Accurate and scalable identification of functional sites by evolutionary tracing.

A common difficulty in post genomics biology is that large-scale techniques of data collection often strip away information on the biological context of these data. The result is a massive number of disconnected observations on sequence, structure, and function from which underlying patterns and biological meaning are obscured. One solution is to build computational filters that pick out sufficiently few facts, relevant to a query, that their relationship is immediately apparent and experimentally testable. Typically, these filters rely on mathematics and statistics, and on first principles from physics and chemistry. We show here that evolution itself can be used to filter sequence and structure data in order to identify evolutionarily important amino acids. A general property of these residues is that they form clusters in native protein structures and point to regions where mutations have the greatest biological impact. The result is an accurate method of functional site annotation that is scalable for structural proteomics.