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bioRxiv · 10.1101/2022.01.18.476733

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

Guerin, N.·Kaserer, T.·Donald, B. R.
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Abstract

Resistance to pharmacological treatments is a major public health challenge. Here we report RO_SCPLOWESISTORC_SCPLOW--a novel structure- and sequence-based algorithm for drug design providing prospective prediction of resistance mutations. RO_SCPLOWESISTORC_SCPLOW computes the Pareto frontier of four resistance-causing criteria: the change in binding affinity ({Delta}Ka) of the (1) drug and (2) endogenous ligand upon a proteins 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 RO_SCPLOWESISTORC_SCPLOW, we applied it to kinase inhibitors targeting EGFR and BRAF in lung adenocarcinoma and melanoma. RO_SCPLOWESISTORC_SCPLOW 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, RO_SCPLOWESISTORC_SCPLOW predictions are consistent with sensitivity data on BRAF inhibitors from both retrospective and prospective experiments using the KinCon biosensor technology. RO_SCPLOWESISTORC_SCPLOW is available in the open-source protein design software OSPREY.

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Guerin, N., Kaserer, T., Donald, B. R.. 2022-01-20. Resistor: an algorithm for predicting resistance mutations using Pareto optimization over multistate protein design and mutational signatures. https://doi.org/10.1101/2022.01.18.476733

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