOP domains, and are stored in the ABAC database The overall methodology used to assess the similarity at protein-protein interfaces is summarized in The ABAC pairs are classified into five categories on the basis of the quality of the superimposition between the two complexes, as illustrated in In the rest of the paper, we present a quantitative analysis of similarity at protein-protein interfaces in ABAC pairs, and then evaluate its significance against a random model. We also survey the similarity of interfaces in terms of evolutionarily conserved residues.We first compute the number of similar elements - atoms, pseudo-atoms or residues - in each partner of the protein complexes after structural superimposition of the common partners A and A′. Domains A and A′ are from homologous domains from the same SCOP family. Consequently, we expect a good level of similarity between them. However, since such similarity results from divergence from a common ancestor and fold conservation, it does not necessarily imply that the similar elements are key determinants for the protein-protein interaction. Domains B and C are from different SCOP superfamilies. They thus have very different structures, but a common ability to bind to the same, or, at least, a similar partner. Similar elements between B and C could thus be a sign of evolutionary convergence to a given binding motif, or indicate which functional groups are essential for the binding.versus the non-homologous sides (B and C) of each complex. Each ABAC pair is thus represented by two points: one for complex AB and one for complex A′C. We previously checked that the sizes of the binding sites on A/A′ and B/C sides are roughly similar . It thus seems that protein interfaces with no detectable similarity can nevertheless bind similar partners.in silico hot spot prediction methods could bring additional information.We should temper this result by noting that the energetic contribution of interfacial residues is uneven; some hot spot residues make major contribution, while other residues are unimportant. Unfortunately, energetic information - requiring extensive mutation analysis - is not available for our full data set, we thus approached this particularity in an indirect way. Although evolutionary conservation is a poor discriminant of hot spots via which they interact with diverse, dissimilar proteins. The binding interfaces of these dissimilar proteins exhibit different atomic/residue patterns, and their conserved residues are not co-localized. It thus suggests that different proteins use their own set of atoms/residues to perform the recognition, as illustrated in Altogether, our results suggest the following picture for promiscuous protein-protein binding: similar, homologous proteins present binding sites with great similarity, A last point to discuss is the existence of structural mimicry at interface. Protein mimicry is an intuitive concept, that has been successfully used in rational design The present study focused on promiscuous binding at the family level. The goal was to find the key determinants that allow unrelated proteins to bind to homologous partners. Our main conclusions are summarized below.Homologous proteins that bind different partners display different levels of structure similarity. Structural variation and residue insertion at the interfaces, as well as global structural variation, are seen in roughly one third of the ABAC pairs. This has to be taken into account in order to properly analyze the similarity of the binding sites.Structural mimicry at the interface of unrelated proteins that bind to homologous partners has been identified, but only for a limited number of ABAC pairs (53 out of 433 pairs), and an even more limited number of protein families (3 out of 105). Interface mimicry is thus probably not as generic as previously thought.Similarity between binding sites of unrelated proteins that bind to the same target is largely insignificant in terms of physico-chemical properties with similar spatial arrangement. That does not exclude the possibility that the same physico-chemical properties could be organized in a different manner between unrelated proteins.Conserved residues within the binding sites of unrelated proteins that bind to the same target are not co-localized.We were not able to find evidence of convergent evolution. Our results support the hypothesis that promiscuous binding is rather achieved by alternative binding strategies for different partners.http://scoppi.biotec.tu-dresden.de/abac/) that contains protein-protein complexes organized in pairs We exploited the data from the ABAC database  SCOP domains spanning several protein chains involved in the binding site are excluded from the analysis for computational simplicity. We also removed complexes with missing atomic coordinates at the binding site, and pairs with very low overlap between the binding sites resulting in no superimposed atoms on the B/C side. Details concerning the minimum overlap size in the data set are given in Table 5 in PDB files of protein-protein complexes were retrieved from the PQS database O : there is a good superimposition between A and A′, 246 pairs,M (mimicry): same as O, but in addition, domains B and C are an example of structural mimicry at the binding site, 53 pairs,E (ensemble conformation): domains A and A′ display alternate conformations at the interface, 63 pairs,I (insertion/deletion): domains A and A′ differ by an insertion/deletion at the interface, 21 pairs,S (superimposition problem): global superimposition between A and A′ is poor, due to structural variability between A and A′, 50 pairs.average values of structural deviation, hence they only reflect global tendencies; furthermore, they depend on the extent of the structural alignments. Also, irmsd computation does not take into account insertion of residues, because they are unaligned. Structural mimicry of B and C domains cannot be detected using rmsd, since domains B and C are unrelated and hence not superimposable. The classification thus ultimately results from a careful visual examination that takes into account all these parameters.For the category M, the geometry of the main chain of B and C domains in the binding site was taken into account. Globally, O and M categories correspond to smaller rmsd between A and A′ domains, and smaller irmsd  compared to category S; categories E and I are intermediate; and categories overlap in terms of rmsd values, see Figure 2 in Our data set is non-redundant in the sense that every SCOP family combination is unique. However, the ABAC pairs are not independent, since the same SCOP family can be shared by several pairs. For example, the SCOP family 49504 (Plastocyanin/azurin-like) is shared by the A/A′ domains of two ABAC pairs:Paracoccus denitrificans complexed with cytochrome c551) versus PDB structure 1gr7, chains BC (dimer of azurins of Pseudomonas aeruginosa),PDB structure 1mg2, chains OP (amicyanin of Enteromorpha prolifera) versus PDB structure 1mda, chains AM (amicyanin of Paracoccus denitrificans complexed with the light chain of methylamine dehydrogenase).PDB structure 7pcy, chains AC  less than similar if both atoms were from the same group. Cutoff As in Binding site similarity was also quantified on a per-residue basis, by representing each residue by its CIn order to take into account the fact that residues are described by a reduced number of points using these simplified representations, the cut-off to detect similar points after complex superimposition was empirically set to 4 Å for the CThe significance of the similarity between binding sites was assessed by bootstrapping. The principle is to generate random binding sites by randomly re-assigning the atom types in the overlapping interfaces. The advantage of this re-sampling is that the sizes of the compared objects are preserved. The procedure was repeated 500 times in order to obtain the distribution of the number of similar atoms (or pseudo-atoms or residues) between two binding sites that can be expected with a random model. The extent of the observed similarity could then be assessed by computing the corresponding P-value, Evolutionarily conserved residues were detected using the ConSurf database During the comparison of binding sites, 131 comparisons out of 433 involved a binding site with no conserved residues when considering A/A′ domains, and 178 out of 433 when considering B/C domains. The analysis of evolutionarily conserved residues is thus inherently based on a smaller data set.Text S1Supporting Figures and Tables.(1.23 MB PDF)Click here for additional data file.


Ortholog detection methods present a powerful approach for finding genes that participate in similar biological processes across different organisms, extending our understanding of interactions between genes across different pathways, and understanding the evolution of gene families.  Drosophila melanogaster, Saccharomyces cerevisiae, Mus musculus and Homo sapiens protein-protein interaction networks extracted from the DIP repository and Mus musculus and Homo sapiens and Sus scrofa gene coexpression networks extracted from NCBI's Gene Expression Omnibus using the decision tree, Naive-Bayes and Support Vector Machine classification algorithms.We exploit features derived from the alignment of protein-protein interaction networks and gene-coexpression networks to reconstruct KEGG orthologs forThe performance of our classifiers in reconstructing KEGG orthologs is compared against a basic reciprocal BLAST hit approach. We provide implementations of the resulting algorithms as part of BiNA, an open source biomolecular network alignment toolkit. With the advent of fast and relatively inexpensive sequencing technology, it has become possible to access and compare genomes from a wide range of organisms including many eukaryotes as well as bacteria and archea through databases such as GenBank , EnsemblSeveral sequence-based approaches currently exist for finding orthologous genes among a set of genomes. For instance, one of the simplest methods is to utilize reciprocal best BLAST hits  across aAgainst this background, we explore a set of graph features that may be utilized in detecting orthologs based on sequence similarity as well as the similarity of their neighborhoods in protein-protein interaction and gene coexpression networks. Furthermore, we construct a set of classifiers that utilize the above features and compare the classifiers to the reciprocal BLAST hits approached for the reconstruction of KEGG orthologs . The basWe utilize the alignment algorithms available as part of the BiNA (Biomolecular Network Alignment) toolkit  as well -10 was used to match protein sequences across species. The KEGG (Kyoto Encyclopedia of Genes and Genomes) [The yeast, fly, mouse and human protein-protein interaction networks were obtained from the Database of Interacting Proteins (DIP) release 1/26/2009 . The seqGenomes)  orthologSus scrofa) [Homo sapiens) [Mus musculus) [-10 was used to match gene sequences across species. The KEGG (Kyoto Encyclopedia of Genes and Genomes) [For detecting orthologs based on gene-coexpression networks, Affymetrix gene expression data was collected from the GEO database for experiments in selected tissues in pigs ( scrofa) , humans sapiens) , and micusculus) . The colusculus)  and compusculus)  with a cGenomes)  orthologk-hop neighborhood-based approach to alignment uses the notion of k-hop neighborhood. The k-hop neighborhood of a vertex G1 is simply a subgraph of G1 that connects V1 that are reachable in k hops from E1. Given two graphs G1 and G2,a mapping matrix P that associates each vertex in V1 with zero or more vertices in V2 (the matrix P can be constructed based on BLAST matches) and a user-specified parameter k, we construct for each vertex k-hop neighborhood xC in G1. We then use the mapping matrix P to obtain the set of matches for vertex V2; and construct the k-hop neighborhood yZ for each matching vertex G2 and k-hop neighborhoods in G2 associated with the vertex G1. We compare each k-hop subgraph xC in G1 with each member of the corresponding collection k-hop subgraph of G2 that is the best match for xC (based on a chosen similarity measure). Figure The proteins in the DIP protein-protein interaction networks for mouse, human, yeast, and fly as well as the gene coexpression networks for mouse, human and pig from GEO were matched using BLAST as shown in Figure k-hop subgraphs, bicomponent clusters extracted from G1 and G2 respectively) is given by:The shortest path graph kernel was first described by Borgwardt and Kriegel . As the O(n3). The shortest path graph kernel has a runtime of O(n4) (where n is the maximum number of nodes in larger of the two graphs being compared). Please see Figure where k-hop subgraphs, bicomponent clusters extracted from G1 and G2 respectively) is given by:The random walk graph kernel  has beenI is the identity matrix, λ is a user-specified variable controlling the length of the random walks ,x K is an nm × nm matrix  ). This is due to the fact that the product graph's adjacency matrix is nm × nm, and the matrix inverse operation takes O(h3) time, where h is the number of rows in the matrix being inverted 3) or O(r6) where r = max). However, runtime complexity of the random walk graph kernel  can be improved to O(r3) by making use of the Sylvester equations as proposed by Borgwardt et al. [We adapted the random walk graph kernel to align protein-protein interaction networks by taking advantage of the reciprocal BLAST hits (RBH) among the proteins in the networks from different species . Naive it et al. . Figure  takes Oh time, whThe BaryCenter score is calculated based on the total shortest path of the node. The shortest path distances for each node in a graph is calculated and the score is assigned to the node based the sum of the lengths of all the shortest paths that pass through the node . More ceBetweenness is a centrality measure of a vertex within a graph. Vertices that occur on many shortest paths between other vertices have a higher betweenness score than nodes that do not occur on many paths .G1, the betweenness score for vertex For a graph Where The degree distribution score is a simple node importance ranker based on the degree of the node. Nodes with a high number of connections will get a high score while nodes with a smaller number of connections will receive a lower score.G1, each node The HITS score represents the "hubs-and-authorities" importance measures for each node in a graph . The scoIn order to establish orthologs between fly, yeast, human, pig and mouse, the 1 hop and 2 hop shortest path and random walk scores, BLAST score, BaryCenter score, betweenness score, degree distribution score and HITS score were computed for each pair of homologs detected by BLAST . The BaryCenter, betweenness, degree distribution and HITS scores were combined using Milenkoviæ et al.'s  formula G1 and G2, where G1 may have much more nodes than G2, thus the node-based scores for G1 may be more likely to be greater than the node-based scores for G2).Where We utilized three broad classes of methods for detecting orthologs:i a in species A and the genes/proteins b1, …, mb in species B are si1, …, sim. The method predicts the gene/protein in species B that has the highest averaged BLAST score as the ortholog to gene/protein ia in species A.• Reciprocal BLAST hits method ,16. The • The reciprocal BLAST score-based classifier takes as input the averaged BLAST scores for each possible pair of genes/proteins and outputs a prediction as to whether the pair are orthologous to each other. This method can predict multiple orthologs from species B for each gene/protein from species A (and vice versa).• The network-based classifier takes as input a vector of pairwise scores (see “Scoring candidate orthologs based on sequence and network similarity" section) computed using the gene-coexpression or protein-protein interaction networks , an open source Biomolecular Network Alignment toolkit. The current implementation includes variants of the shortest path and random walk graph kernels for computing orthologs between pairs of subnetworks and the computation of various graph-based features available in the Java Universal Graph Framework library [The network neighborhood-based homology detection algorithm is implemented in BiNA ( library  such as  library , between library  and HITS library  centraliThe authors declare that they have no competing interests.SVP, CO and OC assembled and verified the datasets for the analysis. FT wrote the algorithms, ran the experiments and wrote the initial draft of the manuscript. CT, MHWG and VH supervised the analysis, design of the algorithms and revisions to the manuscript.mouse_pig_orthologs.csvhuman_pig_orthologs.csv