PECASE: Computational Methods for Genome-Wide Prediction of Protein-Protein Interactions

Princeton University, Princeton NJ

Investigators

Abstract

The long-term goal of this research is to develop computational methods for predicting protein-protein interactions at a genomic level. Protein-protein interactions play a central role in how an organism functions, and computational methods for predicting these interactions will be key to understanding functional pathways within biological systems. The vast amount of biosequence data in a genome makes sophisticated computational analysis a necessity. While computational methods have already proven to be a useful first step for rapid genome-wide identification of putative protein function and structure, research on the problem of computationally determining biologically relevant partners for given protein sequences is just beginning. This project looks at the problem of predicting protein-protein interactions from two complementary viewpoints. For both approaches, the constraint of genomic-level analysis favors development of fast, informatics-based methods. The first part of this proposal focuses on a specific well-characterized structural motif that mediates protein-protein interactions: the parallel, 2-stranded coiled coil. The goal is to develop novel computational techniques that can predict whether two coiled-coil proteins interact with each other. The second part will extend several existing non-structural whole- and cross-genome methodologies that were initially designed for inferring protein function to the problem of predicting protein-protein interactions mediated by particular protein interaction domains. The educational goals of this project include: (1) bioinformatics curriculum development, including a graduate "computing certificate" for molecular biology graduate students; (2) development of interdisciplinary bioinformatics courses at the introductory level and at the graduate research seminar level; and (3) dissemination of instructional material via the internet.