English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Interest in equilibrium-based sampling methods has grown with recent advancesin computational hardware and Markov state modeling (MSM) methods, yetoutstanding questions remain that hinder widespread adoption. Namely, how dosampling strategies explore conformational space and how might this influencepredictions? Here, we seek to answer these questions for four commonly usedsampling methods: 1) a long simulation, 2) many short simulations, 3) adaptivesampling, and 4) FAST. We first develop a theoretical framework foranalytically calculating the probability of discovering states and uncover thedrastic effects of varying the number and length of simulations. We then usekinetic Monte Carlo simulations on a variety of physically inspired landscapesto characterize state discovery and transition pathways. Consistently, we findthat FAST simulations discover target states with the highest probability andtraverse realistic pathways. Furthermore, we uncover the pathology that shortparallel simulations sometimes predict an incorrect transition pathway bycrossing large energy barriers that long simulations would typicallycircumnavigate, which we refer to as pathway tunneling. To protect againsttunneling, we introduce FAST-string, which samples along the highest-fluxtransition paths to refine an MSMs transition probabilities and discriminatebetween competing pathways. Additionally, we compare MSM estimators indescribing thermodynamics and kinetics. For adaptive sampling, we recommendnormalizing the transition counts out of each state after adding pseudo-countsto avoid creating sources or sinks. Lastly, we evaluate our insights fromsimple landscapes with all-atom molecular dynamics simulations of the foldingof the {\lambda}-repressor protein. We find that FAST-contacts predicts thesame folding pathway as long simulations but with orders of magnitude lesssimulation time.

Choice of adaptive sampling strategy impacts state discovery, transition probabilities, and the apparent mechanism of conformational changes