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.

Deciphering Bedaquiline and Clofazimine Resistance in Tuberculosis: An Evolutionary Medicine Approach

“…Another method for prospectively identifying mutations that confer resistance is structure-based modeling, which has recently been combined with machine learning methods to design algorithms for predicting resistance to rifampicin, isoniazid, and pyrazinamide [50][51][52][53][54] . These approaches rely on the fact that resistance-conferring mutations are often located in drug binding pockets or active sites and have distinct biophysical consequences as compared to their susceptible counterparts 55 . This study identifies several resistance mechanisms in Rv0678 with quantifiable structural effects (protein stability, dimer interactions, SNAP2 scores, and interaction with the DNA), suggesting that a structure-based machine learning approach could also be successful for predicting BDQ/CFZ resistance.…”

Insects, Rodents, and Pets as Reservoirs, Vectors, and Sentinels of Antimicrobial Resistance

“…The application of genomic methods in understanding AMR attracted research interest. Extensive genomic studies employing high-throughput sequencing data represent powerful novel approaches that rapidly detect and respond to genetic transformations associated with AMR [ 62 ]. Nevertheless, these studies lack mechanistic details.…”

Prediction of pyrazinamide resistance inMycobacterium tuberculosisusing structure-based machine learning approaches

“…However, these strategies are constrained, respectively, by the relative paucity of sequenced clinical isolates compared to the number of potential resistance-causing mutations and the lack of laboratory capacity to systematically generate and test mutants. Computational modelling approaches 40 can potentially predict the effect of a significant number of missense mutations [41][42][43][44] before they are observed in clinical isolates. Several studies have already trained machine learning models on a number of anti-tuberculars [45][46][47][48] , including pyrazinamide 49 .…”