# Decoding efficacy and resistance space at a drug binding site

## TL;DR

This study uses advanced gene editing to explore all possible mutations at a drug binding site, predicting resistance and aiding drug discovery.

## Contribution

The study introduces a method using multiplex oligo targeting to assess all possible mutations at a drug binding site.

## Key findings

- The method identifies >100 resistance-conferring mutants at the Trypanosoma brucei proteasome.
- Codon variant scores predict resistance and align with in silico and in cellulo observations.
- Fitness profiling reveals constraints on mutational resistance and spontaneous resistance routes.

## Abstract

Assessing and understanding the impacts of all possible mutations at a drug binding site remain challenging. Here we use multiplex oligo targeting for mutational profiling, and computational modelling, to decode efficacy and resistance space at the otherwise native binding site for an anti-trypanosomal proteasome inhibitor. We saturation-edit twenty codons in the Trypanosoma brucei proteasome and subject the resulting libraries to stepwise drug selection and codon variant scoring, yielding dose-response profiles for >100 resistance-conferring mutants. Codon variant scores are predictive of relative resistance observed using a bespoke set of mutants, while fitness profiling reveals otherwise extensive constraints on mutational fitness and resistance space. The resistance profile is predictive of routes to spontaneous drug resistance observed within ‘accessible’, single nucleotide mutational space, while in silico predictions are closely aligned with impacts on drug resistance observed in cellulo. Thus, multiplex oligo targeting facilitates assessment of all possible mutations at a drug binding site.

The authors in this work present a study with multiplexed gene editing that is used to assess all possible mutations at a native drug binding site. The approach yields data that predicts spontaneous resistance, that aligns with in silico predictions, and that promises to facilitate drug discovery.