Discovery of Protein Seq.Struct.Func.Relationships

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DESCRIPTION (provided by applicant): The aim of the proposed research is to develop and provide a user-friendly computational platform for investigating protein sequence-structure-function relationships. Computational tools will be developed and systematically evaluated to exploit various combinations of diverse information (sequence features, structural features, gene expression data, protein interaction data, etc.) with the aim of being able to use them to investigate function from multiple viewpoints. This computational platform for discovery can significantly advance our understanding of biological mechanisms at the molecular level, enhance gene annotation capabilities, and potentially lead to new therapeutic methods. Specific aims of the project include the development of: A suite of algorithms (including new algorithms designed to overcome limitations of existing approaches) and data representations for assigning protein sequences to structural or functional families; Software for rapid and flexible assembly of data sets derived from multiple heterogeneous protein data repositories to support analysis of protein sequence-structure-function relationships; Computational tools for eliciting sequence and structural correlates of functionally important parts of proteins; Computational tools for characterizing and predicting protein-protein interactions; and A set of extensible software modules for analysis of protein structure and function. Predictions generated using these tools will be tested directly in the context of specific biological problems - prediction of candidate ligands for a receptor kinases and prediction of residues that participate in a novel SH3-SH2 interaction, and assembly of antibody variable regions. The algorithms, software, data, and documentation will be made freely available. The proposed integration of flexible tools for generating data sets with software modules for analyzing protein sequence-structure-function relationships represents an enormous improvement over the present situation. The resulting computational platform and tools will find a large community of users in areas ranging from structural biology, to signal transduction, to functional genomics. This research will be closely integrated into the research-based training of graduate students in Computer Science and Bioinformatics and Computational Biology at Iowa State University.