# Improving de novo protein binder design with deep learning

Article · Bennett NR, Coventry B, Goreshnik I, Huang B, Allen A, Vafeados D, Peng YP, Dauparas J, Baek M, Stewart L, DiMaio F, De Munck S, Savvides SN, Baker D

- Archive id: doi:10.1038/s41467-023-38328-5
- Published: 2023-05-06
- Venue: Nature communications
- DOI: https://doi.org/10.1038/s41467-023-38328-5
- PMID: 37149653
- PMCID: PMC10163288
- Citations: 343
- Source URL: https://doi.org/10.1038/s41467-023-38328-5
- PDF: https://europepmc.org/articles/PMC10163288/pdf
- Provenance: europepmc

## 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.

_Abstract reproduced under CC-BY (article license as recorded by Europe PMC; abstract reproduced unmodified apart from markup removal)._

Topics: [AI Protein Design & Structure Prediction](https://space-kitty.com/topics/protein-design)

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