ComPotts: Optimal alignment of coevolutionary models for protein sequences - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Communication Dans Un Congrès Année : 2020

ComPotts: Optimal alignment of coevolutionary models for protein sequences

Résumé

To assign structural and functional annotations to the ever increasing amount of sequenced proteins, the main approach relies on sequence-based homology search methods , e.g. BLAST or the current state-of-the-art methods based on profile Hidden Markov Models (pHMMs), which rely on significant alignments of query sequences to annotated proteins or protein families. While powerful, these approaches do not take coevolution between residues into account. Taking advantage of recent advances in the field of contact prediction, we propose here to represent proteins by Potts models, which model direct couplings between positions in addition to positional composition. Due to the presence of non-local dependencies, aligning two Potts models is computationally hard. To tackle this task, we introduce an Integer Linear Programming formulation of the problem and present ComPotts, an implementation able to compute the optimal alignment of two Potts models representing proteins in tractable time. A first experimentation on 59 low sequence identity pairwise alignments, extracted from 3 reference alignments from sisyphus and BaliBase3 databases, shows that ComPotts finds better alignments than the other tested methods in the majority of these cases.
Fichier principal
Vignette du fichier
jobim_proceedings.pdf (559.13 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-02862213 , version 1 (09-06-2020)

Identifiants

  • HAL Id : hal-02862213 , version 1

Citer

Hugo Talibart, François Coste. ComPotts: Optimal alignment of coevolutionary models for protein sequences. JOBIM 2020 - Journées Ouvertes Biologie, Informatique et Mathématiques, Jun 2020, Montpellier, France. pp.1-8. ⟨hal-02862213⟩
110 Consultations
180 Téléchargements

Partager

Gmail Facebook X LinkedIn More