Bridging the Gap between Sequence and Function

Abstract

Structural genomics initiatives have generated a massive quantity of high resolution structures and sequenced genomes from archaea, bacteria, viruses and eukaryotes. These numbers are expected to grow rapidly within the coming years. The available data constitute an invaluable resource for the prediction of protein structure and function and will allow us to obtain a more comprehensive structure-based understanding of biological function. The acquisition of new biochemical functionality in the course of evolution does not necessarily involve the transfer of whole genes, but may be limited to the transfer of functional domains. In fact, most of a protein's amino acids serve structural roles and may exhibit a low degree of sequence conservation even among closely related genomes. Therefore, it is critical to identify and compare structurally related domains across a wide spectrum of organisms to reveal unique metabolic functionalities.This presentation will outline examples that have combined sequence alignments, homology modeling and biophysical approaches to predict function. Integration of models with existing knowledge about genomic context, biochemical pathways and sparse experimental data, such as small-angle X-ray scattering and spectroscopic data, enables us to accurately identify domains of unknown function. We also show how minor changes in otherwise highly conserved active sites can significantly affect functionality. There is a growing need for intelligent prediction-based strategies that can tap into our enormous genomic and structural databases and help bridge the gap between sequence and function.