- Author
- Nathaniel R. Bennett, Brian Coventry, Inna Goreshnik, Buwei Huang, Aza Allen, Dionne Vafeados, Ying Po Peng, Justas Dauparas, Minkyung Baek, Lance Stewart, Frank DiMaio, Steven De Munck (UGent) , Savvas Savvides (UGent) and David Baker
- Organization
- Project
- Abstract
- Recently it has become possible to de novo design high affinity protein binding proteins from target structural information alone. There is, however, considerable room for improvement as the overall design success rate is low. Here, we explore the augmentation of energy-based protein binder design using deep learning. We find that using AlphaFold2 or RoseTTAFold to assess the probability that a designed sequence adopts the designed monomer structure, and the probability that this structure binds the target as designed, increases design success rates nearly 10-fold. We find further that sequence design using ProteinMPNN rather than Rosetta considerably increases computational efficiency.
- Keywords
- COMPUTATIONAL DESIGN, CRYSTAL-STRUCTURE, INTERLEUKIN-2, RECEPTOR, COMPLEX, IL-2
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-01H7YWB6E6208YC3VJH2Y6H8GX
- MLA
- Bennett, Nathaniel R., et al. “Improving de Novo Protein Binder Design with Deep Learning.” NATURE COMMUNICATIONS, vol. 14, no. 1, 2023, doi:10.1038/s41467-023-38328-5.
- APA
- Bennett, N. R., Coventry, B., Goreshnik, I., Huang, B., Allen, A., Vafeados, D., … Baker, D. (2023). Improving de novo protein binder design with deep learning. NATURE COMMUNICATIONS, 14(1). https://doi.org/10.1038/s41467-023-38328-5
- Chicago author-date
- Bennett, Nathaniel R., Brian Coventry, Inna Goreshnik, Buwei Huang, Aza Allen, Dionne Vafeados, Ying Po Peng, et al. 2023. “Improving de Novo Protein Binder Design with Deep Learning.” NATURE COMMUNICATIONS 14 (1). https://doi.org/10.1038/s41467-023-38328-5.
- Chicago author-date (all authors)
- Bennett, Nathaniel R., Brian Coventry, Inna Goreshnik, Buwei Huang, Aza Allen, Dionne Vafeados, Ying Po Peng, Justas Dauparas, Minkyung Baek, Lance Stewart, Frank DiMaio, Steven De Munck, Savvas Savvides, and David Baker. 2023. “Improving de Novo Protein Binder Design with Deep Learning.” NATURE COMMUNICATIONS 14 (1). doi:10.1038/s41467-023-38328-5.
- Vancouver
- 1.Bennett NR, Coventry B, Goreshnik I, Huang B, Allen A, Vafeados D, et al. Improving de novo protein binder design with deep learning. NATURE COMMUNICATIONS. 2023;14(1).
- IEEE
- [1]N. R. Bennett et al., “Improving de novo protein binder design with deep learning,” NATURE COMMUNICATIONS, vol. 14, no. 1, 2023.
@article{01H7YWB6E6208YC3VJH2Y6H8GX,
abstract = {{Recently it has become possible to de novo design high affinity protein binding proteins from target structural information alone. There is, however, considerable room for improvement as the overall design success rate is low. Here, we explore the augmentation of energy-based protein binder design using deep learning. We find that using AlphaFold2 or RoseTTAFold to assess the probability that a designed sequence adopts the designed monomer structure, and the probability that this structure binds the target as designed, increases design success rates nearly 10-fold. We find further that sequence design using ProteinMPNN rather than Rosetta considerably increases computational efficiency.}},
articleno = {{2625}},
author = {{Bennett, Nathaniel R. and Coventry, Brian and Goreshnik, Inna and Huang, Buwei and Allen, Aza and Vafeados, Dionne and Peng, Ying Po and Dauparas, Justas and Baek, Minkyung and Stewart, Lance and DiMaio, Frank and De Munck, Steven and Savvides, Savvas and Baker, David}},
issn = {{2041-1723}},
journal = {{NATURE COMMUNICATIONS}},
keywords = {{COMPUTATIONAL DESIGN,CRYSTAL-STRUCTURE,INTERLEUKIN-2,RECEPTOR,COMPLEX,IL-2}},
language = {{eng}},
number = {{1}},
pages = {{9}},
title = {{Improving de novo protein binder design with deep learning}},
url = {{http://doi.org/10.1038/s41467-023-38328-5}},
volume = {{14}},
year = {{2023}},
}
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