Publications

66. The CRyPTIC Consortium
Reply: “Epidemiological cut-off values for a 96-well broth microdilution plate for high-throughput research antibiotic susceptibility testing of M. tuberculosis.”
Eur Res J 61:2300426
doi:10.1183/13993003.00426-2023

65. Brankin AE, Fowler PW
Inclusion of minor alleles improves catalogue-based prediction of fluoroquinolone resistance in M. tuberculosis
J Antimicrob Chemother AMR 5(2):dlad039 doi:10.1093/jacamr/dlad039 
bioRxiv preprint doi:10.1101/2022.11.09.515757

64. Sonnenkalb L, Carter JJ, Spitaleri A, Iqbal Z, Hunt M, Malone K, Utpatel C, Cirrillo DM, Rodrigues C, Nilgiriwala KS, the CRyPTIC Consortium, Fowler PW, Merker M, Niemann S.
Deciphering Bedaquiline and Clofazimine Resistance in Tuberculosis: An Evolutionary Medicine Approach
Lancet Microbe doi:10.1016/S2666-5247(23)00002-2 
bioRxiv preprint doi:10.1101/2021.03.19.436148.

63. Hunt M, Letcher B, Malone KM, Nguyen G, Hall MB, Colquhoun RM, Lima L, Schatz M, Ramakr- 2022 ishnan S, The CRyPTIC Consortium, Iqbal Z
Minos: variant adjudication and joint genotyping of cohorts of bacterial genomes
Genome Biology 23:147
doi:10.1186/s13059-022-02714-x
bioRxiv preprint: doi:10.1101/2021.09.15.460475

62. The CRyPTIC Consortium
A data compendium associating the genomes of 12,289 Mycobacterium tuberculosis isolates with quantitative resistance phenotypes to 13 antibiotics
PLoS Biology 20(8):e3001721 doi:10.1371/journal.pbio.3001721
bioRxiv preprint doi:10.1101/2021.09.14.460274

61. The CRyPTIC Consortium
Genome-wide association studies of global Mycobacterium tuberculosis resistance to thirteen antimicrobials in 10,228 genomes
PLoS Biology 20(8):e3001755 doi:10.1371/journal.pbio.3001755
bioRxiv preprint doi:10.1101/2021.09.14.460272

60. Brankin AE, Fowler PW
Predicting antibiotic resistance in complex protein targets using alchemical free energy methods
J Comp Chem 43:1771 doi:10.1002/jcc.26979 
chemRxiv preprint doi:10.26434/chemrxiv-2021-shfgp

59 Fowler PW, Wright C, Spiers H, Zhu T, Baeten EML, Hoosdally SW, Gibertoni Cruz AL, Roohi A, Kouchaki S, Walker TM, Peto TEA, Miller G, Lintott C, Clifton D, Crook DW, Walker AS, The CRyPTIC Consortium
BashTheBug: a crowd of volunteers reproducibly and accurately measure the minimum inhibitory concentrations of 13 antitubercular drugs from photographs of 96-well broth microdilution plates
eLife 11:e75046 doi:10.7554/eLife.75046
bioRxiv preprint doi:10.1101/2021.07.20.453060

58. Hunt M, Swann J, Constantinides B, Fowler PW, Iqbal Z
ReadItAndKeep: rapid decontamination of SARS-CoV-2 sequencing reads
Bioinformatics 38:3291 doi:10.1093/bioinformatics/btac311
bioRxiv preprint doi:10.1101/2022.01.21.477194

57. The CRyPTIC Consortium
Epidemiological cutoff values for a 96-well broth microdilution plate for high-throughput research antibiotic susceptibility testing of M. tuberculosis.
Eur Resp J 60:2200239 doi:10.1183/13993003.00239-2022
medRxiv preprint doi:10.1101/2021.02.24.21252386.

56. Walker TM, Miotto P, Köser CU, Fowler PW, Knaggs J, Iqbal Z, Hunt M, Chindelevitch L, Farhat M, Cirillo DM, Comas I, Posey JE, Omar SV, Peto TEA, Suresh A, Uplekar S, Laurent S, Colman
R, Nathanson CM, Zignol M, Walker AS, the CRyPTIC Consortium, the Seq&Treat Consortium,
Crook DW, Ismail N, Rodwell TC
The 2021 WHO catalogue of Mycobacterium tuberculosis complex mutations associated with drug resistance: A new global standard for molecular diagnostics
Lancet Microbe 3:E265
doi:10.1016/S2666-5247(21)00301-3

55. Adams O, Deme JC, Parker JL, the CRyPTIC Consortium, Fowler PW, Lea SM, Newstead S
Cryo-EM Structure and Resistance Landscape of M. tuberculosis MmpL3: An Emergent Therapeutic Target
Structure 29:1182
doi:10.1016/j.str.2021.06.013

54. Ismail NA, Nathanson CM, Korobitsyn A, Zignol M, Kasaeva T, Rodwell T, Miotto P, Walker TM, 2021 66 Fowler PW, Knaggs J, Iqbal Z, Hunt M, Chindelevitch L, Farhat M, Cirillo D, Crook DW, Comas I, Posey J, Vally Omar S, Peto TEA, Walker AS, Suresh A, Uplekar S, Laurent S, Colman R
Catalogue of mutations in M. tuberculosis complex and their association with drug resistance.
World Health Organization ISBN: 9789240028173

53. Lumley SF, Rodger G, Constantinides B, Sanderson N, Chau KK, Street TL, O’Donnell D, Howarth A, Hatch SB, Marsden BD, Cox S, James T, Warren F, Peck LJ, Ritter TG, de Toledo Z, Warren L, Axten D, Cornall RJ, Jones EY, Stuart DI, Screaton G, Ebner D, Hoosdally S, Chand M,
Oxford University Hospitals Staff Testing Group, Crook DW, O’Donnell A, Conlon CP, Pouwels KB, Walker AS, Peto TEA, Hopkins S, Walker TM, Stoesser NE, Matthews PC, Jeffery K, Eyre DW
An observational cohort study on the incidence of SARS-CoV-2 infection and B.1.1.7 variant infection in healthcare workers by antibody and vaccination status
accepted by Clin Infec Dis doi: 10.1093/cid/ciab608
medRxiv preprint doi: 10.1101/2021.03.09.21253218

52. Lumley SF, O’Donnell D, Stoesser NE, Matthews PC, Howarth A, Hatch, SB, Marsden BD, et al. and the Oxford University Hospitals Staff Testing Group
Antibody Status and Incidence of SARS-CoV-2 Infection in Health Care Workers
N Eng J Med 384:533
doi:10.1056/NEJMoa2034545

51. Lumley SF, Wei J, O’Donnell D, Stoesser NE, Matthews PC, Howarth A, Hatch SB, Marsden BD, Cox S, James T, Peck LJ, Ritter TG, de Toledo Z, Cornall RJ, Jones EY, Stuart DI, Screaton G,
Ebner D, Hoosdally S, Crook DW, Conlon CP, Pouwels KB, Walker AS, Peto TEA, Walker TM,
Jeffery K, Eyre DW, Oxford University Hospitals Staff Testing Group
The Duration, Dynamics, and Determinants of SARS-CoV-2 Antibody Responses in Individual Healthcare Workers
Clin Infec Dis 73:e699
doi: 10.1093/cid/ciab004
and medRxiv preprint doi: 10.1101/2020.11.02.20224824

50. Fowler PW
How quickly can we predict trimethoprim resistance using alchemical free energy methods?
Interface Focus 10:20190141
doi: 10.1098/rsfs.2019.0141 and bioRxiv preprint doi:10.1101/2020.01.13.904664

49. Eyre DW, Lumley SF, Donnell DO, Campbell M, Sims E, Lawson E, Warren F, James T, Cox S, Howarth A, Doherty G, Hatch SB, Kavanagh J, Chau KK, Fowler PW, Swann J, Volk D, Yang-Turner F, Stoesser NE, Matthews PC, Dudareva M, Davies T, Shaw RH, Peto L, et al.
Differential occupational risks to healthcare workers from SARS-CoV-2 observed during a prospective observational study
eLife 9:e60675
doi: 10.7554/eLife.60675 and medRxiv preprint
doi:10.1101/2020.06.24.20135038

48. Davies TJ, Stoesser N, Sheppard AE, Abuoun M, Fowler PW, Swann J, Quan P, Griffiths D, Vaughan A, Morgan M, Phan HTT, Jeffery KJM, Andersson M, Eillington M, Ekelund O, Mathers A, Bonomo R, Woodford N, Crook DW, Peto TEA, Anjum M, Walker AS
Reconciling the potentially irreconcilable? Genotypic and phenotypic amoxicillin-clavulanate resistance in E. coli.
Antimicrobial Agent Chemo 64:e02026
doi: 10.1128/AAC.02026-19 and preprint doi:10.1101/511402

47. Wilson DJ, CRyPTIC Consortium
GenomegaMap: within-species genome-wide dN/dS estimation from over 10,000 genomes.
Mol Biol Evol msaa0699
doi: 10.1093/molbev/msaa069 and biorXiv preprint doi: 10.1101/523316

46. Merker M, Kohl TA, Barilar I, Andres S, Fowler PW, Chryssanthou E, Ängeby K, Jureen P, Moradigaravand D, Parkhill J, Peacock SJ, Schön T, Maurer F, Walker TM, Köser C, Niemann S.
Phylogenetically informative mutations in genes implicated in antibiotic resistance in Mycobacterium tuberculosis complex.
Genome Medicine 12:27
doi: 10.1186/s13073-020-00726-5

45. Hunt M, Bradley P, Lapierre SG, Heys S, Thomsit M, Hall MB, Malone KM, Wintringer P, Walker TM, Cirillo DM, Comas I, Farhat MR, Fowler PW, Gardy J, Ismail N, Kohl TA, Mathys V, Merker M, Niemann S, Omar SV, Sintchenko V, Smith G, van Soolingen D, Supply P, Tahseen S, Wilcox M, Arandjelovic I, Peto TEA, Crook DW, Iqbal Z.
Antibiotic resistance prediction for Mycobacterium tuberculosis from genome sequence data with Mykrobe
Wellcome Open Research 4:191
doi:10.12688/wellcomeopenres.15603.1

44. Brankin AE, Fowler PW
Predicting Resistance is (Not) Futile.
ACS Cent Sci. 5:1312
doi: 10.1021/acscentsci.9b00791

43. Collins JJ, Brown E, Baym M, Wright GD, Dantas G, Burrows L, Liu GY, Fowler PW, Whitchurch CB, Skelly AN, Honda K, Strathdee SA, Patterson TL.
Overcoming Antibiotic Resistance.
Cell Host Microbe 26:8.
doi: 10.1016/j.chom.2019.06.007

42. Yang-Turner F, Volk D, Fowler PW, Hoosdally S, Peto TEA, Crook DW
Scalable Pathogen Pipeline Platform (SP3) Enabling Unified Genomic Data Analysis with Elastic Cloud Computing
IEEE Cloud 2019 478
doi: 10.1109/CLOUD.2019.00083

41. Yang Y, Walker TM, Walker AS, Wilson DJ, Peto TEA, Crook DW, Shamout F, CRyPTIC Consortium, Zhu T, Clifton DA
DeepAMR for predicting co-occurrent resistance of Mycobacterium tuberculosis
Bioinformatics 35:3240
doi: 10.1093/bioinformatics/btz067

40. Kouchaki S, Yang Y, Walker TM, Walker AS, Wilson DJ, Peto TEA, Crook DW, CRyPTIC Consortium, Clifton DA
Application of machine learning techniques to tuberculosis drug resistance analysis
Bioinformatics 35:2276
doi: 10.1093/bioinformatics/bty949

39. Fowler PW, Gibertoni-Cruz AL, Hoosdally S, Jarrett L, Borroni E, Chiacchiaretta M, Rathod P, Lehmann S, Molodtsov N, Grazian C, Walker TM, Robinson E, Hoffmann H, Peto TEA, Cirillo DM, Smith EG, Crook DW
Automated detection of bacterial growth on 96-well plates for high-throughput drug susceptibility testing of Mycobacterium tuberculosis
Microbiology 164:1522
doi: 10.1099/mic.0.000733 and biorXiv preprint doi: 10.1101/229427

38. The CRyPTIC Consortium and the 100,000 Genomes Project
Prediction of Susceptibility to First-Line Tuberculosis Drugs by DNA Sequencing
N Eng J Med 379:1403
doi: 10.1056/NEJMoa1800474

37. Rancoita PMV, Cugnata F, Gibertoni-Cruz AL, Borroni E, Hoosdally SJ, Walker TM, Grazian C, 2018 12 Davies TJ, Peto TEA, Crook DW, Fowler PW, Cirillo DM and the CRyPTIC consortium
Validating a 14-Drug Microtiter Plate Containing Bedaquiline and Delamanid for Large-Scale Re-
search Susceptibility Testing of Mycobacterium tuberculosis
Antimicrob Agent Chemo 62:e00344
doi: 10.1128/AAC.00344-18 and biorXiv preprint doi: 10.1101/244731

36. Fowler PW, Coles K, Gordon NC, Kearns AM, Llewelyn MJ, Peto TEA, Crook DW, Walker AS
Robust prediction of resistance to trimethoprim in S. aureus
Cell Chem Biol 25:339
doi: 10.1016/j.chembiol.2017.12.009

35. Duncan A, Reddy T, Koldsø H, Helie J, Fowler PW, Chavent M, Sansom MSP Protein crowding and lipid complexity influence the nanoscale dynamic organization of ion channels in cell membranes
Sci. Rep. 7:16647
doi: 10.1038/s41598-017-16865-6

34. Fowler PW, Sansom MSP, Reithmeier RAF
Effect of the Southeast Asian Ovalocytosis Deletion on the Conformational Dynamics of the Signal-Anchor Transmembrane Segment 1 of the Red Cell Anion Exchanger 1 (AE1, Band 3).
Biochemistry56:712
doi: 10.1021/acs.biochem.6b00966

33. Fowler PW, Helie J, Duncan AL,Chavent M, Koldsø H, Sansom MSP
Membrane stiffness is modified by integral membrane proteins.
Soft Matter 12:7792
doi: 10.1039/c6sm01186a

32. Fowler PW, Williamson JJ, Sansom MSP, Olmsted PD
Roles of interleaflet coupling and hydrophobic mismatch in lipid membrane phase-separation kinetics.
J Am Chem Soc 138:11633
doi: 10.1021/jacs.6b04880

31. Koldsø H, Reddy T, Fowler PW, Duncan AL, Sansom MSP
Membrane compartmentalization reduces the mobility of lipids and proteins within a model plasma membrane.
J Phys Chem B 120:8873
doi: 10.1021/acs.jpcb.6b05846

30. Samsudin F, Parker JL, Sansom MSP, Newstead and Fowler PW
Accurate prediction of ligand affinities for a peptide transporter.
Cell Chem Biol 23:299
doi: 10.1016/j.chembiol.2015.11.015

29. Beale JH, Parker JL, Samsudin F, Barret AL, Senan A, Bird LE, Scott D, Owens RJ, Sansom MSP, 2015 23 Tucker SJ, Meredith D, Fowler PW and Newstead S
Crystal structures of the extracellular domain from PepT1 and PepT2 provide novel insights into mammalian peptide transport.
Structure 23:1889.
doi: 10.1016/j.str.2015.07.016

28. Jefferys E, Sands Z, Shi J, Sansom MSP and Fowler PW
Alchembed: A computational method for incorporating proteins into complex lipid geometries.
J Chem Theo Comp. 11:2743
doi: 10.1021/ct501111d

27. Reddy T, Shorthouse D, Parton D, Jefferys E, Fowler PW, Chavent M, Baaden M, Sansom MSP (2015)
Nothing to sneeze at: a dynamic and integrative computational model of an influenza A virion.
Structure. 23:584.
doi: 10.1016/j.str.2014.12.019

26. Fowler PW, Orwick-Rydmark M, Radestock S, Solcan N, Dijkman P, Lyons JA, Kwok J, Caffery M, Watts A, Forrest LR and Newstead S (2015)
Gating topology of the proton coupled oligopeptide symporters.
Structure 23:290.
doi: 10.1016/j.str.2014.12.012

25. Fowler PW, Bollepalli MK, Rapedius M, Shang L, Nematian E, Sansom MSP, Tucker SJ, Baukrowitz T (2014)
Insights into the structural nature of the transition state in the Kir channel gating pathway.
Channels 8:551
doi: 10.4161/19336950.2014.962371

24. Bollepalli MK, Fowler PW, Rapedius M, Shang L, Sansom MSP, Tucker SJ, Baukrowitz T (2014)
State dependent network connectivity determines gating in a K+ channel.
Structure 22:1037

23. Jefferys E, Sansom MSP and Fowler PW (2014)
NRas slows the rate at which a model lipid bilayer phase separates.
RSC Faraday Discussion 169:209
doi: 10.1039/c3fd00131h

22. Stelzl L, Fowler PW, Sansom MSP and Beckstein O (2014)
Flexible gates generate occluded intermediates in the transport cycle of LacY.
J Mol Biol 426:735
doi: 10.1016/j.jmb.2013.10.024

21. Fowler PW, Beckstein O, Abad E and Sansom MSP (2013)
Detailed examination of a single conduction event in a potassium channel.
J Phys Chem Lett 4:3104
doi: 10.1021/jz4014079

20. Fowler PW, Abad E, Beckstein O and Sansom MSP (2013)
Energetics of multi-ion conduction pathways in potassium ion channels.
J Chem Theo Comp 9:5176
doi: 10.1021/ct4005933

19. Fowler PW and Sansom MSP (2013)
The pore of voltage-gated potassium ion channels is strained when closed.
Nature Communications 4:1872
doi: 10.1038/ncomms2858

18. Solcan N, Kwok J, Fowler PW, Cameron AD, Drew D, Iwata S and Newstead S (2012)
Alternating access mechanism in the POT family of oligopeptide transporters.
EMBO J 31:3411
doi: 10.1038/emboj.2012.157

17. Newstead S, Drew D, Cameron AD, Postis VLG, Xia X, Fowler PW, Carpenter EP, Sansom MSP, McPheron MJ, Baldwin SA and Iwata S (2011)
Crystal structure of a prokaryotic homologue of the mammalian oligopeptide-proton symporters, PepT1 and PepT2.
EMBO J 30:417
doi: 10.1038/emboj.2010.309

16. Paynter JJ, Andres-Enguix I, Fowler PW, Tottey S, Cheng W, Enkvetchakul D, Bavro VN, Kusakabe Y, Sansom MSP, Robinson NJ, Nichols CG and Tucker SJ (2010)
Functional complementation and genetic deletion studies of KirBac channels.
J Biol Chem 285:40754
doi: 10.1074/jbc.M110.175687

15. Newstead S, Fowler PW, Bilton P, Carpenter E, Sadler P, Campopiano D, Sansom M, Iwata S (2009)
Insights into how nucleotide-binding domains power ABC transport.
Structure 17:1213
doi: 10.1016/j.str.2009.07.009

14. Abad E, Reingruber J, Fowler PW and Sansom MSP (2009)
A novel rate theory approach to transport in ion channels.
TACC 1102:236
doi: 10.1063/1.3108380

13. Tai K, Fowler PW, Mokrab Y, Stansfield P and Sansom MSP (2008)
Molecular modeling and simulation studies of ion channel structures, dynamics & mechanisms.
Methods Nano Cell Biol 90:233
doi: 10.1016/S0091-679X(08)00812-1

12. Psachoulia E, Fowler PW, Bond PJ and Sansom MSP (2008)
Helix-helix interactions in membrane proteins: Coarse grained simulations of Glycophorin helix dimerization.
Biochemistry. 47:10503
doi: 10.1021/bi800678t

11. Fowler PW, Tai K and Sansom MSP (2008)
The selectivity of K+ ion channels: testing the hypotheses.
Biophys J 95:5062
doi: 10.1529/biophysj.108.132035

10. Rapedius M, Paynter J, Fowler PW, Shang L, Sansom MSP, Tucker SJ and Baukrowitz T (2007)
Control of pH and PIP2 gating in heteromeric Kir4.1/Kir5.1 channels by h-bonding at the helix bundle crossing.
Channels. 1:327
doi: 10.4161/chan.5176

9. Rapedius M, Fowler PW, Shang L, Sansom MSP, Tucker SJ and Baukrowitz T (2007)
H-Bonding at the helix-bundle crossing controls gating in Kir potassium channels.
Neuron 55:602
doi: 10.1016/j.neuron.2007.07.026

8. Fowler PW, Geroult S, Jha S, Waksman G and Coveney PV (2007)
Rapid, accurate and precise calculation of relative binding affinities for the SH2 domain using a computational grid.
J Comp Theo Chem 3:1193
doi: 10.1021/ct6003017

7. Fowler PW , Balali-Mood K , Deol SS, Coveney PV and Sansom MSP (2007)
Monotopic proteins and lipid bilayers: a comparative study.
Biochemistry. 46:3108
doi: 10.1021/bi602455n

6. Fowler PW and Coveney PV (2006)
A computational protocol for the integration of the monotopic protein PGHS into a bilayer. Biophys J 91:401
doi: 10.1529/biophysj.105.077784

5. Coveney PV and Fowler PW (2006)
Modelling biological complexity: a physical scientist’s perspective.
J R Soc Interface 2:267
doi: 10.1098/rsif.2005.0045

4. Fowler PW, Jha S and Coveney PV (2005)
Grid-based steered thermodynamic integration accelerates the calculation of binding affinities. Phil Trans R Soc Lond A 363(1833):1999
doi: 10.1098/rsta.2005.1625

3. Giordanetto F, Fowler PW, Saqi M, and Coveney PV (2005)
Large scale molecular dynamics simulation of native and mutant DHPS complexes suggests the molecular basis for dihydropteroate synthase drug resistance.
Phil Trans R Soc Lond A 363:(1833):2055
doi: 10.1098/rsta.2005.1629

2. Fowler PW, Coveney PV, Jha S and Wan S (2004)
Exact calculation of peptide-protein binding energies by steered thermodynamic integration using high performance computing grids.
Proceedings of the UK e-Science All Hands Meeting

1. Al-Mushadani O, Boghosian BM, Coveney PV, Fowler PW, Maillet JB and Wilson JL (1998)
Lattice-Gas Simulations of Ternary Amphiphilic Fluid Flow in Porous Media.
Int J Mod Phys C 9:1479
doi: 10.1142/S0129183198001345