Decoding efficacy and resistance space at a drug binding site

This article is a preprint and has not been certified by peer review

Interactions between drugs and their targets impact efficacy and, when altered by mutation, can result in resistance ^1-3. Assessing and understanding the impacts of all possible mutations at a drug binding site remain challenging, however ^4-6. Here, we used Multiplex Oligo Targeting (MOT) for mutational profiling, and computational modelling, to decode efficacy and resistance space at the otherwise native binding site for a low nanomolar potency, anti-trypanosomal, proteasome inhibitor ⁷. We saturation-edited twenty codons in the Trypanosoma brucei proteasome β5 subunit and subjected the resulting MOT libraries to stepwise drug selection. Amplicon sequencing, and codon variant scoring, yielding dose-response profiles for >100 resistance-conferring mutants, among 1,280 possible codon variants. Codon variant scores were predictive of relative resistance observed using a bespoke set of mutants, while fitness profiling revealed otherwise extensive constraints on mutational fitness and resistance space. The resistance profile that emerged allowed us to readily predict routes to spontaneous drug resistance observed within ‘accessible’, single nucleotide mutational space. In silico analysis of β5 subunit mutations predicted impacts on ligand affinity via steric effects, hydrogen-bonding and lipophilicity, which when combined with predictions of proteasome function perturbing mutations, were closely aligned with observed impacts on drug resistance. We conclude that MOT-library profiling facilitates assessment of all possible mutations at a drug binding site. Further decoding of drug target structure-activity relationships and drug resistance space will facilitate the design of more effective and durable drugs.