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

Abstract

AbstractResistance to pharmacological treatments is a major public health challenge. Here we report Resistor—a novel structure- and sequence-based algorithm for drug design providing prospective prediction of resistance mutations. 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. To validate Resistor, we applied it to kinase inhibitors targeting EGFR and BRAF in lung adenocarcinoma and melanoma. Resistor correctly identified eight clinically significant EGFR resistance mutations, including the “gatekeeper” T790M mutation to erlotinib and gefitinib and five known resistance mutations to osimertinib. Furthermore, Resistor predictions are consistent with sensitivity data on BRAF inhibitors from both retrospective and prospective experiments using the KinCon biosensor technology. Resistor is available in the open-source protein design software OSPREY.