Clustering protein environments for function prediction: finding PROSITE motifs in 3D

Background: Structural genomics initiatives are producing increasing numbers of threedimensional (3D) structures for which there is little functional information. Structure-based annotation of molecular function is therefore becoming critical. We previously presented FEATURE, a method for describing microenvironments around functional sites in proteins. However, FEATURE uses supervised machine learning and so is limited to building models for sites of known importance and location. We hypothesized that there are a large number of sites in proteins that are associated with function that have not yet been recognized. Toward that end, we have developed a method for clustering protein microenvironments in order to evaluate the potential for discovering novel sites that have not been previously identified. Results: We have prototyped a computational method for rapid clustering of millions of microenvironments in order to discover residues whose surrounding environments are similar and whic...

• Type: Journal
• Year: 2007
• Where: BMCBI
• Authors: Sungroh Yoon, Jessica C. Ebert, Eui-Young Chung, Giovanni De Micheli, Russ B. Altman
• Keywords: BMCBI 2007, Functional Sites, Little Functional Information, Protein