Cargando…
Deducing high-accuracy protein contact-maps from a triplet of coevolutionary matrices through deep residual convolutional networks
The topology of protein folds can be specified by the inter-residue contact-maps and accurate contact-map prediction can help ab initio structure folding. We developed TripletRes to deduce protein contact-maps from discretized distance profiles by end-to-end training of deep residual neural-networks...
Autores principales: | Li, Yang, Zhang, Chengxin, Bell, Eric W., Zheng, Wei, Zhou, Xiaogen, Yu, Dong-Jun, Zhang, Yang |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8026059/ https://www.ncbi.nlm.nih.gov/pubmed/33770072 http://dx.doi.org/10.1371/journal.pcbi.1008865 |
Ejemplares similares
-
Folding non-homologous proteins by coupling deep-learning contact maps with I-TASSER assembly simulations
por: Zheng, Wei, et al.
Publicado: (2021) -
The coevolutionary process /
por: Thompson, John N.
Publicado: (1994) -
Improving fragment-based ab initio protein structure assembly using low-accuracy contact-map predictions
por: Mortuza, S. M., et al.
Publicado: (2021) -
Progressive and accurate assembly of multi-domain protein structures from cryo-EM density maps
por: Zhou, Xiaogen, et al.
Publicado: (2020) -
A deep dilated convolutional residual network for predicting interchain contacts of protein homodimers
por: Roy, Raj S, et al.
Publicado: (2022)