# Rapid, accurate, and reproducible de novo prediction of resistance to antituberculars

## TL;DR

This paper introduces a fast and accurate method to predict drug resistance in tuberculosis using computer simulations, helping improve diagnosis and treatment strategies.

## Contribution

The study introduces TIES_PM, a novel ensemble-based molecular dynamics method for predicting rifampicin resistance in tuberculosis.

## Key findings

- TIES_PM accurately predicts rifampicin resistance in RNA polymerase for 61 clinically observed mutations.
- The method identifies ambiguous cases that suggest alternative resistance mechanisms.
- TIES_PM is rapid, cost-effective, and scalable for drug resistance screening in research and clinical settings.

## Abstract

As one of the deadliest infectious diseases in the world, tuberculosis is
responsible for millions of new cases and deaths reported annually. The rise
of drug-resistant tuberculosis, particularly resistance to first-line
treatments like rifampicin, presents a critical challenge for global health,
which complicates the treatment strategies and calls for effective
diagnostic and predictive tools. In this study, we apply an ensemble-based
molecular dynamics computer simulation method, TIES_PM, to estimate the
binding affinity through free energy calculations and predict rifampicin
resistance in RNA polymerase. By analyzing 61 mutations, including those in
the rifampicin resistance-determining region, TIES_PM produces reliable
results in good agreement with clinical reference and identifies abnormal
data points indicating alternative mechanisms of resistance. In the future,
TIES_PM is capable of identifying and selecting leads with a lower risk of
resistance evolution and, for smaller proteins, it may systematically
predict antibiotic resistance by analyzing all possible codon permutations.
Moreover, its flexibility allows for extending predictions to other
first-line drugs and drug-resistant diseases. TIES_PM provides a rapid,
accurate, low-cost, and scalable supplement to current diagnostic pipelines,
particularly for drug resistance screening in both research and clinical
domains.

Antimicrobial resistance (AMR), a global threat, challenges early
diagnosis and treatment of tuberculosis (TB). This study employs
TIES_PM, a free-energy calculation method, to efficiently predict AMR by
quantifying how mutations in bacterial RNA polymerase (RNAP) affect
rifampicin (RIF) binding. On simulating 61 clinically observed
mutations, the results align with WHO classifications and reveal
ambiguous cases, suggesting alternative resistance mechanisms. Each
mutation requires ~5 h, offering rapid, cost-effective predictions. An
ensemble approach ensures statistical robustness. TIES_PM can be
extended to smaller proteins for systematic codon permutation analysis,
enabling comprehensive antibiotic resistance prediction, or adapted to
identify low-resistance-risk drug leads. It also applies to other TB
drugs and resistant pathogens, supporting personalized therapy and
global AMR surveillance. This work provides novel tools to refine
resistance mutation databases and phenotypic classification standards,
enhancing early diagnosis while advancing translational research and
infectious disease control.