Subpopulations in clinical samples ofM. tuberculosiscan give rise to rifampicin resistance and shed light on how resistance is acquired
Brunner VM. and Fowler PW., (2025)
Predicting rifampicin resistance inM. tuberculosisusing machine learning informed by protein structural and chemical features
Lynch CI. et al, (2025), ERJ Open Research, 00952 - 2024
Infection Inspection: using the power of citizen science for image-based prediction of antibiotic resistance in Escherichia coli treated with ciprofloxacin
Farrar A. et al, (2024), Scientific Reports, 14
Quantitative drug susceptibility testing for Mycobacterium tuberculosis using unassembled sequencing data and machine learning
(2024), PLOS Computational Biology, 20, e1012260 - e1012260
Prediction of pyrazinamide resistance in Mycobacterium tuberculosis using structure-based machine-learning approaches
Carter JJ. et al, (2024), JAC-Antimicrobial Resistance, 6
Compensatory mutations are associated with increased in vitro growth in resistant clinical samples of Mycobacterium tuberculosis
Brunner VM. and Fowler PW., (2024), Microbial Genomics, 10
Quantitative measurement of antibiotic resistance in Mycobacterium tuberculosis reveals genetic determinants of resistance and susceptibility in a target gene approach
Barilar I. et al, (2024), Nature Communications, 15
Discordance between different bioinformatic methods for identifying resistance genes from short-read genomic data, with a focus on Escherichia coli
Davies TJ. et al, (2023), Microbial Genomics, 9
Reply: Epidemiological cut-off values for a 96-well broth microdilution plate for high-throughput research antibiotic susceptibility testing ofM. tuberculosis
(2023), European Respiratory Journal, 61, 2300426 - 2300426
Inclusion of minor alleles improves catalogue-based prediction of fluoroquinolone resistance inMycobacterium tuberculosis
Brankin AE. and Fowler PW., (2023), JAC-Antimicrobial Resistance, 5
Compensatory mutations are associated with increasedin vitrogrowth in resistant clinical samples ofMycobacterium tuberculosis
Brunner V. and Fowler P., (2023)
Predicting antibiotic resistance in complex protein targets using alchemical free energy methods
Brankin AE. and Fowler PW., (2022), Journal of Computational Chemistry, 43, 1771 - 1782
Epidemiological cut-off values for a 96-well broth microdilution plate for high-throughput research antibiotic susceptibility testing ofM. tuberculosis
(2022), European Respiratory Journal, 60, 2200239 - 2200239
Genome-wide association studies of global Mycobacterium tuberculosis resistance to 13 antimicrobials in 10,228 genomes identify new resistance mechanisms
(2022), PLOS Biology, 20, e3001755 - e3001755
A data compendium associating the genomes of 12,289 Mycobacterium tuberculosis isolates with quantitative resistance phenotypes to 13 antibiotics.
The CRyPTIC Consortium None., (2022), PLoS biology, 20
High fluoroquinolone resistance proportions among multidrug-resistant tuberculosis driven by dominant L2 Mycobacterium tuberculosis clones in the Mumbai Metropolitan Region.
Dreyer V. et al, (2022), Genome medicine, 14
ReadItAndKeep: rapid decontamination of SARS-CoV-2 sequencing reads
Hunt M. et al, (2022), Bioinformatics
An Observational Cohort Study on the Incidence of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Infection and B.1.1.7 Variant Infection in Healthcare Workers by Antibody and Vaccination Status
Lumley SF. et al, (2022), Clinical Infectious Diseases, 74, 1208 - 1219
The 2021 WHO catalogue of Mycobacterium tuberculosis complex mutations associated with drug resistance: a genotypic analysis
Walker TM. et al, (2022), The Lancet Microbe, 3, e265 - e273
Rapid turnaround multiplex sequencing of SARS-CoV-2: comparing tiling amplicon protocol performance
Constantinides B. et al, (2022)
The Duration, Dynamics, and Determinants of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Antibody Responses in Individual Healthcare Workers
Lumley SF. et al, (2021), Clinical Infectious Diseases, 73, e699 - e709
Deciphering Bedaquiline and Clofazimine Resistance in Tuberculosis: An Evolutionary Medicine Approach
Sonnenkalb L. et al, (2021)
Antibody Status and Incidence of SARS-CoV-2 Infection in Health Care Workers
Lumley SF. et al, (2021), New England Journal of Medicine, 384, 533 - 540
Antibiotic resistance prediction for Mycobacterium tuberculosis from genome sequence data with Mykrobe
Hunt M. et al, (2019), Wellcome Open Research, 4, 191 - 191
Prediction of Susceptibility to First-Line Tuberculosis Drugs by DNA Sequencing
(2018), New England Journal of Medicine, 379, 1403 - 1415
Robust Prediction of Resistance to Trimethoprim in Staphylococcus aureus
Fowler PW. et al, (2018), Cell Chemical Biology, 25, 339 - 349.e4
Roles of Interleaflet Coupling and Hydrophobic Mismatch in Lipid Membrane Phase-Separation Kinetics
Fowler PW. et al, (2016), Journal of the American Chemical Society, 138, 11633 - 11642
Accurate Prediction of Ligand Affinities for a Proton-Dependent Oligopeptide Transporter
Samsudin F. et al, (2016), Cell Chemical Biology, 23, 299 - 309
Membrane stiffness is modified by integral membrane proteins
Fowler PW. et al, (2016), Soft Matter, 12, 7792 - 7803
Crystal Structures of the Extracellular Domain from PepT1 and PepT2 Provide Novel Insights into Mammalian Peptide Transport
Beale JH. et al, (2015), Structure, 23, 1889 - 1899
Nothing to Sneeze At: A Dynamic and Integrative Computational Model of an Influenza A Virion
Reddy T. et al, (2015), Structure, 23, 584 - 597
How Membrane Curvature Drives the Up-Concentration of N-Ras Proteins to Ordered Lipid Domains : Correlation of In Vivo and In Vitro Experiments with Mean Field Theory Calculations and Coarse Grain Simulations
Hatzakis NS. et al, (2014), BIOPHYSICAL JOURNAL, 106, 713A - 713A
Energetics of Multi-Ion Conduction Pathways in Potassium Ion Channels
Fowler PW. et al, (2013), Journal of Chemical Theory and Computation, 9, 5176 - 5189
The pore of voltage-gated potassium ion channels is strained when closed
Fowler PW. and Sansom MSP., (2013), Nature Communications, 4