Combining structure and genomics to understand antimicrobial resistance.

Area of Science:

• Genomics and computational biology
• Molecular biology and drug discovery

Background:

• Antimicrobials have revolutionized health, but their misuse drives antimicrobial resistance (AMR).
• AMR poses a significant threat to public health and animal husbandry.
• Genetic mutations, particularly non-synonymous single nucleotide polymorphisms (nsSNPs), are a key mechanism of AMR development.

Purpose of the Study:

• To review computational tools for analyzing mutations linked to AMR.
• To highlight the application of these tools in understanding drug resistance in pathogens like *Mycobacterium tuberculosis*.
• To bridge genomic findings with biophysical effects for mechanistic insights into AMR.

Main Methods:

• Review of sequence and structure-based computational tools.
• Focus on tools used to investigate mutational effects on drug targets.
• Integration of genomic data with biophysical mutation effect analysis.

Main Results:

• Genomic studies combined with computational analysis can rapidly detect AMR-linked mutations.
• Computational tools provide mechanistic insights into how mutations affect protein function and evolution.
• The study identifies tools applicable to clinically important pathogens, including *Mycobacterium tuberculosis*.

Conclusions:

• Combining genomic and computational approaches reveals the molecular basis of AMR.
• Understanding the biophysical effects of mutations is crucial for combating AMR.
• Mechanistic insights into drug resistance can inform strategies to limit AMR's impact.