Identifying novel β-lactamase substrate activity through in silico prediction of antimicrobial resistance.

ABSTRACT: Diagnosing antimicrobial resistance (AMR) in the clinic is based on empirical evidence and current gold standard laboratory phenotypic methods. Genotypic methods have the potential advantages of being faster and cheaper, and having improved mechanistic resolution over phenotypic methods. We generated and applied rule-based and logistic regression models to predict the AMR phenotype from Escherichia coli and Pseudomonas aeruginosa multidrug-resistant clinical isolate genomes. By inspecting and evaluating these models, we identified previously unknown β-lactamase substrate activities. In total, 22 unknown β-lactamase substrate activities were experimentally validated using targeted gene expression studies. Our results demonstrate that generating and analysing predictive models

SUBMITTER: Tsang KK 

PROVIDER: S-EPMC8115898 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature