Protein Sequence Design with a Learned Potential

Overview

Journal Nat Commun

Specialty Biology

Date 2022 Feb 9

PMID 35136054

Authors

Namrata Anand

Raphael Eguchi

Irimpan I Mathews

Carla P Perez

Alexander Derry

Russ B Altman

Po-Ssu Huang

Affiliations

Soon will be listed here.

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.

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