Multiscale biomolecular simulations

From method developments to applications to neurobiology


The group develops and applies multiscale methods in biomolecular simulation (from quantum to coarse grain) aimed at understanding structure/function of molecules of neurobiological interest and their complexes.  The methods span from coarse grain/molecular mechanics to quantum mechanics/molecular mechanics and they are applied to investigate the mode of action of neuroreceptors and enzymatic catalysis. The group also applies advanced simulation techniques and machine learning approaches to calculate residence times, a parameter of great importance in neuropharmacology, as well as to study drugs binding to targets such as RNA and neuroreceptors.

Research Topics

  • Simulation
  • Supercomputing
  • Machine learning
  • Artificial Intelligence
  • Hybrid quantum mechanics / molecular mechanics simulations
  • Hybrid coarse grain / molecular mechanics simulations
  • Enhanced sampling
  • Kinetics calculations
  • Machine-learning-accelerated molecular simulations
  • Applications to proteins and nucleic acids, especially in neurobiologically relevant processes


Prof. Dr. Paolo Carloni


Building 16.15 / Room 3010

+49 2461/61-8941


Selected Publications
  1. van Keulen SC, Martin J, Colizzi F, Frezza E, Trpevski D, Diaz NC, Vidossich P, Rothlisberger U, Hellgren Kotaleski J, Wade RC, Carloni P, Multiscale molecular simulations to investigate adenylyl cyclase‐based signaling in the brain, 2022/6/14, WIREs Comp. Mol. Sci (2022). IF 25.1

    Review on multiscale modeling, from quantum to coarse grain, of enzymes involved in a variety of GPCRs-based neurological processes.

  2. Meyer M, Jurek B, Alfonso-Prieto M, Ribeiro R, Milenkovic VM, Winter J, Hoffmann P, Wetzel CH, Giorgetti A, Carloni P, Neumann ID. Structure-function relationships of the disease-linked A218T oxytocin receptor variant. Mol Psychiatry. 27(2): 907-917 (2022). IF:13.4

    Impact of a point mutation of the neuronal GPCR oxytocin receptor,  associated with autism, on structure and signaling.

  3. Rizzi A, Carloni P, Parrinello M. Targeted Free Energy Perturbation Revisited: Accurate Free Energies from Mapped Reference Potentials. J Phys Chem Lett. 12(39): 9449-9454 (2021). IF: 6.8

    Applications of ML for molecular simulations-based free energy calculations.

  4. Ansari N, Rizzi V, Carloni P, Parrinello M. Water-Triggered, Irreversible Conformational Change of SARS-CoV-2 Main Protease on Passing from the Solid State to Aqueous Solution. J Am Chem Soc. 143(33): 12930-12934 (2021). IF: 16.4

    State of the art machine learning assisted enhanced sampling methods.

  5. Bolnykh V, Rossetti G, Rothlisberger U, Carloni P. Expanding the boundaries of ligand–target modeling by exascale calculations. Wiley Interdisciplinary Reviews: Computational Molecular Science 11(4): e1535 (2021). IF: 25.1

    Review of biomolecular simulations towards the exascale.

  6. Chiariello MG, Bolnykh V, Ippoliti E, Meloni S, Olsen JM, Beck T, Rothlisberger U, Fahlke C, Carloni P. Molecular basis of CLC antiporter inhibition by fluoride. J. Am. Chem. Soc. 142(16): 7254-7258 (2020). IF: 16.4

    Application of our massively parallel code MiMiC to a large biological system.

  7. Lyu W, Arnesano F, Carloni P, Natile G., Rossetti G. Effect of in vivo post-translational modifications of the HMGB1 protein upon binding to platinated DNA: a molecular simulation study. Nucleic Acids Res. 46(22): 11687-11697 (2018). IF: 19.2

    Application of the force matching method to obtain accurate force fields from QM/MM simulations.

  8. Casasnovas R. Limongelli V. Tiwary P. Carloni P. Parrinello M. Unbinding Kinetics of a p38 MAP Kinase Type II Inhibitor from Metadynamics Simulations. Journal of the American Chemical Society 139(13): 4780-4788 (2017). IF: 16.4

    One of the first applications of metadynamics-based kinetics calculations.

Last Modified: 12.06.2024