Improving de novo protein binder design with deep learning

By N. Bennett, B. Coventry, I. Goreshnik, Buwei Huang, Aza Allen, D. Vafeados, Ying Po Peng, J. Dauparas, Minkyung Baek, Lance Stewart and 4 more. 2022

367 citations recorded by the source corpus.

Recently, a pipeline for the design of protein-binding proteins using only the structure of the target protein was reported. Here, the authors report that the incorporation of deep learning methods into the original pipeline increases experimental success rate by ten-fold. 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.

Subfields: Molecular Biology

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In Molecular Biology

Citations in the corpus

DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking (2022, background): “[19] Nathaniel Bennett, Brian Coventry, Inna Goreshnik, Buwei Huang, Aza Allen, Dionne Vafeados, Ying Po Peng, Justas Dauparas, Minkyung Baek, Lance Stewart, et al.”
Diffusion probabilistic modeling of protein backbones in 3D for the motif-scaffolding problem (2022, methodology): “Our choice of utilizing ProteinMPNN and AF2 (without MSAs) is motivated by their empirical success in various de novo protein design tasks and the ability to recapitulate native proteins (Dauparas et al., 2022; Bennett et al., 2022).”
Broadly applicable and accurate protein design by integrating structure prediction networks and diffusion generative models (2022, neutral): “S2A-B), these metrics have been demonstrated to be good predictors of experimental success 1,4,21 .”