In the Protein Data Bank, there are over 11,800 structural genomics (SG) proteins and most of these are of unknown or uncertain function. The present method to predict function, Structurally Aligned Local Sites of Activity (SALSAs), uses functional active site predictors, Theoretical Microscopic Anomalous Titration Curve Shapes (THEMATICS) and Partial Order Optimum Likelihood (POOL), to first determine the important active site residues from the 3D structure. Second, these local active site structures are aligned to compare proteins of known function with SG proteins. Third, those matches are assigned scores with a similarity scoring matrix to assign correct annotation.  We apply this method to the ribulose-phosphate binding barrel (RPBB) superfamily and annotate twenty-eight SG proteins. The superfamily is divided into eight subgroups and their active sites are clearly differentiated. Literature information and the Catalytic Site Atlas (CSA) are used to verify the correct location of the active site for each subgroup.

Structurally aligned local sites of activity (SALSAs) computational method for the prediction of function of structural genomics proteins

245th National Meeting (2013)

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