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A multitude of measures have been proposed to quantify the similarity betweenprotein 3-D structure. Among these measures, contact map overlap (CMO)maximization deserved sustained attention during past decade because it offersa fine estimation of the natural homology relation between proteins. Despitethis large involvement of the bioinformatics and computer science community,the performance of known algorithms remains modest. Due to the complexity ofthe problem, they got stuck on relatively small instances and are notapplicable for large scale comparison. This paper offers a clear improvementover past methods in this respect. We present a new integer programming modelfor CMO and propose an exact B &B algorithm with bounds computed by solvingLagrangian relaxation. The efficiency of the approach is demonstrated on apopular small benchmark (Skolnick set, 40 domains). On this set our algorithmsignificantly outperforms the best existing exact algorithms, and yet provideslower and upper bounds of better quality. Some hard CMO instances have beensolved for the first time and within reasonable time limits. From the values ofthe running time and the relative gap (relative difference between upper andlower bounds), we obtained the right classification for this test. Theseencouraging result led us to design a harder benchmark to better assess theclassification capability of our approach. We constructed a large scale set of300 protein domains (a subset of ASTRAL database) that we have called Proteus300. Using the relative gap of any of the 44850 couples as a similaritymeasure, we obtained a classification in very good agreement with SCOP. Ouralgorithm provides thus a powerful classification tool for large structuredatabases.

Towards Structural Classification of Proteins based on Contact Map Overlap