Article

The structural context of mutations in proteins predicts their effect on antibiotic resistance

2025-09-25

Abstract excerpt

In Mycobacterium tuberculosis, a prevalent and deadly pathogen, resistance to antibiotics evolves primarily through non-synonymous mutations in proteins. Sequence-based analyses are currently used to understand the genetic basis of antibiotic resistance, either via genotype-phenotype association, or via signals of convergent evolution. These methods focus on primary sequence and often neglect other biological sig...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 74d751cb-ce55-58c4-a596-a2d9ac057473
DOI: 10.1101/2025.09.23.676583

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

The structural context of mutations in proteins predicts their effect on antibiotic resistanceDOI 10.1101/2025.09.23.676583Read

Machine learning and structural analysis of Mycobacterium tuberculosis pan-genome identifies genetic signatures of antibiotic resistanceSemantic proximity Read

An open-access dashboard to interrogate the genetic diversity of Mycobacterium tuberculosis clinical isolates.Semantic proximity Read

Prediction of impacts of mutations on protein structure and interactions: SDM, a statistical approach, and mCSM, using machine learning.Semantic proximity Read

Regression for accurate and sensitive grading of mutations diagnostic of antibiotic resistance in<i>Mycobacterium tuberculosis</i>Semantic proximity Read

FARM: Forecasting Antibiotic Resistance in <i>Mycobacterium tuberculosis</i> using biophysics and machine learningSemantic proximity Read

Single nucleotide variation catalogue from clinical isolates mapped on tertiary and quaternary structures of ESX-1 related proteins reveals critical regions as putative Mtb therapeutic targetsSemantic proximity Read

Computational modeling and bioinformatic analyses of functional mutations in drug target genes in Mycobacterium tuberculosisSemantic proximity Read

Structure-aware <i>M. tuberculosis</i> functional annotation uncloaks resistance, metabolic, and virulence genesSemantic proximity Read

Multivariable regression models improve accuracy and sensitive grading of antibiotic resistance mutations in Mycobacterium tuberculosis.Semantic proximity Read

Prediction of rifampicin resistance beyond the RRDR using structure-based machine learning approachesSemantic proximity Read

Structure-Aware Mycobacterium tuberculosis Functional Annotation Uncloaks Resistance, Metabolic, and Virulence GenesSemantic proximity Read

Mycobacterium tuberculosis uses intrinsically disordered, fast evolving proteins to interact with conserved host factorsSemantic proximity Read