Alignment-based protein mutational landscape prediction: doing more with less - CNRS - Centre national de la recherche scientifique Accéder directement au contenu
Pré-Publication, Document De Travail Année : 2022

Alignment-based protein mutational landscape prediction: doing more with less

Marina Abakarova
Céline Marquet
Burkhard Rost

Résumé

Recent efforts for democratising protein structure prediction have leveraged the MMseqs2 algorithm to efficiently generate multiple sequence alignments with high diversity and a limited number of sequences. Here, we investigated the usefulness of this strategy for mutational outcome prediction. We place ourselves in a context where we only exploit information coming from the input alignment for making predictions. Through a large-scale assessment of ≈1.5M missense variants across 72 protein families, we show that the MMseqs2-based protocol implemented in ColabFold compares favourably with tools and resources relying on profile-Hidden Markov Models. Our study demonstrates the feasibility of simultaneously providing high-quality and compute-efficient alignment-based predictions for the mutational landscape of entire proteomes.
Fichier non déposé

Dates et versions

hal-03903639 , version 1 (16-12-2022)

Identifiants

  • HAL Id : hal-03903639 , version 1

Citer

Marina Abakarova, Céline Marquet, Michael Rera, Burkhard Rost, Elodie Laine. Alignment-based protein mutational landscape prediction: doing more with less. 2022. ⟨hal-03903639⟩
11 Consultations
0 Téléchargements

Partager

Gmail Facebook X LinkedIn More