Springe direkt zu Inhalt

Project C7

Structural bases of proton dynamics in viroporins by multiscale modelling and simulation

Principal Investigator: Prof. Dr. Cecilia Clementi  (FUB)

This project focuses on the development and application of methods for the characterization of the long timescale conformational transitions coupled with protonation dynamics in proteins. By combining statistical mechanics, machine learning and molecular dynamic simulations, we will design coarse-grained and multiscale models to study the global structural changes coupled with deprotonation/reprotonation events in phytochromes, and the structural bases underlying proton conductance of a set of newly discovered viral proton channels (viroporins).


2021 - 2024

Arts, M., Garcia Satorras, V., Huang, C.-W., Zügner, D., Federici, M., Clementi, C., Noé, F., Pinsler, R. and van den Berg, R. (2023). Two for One: Diffusion Models and Force Fields for Coarse-Grained Molecular Dynamics. J Chem Theory Comput, 19, 18: 6151-6159. doi: 10.1021/acs.jctc.3c00702.

Durumeric, A.E.P., Charron, N.E., Templeton, C., Musil, F., Bonneau, K., Pasos-Trejo, A.S., Chen, Y., Kelkar, A., Noé, F. and Clementi, C. (2023). Machine learned coarse-grained protein force-fields: Are we there yet? Curr Opin Struct Biol, 79, 102533. doi: 10.1016/j.sbi.2023.102533.

Glielmo, A., Husic, B.E., Rodriguez, A., Clementi, C., Noé, F., and Laio, A. (2021). Unsupervised Learning Methods for Molecular Simulation Data. Chem Rev, 121, 16: 9722–9758. doi: 10.1021/acs.chemrev.0c01195.

Köhler, J., Chen, Y., Krämer, A., Clementi, C. and Noé, F. (2023). Flow-Matching: Efficient Coarse-Graining of Molecular Dynamics without Forces. J Chem Theory and Comput, 19, 3: 942-952. doi: 10.1021/acs.jctc.3c00016.

Krämer, A., Durumeric, A.E.P., Charron, N.E., Chen, Y., Clementi, C. and Noé, F. (2023). Statistically Optimal Force Aggregation for Coarse-Graining Molecular Dynamics. J Phys Chem Lett, 14, 17: 3970-3979. doi: 10.1021/acs.jpclett.3c00444.

Lin, X., George, J.T., Schafer, N.P., Chau, K.N., Birnbaum, M.E., Clementi, C., Onuchic, J.N. and Levine, H. (2021). Rapid assessment of T-cell receptor specificity of the immune repertoire. Nat Comput Sci, 1: 362–373. doi: 10.1038/s43588-021-00076-1.

Majewski, M., Pérez, A., Thölke, P., Doerr, S., Charron, N.E., Giorgino, T., Husic, B.E., Clementi, C., Noé, F. and De Fabritiis, G. (2023). Machine learning coarse-grained potentials of protein thermodynamics. Nat Commun, 14, 1: 5739. doi: 10.1038/s41467-023-41343-1.

Musil, F., Zaporozhets, I., Noé, F., Clementi, C. and Kapil, V. (2022). Quantum dynamics using path integral coarse-graining. Chem Phys. doi: 10.1063/5.0120386.

Wang, J., Charron, N., Husic, B., Olsson, S., Noé, F., and Clementi, C. (2021). Multi-body effects in a coarse-grained protein force field. J Chem Phys, 154, 164113. doi: 10.1063/5.0041022.

Yang, W., Templeton, C., Rosenberger, D., Bittracher, A., Nüske, F., Noé, F. and Clementi, C. (2023). Slicing and Dicing: Optimal Coarse-Grained Representation to Preserve Molecular Kinetics. ACS Cent Sci. doi: 10.1021/acscentsci.2c01200.

Zaporozhets, I. and Clementi, C. (2023). Multibody Terms in Protein Coarse-Grained Models: A Top-Down Perspective. J Phys Chem B. doi: 10.1021/acs.jpcb.3c04493.