Development of a Database for the Discovery and Annotation of Functional Sites in Protein Structures

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This proposal aims to develop novel and general computational tools to discover functional sites in protein structures. These sites control nearly all aspects of cellular chemistry and their identification has important applications, such as to understand the basis of protein function, and to modify cellular pathways by engineering protein mimetics or inhibitors. Until now, however, functional sites could be identified reliably only after exhaustive mutational analysis: a laboratory process that is slow, labor intensive, limited by assay sensitivity, and which is protein specific. Here, we propose instead to identify functional sites with new algorithms for sequence analysis and for the efficient geometric comparison of important features in protein structures. This builds on our preliminary work on the Evolutionary Trace (ET) method. First, in proteins with sufficient evolutionary information, we will use ET to identify the key geometric and chemical features of active sites and summarize these into 3-dimensional molds. Second, in protein structures lacking sufficient evolutionary information, we will search for look-alike areas that mimic these molds. At a time when the outpour of raw sequence and structure date overwhelms conventional means of analysis, this low-cost strategy will reveal the regions of protein structures that are most relevant to function, and therefore to experimentation.