SAINT-Angle: self-attention augmented inception-inside-inception network and transfer learning improve protein backbone torsion angle prediction.
Journal
Bioinformatics advances
ISSN: 2635-0041
Titre abrégé: Bioinform Adv
Pays: England
ID NLM: 9918282081306676
Informations de publication
Date de publication:
2023
2023
Historique:
received:
29
11
2022
revised:
21
02
2023
accepted:
04
04
2023
medline:
24
4
2023
pubmed:
24
4
2023
entrez:
24
04
2023
Statut:
epublish
Résumé
Protein structure provides insight into how proteins interact with one another as well as their functions in living organisms. Protein backbone torsion angles ( We present SAINT-Angle, a highly accurate method for predicting protein backbone torsion angles using a self-attention-based deep learning network called SAINT, which was previously developed for the protein secondary structure prediction. We extended and improved the existing SAINT architecture as well as used transfer learning to predict backbone angles. We compared the performance of SAINT-Angle with the state-of-the-art methods through an extensive evaluation study on a collection of benchmark datasets, namely, TEST2016, TEST2018, TEST2020-HQ, CAMEO and CASP. The experimental results suggest that our proposed self-attention-based network, together with transfer learning, has achieved notable improvements over the best alternate methods. SAINT-Angle is freely available as an open-source project at https://github.com/bayzidlab/SAINT-Angle. Supplementary data are available at
Identifiants
pubmed: 37092035
doi: 10.1093/bioadv/vbad042
pii: vbad042
pmc: PMC10115468
doi:
Types de publication
Journal Article
Langues
eng
Pagination
vbad042Informations de copyright
© The Author(s) 2023. Published by Oxford University Press.
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