Quantitative Models of Molecular Dynamics from Sparse Simulation and Experimental Data

Abstract

Studying the long-timescale behavior of proteins is the key to understanding many of the fundamental processes of life. Molecular Dynamics (MD) simulations and biophysical experiments probe the dynamics of such systems. However, while MD aims to simulate the processes detected in experiments, their predictions are often not in quantitative agreement. Reconciling these differences is a significant opportunity to build quantitative mechanistic models of these systems. To this end, here we present dynamic Augmented Markov Models (dynAMMo), a new approach to integrate dynamic experimental observables, such as NMR relaxation dispersion data, with a Markov state model derived from MD simulation statistics. We find that integrating experimental data that are sensitive to dynamic processes allows us to accurately recover the unbiased kinetics from biased MD simulations. Further, we show that dynAMMo can recover exchange processes not observed in MD data and yield a kinetic model reconciling experiment and disconnected simulations, something which has not yet been possible. We demonstrate the effectiveness of dynAMMo using well-controlled model systems and show the broad applicability of the method on a well-studied protein system. Our approach opens up a wealth of new opportunities to quantitatively study protein structure and dynamics from a mechanistic point of view.