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

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 (ΔK_a) 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.