A multi-objective optimization approach accurately resolves protein domain architectures.
J S Bernardes, F R J Vieira, G Zaverucha, A Carbone
Author Information
J S Bernardes: Sorbonne Universités, UPMC Univ-Paris 6, CNRS, UMR 7238, Laboratoire de Biologie Computationnelle et Quantitative, 15 rue de l'Ecole de Médecine, 75006 Paris.
F R J Vieira: CNRS, UMR 7606, Laboratoire d'Informatique de Paris 6, 75005 Paris, France and COPPE-UFRJ, Programa de Engenharia de Sistemas e Computação, Rio de Janeiro, Brazil.
G Zaverucha: COPPE-UFRJ, Programa de Engenharia de Sistemas e Computação, Rio de Janeiro, Brazil.
A Carbone: Sorbonne Universités, UPMC Univ-Paris 6, CNRS, UMR 7238, Laboratoire de Biologie Computationnelle et Quantitative, 15 rue de l'Ecole de Médecine, 75006 Paris, Institut Universitaire de France, 75005 Paris.
MOTIVATION: Given a protein sequence and a number of potential domains matching it, what are the domain content and the most likely domain architecture for the sequence? This problem is of fundamental importance in protein annotation, constituting one of the main steps of all predictive annotation strategies. On the other hand, when potential domains are several and in conflict because of overlapping domain boundaries, finding a solution for the problem might become difficult. An accurate prediction of the domain architecture of a multi-domain protein provides important information for function prediction, comparative genomics and molecular evolution. RESULTS: We developed DAMA (Domain Annotation by a Multi-objective Approach), a novel approach that identifies architectures through a multi-objective optimization algorithm combining scores of domain matches, previously observed multi-domain co-occurrence and domain overlapping. DAMA has been validated on a known benchmark dataset based on CATH structural domain assignments and on the set of Plasmodium falciparum proteins. When compared with existing tools on both datasets, it outperforms all of them. AVAILABILITY AND IMPLEMENTATION: DAMA software is implemented in C++ and the source code can be found at http://www.lcqb.upmc.fr/DAMA. CONTACT: juliana.silva_bernardes@upmc.fr or alessandra.carbone@lip6.fr SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
References
Bioinformatics. 2009 Dec 1;25(23):3077-83
[PMID: 19786484]
