Computational design of a modular protein sense-response system

Anum Glasgow(University of California, San Francisco), Yao-Ming Huang(University of California, San Francisco), Daniel J. Mandell(University of California, San Francisco), Michael C. Thompson(University of California, San Francisco), Ryan Ritterson(University of California, San Francisco), Amanda L. Loshbaugh(University of California, San Francisco), J. Pellegrino(University of California, San Francisco), Cody Krivacic(University of California, San Francisco), Roland A. Pache(University of California, San Francisco), Kyle A. Barlow(University of California, San Francisco), Noah Ollikainen(University of California, San Francisco), Deborah Jeon(University of California, San Francisco), Mark J. S. Kelly(University of California, San Francisco), James S. Fraser(University of California, San Francisco), Tanja Kortemme(University of California, San Francisco)
Science
November 22, 2019
Cited by 135Open Access
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Abstract

Sensing and responding to signals is a fundamental ability of living systems, but despite substantial progress in the computational design of new protein structures, there is no general approach for engineering arbitrary new protein sensors. Here, we describe a generalizable computational strategy for designing sensor-actuator proteins by building binding sites de novo into heterodimeric protein-protein interfaces and coupling ligand sensing to modular actuation through split reporters. Using this approach, we designed protein sensors that respond to farnesyl pyrophosphate, a metabolic intermediate in the production of valuable compounds. The sensors are functional in vitro and in cells, and the crystal structure of the engineered binding site closely matches the design model. Our computational design strategy opens broad avenues to link biological outputs to new signals.


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