2. Academic Validation
  3. Resistor: An algorithm for predicting resistance mutations via Pareto optimization over multistate protein design and mutational signatures

Resistor: An algorithm for predicting resistance mutations via Pareto optimization over multistate protein design and mutational signatures

  • Cell Syst. 2022 Oct 19;13(10):830-843.e3. doi: 10.1016/j.cels.2022.09.003.
Nathan Guerin 1,  Andreas Feichtner 2,  Eduard Stefan 3,  Teresa Kaserer 4,  Bruce R Donald 5
Affiliations

Affiliations

  • 1 Department of Computer Science, Duke University, Durham, NC 27708, USA.
  • 2 Institute of Biochemistry and Center for Molecular Biosciences, University of Innsbruck, Innsbruck, 6020 Tyrol, Austria.
  • 3 Institute of Biochemistry and Center for Molecular Biosciences, University of Innsbruck, Innsbruck, 6020 Tyrol, Austria; Tyrolean Cancer Research Institute, Innsbruck, 6020 Tyrol, Austria.
  • 4 Institute of Pharmacy/Pharmaceutical Chemistry, University of Innsbruck, Innsbruck, 6020 Tyrol, Austria. Electronic address: [email protected].
  • 5 Department of Computer Science, Duke University, Durham, NC 27708, USA; Department of Biochemistry, Duke University Medical Center, Durham, NC 27710, USA; Department of Chemistry, Duke University, Durham, NC 27708, USA; Department of Mathematics, Duke University, Durham, NC 27708, USA. Electronic address: [email protected].
PMID: 36265469 DOI: 10.1016/j.cels.2022.09.003
Abstract

Resistance to pharmacological treatments is a major public health challenge. Here, we introduce Resistor-a structure- and sequence-based algorithm that prospectively predicts resistance mutations for drug design. Resistor computes the Pareto frontier of four resistance-causing criteria: the change in binding affinity (ΔKa) of the (1) drug and (2) endogenous ligand upon a protein's mutation; (3) the probability a mutation will occur based on empirically derived mutational signatures; and (4) the cardinality of mutations comprising a hotspot. For validation, we applied Resistor to EGFR and BRaf kinase inhibitors treating lung adenocarcinoma and melanoma. Resistor correctly identified eight clinically significant EGFR resistance mutations, including the erlotinib and gefitinib "gatekeeper" T790M mutation and five known osimertinib resistance mutations. Furthermore, Resistor predictions are consistent with BRaf Inhibitor sensitivity data from both retrospective and prospective experiments using KinCon biosensors. Resistor is available in the open-source protein design software OSPREY.

Keywords

BRAF; EGFR; KinCon; OSPREY; PLX8394; Pareto optimization; cancer; computational protein design; dabrafenib; drug resistance; encorafenib; erlotinib; gefitinib; kinase conformation reporter assay; kinase conformations; multistate design; mutational signatures; osimertinib; resistance mutations; vemurafenib.

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