Decoding efficacy and resistance space at a drug binding site.

Area of Science:

• Drug discovery and development
• Computational biology
• Parasitology

Background:

• Understanding drug resistance mechanisms is crucial for developing effective therapeutics.
• Assessing the impact of all possible mutations at a drug binding site is computationally and experimentally challenging.
• The proteasome is a validated drug target for treating parasitic diseases like trypanosomiasis.

Purpose of the Study:

• To decode the efficacy and resistance landscape of an anti-trypanosomal proteasome inhibitor.
• To assess the impact of all possible mutations at the drug binding site.
• To develop a predictive model for drug resistance.

Main Methods:

• Multiplex oligo targeting for saturation mutagenesis of twenty codons in the Trypanosoma brucei proteasome.
• Stepwise drug selection and codon variant scoring to generate dose-response profiles for mutants.
• Computational modeling and in silico predictions of mutation impacts.
• Fitness profiling to identify constraints on mutational fitness.

Main Results:

• >100 resistance-conferring mutants were identified with detailed dose-response profiles.
• Codon variant scores accurately predicted relative drug resistance.
• Fitness profiling revealed extensive constraints on mutational fitness and resistance space.
• In silico predictions closely aligned with experimentally observed drug resistance in cellulo.

Conclusions:

• Multiplex oligo targeting is an effective method for assessing all possible mutations at a drug binding site.
• The study provides a comprehensive understanding of the resistance landscape for an anti-trypanosomal proteasome inhibitor.
• The findings facilitate the prediction of drug resistance pathways and inform the development of next-generation therapeutics.