Protein sequence design with a learned potential

By Namrata Anand, Raphael R. Eguchi, I. Mathews, Carla P. Perez, Alexander Derry, Russ B. Altman, Po-Ssu Huang. 2022

69 citations recorded by the source corpus.

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. Rational protein design to achieve a given protein backbone conformation is needed to engineer specific functions. Here Anand et al. describe a machine learning method using a learned neural network potential for fixed-backbone protein design.

Subfields: Materials Chemistry, Molecular Biology, Computational Biology & Bioinformatics

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In Materials Chemistry

1. Gevorg Grigoryan
2. John Ingraham

In Molecular Biology

1. Zak Costello
2. Gevorg Grigoryan
3. John Ingraham
4. Kevin Kaichuang Yang

In Computational Biology & Bioinformatics

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2. Gevorg Grigoryan
3. John Ingraham

Citations in the corpus

• Robust deep learning based protein sequence design using ProteinMPNN (2022, neutral): “…message passing method on which ProteinMPNN is based, but have focused on the monomer design problem, achieve lower native sequence recoveries, and with the exception of a TIM barrel design study (6) have not been extensively validated using crystallography and cryoEM to evaluate design accuracy.”
• Illuminating protein space with a programmable generative model (2022, background): “7, 8 and 15), but with conditional sequence and side-chain decoding layers that build on previous studies 15,16 that have seen further refinement and experimental validation 32–34 .”