Protocol for predicting drug-resistant protein mutations to an ERK2 inhibitor using RESISTOR

Nathan Guerin; Teresa Kaserer; Bruce R Donald

doi:10.1016/j.xpro.2023.102170

Protocol for predicting drug-resistant protein mutations to an ERK2 inhibitor using RESISTOR

STAR Protoc. 2023 Apr 27;4(2):102170. doi: 10.1016/j.xpro.2023.102170. Online ahead of print.

Authors

Nathan Guerin¹, Teresa Kaserer², Bruce R Donald³

Affiliations

¹ Department of Computer Science, Duke University, Durham, NC 27708, USA. Electronic address: osprey@cs.duke.edu.
² Institute of Pharmacy/Pharmaceutical Chemistry, University of Innsbruck, 6020 Innsbruck Austria.
³ Department of Computer Science, Duke University, Durham, NC 27708, USA; Department of Biochemistry, Duke University Medical Center, Durham, NC 22710, USA; Department of Chemistry, Duke University, Durham, NC 27708, USA; Department of Mathematics, Duke University, Durham, NC 27708, USA. Electronic address: brd+sp22@cs.duke.edu.

Abstract

Prospective predictions of drug-resistant protein mutants could improve the design of therapeutics less prone to resistance. Here, we describe RESISTOR, an algorithm that uses structure- and sequence-based criteria to predict resistance mutations. We demonstrate the process of using RESISTOR to predict ERK2 mutants likely to arise in melanoma ablating the efficacy of the ERK1/2 inhibitor SCH779284. RESISTOR is included in the free and open-source computational protein design software OSPREY. For complete details on the use and execution of this protocol, please refer to Guerin et al..¹.

Keywords: Bioinformatics; Biotechnology and Bioengineering; Cancer; Computer Sciences; High-throughput Screening; Protein Biochemistry; Structural Biology.

Grants and funding

R35 GM144042/GM/NIGMS NIH HHS/United States