The molecular dynamics of proteins and peptides is a hierarchical process which involves characteristic time scales ranging from 10-12 seconds to 100 seconds. Although the physical models of the local intramolecular interactions are relatively well developed, and molecular dynamics simulations have proven successful in recovering the dynamics of large-scale biomolecular systems, a mathematical understanding of how local interactions in the molecular root model give rise to a cascade of processes on different time scales is still lacking.
In this project we will investigate how these scaling cascades arise from the physical models of molecular dynamics and develop mathematical tools for their analysis. Our root model is a diffusion in a high-dimensional potential energy landscape that models the local interactions between atoms or groups of atoms. The local interactions in the molecular force .eld (i.e., the gradient of the potential energy) then induce long-range effects and may give rise to the observed long time scales on the order of seconds. Yet the predictability of molecular dynamics with respect to variations in the physical parameters (e.g., force .eld parameters) or boundary conditions (e.g., temperature) is remarkably poor, the reason being the nonlinearity, the large dimensionality of the models and noise present in the systems, which altogether promote large-scale effects induced by small noise or slow collective motions of atoms or groups of atoms.
For molecular systems with reversible dynamics, the relevant so-called implied time scales are related to the dominant eigenvalues of the underlying Markov generator. These eigenvalues can be estimated from molecular dynamics simulations and serve as approximations of experimentally measurable quantities. In molecular dynamics simulations it is possible to selectively tune the strength of a speci.c physical interaction (e.g., strength of long-range forces between different amino acids) or boundary conditions (e.g., temperature or pH), rendering them an ideal tool for analyzing the connection between root model and observed time scales. To investigate how the cascades of time scales arise in molecular dynamics we will extend numerical continuation methods for dynamical systems to stochastic molecular systems in order to study the changes in the implied time scales under variation of force .eld parameters or boundary conditions. We will compare analytical results to results from numerical simulations (classical and ab-initio molecular dynamics) and to results from infrared (IR) spectroscopy.
Despite its popularity in the protein folding community, implied time scales are only one possible way to quantify molecular dynamics time scales. For instance, the exponential convergence rate towards the thermodynamic equilibrium state is closely linked to experimentally measurable quantities. A second focus of the project is therefore to compare quantities which represent these relaxation time scales. To this end we will extend the numerical continuation approach to other observables, such as entropy production rates that, in certain cases, can be related to the shape of the molecular potential or Hankel singular values that characterize the response of the system to the environmental noise and can be related to the typical residence time of a conformation.
The understanding how scaling cascades in protein dynamics originate from the known hierarchy of physical interactions will be crucial for the development of multi-scale models, which consistently capture time scales on any desired level of coarseness. Moreover it will yield insight into biological phenomena such as allosteric regulation mechanisms or pathological misfolding events caused by single-point mutations.
Keller, B.G. and Aleksic, S. and Donati, L. (2019) Markov State Models in drug design. In: Biomolecular Simulations in Structure-based Drug Discovery. Methods and Principles in Medicinal Chemistry, 75 . Wiley-Interscience, Weinheim, pp. 67-86. ISBN 978-3-527-34265-5
Fackeldey, K. and Koltai, P. and Névir, P. and Rust, H.W. and Schild, A and Weber, M. (2019) From Metastable to Coherent Sets – time-discretization schemes. Chaos: An Interdisciplinary Journal of Nonlinear Science, 29 (1). 012101. ISSN 1054-1500 (print); 1089-7682 (online)
Donati, L. and Heida, M. and Weber, M. and Keller, B. (2018) Estimation of the infinitesimal generator by square-root approximation. Journal of Physics: Condensed Matter, 30 (42). p. 425201. ISSN 0953-8984, ESSN: 1361-648X
Donati, L. and Keller, B. (2018) Girsanov reweighting for metadynamics simulations. Journal of Chemical Physics, 149 (7). 072335. ISSN 0021-9606
Hassan, I. and Donati, L. and Stensitzki, T. and Keller, B. and Heyne, K. and Imhof, P. (2018) The Vibrational Spectrum of the hydrated Alanine-Leucine Peptide in the Amide region from IR experiments and First Principles Calculation. Chem. Phys. Lett. . pp. 1-26. ISSN 0009-2614
Neureither, L. and Hartmann, C. (2018) Time scales and exponential trends to equilibrium: Gaussian model problems. Proceedings of the Institut Henri Poincaré . (Submitted)
Stensitzki, T. and Yang, Y. and Kozich, V. and Ahmed, A.A. and Kössl, F. and Kühn, O. and Heyne, K. (2018) Acceleration of a ground-state reaction by selective femtosecond-infrared-laser-pulse excitation. Nature Chemistry, 10 . pp. 126-131.
Manz, C. and Kobitski, A. and Samanta, A. and Keller, B.G. and Jäschke, A. and Nienhaus, G.U. (2017) Single-molecule FRET reveals the energy landscape of the full-length SAM-I riboswitch. Nat. Chem. Biol., 13 . pp. 1172-1178.
Donati, L. and Hartmann, C. and Keller, B.G. (2017) Girsanov reweighting for path ensembles and Markov state models. Journal of Chemical Physics, 146 (24). p. 244112. ISSN 0021-9606
Quer, J. and Donati, L. and Keller, B.G. and Weber, M. (2017) An automatic adaptive importance sampling algorithm for molecular dynamics in reaction coordinates. SIAM J. Sci. Comput. . pp. 1-19. ISSN 1064-8275 (print) (In Press)
Zhang, W. and Hartmann, C. and Schütte, Ch. (2016) Effective dynamics along given reaction coordinates, and reaction rate theory. Faraday discussions, 195 . pp. 365-394. ISSN 1359-6640
Lemke, O. and Keller, B.G. (2016) Density-based cluster algorithms for the identification of core sets. Journal of Chemical Physics, 145 (164104).
Vitalini, F. and Noé, F. and Keller, B. (2016) Molecular dynamics simulations data of the twenty encoded amino acids in different force fields. Data in Brief, 7 . pp. 582-590.
Bittracher, Andreas and Hartmann, C. and Junge, O. and Koltai, Péter (2015) Pseudo generators for under-resolved molecular dynamics. The European Physical Journal Special Topics, 224 (12). pp. 2463-2490. ISSN 1951-6355
Hartmann, C. and Delle Site, L. (2015) Scale Bridging in Molecular Simulation. The European Physical Journal Special Topics, 224 (12). pp. 2173-2176. ISSN 1951-6355