RT Journal Article
SR Electronic
T1 Large-scale design and refinement of stable proteins using sequence-only models
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.03.12.435185
DO 10.1101/2021.03.12.435185
A1 Jedediah M. Singer
A1 Scott Novotney
A1 Devin Strickland
A1 Hugh K. Haddox
A1 Nicholas Leiby
A1 Gabriel J. Rocklin
A1 Cameron M. Chow
A1 Anindya Roy
A1 Asim K. Bera
A1 Francis C. Motta
A1 Longxing Cao
A1 Eva-Maria Strauch
A1 Tamuka M. Chidyausiku
A1 Alex Ford
A1 Ethan Ho
A1 Craig O. Mackenzie
A1 Hamed Eramian
A1 Frank DiMaio
A1 Gevorg Grigoryan
A1 Matthew Vaughn
A1 Lance J. Stewart
A1 David Baker
A1 Eric Klavins
YR 2021
UL http://biorxiv.org/content/early/2021/04/06/2021.03.12.435185.abstract
AB 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.Competing Interest StatementJMS and NL are employed by Two Six Technologies, which has filed a patent on a portion of the technology described in this manuscript.