Publications

Highlights

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

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)

Shedding light on the dock-lock mechanism in amyloid fibril growth using Markov State Models

Amyloid fibril formation from a docked to a locked state occurs along a set of pathways containing highly metastable trapping states. pH will influence the population of the different metastable trapping states.

M. Schor, A.S.J.S. Mey, F. Noé, C.E. MacPhee

J. Phys. Chem. Lett. 6, 1076 (2015)

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. Automated Assessment of Binding Affinity via Alchemical Free Energy Calculations
  2. M. Kuhn, S. Firth-Clark, P. Tosco, A.S.J.S. Mey, M. Mackey, J. Michel
    chemrxiv

Full List

  1. Geometric constraints in protein folding
  2. N. Molkenthin, S. Mühle, A.S.J.S. Mey, M. Timme
    Plos One (accepted)
  3. Dynamic design: manipulation of millisecond timescale motions on the energy landscape of Cyclophilin A
  4. J. Juárez-Jiménez, A. Gupta, G. Karunanithy, A.S.J.S. Mey, et al.
    Chemical Science (accepted)
  5. BioSimSpace: An interoperable Python framework for biomolecular simulation
  6. L.O. Hedges, A.S.J.S. Mey, C.A. Laughton, et al.
    JOSS, 4, 1831 (2019)
  7. Allosteric effects in a catalytically impaired variant of the enzyme Cyclophilin A may be explained by changes in nano-microsecond time scale motions
  8. P. Wapeesittipan, A.S.J.S. Mey, M. Walkinshaw, J. Michel
    Comms. Chem. 2 41 (2019)
  9. Effect of automation on the accuracy of alchemical free energy calculation protocols over a set of ACK1 inhibitors
  10. J.M. Granadino-Roldan*, A.S.J.S. Mey*, J.J. Perez, S. Bosisio, J. Rubio-Martinez, J. Michel
    PloS One 14, e0213217 (2019)
  11. Impact of domain knowledge on blinded predictions of binding energies by alchemical free energy calculations
  12. A.S.J.S. Mey, J. Juárez-Jiménez, J. Michel
    J. Comput. Aided. Mol. Des. 32, 199 (2018)
  13. Blinded predictions of host-guest standard free energies of binding in the SAMPL5 challenge
  14. S. Bosisio, A.S.J.S. Mey, J. Michel,
    J. Comput. Aided. Mol. Des. 31, 61 (2017)
  15. Analytical methods for structural ensembles and dynamics of intrinsically disordered proteins
  16. M. Schor, A.S.J.S. Mey, C.E. MacPhee
    Biophys. Rev. 8, 429 (2016)
  17. Blinded predictions of distribution coefficients in the SAMPL5 challenge
  18. S. Bosisio, A.S.J.S. Mey, J. Michel
    J. Comput. Aided. Mol. Des. 30, 1101 (2016)
  19. Blinded predictions of binding modes and energies of HSP90-α ligands for the 2015 D3R Grand Challenge
  20. A.S.J.S. Mey*, J. Juárez-Jiménez*, A. Hennessy, J. Michel
    Bioorg. Med. Chem. 24, 4890 (2016)
  21. Elucidation of Non-Additive Effects in Protein-Ligand Binding Energies: Thrombin as a Case Study,
  22. G. Calabrò, C.J. Woods, F. Powlesland, A.S.J.S Mey, A.J. Mulholland, J. Michel
    J. Phys. Chem. B 120, 5340 (2016)
  23. Shedding light on the dock-lock mechanism in amyloid fibril growth using Markov State Models
  24. M. Schor, A.S.J.S. Mey, F. Noé, C.E. MacPhee
    J. Phys. Chem. Lett. 6, 1076 (2015)
  25. Dynamic Properties of Forcefields
  26. F. Vitalini*, A.S.J.S. Mey*, F. Noé and B.G. Keller
    J. Chem. Phys. 142, 084101 (2015)
  27. Statistically optimal analysis of state-discretized trajectory data from multiple thermodynamic states
  28. H. Wu, A.S.J.S. Mey, E. Rosta, F. Noé
    J. Chem. Phys. 141, 214106 (2014)
  29. xTRAM: Estimating equilibrium expectations from time-correlated simulation data at multiple thermodynamic states
  30. A.S.J.S. Mey, H. Wu, and F. Noé
    Phys. Rev. X 4, 041018 (2014)
  31. Rare-event trajectory ensemble analysis reveals metastable dynamical phases in lattice proteins
  32. A.S.J.S. Mey, P.L. Geissler and J.P. Garrahan
    Phys. Rev. E 89, 032109 (2014)
  33. Variational approach to molecular kinetics
  34. F. Nüske, B.G. Keller, G. Pérez-Hernández, A.S.J.S. Mey, F. Noé
    J. Chem. Theory Comput. 10, 1739 (2014)
  35. EMMA - A software package for Markov model building and analysis
  36. M. Senne, B. Trendelkamp-Schroer, A.S.J.S. Mey, C. Schütte, F. Noé
    J. Chem. Theory Comput. 8, 2223 (2012)
  37. Thermodynamics of trajectories of the one-dimensional Ising model
  38. E.S. Loscar, A.S.J.S. Mey, J.P. Garrahan
    J. Stat. Mech. 2011, P12011 (2011)