Molecular dynamics is an important research method in the study of biomolecules, but is often limited by insufficient sampling. Enhanced sampling methods emerged to faster explore the conformational space of a biomolecular system. At Molecular Biophysics Stockholm, we develop and apply various enhanced sampling methods for different purposes. We show a few prominent examples below.
The String Method with Swarms of Trajectories
The string method with swarms of trajectories finds the most probable transition path between two end states. We developed a generalized protocol of the simulation method with automatic parameter selection, significantly reducing the efforts required to carry out and optimize this kind of simulation study. As a result, we could efficiently derive the activation pathway of a GPCR and the free energy landscape of activation. We use this method to study membrane proteins with well-defined end states to reveal the physiological transition mechanism.
Adaptive Sampling Methods
With adaptive sampling, we increase the efficiency of MD without applying an artificial force to the system. By running several simulation replicas in parallel and adaptively reseeding a subset of the replicas, we can enhance exploration of a proteins’ conformational landscape or derive single well-equilibrated states from a starting structure. We actively apply adaptive sampling to obtain a quantitative comparison of ligand induced GPCR states, as well as to refine Markov state models of Calmodulin.
Temperature-enhanced methods, such as temperature replica exchange (T-REMD) and replica exchange solute tempering/scaling (REST) are useful when the system lacks structural end-states and the important degrees of freedom are unknown. We use these methods to explore the conformational landscapes of small proteins such as calmodulin. A fast exploration of the conformational ensemble enables us to identify and characterize states with data analysis, or seed starting configurations to launch new simulations from.
Energy landscapes reveal agonist’s control of GPCR activation via microswitches O Fleetwood, P Matricon, J Carlsson, L Delemotte Biochemistry 59, 880–891,
Inference of calmodulin’s Ca²⁺-dependent free energy landscapes via Gaussian mixture model validation AM Westerlund, TJ Harpole, C Blau and L Delemotte, J Chem Theor Comp,14, 1, 63-71
Effect of Ca2+ on the promiscuous target-protein binding mechanism of calmodulin A Westerlund, L Delemotte, PloS Comp Biol, DOI: 10.1371/journal.pcbi.1006072
Conformational landscapes of membrane proteins delineated by enhanced sampling molecular dynamics simulations TJ Harpole, L Delemotte, BBA Biomem, 1860 (4) 909-926
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