Folding non-homologous proteins by coupling deep-learning contact maps with I-TASSER assembly simulations.
Cell Rep Methods
; 1(3)2021 Jul 26.
Artigo
em Inglês
| MEDLINE | ID: covidwho-1275250
ABSTRACT
Structure prediction for proteins lacking homologous templates in the Protein Data Bank (PDB) remains a significant unsolved problem. We developed a protocol, C-I-TASSER, to integrate interresidue contact maps from deep neural-network learning with the cutting-edge I-TASSER fragment assembly simulations. Large-scale benchmark tests showed that C-I-TASSER can fold more than twice the number of non-homologous proteins than the I-TASSER, which does not use contacts. When applied to a folding experiment on 8,266 unsolved Pfam families, C-I-TASSER successfully folded 4,162 domain families, including 504 folds that are not found in the PDB. Furthermore, it created correct folds for 85% of proteins in the SARS-CoV-2 genome, despite the quick mutation rate of the virus and sparse sequence profiles. The results demonstrated the critical importance of coupling whole-genome and metagenome-based evolutionary information with optimal structure assembly simulations for solving the problem of non-homologous protein structure prediction.
Texto completo:
Disponível
Coleções:
Bases de dados internacionais
Base de dados:
MEDLINE
Tipo de estudo:
Estudo prognóstico
Idioma:
Inglês
Ano de publicação:
2021
Tipo de documento:
Artigo
País de afiliação:
J.crmeth.2021.100014
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