Rescuing off-equilibrium simulation data through dynamic experimental data with dynAMMo

Abstract

Long-timescale behavior of proteins is fundamental to many biological processes. Molecular dynamics (MD) simulations and biophysical experiments are often used to study protein dynamics. However, high computational demands of MD limit what timescales are feasible to study, often missing rare events, which are critical to explain experiments. On the other hand, experiments are limited by low resolution. We present dynamic augmented Markov models (dynAMMo) to bridge the gap between these data and overcome their respective limitations. For the first time, dynAMMo enables the construction of mechanistic models of slow exchange processes that have been not observed in MD data by integrating dynamic experimental observables. As a consequence, dynAMMo allows us to bypass costly and extensive simulations, yet providing mechanistic insights of the system. Validated with controlled model systems and a well-studied protein, dynAMMo offers a new approach to quantitatively model protein dynamics on long timescales in an unprecedented manner.

Folding-upon-binding pathways of an intrinsically disordered protein from a deep Markov state model

“…By identifying a network of states and transition rates, including encounter complexes and non-native binding pathways, it is possible to compare the effects of mutants on multiple interaction pathways (i.e., non-native binding vs. native binding, or the rate of transitions between different native-like bound states), as compared to utilizing simplifying assumptions of single on-rates, off-rates, folding rates, and unfolding rates when comparing simulations and experiments. In particular, we believe that MSMs derived with the methods presented here, when combined with the recently developed augmented Markov model formalism, where MSM state populations and transition rates refit using maximum-entropy methods to match agreement with experimental NMR spin relaxation data, will provide an eloquent approach to assess the agreement between MD simulations and NMR ( 118 , 119 ). To illustrate this, we have predicted backbone NMR chemical shifts for each state identified by our deep MSM ( SI Appendix , Fig.…”