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Improving de novo protein binder design with deep learning

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 u...

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Detalles Bibliográficos
Autores principales: Bennett, Nathaniel R., Coventry, Brian, Goreshnik, Inna, Huang, Buwei, Allen, Aza, Vafeados, Dionne, Peng, Ying Po, Dauparas, Justas, Baek, Minkyung, Stewart, Lance, DiMaio, Frank, De Munck, Steven, Savvides, Savvas N., Baker, David
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10163288/
https://www.ncbi.nlm.nih.gov/pubmed/37149653
http://dx.doi.org/10.1038/s41467-023-38328-5
Descripción
Sumario: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.