Combining structure and genomics to understand antimicrobial resistance

Abstract

Antimicrobials against bacterial, viral and parasitic pathogens have transformed human and animal health. Nevertheless, their widespread use (and misuse) has led to the emergence of antimicrobial resistance (AMR) which poses a potentially catastrophic threat to public health and animal husbandry. There are several routes, both intrinsic and acquired, by which AMR can develop. One major route is through non-synonymous single nucleotide polymorphisms (nsSNPs) in coding regions. Large scale genomic studies using high-throughput sequencing data have provided powerful new ways to rapidly detect and respond to such genetic mutations linked to AMR. However, these studies are limited in their mechanistic insight. Computational tools can rapidly and inexpensively evaluate the effect of mutations on protein function and evolution. Subsequent insights can then inform experimental studies, and direct existing or new computational methods. Here we review a range of sequence and structure-based computational tools, focussing on tools successfully used to investigate mutational effect on drug targets in clinically important pathogens, particularly Mycobacterium tuberculosis. Combining genomic results with the biophysical effects of mutations can help reveal the molecular basis and consequences of resistance development. Furthermore, we summarise how the application of such a mechanistic understanding of drug resistance can be applied to limit the impact of AMR.

Keywords: Antimicrobial resistance; Genome wide association studies; Machine learning; Pathogen surveillance; Structural bioinformatics; Tuberculosis.

Fig. 1

A summary of mutational Cut-off Scanning Matrix (mCSM) method and its application in measuring mutational effects on protein stability (mCSM DUET), protein–protein interaction (mCSM-PPI, mCSM-PPI2), protein-nucleic acid (mCSM-NA) and protein–ligand affinity (mCSM-lig).

Fig. 2

Structure of katG in complex with the drug isoniazid (INH) coloured by 378 mutational positions linked to 571 SNPs. Areas marked in pink are associated with one or more mutations. HEM is denoted in red, INH is denoted as spheres. Parts a) and b) denote the structure in two different orientations, rotated by 180°. Figure rendered using UCSF Chimera, Version 1.13.1. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 3

Relationship between the impact of katG mutations on Protein stability (DUET) with Odds Ratio (OR), Allele Frequency (AF) and Mtb lineages. a) Pairwise correlations between DUET protein stability and GWAS measures of OR and AF of 566 mutations (total number of mutations with associated OR). The upper panel in both plots include the pairwise Spearman correlation values (denoted by ρ) along with their statistical significance (***P < 0.001). b) Lineage distribution of samples with katG mutations showing Mtb lineages 1–4 according to DUET protein stability ranging from red (-1, most destabilising) to blue (+1, most stabilising). The number of samples within each lineage are: Lineage 1 (n = 2448), Lineage 2 (n = 6813), Lineage 3 (n = 5020) and Lineage 4 (n = 2739). The number of samples contribute to the 566 katG mutations. Figure generated using R statistical software, version 3.6.1. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)