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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Significance An important challenge has been to develop computer methods that can predict protein native structures from their sequences and satisfy the thermodynamic principle of Boltzmann's Law, which requires that the sampling method obey detailed balance. The latter is needed to study mechanisms and dynamics, which require an understanding of relative populations of states. These dual goals are met by atomistic model simulations, but they have been too expensive computationally. Here, we join together molecular dynamics (MD) with Bayesian inferences derived from loose insights (proteins have “hydrophobic cores” and “secondary structures”). We show that this method can speed up MD simulations by up to five orders of magnitude, allowing for the accurate predictions of small native protein structures with only atomistic potentials.

Accelerating molecular simulations of proteins using Bayesian inference on weak information