Abstract
As a result of high‐throughput protein structure initiatives, over 14,400 protein structures have been solved by Structural Genomics (SG) centers and participating research groups. While the totality of SG data represents a tremendous contribution to genomics and structural biology, reliable functional information for these proteins is generally lacking. Better functional predictions for SG proteins will add substantial value to the structural information already obtained. Our method described herein, Graph Representation of Active Sites for Prediction of Function (GRASP‐Func), predicts quickly and accurately the biochemical function of proteins by representing residues at the predicted local active site as graphs rather than in Cartesian coordinates. We compare the GRASP‐Func method to our previously reported method, Structurally Aligned Local Sites of Activity (SALSA), using the Ribulose Phosphate Binding Barrel (RPBB), 6‐Hairpin Glycosidase (6‐HG), and Concanavalin A‐like Lectins/Glucanase (CAL/G) superfamilies as test cases. In each of the superfamilies, SALSA and the much faster method GRASP‐Func yield similar correct classification of previously characterized proteins, providing a validated benchmark for the new method. In addition, we analyzed SG proteins using our SALSA and GRASP‐Func methods to predict function. Forty‐one SG proteins in the RPBB superfamily, nine SG proteins in the 6‐HG superfamily, and one SG protein in the CAL/G superfamily were successfully classified into one of the functional families in their respective superfamily by both methods. This improved, faster, validated computational method can yield more reliable predictions of function that can be used for a wide variety of applications by the community.
Highlights
A wealth of new protein structures has been reported by structural genomics (SG) initiatives since 2000, but determination of the biochemical function of these structures has proved to be much more difficult than originally envisioned
Current methods for assigning biochemical function are generally informatics based; sequence and structure comparisons are made between the query protein and other proteins in large databases, and functional assignments are transferred based on sequence or structure similarity with previously annotated proteins
We present analysis of the three superfamilies with a new approach, wherein predicted sets of residues are expressed as graphs and local alignments are generated based on the graph representation
Summary
A wealth of new protein structures has been reported by structural genomics (SG) initiatives since 2000, but determination of the biochemical function of these structures has proved to be much more difficult than originally envisioned. A new approach to the local structure matching, Graph Representation of Active Sites for Prediction of Function (GRASP-Func), is introduced; instead of using a Cartesian coordinate representation of the active site residues and relying on global multiple structure alignments as was done previously,[14,15,19] the predicted sets of active residues are expressed in a topological graph representation. We present analysis of the three superfamilies with a new approach, wherein predicted sets of residues are expressed as graphs and local alignments are generated based on the graph representation This new approach produces locally aligned signatures much faster and allows for more rapid, facile, larger-scale functional classification of protein structures
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