Prediction of protein-protein interactions: unifying evolution and structure at protein interfaces. Phys Biol 8:035006

Koc University, Center for Computational Biology and Bioinformatics, and College of Engineering, Rumelifeneri Yolu, 34450 Sariyer Istanbul, Turkey.
Physical Biology (Impact Factor: 2.54). 06/2011; 8(3):035006. DOI: 10.1088/1478-3975/8/3/035006
Source: PubMed


The vast majority of the chores in the living cell involve protein-protein interactions. Providing details of protein interactions at the residue level and incorporating them into protein interaction networks are crucial toward the elucidation of a dynamic picture of cells. Despite the rapid increase in the number of structurally known protein complexes, we are still far away from a complete network. Given experimental limitations, computational modeling of protein interactions is a prerequisite to proceed on the way to complete structural networks. In this work, we focus on the question 'how do proteins interact?' rather than 'which proteins interact?' and we review structure-based protein-protein interaction prediction approaches. As a sample approach for modeling protein interactions, PRISM is detailed which combines structural similarity and evolutionary conservation in protein interfaces to infer structures of complexes in the protein interaction network. This will ultimately help us to understand the role of protein interfaces in predicting bound conformations.

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    • "In these methods, a protein complex is modeled using sequence or structural similarity to a known template protein complex. Template based methods can either use sequence or structural homology or interface similarity [21]. However, these methods are applicable only when template complexes or interfaces exist for a query complex. "
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    ABSTRACT: We present a novel partner-specific protein-protein interaction site prediction method called PAIRpred. Unlike most existing machine learning binding site prediction methods, PAIRpred uses information from both proteins in a protein complex to generate predict pairs of interacting residues from the two proteins. PAIRpred captures sequence and structure information about residue pairs through pairwise kernels that are used for training a support vector machine classifier. As a result, PAIRpred presents a more detailed model of protein binding, and offers state of the art accuracy in predicting binding sites at the protein level as well as inter-protein residue contacts at the complex level. We demonstrate PAIRpred's performance on Docking Benchmark 4.0 and recent CAPRI targets. We present a detailed performance analysis outlining the contribution of different sequence and structure features, together with a comparison to a variety of existing interface prediction techniques. We have also studied the impact of binding-associated conformational change on prediction accuracy and found PAIRpred to be more robust to such structural changes than existing schemes. As an illustration of potential applications of PAIRpred, we provide a case study in which PAIRpred is used to analyze the nature and specificity of the interface in the interaction of human ISG15 protein with NS1 protein from inuenza A virus. Python code for PAIRpred is available at: © Proteins 2013;. © 2013 Wiley Periodicals, Inc.
    Proteins Structure Function and Bioinformatics 07/2014; 82(7). DOI:10.1002/prot.24479 · 2.63 Impact Factor
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    • "Using only interface architectures as templates, independent of global sequence or structure homology, is a significant improvement on this scheme, because it was found that similar protein interface architectures are reused by structurally and functionally different protein pairs (Tuncbag et al., 2011a,b; Gunther et al., 2007; Kundrotas and Vakser, 2010; Sinha et al., 2010, 2012). In the PRISM protocol (Tuncbag et al., 2011a,b) the target proteins, after being transformed onto the template complex, additionally undergo flexible refinement. The performance of various docking and scoring methods, servers and groups are evaluated in a community-wide experiment , the Critical Assessment of Predicted Interactions (CAPRI) (Lensink and Wodak, 2013; Janin, 2013). "
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    ABSTRACT: Understanding the molecular basis of protein function remains a central goal of biology, with the hope to elucidate the role of human genes in health and in disease, and to rationally design therapies through targeted molecular perturbations. We review here some of the computational techniques and resources available for characterizing a critical aspect of protein function - those mediated by protein-protein interactions (PPI). We describe several applications and recent successes of the Evolutionary Trace (ET) in identifying molecular events and shapes that underlie protein function and specificity in both eukaryotes and prokaryotes. ET is a part of analytical approaches based on the successes and failures of evolution that enable the rational control of PPI.
    Progress in Biophysics and Molecular Biology 05/2014; 116(2-3). DOI:10.1016/j.pbiomolbio.2014.05.004 · 2.27 Impact Factor
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    • "In this study, we constructed the structural pathway based on protein-protein interactions (PPIs). The commonly used node-and-edge description of pathways, where nodes represent proteins and edges the interactions between them, are useful, but do not provide structural interaction detail [33,34]. Further, in many cases, such as IL-10 and the receptors in this study, the available structural interaction data of the proteins are incomplete. "
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    ABSTRACT: BACKGROUND: Inflammation has significant roles in all phases of tumor development, including initiation, progression and metastasis. Interleukin-10 (IL-10) is a well-known immuno-modulatory cytokine with an anti-inflammatory activity. Lack of IL-10 allows induction of pro-inflammatory cytokines and hinders anti-tumor immunity, thereby favoring tumor growth. The IL-10 network is among the most important paths linking cancer and inflammation. The simple node-and-edge network representation is useful, but limited, hampering the understanding of the mechanistic details of signaling pathways. Structural networks complete the missing parts, and provide details. The IL-10 structural network may shed light on the mechanisms through which disease-related mutations work and the pathogenesis of malignancies.
    BMC Genomics 05/2014; 15 Suppl 4(Suppl 4):S2. DOI:10.1186/1471-2164-15-S4-S2 · 3.99 Impact Factor
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