ISPRED-SEQ: Deep Neural Networks and Embeddings for Predicting Interaction Sites in Protein Sequences.
PPI prediction
deep neural networks
embedding
end-to-end models
protein sequence
Journal
Journal of molecular biology
ISSN: 1089-8638
Titre abrégé: J Mol Biol
Pays: Netherlands
ID NLM: 2985088R
Informations de publication
Date de publication:
15 07 2023
15 07 2023
Historique:
received:
21
11
2022
revised:
09
01
2023
accepted:
10
01
2023
medline:
27
6
2023
pubmed:
26
6
2023
entrez:
25
6
2023
Statut:
ppublish
Résumé
The knowledge of protein-protein interaction sites (PPIs) is crucial for protein functional annotation. Here we address the problem focusing on the prediction of putative PPIs considering as input protein sequences. The issue is important given the huge volume of protein sequences compared to experimental and/or computed structures. Taking advantage of protein language models, recently developed, and Deep Neural networks, here we describe ISPRED-SEQ, which overpasses state-of-the-art predictors addressing the same problem. ISPRED-SEQ is freely available for testing at https://ispredws.biocomp.unibo.it.
Identifiants
pubmed: 37356906
pii: S0022-2836(23)00019-0
doi: 10.1016/j.jmb.2023.167963
pii:
doi:
Substances chimiques
Proteins
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
167963Informations de copyright
Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.