Journal
SCIENCE
Volume 369, Issue 6502, Pages 440-+Publisher
AMER ASSOC ADVANCEMENT SCIENCE
DOI: 10.1126/science.aba3304
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Funding
- NIH [RO1GM12345]
- Robert A. Welch Foundation [I-1366]
- University of Chicago Center for Data and Computing
- Green Center for Systems Biology at UT Southwestern Medical Center
- EU [734439]
- Agence Nationale de la Recherche [CE30-0021-01]
- Swiss National Science Foundation [310030M_182648, 310030B_176405]
- Swiss National Science Foundation (SNF) [310030B_176405, 310030M_182648] Funding Source: Swiss National Science Foundation (SNF)
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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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