antimicrobial resistance clinical microbiology computing grants research tuberculosis

Postdoctoral position advertised

Through the CompBioMed2 EU Centre of Excellence project I have funding to appoint a postdoctoral researcher to develop machine-learning models to predict whether an infection is susceptible to an antibiotic. The need for predictive methods, such as these, will grow in the coming years as more of clinical microbiology transitions to using genetics to infer […]

antimicrobial resistance clinical microbiology computing distributed computing grants


I’ve been working on this for the last few months and very happy that we can now share our plans. Through a very generous donation by ORACLE, a group of researchers led by ModMedMicro at Oxford, are developing a cloud-based clinical microbiology genetics processing service, called the Global Pathogen Analysis System (GPAS). GPAS is still […]

antimicrobial resistance clinical microbiology computing grants research

Research position advertised

Come and work with me on antimicrobial resistance! Advert here. Broadly the idea is to develop our work using machine learning and molecular simulation to predict whether individual bacterial protein mutations confer resistance to an antibiotic (or not). Any questions please get in touch. For more details please see the advert, especially the lists of […]

antimicrobial resistance computing distributed computing GPUs molecular dynamics publication research

New publication: how quickly can be calculate the effect of a mutation on an antibiotic?

The idea for this paper arose during talking over coffee at the BioExcel Alchemical Free Energy workshop in May 2019. We’d previously shown that alchemical free energy methods could successfully predict which mutations in S. aureus DHFR  confer resistance to trimethoprim (and crucially, which do not). That is all well and good, but to do […]

antimicrobial resistance clinical microbiology computing tuberculosis

AMyGDA now available from GitHub

AMyGDA is a python module that analyses photographs of 96-well plates and, by examining each well for bacterial growth, is able to read a series of minimum inhibitory concentrations for the antibiotics present on a plate. Previously it was only available to download from this website (due to licensing) if you gave your email address […]

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.

computing GPUs molecular dynamics


HECBioSim advertised for proposals to use JADE, the new Tier-2 UK GPU high performance computer back in April 2019. JADE is built around NVIDIA DGX-1s, each of which contains 8 Tesla V100 GPUs. I’d previously run some alchemical free energy calculations on ARCHER, the Tier-1 UK academic supercomputer that has a conventional architecture, thanks to […]

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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BioExcel Alchemical Free Energy workshop

Last month I was invited to give a talk on using alchemical free energy methods to predict antimicrobial resistance at a workshop in Göttingen organised by the Max Planck Institute for Biophysical Chemistry on behalf of BioExcel. You can read more about the meeting, which I hope will become a biennial event, here.


Compression FASTA files natively in Python

The M. tuberculosis genome is pretty small, only 4.4 million nucleotides, so storing all that as plaintext means each genome is 4.2MB, but when you have tens of thousands of genomes it starts to add up, particularly as I want to keep my data tree on my workstation so I can view the images produced […]