Protein oligomer modeling guided by predicted interchain contacts in CASP14.
Proteins
; 89(12): 1824-1833, 2021 12.
Article
en En
| MEDLINE
| ID: mdl-34324224
ABSTRACT
For CASP14, we developed deep learning-based methods for predicting homo-oligomeric and hetero-oligomeric contacts and used them for oligomer modeling. To build structure models, we developed an oligomer structure generation method that utilizes predicted interchain contacts to guide iterative restrained minimization from random backbone structures. We supplemented this gradient-based fold-and-dock method with template-based and ab initio docking approaches using deep learning-based subunit predictions on 29 assembly targets. These methods produced oligomer models with summed Z-scores 5.5 units higher than the next best group, with the fold-and-dock method having the best relative performance. Over the eight targets for which this method was used, the best of the five submitted models had average oligomer TM-score of 0.71 (average oligomer TM-score of the next best group 0.64), and explicit modeling of inter-subunit interactions improved modeling of six out of 40 individual domains (ΔGDT-TS > 2.0).
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Conformación Proteica
/
Programas Informáticos
/
Proteínas
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Modelos Moleculares
Tipo de estudio:
Prognostic_studies
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Risk_factors_studies
Idioma:
En
Revista:
Proteins
Asunto de la revista:
BIOQUIMICA
Año:
2021
Tipo del documento:
Article
País de afiliación:
Estados Unidos
Pais de publicación:
EEUU
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ESTADOS UNIDOS
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ESTADOS UNIDOS DA AMERICA
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EUA
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UNITED STATES
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UNITED STATES OF AMERICA
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US
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USA