Towards Structural Classification of Proteins based on Contact Map Overlap

Rumen Andonov 1, * Nicola Yanev 2 Noël Malod-Dognin 1
* Auteur correspondant
1 SYMBIOSE - Biological systems and models, bioinformatics and sequences
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
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.
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Soumis le : mardi 27 novembre 2007 - 16:34:00
Dernière modification le : jeudi 11 janvier 2018 - 06:20:10
Document(s) archivé(s) le : mardi 21 septembre 2010 - 15:26:55


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  • HAL Id : inria-00192206, version 2
  • ARXIV : 0711.4208


Rumen Andonov, Nicola Yanev, Noël Malod-Dognin. Towards Structural Classification of Proteins based on Contact Map Overlap. [Research Report] RR-6370, INRIA. 2007, pp.20. 〈inria-00192206v2〉



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