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Protein threading using residue co-variation and deep learning

MOTIVATION: Template-based modeling, including homology modeling and protein threading, is a popular method for protein 3D structure prediction. However, alignment generation and template selection for protein sequences without close templates remain very challenging. RESULTS: We present a new metho...

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Detalles Bibliográficos
Autores principales: Zhu, Jianwei, Wang, Sheng, Bu, Dongbo, Xu, Jinbo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6022550/
https://www.ncbi.nlm.nih.gov/pubmed/29949980
http://dx.doi.org/10.1093/bioinformatics/bty278
Descripción
Sumario:MOTIVATION: Template-based modeling, including homology modeling and protein threading, is a popular method for protein 3D structure prediction. However, alignment generation and template selection for protein sequences without close templates remain very challenging. RESULTS: We present a new method called DeepThreader to improve protein threading, including both alignment generation and template selection, by making use of deep learning (DL) and residue co-variation information. Our method first employs DL to predict inter-residue distance distribution from residue co-variation and sequential information (e.g. sequence profile and predicted secondary structure), and then builds sequence-template alignment by integrating predicted distance information and sequential features through an ADMM algorithm. Experimental results suggest that predicted inter-residue distance is helpful to both protein alignment and template selection especially for protein sequences without very close templates, and that our method outperforms currently popular homology modeling method HHpred and threading method CNFpred by a large margin and greatly outperforms the latest contact-assisted protein threading method EigenTHREADER. AVAILABILITY AND IMPLEMENTATION: http://raptorx.uchicago.edu/ SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.