Publikationen

  • Explorative Datenanalyse, Dimensionsreduktion: P20, P3, P12, P15
  • Kombinierende stochastische Modellierung von Daten aus multiplen Quellen: P7, P10, P14
  • Modellselektion in der Zeitreihenanalyse: P12, P9
  • Unsicherheitsquantifizierung: P17, P9
  • Feature selection: P12
  • Maximum-likelihood Methoden: P7, P17
  • Software und Tutorials: P18
  • Anwendungen Protein-Protein-Interaktion: P5, P6
  • Anwendungen Konformationdynamik: P1, P2, P11, P5, P6
  • Anwendungen Ligandenbindung: P1, P4, P6

Publikationen

  1. Paul, Thomas, Roux, Diversity of long-lived intermediates along the binding pathway of Imatinib to Abl kinase revealed by MD simulations J. Chem. Theory Comput., 16, 7852, (2020)
  2. Paul, Meng, Roux, Identification of druggable kinase target conformations using Markov model metastable states analysis of apo Abl J. Chem. Theory Comput., 16, 1896, (2020), PubMed
  3. Paul, Wu, Vossel, de Groot, Noé, Identification of kinetic order parameters for non-equilibrium dynamics J. Chem. Phys., 150, 164120 (2019), arXiv
  4. Paul, Markov state modeling of binding and conformational changes of proteins, Dissertation, Universität Potsdam (2017)
  5. Paul, Noé, Weikl, Identifying Conformational-Selection and Induced-Fit Aspects in the Binding-Induced Folding of PMI from Markov State Modeling of Atomistic Simulations J. Phys. Chem. B, 122, 5649 (2018)
  6. Paul, Wehmeyer, Abualrous, Wu, Schöneberg, Freund, Weikl, Noé, Protein-peptide association kinetics beyond the seconds timescale from atomistic simulations, Nat. Commun. 8, 1095 (2017)
  7. Wu, Paul, Wehmeyer, Noé, Multiensemble Markov models of molecular thermodynamics and kinetics, Proc. Natl. Acad. Sci. USA. 113, E3221 (2016)
  8. Paul, Weikl, How to distinguish conformational selection and induced fit based on chemical relaxation rates, PLoS comput. biol. 12, e1005067 (2016), arXiv
  9. Münch, Paul, Schmauder, Benndorf, Bayesian Hidden Markov modeling and model selection by Kalman filtering applied to multi-dimensional data of ion channels (preprint) bioRxiv (2020)
  10. He, Paul, Roux, A critical perspective on Markov state model treatments of protein–protein association using coarse-grained simulationss J. Chem. Phys. 154, 084101 (2021)
  11. Dodd, Botto, Paul, Fernandez-Leiro, Lamers and Ivanov, Polymerization and editing modes of a high-fidelity DNA polymerase are linked by a well-defined path Nat. Commun. (2020)
  12. Scherer, Husic, Hoffmann, Paul, Wu, Noé, Variational Selection of Features for Molecular Kinetics, J. Chem. Phys., 150, 194108 (2019), arXiv
  13. Pinamonti, Paul, Noé, Rodriguez, Bussi, The mechanism of RNA base fraying: molecular dynamics simulations analyzed with core-set Markov state models, J. Chem. Phys. 150, 154123 (2019), arXiv
  14. Olsson, Wu, Paul, Clemeni, Noé, Combining Experimental and Simulation Data via Augmented Markov Models, Proc. Natl. Acad. Sci. USA. 31, 8265 (2017)
  15. Wu, Nüske, Paul, Klus, Koltai, Noé, Variational Koopman models: slow collective variables and molecular kinetics from short off-equilibrium simulations, J. Chem. Phys. 146, 154104 (2017), arXiv
  16. Pinamonti, Zhao, Condon, Paul, Noé, Turner, Bussi, Predicting the kinetics of RNA oligonucleotides using Markov state models, J. Chem. Theory Comput. 13, 926 (2017), arXiv
  17. Trendelkamp-Schroer, Wu, Paul, Noé, Estimation and uncertainty of reversible Markov models, J. Chem. Phys. 143, 174101 (2015), arXiv
  18. Scherer, Trendelkamp-Schroer, Paul, et al. PyEMMA 2: A Software Package for Estimation, Validation, and Analysis of Markov Models, J. Chem. Theory Comput. 11, 5525 (2015), FU Publication Server
  19. Weikl, Paul, Conformational selection in protein binding and function, Protein Science 23, 1508 (2014), PubMed
  20. Pérez-Hernández, Paul, Giorgino, De Fabritiis, Noé, Identification of slow molecular order parameters for Markov model construction, J. Chem. Phys. 139, 015102 (2013), arXiv
  21. Paul Dreidimensionale Erregungswellen in oszillatorischen Medien, Technische Universität Berlin (2011)