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Protein oligomer modeling guided by predicted interchain contacts in CASP14
Cited 12 time in
Web of Science
Cited 13 time in Scopus
- Authors
- Issue Date
- 2021-12
- Publisher
- Wiley-Liss Inc
- Citation
- Proteins: Structure, Function and Genetics, Vol.89 No.12, pp.1824-1833
- 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 (Delta GDT-TS > 2.0).
- ISSN
- 0887-3585
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