Large-scale design and refinement of stable proteins using sequence-only models
Hugh K. Haddox,
Gabriel J Rocklin,
Cameron M. Chow,
Asim K. Bera,
Francis C Motta,
Tamuka M. Chidyausiku,
Craig O Mackenzie,
Lance J Stewart,
Posted 12 Mar 2021
bioRxiv DOI: 10.1101/2021.03.12.435185
Posted 12 Mar 2021
Engineered proteins generally must possess a stable structure in order to achieve their designed function. Stable designs, however, are astronomically rare within the space of all possible amino acid sequences. As a consequence, many designs must be tested computationally and experimentally in order to find stable ones, which is expensive in terms of time and resources. Here we report a neural network model that predicts protein stability based only on sequences of amino acids, and demonstrate its performance by evaluating the stability of almost 200,000 novel proteins. These include a wide range of sequence perturbations, providing a baseline for future work in the field. We also report a second neural network model that is able to generate novel stable proteins. Finally, we show that the predictive model can be used to substantially increase the stability of both expert-designed and model-generated proteins.
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