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Characterization of domain-peptide interaction interface: a generic structure-based model to decipher the binding specificity of SH3 domains.

ABSTRACT: Extensive efforts have been devoted to determining the binding specificity of Src homology 3 (SH3) domains usually in a case-by-case manner. A generic structure-based model is necessary to decipher the protein recognition code of the entire domain family. In this study, we have developed a general framework that combines molecular modeling and a machine learning algorithm to capture the energetic characteristics of the domain-peptide interactions and predict the binding specificity of the SH3 domain family. Our model is not trained for individual SH3 domains; rather it is a generic model for the entire domain family. Our model not only achieved satisfactory prediction accuracy but also provided structural insights into which residues are important for the binding specificity. The success of our framework on SH3 domains suggests that it is possible to establish a theoretical model to decipher the protein recognition code of any modular domain.

SUBMITTER: Hou T

PROVIDER: S-EPMC2667344 | biostudies-literature | 2009 Apr

REPOSITORIES: biostudies-literature

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Characterization of domain-peptide interaction interface: a generic structure-based model to decipher the binding specificity of SH3 domains.

Hou Tingjun T Xu Zheng Z Zhang Wei W McLaughlin William A WA Case David A DA Xu Yang Y Wang Wei W

Molecular & cellular proteomics : MCP 20081120 4

Extensive efforts have been devoted to determining the binding specificity of Src homology 3 (SH3) domains usually in a case-by-case manner. A generic structure-based model is necessary to decipher the protein recognition code of the entire domain family. In this study, we have developed a general framework that combines molecular modeling and a machine learning algorithm to capture the energetic characteristics of the domain-peptide interactions and predict the binding specificity of the SH3 do ...[more]

PMID: 19023120

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Project description:MotivationState-of-the-art experimental data for determining binding specificities of peptide recognition modules (PRMs) is obtained by high-throughput approaches like peptide arrays. Most prediction tools applicable to this kind of data are based on an initial multiple alignment of the peptide ligands. Building an initial alignment can be error-prone, especially in the case of the proline-rich peptides bound by the SH3 domains.ResultsHere, we present a machine-learning approach based on an efficient graph-kernel technique to predict the specificity of a large set of 70 human SH3 domains, which are an important class of PRMs. The graph-kernel strategy allows us to (i) integrate several types of physico-chemical information for each amino acid, (ii) consider high-order correlations between these features and (iii) eliminate the need for an initial peptide alignment. We build specialized models for each human SH3 domain and achieve competitive predictive performance of 0.73 area under precision-recall curve, compared with 0.27 area under precision-recall curve for state-of-the-art methods based on position weight matrices. We show that better models can be obtained when we use information on the noninteracting peptides (negative examples), which is currently not used by the state-of-the art approaches based on position weight matrices. To this end, we analyze two strategies to identify subsets of high confidence negative data. The techniques introduced here are more general and hence can also be used for any other protein domains, which interact with short peptides (i.e. other PRMs).AvailabilityThe program with the predictive models can be found at http://www.bioinf.uni-freiburg.de/Software/SH3PepInt/SH3PepInt.tar.gz. We also provide a genome-wide prediction for all 70 human SH3 domains, which can be found under http://www.bioinf.uni-freiburg.de/Software/SH3PepInt/Genome-Wide-Predictions.tar.gz.Supplementary informationSupplementary data are available at Bioinformatics online.

Project description:To explore the conservation of Src homology 3 (SH3) domain-mediated networks in evolution, we compared the specificity landscape of these domains among four yeast species, Saccharomyces cerevisiae, Ashbya gossypii, Candida albicans, and Schizosaccharomyces pombe, encompassing 400 million years of evolution. We first aligned and catalogued the families of SH3-containing proteins in these four species to determine the relationships between homologous domains. Then, we tagged and purified all soluble SH3 domains (82 in total) to perform a quantitative peptide assay (SPOT) for each SH3 domain. All SPOT readouts were hierarchically clustered and we observed that the organization of the SH3 specificity landscape in three distinct profile classes remains conserved across these four yeast species. We also produced a specificity profile for each SH3 domain from manually aligned top SPOT hits and compared the within-family binding motif consensus. This analysis revealed a striking example of binding motif divergence in a C. albicans Rvs167 paralog, which cannot be explained by overall SH3 sequence or interface residue divergence, and we validated this specificity change with a yeast two-hybrid (Y2H) assay. In addition, we show that position-weighted matrices (PWM) compiled from SPOT assays can be used for binding motif screening in potential binding partners and present cases where motifs are either conserved or lost among homologous SH3 interacting proteins. Finally, by comparing pairwise SH3 sequence identity to binding profile correlation we show that for ~75% of all analyzed families the SH3 specificity profile was remarkably conserved over a large evolutionary distance. Thus, a high sequence identity within an SH3 domain family predicts conserved binding specificity, whereas divergence in sequence identity often coincided with a change in binding specificity within this family. As such, our results are important for future studies aimed at unraveling complex specificity networks of peptide recognition domains in higher eukaryotes, including mammals.

Project description:Protein-protein interactions play an essential role in cellular processes. Certain proteins form stable complexes with their partner proteins, whereas others function by forming transient complexes. The conventional protein-protein interaction model describes an interaction between two proteins under the assumption that a protein binds to its partner protein through a single binding site. In this study, we improved the conventional interaction model by developing a Multiple-Site (MS) model in which a protein binds to its partner protein through closely located multiple binding sites on a surface of the partner protein by transiently docking at each binding site with individual binding free energies. To test this model, we used the protein-protein interaction mediated by Src homology 3 (SH3) domains. SH3 domains recognize their partners via a weak, transient interaction and are therefore promiscuous in nature. Because the MS model requires large amounts of data compared with the conventional interaction model, we used experimental data from the positionally addressable syntheses of peptides on cellulose membranes (SPOT-synthesis) technique. From the analysis of the experimental data, individual binding free energies for each binding site of peptides were extracted. A comparison of the individual binding free energies from the analysis with those from atomistic force fields gave a correlation coefficient of 0.66. Furthermore, application of the MS model to 10 SH3 domains lowers the prediction error by up to 9% compared with the conventional interaction model. This improvement in prediction originates from a more realistic description of complex formation than the conventional interaction model. The results suggested that, in many cases, SH3 domains increased the protein complex population through multiple binding sites of their partner proteins. Our study indicates that the consideration of general complex formation is important for the accurate description of protein complex formation, and especially for those of weak or transient protein complexes.

Dataset Information

Characterization of domain-peptide interaction interface: a generic structure-based model to decipher the binding specificity of SH3 domains.

Publications

Characterization of domain-peptide interaction interface: a generic structure-based model to decipher the binding specificity of SH3 domains.

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