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Poster Communications Year : 2022

PPalign: optimal alignment of Potts models representing proteins with direct coupling information

Abstract

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, 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, our approach, recently published in BMC Bioinformatics, proposes to represent proteins by Potts models, which model direct couplings between positions in addition to positional composition, and to compare proteins by aligning these models. Due to non-local dependencies, the problem of aligning Potts models is hard and remains the main computational bottleneck for their use.
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Dates and versions

hal-03926272 , version 1 (06-01-2023)

Identifiers

  • HAL Id : hal-03926272 , version 1

Cite

Hugo Talibart, François Coste, Mathilde Carpentier. PPalign: optimal alignment of Potts models representing proteins with direct coupling information. ISMB 2022 - 30th Conference on Intelligent Systems for Molecular Biology, Jul 2022, Madison, United States. pp.1-1. ⟨hal-03926272⟩
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