TY - JOUR
T1 - An evolution-based model for designing chorismate mutase enzymes
AU - Russ, William P.
AU - Figliuzzi, Matteo
AU - Stocker, Christian
AU - Barrat-Charlaix, Pierre
AU - Socolich, Michael
AU - Kast, Peter
AU - Hilvert, Donald
AU - Monasson, Remi
AU - Cocco, Simona
AU - Weigt, Martin
AU - Ranganathan, Rama
N1 - Publisher Copyright:
© 2020 American Association for the Advancement of Science. All rights reserved.
PY - 2020/7/24
Y1 - 2020/7/24
N2 - The rational design of enzymes is an important goal for both fundamental and practical reasons. Here, we describe a process to learn the constraints for specifying proteins purely from evolutionary sequence data, design and build libraries of synthetic genes, and test them for activity in vivo using a quantitative complementation assay. For chorismate mutase, a key enzyme in the biosynthesis of aromatic amino acids, we demonstrate the design of natural-like catalytic function with substantial sequence diversity. Further optimization focuses the generative model toward function in a specific genomic context. The data show that sequence-based statistical models suffice to specify proteins and provide access to an enormous space of functional sequences. This result provides a foundation for a general process for evolution-based design of artificial proteins.
AB - The rational design of enzymes is an important goal for both fundamental and practical reasons. Here, we describe a process to learn the constraints for specifying proteins purely from evolutionary sequence data, design and build libraries of synthetic genes, and test them for activity in vivo using a quantitative complementation assay. For chorismate mutase, a key enzyme in the biosynthesis of aromatic amino acids, we demonstrate the design of natural-like catalytic function with substantial sequence diversity. Further optimization focuses the generative model toward function in a specific genomic context. The data show that sequence-based statistical models suffice to specify proteins and provide access to an enormous space of functional sequences. This result provides a foundation for a general process for evolution-based design of artificial proteins.
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U2 - 10.1126/science.aba3304
DO - 10.1126/science.aba3304
M3 - Article
C2 - 32703877
AN - SCOPUS:85088524641
SN - 0036-8075
VL - 369
SP - 440
EP - 445
JO - Science
JF - Science
IS - 6502
ER -