Publications

Highlights

(For a full list see below or go to Google Scholar)

SILVR: Guided Diffusion for Molecule Generation

SILVR is a method for conditioning a pre-trained equivariant diffusion model to generate new molecules from X-ray fragements.

Nicholas T. Runcie, Antonia S.J.S. Mey

J. Chem. Inf. Model. 63, 19, 5996–6005 (2023)

The paper was selected as an Editor’s Choice article

Allosteric effects in a catalytically impaired variant of the enzyme Cyclophilin A may be explained by changes in nano-microsecond time scale motions

Molecular simulations can give an alternative explanation for the reduced catalytic rate of different Cyclophilin A mutants that is experimentally testable.

P. Wapeesittipan, A.S.J.S. Mey, M. Walkinshaw, J. Michel

Comms. Chem. 2 41 (2019)

Impact of domain knowledge on blinded predictions of binding energies by alchemical free energy calculations

Accounting for differently charged ligands can be difficult in simulations, a framework for charging corrections was tested as part of a blinded challenge.

A.S.J.S. Mey, J. Juárez-Jiménez, J. Michel

J. Comput. Aided. Mol. Des. 32, 199 (2018)

Blinded predictions of binding modes and energies of HSP90-α ligands for the 2015 D3R Grand Challenge

Alchemical free energy calculation for HSP90-α using SOMD give competitive results in a blinded prediction challenge.

A.S.J.S. Mey, J. Juárez-Jiménez, A. Hennessy, J. Michel

Bioorg. Med. Chem. 24, 4890 (2016)

Dynamic Properties of Forcefields

Protein dynamics is highly dependent on the choice of molecular forcefield. The speed of different forcefields is assessed.

F. Vitalini, A.S.J.S. Mey, F. Noé and B.G. Keller

J. Chem. Phys. 142, 084101 (2015)

Most read article of in JCP in 2015

xTRAM: Estimating equilibrium expectations from time-correlated simulation data at multiple thermodynamic states

A Bayesian estimator for enhanced molecular simulation to estimate both thermodynamic properties and dynamic properties.

A.S.J.S. Mey, H. Wu, and F. Noé

Phys. Rev. X 4, 041018 (2014)

 

Preprints

  1. Sire: An Interoperability Engine for Prototyping Algorithms and Exchanging Information Between Molecular Simulation Programs
  2. C. Woods, L. Hedges, A. Mulholland, M. Malaisree, P. Tosco, H. Loeffler, M. Suruzhon, M. Burman, S. Bariami, S. Bosisio, G. Calabro, F. Clark, A. S.J.S. Mey, J. Michel
    chemrxiv-2024-gnm1z
  3. Benchmarking active learning protocols for ligand binding affinity prediction
  4. R. Gorantla, A. Kubincova, B. Suutari, B. P. Cossins, A. S.J.S. Mey
    bioRxiv 2023.11. 24.568570
  5. mRNA interactions with disordered regions control protein activity
  6. Y. Luo, S. Pratihar, E. Horste, S. Mitschka, A. S.J.S. Mey, H.M. Al-Hashimi, C. Mayr
    bioRxiv 2023.02.18.529068

Full List

  1. Markove State Models: to optimize or not to optimize
  2. R. Arbon, Y. Zhu, A. S.J.S. Mey
    J. Chem. Theory Comput. 20, 977–988 (2024)
  3. A suite of tutorials for the BioSimSpace framework for interoperable biomolecular simulation
  4. L. O. Hedges, S. Bariami, M. Burman, F. Clark, B. P. Cossins, A. Hardie, A. M. Herz, D. Lukauskis, A. S.J.S. Mey, J. Michel, J. Scheen, M. Suruzhon, C. J. Woods, Z. Wu
    Living Journal of Computational Molecular Science 5 (1), 2375-2375
  5. From Proteins to Ligands: Decoding Deep Learning Methods for Binding Affinity Prediction
  6. Rohan Gorantla, Alzbeta Kubincova, Andrea Y. Weisse, Antonia S.J.S. Mey
    J. Chem. Inf. Model. (2023)
  7. SILVR: Guided Diffusion for Molecule Generation
  8. Nicholas T. Runcie, Antonia S.J.S. Mey
    J. Chem. Inf. Model. 63, 19, 5996–6005 (2023)
  9. Course Materials for an Introduction to Data-Driven Chemistry
  10. James Cumby, Valentina Erastova, Matteo T. Degiacomi, J. Jasmin Güven, Claire L. Hobday, Antonia S.J.S. Mey, Hannah Pollak, Rafal Szabla
    J. Open Source Ed. 6, 192 (2023)
  11. What geometrically constrained models can tell us about real-world protein contact maps
  12. J. Jasmin Güven, Nora Molkenthin, Steffen Mühle, Antonia S.J.S. Mey
    Phys. Biol. 20 046004 (2023)
  13. Efficient Purification of Cowpea Chlorotic Mottle Virus by a Novel Peptide Aptamer
  14. G. Tscheuschner, M. Ponader, C. Raab, P. S. Weider, R. Hartfiel, J. O. Kaufmann, J. L. Völzke, G. Bosc-Bierne, C. Prinz, T. Schwaar, P. Andrle, H. Bäßler, K. Nguyen, Y. Zhu, A.S.J.S. Mey, A. Mostafa, I. Bald, M. G. Weller
    Viruses, 15(3), 697 (2023)
  15. Chapter 8: The IMAGINARY Journey to Open Mathematics Engagement: History and Current Projects
  16. E. Londaits, A. Matt, A.S.J.S. Mey, D. Ramos, C. Stussak, B. Violet
    World Scientific Series on Science Communication pp. 135-163 (2023)
  17. Best practices for constructing, preparing, and evaluating protein-ligand binding affinity benchmarks
  18. D.F. Hahn, C.I. Bayly, H.E. Bruce Macdonald, J.D. Chodera, A.S.J.S. Mey, D.L. Mobley, L. Perez Benito, C.E. M. Schindler, G.Tresadern, G.L. Warren
    Living J. Comp. Mol. Sci. 4(1), 1497 (2022)
  19. Dynamic Profiling of β-Coronavirus 3CL Mpro Protease Ligand-Binding Sites
  20. E. Cho, M. Rosa, R. Anjum, S. Mehmood, M. Soban, M. Mujtaba, K. Bux, S. C. Dantu, A. Pandini, J. Yin, H. Ma, A. Ramanathan, B. Islam, A.S.J.S. Mey, D. Bhowmik, S. Haider
    J. Chem. Inf. Model. 61, 6, 3058–3073 (2021)
  21. Implementation of the QUBE Force Field in SOMD for High-Throughput Alchemical Free-Energy Calculations
  22. L. Nelson, S. Bariami, C. Ringrose, J. Horton, V. Kurdekar, A.S.J.S. Mey, J. Michel, D. Cole
    J. Chem. Inf. Model. 61, 5, 2124–2130 (2021)
  23. Best Practices for Alchemical Free Energy Calculations
  24. A.S.J.S. Mey, B. Allen, H.E. Bruce Macdonald, J.D. Chodera, D. Hahn, M. Kuhn, J. Michel, D.L. Mobley, L.N. Naden, S. Prasad, A. Rizzi, J. Scheen, M.R. Shirts, G. Tresadern, H. Xu
    Living J. Comp. Mol. Sci. 2 (1), 18378 (2020)
  25. A Hybrid Alchemical Free Energy and Machine Learning Methodology for the Calculation of Absolute Hydration Free Energies of Small Molecules
  26. J. Scheen, W. Wu, A.S.J.S. Mey, P. Tosco, M. Mackey, J. Michel
    J. Chem. Inf. Model. 60, 11, 5331–5339 (2020)
  27. Assessment of Binding Affinity via Alchemical Free-Energy Calculations
  28. M. Kuhn, S. Firth-Clark, P. Tosco, A.S.J.S. Mey, M. Mackey, J. Michel
    J. Chem. Inf. Model. 60, 6, 3120–3130 (2020)
  29. Self-organized emergence of folded protein-like network structures from geometric constraints
  30. N. Molkenthin, S. Mühle, A.S.J.S. Mey, M. Timme
    PLoS ONE 15(2), e0229230 (2020)
  31. Dynamic design: manipulation of millisecond timescale motions on the energy landscape of Cyclophilin A
  32. J. Juárez-Jiménez, A. Gupta, G. Karunanithy, A.S.J.S. Mey, et al.
    Chem. Sci., 11, 2670-2680 (2020)
  33. BioSimSpace: An interoperable Python framework for biomolecular simulation
  34. L.O. Hedges, A.S.J.S. Mey, C.A. Laughton, et al.
    JOSS, 4, 1831 (2019)
  35. Allosteric effects in a catalytically impaired variant of the enzyme Cyclophilin A may be explained by changes in nano-microsecond time scale motions
  36. P. Wapeesittipan, A.S.J.S. Mey, M. Walkinshaw, J. Michel
    Comms. Chem. 2 41 (2019)
  37. Effect of automation on the accuracy of alchemical free energy calculation protocols over a set of ACK1 inhibitors
  38. J.M. Granadino-Roldan*, A.S.J.S. Mey*, J.J. Perez, S. Bosisio, J. Rubio-Martinez, J. Michel
    PloS One 14, e0213217 (2019)
  39. Impact of domain knowledge on blinded predictions of binding energies by alchemical free energy calculations
  40. A.S.J.S. Mey, J. Juárez-Jiménez, J. Michel
    J. Comput. Aided. Mol. Des. 32, 199 (2018)
  41. Blinded predictions of host-guest standard free energies of binding in the SAMPL5 challenge
  42. S. Bosisio, A.S.J.S. Mey, J. Michel,
    J. Comput. Aided. Mol. Des. 31, 61 (2017)
  43. Analytical methods for structural ensembles and dynamics of intrinsically disordered proteins
  44. M. Schor, A.S.J.S. Mey, C.E. MacPhee
    Biophys. Rev. 8, 429 (2016)
  45. Blinded predictions of distribution coefficients in the SAMPL5 challenge
  46. S. Bosisio, A.S.J.S. Mey, J. Michel
    J. Comput. Aided. Mol. Des. 30, 1101 (2016)
  47. Blinded predictions of binding modes and energies of HSP90-α ligands for the 2015 D3R Grand Challenge
  48. A.S.J.S. Mey*, J. Juárez-Jiménez*, A. Hennessy, J. Michel
    Bioorg. Med. Chem. 24, 4890 (2016)
  49. Elucidation of Non-Additive Effects in Protein-Ligand Binding Energies: Thrombin as a Case Study,
  50. G. Calabrò, C.J. Woods, F. Powlesland, A.S.J.S Mey, A.J. Mulholland, J. Michel
    J. Phys. Chem. B 120, 5340 (2016)
  51. Shedding light on the dock-lock mechanism in amyloid fibril growth using Markov State Models
  52. M. Schor, A.S.J.S. Mey, F. Noé, C.E. MacPhee
    J. Phys. Chem. Lett. 6, 1076 (2015)
  53. Dynamic Properties of Forcefields
  54. F. Vitalini*, A.S.J.S. Mey*, F. Noé and B.G. Keller
    J. Chem. Phys. 142, 084101 (2015)
  55. Statistically optimal analysis of state-discretized trajectory data from multiple thermodynamic states
  56. H. Wu, A.S.J.S. Mey, E. Rosta, F. Noé
    J. Chem. Phys. 141, 214106 (2014)
  57. xTRAM: Estimating equilibrium expectations from time-correlated simulation data at multiple thermodynamic states
  58. A.S.J.S. Mey, H. Wu, and F. Noé
    Phys. Rev. X 4, 041018 (2014)
  59. Rare-event trajectory ensemble analysis reveals metastable dynamical phases in lattice proteins
  60. A.S.J.S. Mey, P.L. Geissler and J.P. Garrahan
    Phys. Rev. E 89, 032109 (2014)
  61. Variational approach to molecular kinetics
  62. F. Nüske, B.G. Keller, G. Pérez-Hernández, A.S.J.S. Mey, F. Noé
    J. Chem. Theory Comput. 10, 1739 (2014)
  63. EMMA - A software package for Markov model building and analysis
  64. M. Senne, B. Trendelkamp-Schroer, A.S.J.S. Mey, C. Schütte, F. Noé
    J. Chem. Theory Comput. 8, 2223 (2012)
  65. Thermodynamics of trajectories of the one-dimensional Ising model
  66. E.S. Loscar, A.S.J.S. Mey, J.P. Garrahan
    J. Stat. Mech. 2011, P12011 (2011)