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antimicrobial resistance clinical microbiology publication research

New publication: Reconciling the potentially irreconcilable? Genotypic and phenotypic amoxicillin-clavulanate resistance in Escherichia coli.

Clinical microbiology often assumes a sample is resistant or susceptible. Making such a classification relies on applying a threshold (usually called a cutoff) to quantitative data, such as minimum inhibitory concentrations (MICs). If the MICs are strongly bimodal, then this is trivial and reproducibility is guaranteed. If the MICs are unimodal, then one is left […]

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antimicrobial resistance clinical microbiology publication research tuberculosis

New publication: Phylogenetically informative mutations in genes implicated in antibiotic resistance in Mycobacterium tuberculosis complex

Although the population structure M. tuberculosis is clonal, one must be careful when inferring the effect of individual mutations on the effect of an antibiotic. Purely because a mutation appears to define a phylogeny does not mean it has no effect on the minimum inhibitory concentration. Read more here (Open Access).

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antimicrobial resistance clinical microbiology computing GPUs molecular dynamics publication research

New preprint: rapid prediction of AMR by free energy methods

The story behind this preprint goes back to the workshop on free energy methods run by BioExcel in Göttingen in May 2019. I gave a talk, based in part on the work I’d previously published showing how alchemical free energy methods are able to predict which mutations in S. aureus DHFR confer resistance to trimethoprim.

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antimicrobial resistance molecular dynamics publication

New publication: Predicting resistance is (not) futile

Our “First Reactions” article has been published in ACS Central Science. We discuss the paper, Predicting Kinase Inhibitor Resistance: Physics-Based and Data-Driven Approaches, by Matteo Aldeghi, Vytautas Gapsys and Bert de Groot, which is in the same issue of the journal. Aldeghi et al. apply a series of methods to try and predict the effect […]

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antimicrobial resistance clinical microbiology publication research tuberculosis

New preprint: Predicting pyrazinamide resistance by machine learning

Usually, the protein that an antibiotic binds is essential for bacterial survival, which is how the drug has its effect. In this case, relatively few protein mutations arise that confer resistance, they are often subtle in nature and one can try to predict the phenotype of a protein mutation by considering how it affects the […]

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antimicrobial resistance citizen science clinical microbiology publication tuberculosis

New publication: Automated detection of bacterial growth on 96-well plates for high-throughput drug susceptibility testing of M. tuberculosis

In this Microbiology paper we show how a Python package, called the Automated Mycobacterial Detection Growth Algorithm (AMyDGA for short), can be used to independently read a 96-well plate designed for determining the minimum inhibitory concentration of 14 different anti-tubercular drugs. AMyGDA is reproducible and shows promising levels of accuracy. Where it fails, it does in […]