De novo design of stable proteins that efficaciously inhibit oncogenic G proteins

Author:

Cummins Matthew C.1ORCID,Tripathy Ashutosh2,Sondek John123,Kuhlman Brian23ORCID

Affiliation:

1. Department of Pharmacology University of North Carolina School of Medicine Chapel Hill North Carolina USA

2. Department of Biochemistry and Biophysics University of North Carolina School of Medicine Chapel Hill North Carolina USA

3. Lineberger Comprehensive Cancer Center University of North Carolina at Chapel Hill Chapel Hill North Carolina USA

Abstract

AbstractMany protein therapeutics are competitive inhibitors that function by binding to endogenous proteins and preventing them from interacting with native partners. One effective strategy for engineering competitive inhibitors is to graft structural motifs from a native partner into a host protein. Here, we develop and experimentally test a computational protocol for embedding binding motifs in de novo designed proteins. The protocol uses an “inside‐out” approach: Starting with a structural model of the binding motif docked against the target protein, the de novo protein is built by growing new structural elements off the termini of the binding motif. During backbone assembly, a score function favors backbones that introduce new tertiary contacts within the designed protein and do not introduce clashes with the target binding partner. Final sequences are designed and optimized using the molecular modeling program Rosetta. To test our protocol, we designed small helical proteins to inhibit the interaction between Gαq and its effector PLC‐β isozymes. Several of the designed proteins remain folded above 90°C and bind to Gαq with equilibrium dissociation constants tighter than 80 nM. In cellular assays with oncogenic variants of Gαq, the designed proteins inhibit activation of PLC‐β isozymes and Dbl‐family RhoGEFs. Our results demonstrate that computational protein design, in combination with motif grafting, can be used to directly generate potent inhibitors without further optimization via high throughput screening or selection.

Funder

National Institute of General Medical Sciences

Publisher

Wiley

Subject

Molecular Biology,Biochemistry

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