Protein sequence design with a learned potential

Namrata Anand, Raphael Eguchi, Irimpan I. Mathews, Carla P. Perez, Alexander Derry, Russ B. Altman and Po-Ssu Huang ()
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Namrata Anand: Stanford University
Raphael Eguchi: Stanford University
Irimpan I. Mathews: Stanford Synchrotron Radiation Lightsource
Carla P. Perez: Stanford University
Alexander Derry: Stanford University
Russ B. Altman: Stanford University
Po-Ssu Huang: Stanford University

Nature Communications, 2022, vol. 13, issue 1, 1-11

Abstract: Abstract The task of protein sequence design is central to nearly all rational protein engineering problems, and enormous effort has gone into the development of energy functions to guide design. Here, we investigate the capability of a deep neural network model to automate design of sequences onto protein backbones, having learned directly from crystal structure data and without any human-specified priors. The model generalizes to native topologies not seen during training, producing experimentally stable designs. We evaluate the generalizability of our method to a de novo TIM-barrel scaffold. The model produces novel sequences, and high-resolution crystal structures of two designs show excellent agreement with in silico models. Our findings demonstrate the tractability of an entirely learned method for protein sequence design.

Date: 2022
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DOI: 10.1038/s41467-022-28313-9

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