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.
Data availability
The high-throughput sequencing data generated for this study have been deposited at the Sequence Read Archive under accession code PRJNA1295514 (https://www.ncbi.nlm.nih.gov/bioproject/1295514). Source data for Figs. 1c, 2b–d, 3a, 4b, c and 5a, b are available as a Source Data file and for Figs. 6c, d, f, g, 7a, b, Supplementary Figs. 7c and 10a,b in the Github repository (https://github.com/velocirraptor23/Decoding-efficacy-and-resistance-space-at-a-drug-binding-site-/tree/main) and Zenodo https://zenodo.org/records/18195459. The homology model used in Figs. 6 and 7 is available in ModelArchive (https://modelarchive.org/doi/10.5452/ma-cxis8).
Code availability
The code used to develop the homology model, perform analyses and generate results in this study is publicly available and has been deposited in Github at https://github.com/velocirraptor23/Decoding-efficacy-and-resistance-space-at-a-drug-binding-site-/tree/main, under MIT license. The specific version of the code associated with this publication is archived in Zenodo and is accessible via https://zenodo.org/records/1819545941.
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Acknowledgements
We thank David Robinson for assistance with structural analysis and Anna Creelman and Hayley Bell for assistance with PCR and sequencing. This work was supported by a Wellcome Centre Award (223608/Z/21/Z to D.H. as co-applicant) and a Wellcome Investigator Award (217105/Z/19/Z to D.H.).
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The in cellulo experiments were designed by S.A., M.R. and D.H. and carried out by S.A. and M.R. Compound selection was carried out by M.D.R and M.T. Sequence data analysis was performed by M.T. Data analyses were performed by S.A., M.T. and D.H. Computational modelling was performed by C.M-M, J.S.S. and P.E.G.F.I. The work was supervised by D.H. and M.J.B. The manuscript was written by S.A., C.M-M. and D.H. The manuscript was edited by all authors.
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Altmann, S., Mendoza-Martinez, C., Ridgway, M. et al. Decoding efficacy and resistance space at a drug binding site. Nat Commun (2026). https://doi.org/10.1038/s41467-026-69187-5
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DOI: https://doi.org/10.1038/s41467-026-69187-5
