ABI Development: Utilization of Diverse Data in Exploring Protein-Protein Interactions

Sponsor: National Science Foundation (NSF)

Award Number: DBI-1458509

Abstract:

Each living cell is packed with proteins that continuously interact with each other in response to envirtonmental and other signals to control the cell’s growth and eventual fate. The analysis of such interactions is crucial for understanding signal transduction pathways that drive cell differentian during development and throughout the life an an organism. These pathways are often involed in disease progression, such as cancer, so a broader impact of understanding these pathways could be the design of drugs to modulate such pathways. Moreover, such an understanding of protein protein interactions may aid the develpment of protein-based biomaterials. Current technologies exist for providing a blueprint for large protein interaction networks, but a deeper understanding requires detailed structures of the complexes formed by protein pairs or partners. Structural information is frequently difficult or even impossible to obtain by experimental tools, emphasizing the need for computational approaches. Vajda and Kozakov at Boston University have developed the web based server ClusPro for predicting the three dimensional structures of protein-protein complexes. According to the worldwide experiment CAPRI (Critical Assessment of Predicted Interactions), ClusPro consistently has been the best protein-protein docking server. It has over 5000 registered users and performs around 3500 docking calculations each month. Structures generated by the server have been reported in over 350 research papers. The goal of this project is to develop the next generation of ClusPro that will be able to optimally utilize the vast amount of data accumulated in public bioinformatics databases in order to improve the reliability and accuracy of the predicted structures. Since the server is used primarily by biological and chemical scientists who may not have expertise in the use of current bioinformatics tools, the new version will provide convenient access to state-of-art methods. Integration of bioinformatics and computational biophysics approaches, in combination with experimental validation, will result in a unique and powerful research tool. The increased availability of protein complex structures will have a major impact in many areas of biology, biochemistry, and biotechnology. All software developed will be released free of charge for academic and governmental use. In addition, the project will be used to train a new generation of graduate students, who will be able to optimally combine data from a variety of experimental and bioinformatics techniques with high performance computing. The use of the server will also be incorporated into undergraduate courses to teach aspects of bioinformatics and biophysical principles of molecular recognition.

The current version of ClusPro systematically samples the conformational space of a target protein complex, and scores the structures using physics based energy functions. The major shortcoming of this approach is that it performs docking without consideration of the large body of interaction, sequence, structural, and experimental information available in public databases. Rather than simply generating and scoring docked structures of the starting proteins, the new approach will use their sequences to collect all orthologs to identify the globular domains and inter-domain regions that may contain short linear motifs (SLIMs). Domains that occur in a number of interacting protein pairs are likely to mediate the interaction, enabling the identification of the specific domains that interact with domains or SLIMs of the partner protein. Superimposing the domains from different orthologs identifies the conserved regions. Since the interface in complexes is sequentially and structurally more conserved relative to the rest of the protein, for the case of domain-domain interactions docking just the key segments generally yields near-native docked structures. Docking key segments from different orthologs and selecting consensus models improves the reliability of predicted structures. This approach substantially improves the docking of proteins that have flexible loops, and will be extended to the docking of homology models. If the interface includes a SLIM, docking structures of peptide fragments that contain the motif extends the method to proteins with interactions mediated by flexible or unstructured regions. The algorithms to be developed will be implemented in ClusPro, which will provide substantial new information on protein-protein complexes. ClustPro software can be accessed at http://cluspro.bu.edu.

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