Identification of recurring protein structure microenvironments and discovery of novel functional sites around CYS residues.
BACKGROUND
The emergence of structural genomics presents significant challenges in the annotation of biologically uncharacterized proteins. Unfortunately, our ability to analyze these proteins is restricted by the limited catalog of known molecular functions and their associated 3D motifs.


RESULTS
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 A 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.


CONCLUSIONS
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.
Wu
Shirley
S
23andMe, 1390 Shorebird Way, Mountain View, CA, USA.
Liu
Tianyun
T
Altman
Russ B
RB
D001665:Binding Sites; D016000:Cluster Analysis; D019295:Computational Biology; D003545:Cysteine / Q000737:chemistry*; D030562:Databases, Protein; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry*
D011506:Proteins; D003545:Cysteine