Estimating probabilistic context-free grammars for proteins using contact map constraints - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue PeerJ Année : 2019

Estimating probabilistic context-free grammars for proteins using contact map constraints

Résumé

Interactions between amino acids that are close in the spatial structure, but not necessarily in the sequence, play important structural and functional roles in proteins. These non-local interactions ought to be taken into account when modeling collections of proteins. Yet the most popular representations of sets of related protein sequences remain the profile Hidden Markov Models. By modeling independently the distributions of the conserved columns from an underlying multiple sequence alignment of the proteins, these models are unable to capture dependencies between theprotein residues. Non-local interactions can be represented by using more expressive grammatical models. However, learning such grammars is difficult. In this work, we propose to use information on protein contacts to facilitate the training of probabilistic context-free grammars representing families of protein sequences. We develop the theory behind the introduction of contact constraints in maximum-likelihood and contrastive estimation schemes and implement it in a machine learning framework for protein grammars. The proposed framework is tested on samples of protein motifs in comparison with learning without contact constraints. The evaluation shows high fidelity of grammatical descriptors to protein structures and improved precision in recognizing sequences. Finally, we present an example of using our method in a practical setting and demonstrate its potential beyond the current state of the art by creating a grammatical model of a meta-family of protein motifs. We conclude that the current piece of research is a significant step towards more flexible and accurate modeling of collections of protein sequences. The software package is made available to thecommunity.
Fichier principal
Vignette du fichier
peerj-6559.pdf (3.45 Mo) Télécharger le fichier
Origine : Fichiers éditeurs autorisés sur une archive ouverte

Dates et versions

hal-02400871 , version 1 (04-06-2021)

Identifiants

Citer

Witold Dyrka, Mateusz Pyzik, François Coste, Hugo Talibart. Estimating probabilistic context-free grammars for proteins using contact map constraints. PeerJ, 2019, 7, pp.1-35. ⟨10.7717/peerj.6559⟩. ⟨hal-02400871⟩
91 Consultations
38 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More