Identification of recurring protein structure microenvironments and discovery of novel functional sites around CYS residues

In order to identify novel 3D motifs that may be associated with molecular functions, we employ an unsupervised, two-phase clustering approach that combines k-means and hierarchical clustering with knowledge-informed cluster selection and annotation methods. We applied the approach to approximately 20,000 cysteine-based protein microenvironments (3D regions 7.5 ？ in radius) and identified 70 interesting clusters, some of which represent known motifs (e.g. metal binding and phosphatase activity), and some of which are novel, including several zinc binding sites. Detailed annotation results are available online for all 70 clusters at http://feature.stanford.edu/clustering/cys webcite.The use of microenvironments instead of backbone geometric criteria enables flexible exploration of protein function space, and detection of recurring motifs that are discontinuous in sequence and diverse in structure. Clustering microenvironments may thus help to functionally characterize novel proteins and better understand the protein structure-function relationship.Protein function and structure are inherently linked, with molecular interactions determined by the shape and energetics of the participating structures. Knowledge of structure is especially important for elucidating detailed molecular mechanisms of function for the development of disease therapeutics and pharmaceuticals. Galvanized by the Protein Structure Initiative, the field of structural genomics has begun to solve the structures of proteins in high-throughput [1-3]. By solving representative structures throughout protein structure space, researchers can more fully determine the relationship between protein structure and function [4]. Many of the solved structural genomics targets, however, lack annotation regarding the proteins' biological functions.Numerous methods exist for predicting protein function computationally, with most using some kind of sequence or structure-based similarity to match the query protein to o