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Exploring evolutionary trajectories of drug resistance

Linfeng Hu1, Aoxuan Zhang1, Arieh Warshel1

  • 1Department of Chemistry, University of Southern California, Los Angeles, CA 90089.

Proceedings of the National Academy of Sciences of the United States of America
|November 4, 2025
PubMed

Insights

Predicting drug resistance is key to developing new therapies. This study shows that a limited mutational space, like in HCV protease, allows for better prediction of resistance evolution and drug design.

Area of Science:

  • Virology
  • Computational Biology
  • Drug Discovery

Background:

  • Drug resistance is a major global health threat, demanding novel therapeutic strategies.
  • Predicting drug-resistant mutations and designing effective drugs against evolving targets remains a significant challenge.
  • Previous methods for assessing drug resistance using vitality values lacked accuracy due to insufficient data on mutation tendencies and protein stability.

Purpose of the Study:

  • To investigate the correlation between drug resistance and protein energy landscapes.
  • To identify a suitable model system for predicting drug resistance evolution.
  • To guide the development of robust therapeutic candidates against drug-resistant viruses.

Main Methods:

  • Utilized the Stanford University HIV Drug Resistance Database to analyze drug resistance patterns.
  • Correlated drug resistance (quantified as [Formula: see text]) with Maximum Entropy energy ([Formula: see text]) and the number of mutations.
  • Examined the mutational landscape of Hepatitis C Virus (HCV) protease as a model system with limited mutagenesis.

Main Results:

  • A positive correlation was observed between drug resistance and Maximum Entropy energy in HIV.
  • Both drug resistance and vitality were correlated with the number of mutations, suggesting a trade-off between resistance and viral function.
  • In HCV protease, resistance substitutions were found to cluster at low [Formula: see text] values, indicating a restricted mutational space.

Conclusions:

  • The limited mutational landscape of HCV protease enables the prediction of evolutionary pathways for drug resistance.
  • Maximum Entropy energy ([Formula: see text]) can serve as a predictor for evolutionary pathways of resistance in constrained systems.
  • This approach can guide the identification of therapeutic candidates that maintain efficacy against evolving drug resistance.
Keywords:
HIV proteaseantiviral drug designdrug resistancemaximum entropyvitality metric

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