Towards Structural Classification of Proteins based on Contact Map Overlap

TL;DR

This paper introduces a new exact algorithm for contact map overlap maximization in protein structures, significantly improving performance on small and large datasets, enabling better classification of proteins based on 3D structure similarity.

Contribution

It presents a novel integer programming model and an exact branch-and-bound algorithm with bounds from Lagrangian relaxation, outperforming existing methods on benchmark datasets.

Findings

Successfully solved hard CMO instances for the first time.

Achieved better bounds and faster solutions on the Skolnick set.

Produced protein classification results in strong agreement with SCOP.

Abstract

A multitude of measures have been proposed to quantify the similarity between protein 3-D structure. Among these measures, contact map overlap (CMO) maximization deserved sustained attention during past decade because it offers a fine estimation of the natural homology relation between proteins. Despite this large involvement of the bioinformatics and computer science community, the performance of known algorithms remains modest. Due to the complexity of the problem, they got stuck on relatively small instances and are not applicable for large scale comparison. This paper offers a clear improvement over past methods in this respect. We present a new integer programming model for CMO and propose an exact B &B algorithm with bounds computed by solving Lagrangian relaxation. The efficiency of the approach is demonstrated on a popular small benchmark (Skolnick set, 40 domains). On this set our algorithm significantly outperforms the best existing exact algorithms, and yet provides lower and upper bounds of better quality. Some hard CMO instances have been solved for the first time and within reasonable time limits. From the values of the running time and the relative gap (relative difference between upper and lower bounds), we obtained the right classification for this test. These encouraging result led us to design a harder benchmark to better assess the classification capability of our approach. We constructed a large scale set of 300 protein domains (a subset of ASTRAL database) that we have called Proteus 300. Using the relative gap of any of the 44850 couples as a similarity measure, we obtained a classification in very good agreement with SCOP. Our algorithm provides thus a powerful classification tool for large structure databases.