An evaluation of force field accuracy for the mini-protein chignolin using Markov state models

Marshall, Tim; Raddi, Robert; Voelz, Vincent A.

doi:10.1016/j.bpj.2022.11.2275

Cited by 1 publication

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“…To further demonstrate its utility, we apply BICePs to a series of prior conformational populations derived from all-atom simulations of the beta-hairpin chignolin CLN001, 32 using published NMR measurements as experimental restraints. 33 From over 20 µs of aggregate simulation trajectory data for each force field in TIP3P explicit solvent, Markov state models (MSMs) of chignolin folding were constructed using various numbers {5, 10, 50, 75, 100, 500} of microstates defined by conformational clustering (see Methods).…”

Section: Biceps Reweights Conformational Populations and Ranks The Ac...mentioning

confidence: 99%

“…Using these conformational state definitions, Markov State Models (MSMs) with lag time 20 ns were constructed using a maximum-likelihood estimator, with a bootstrapping procedure (randomly selecting 50% of the trajectories as input data over 5 trials) to estimate equilibrium state populations p(X) and their uncertainties. Full details are described in Marshall et al 32 Six models of varying levels of coase graining of conformational space were constructed for each force field. Markov state models (MSM) were built , where ensemble averaged forward model data was averaged over 20 snapshots for each microstate.…”

Section: Markov State Modelsmentioning

confidence: 99%

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Model selection using replica averaging with Bayesian inference of conformational populations

Raddi

Marshall

Ge³

et al. 2023

Preprint

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Bayesian Inference of Conformational Populations (BICePs) is a reweighting algorithm that reconciles simulated ensembles with sparse and/or noisy observables, by sampling the full posterior distribution of conformational populations in the presence of experimental restraints. By modifying BICePs to use replica-averaging in its forward model, BICePs becomes similar to other MaxEnt approaches, but with the significant advantages of (1) being able to sample over the posterior distribution of uncertainties due to random and systematic error, with improved likelihoods to deal with outliers, and (2) having an objective score for model selection, a free energy-like quantity called the BICePs score. To demonstrate the power of our approach, we used BICePs to reweight conformational ensembles of the mini-protein chignolin simulated in nine different force fields with TIP3P water, using a set of 158 experimental measurements (139 NOE distances, 13 chemical shifts, and 6 vicinal $J$-coupling constants for H$^{\text{N}}$ and H$^{\alpha}$. In all cases, reweighted populations favor the correctly folded conformation. The BICePs score, which reports the free energy of "turning on" conformational populations along with experimental restraints, provides a metric to evaluate each force field. For the nine force fields tested (A14SB, A99SB-ildn, A99, A99SBnmr1-ildn, A99SB, C22star, C27, C36, OPLS-aa), we obtain results consistent with previous work that used a conventional $\chi^{2}$ metric for model selection for small polypeptides and ubiquitin (Beauchamp et al 2012). These results suggest a powerful role for BICePs in future applications requiring ensemble reweighting and model selection.

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Section: Biceps Reweights Conformational Populations and Ranks The Ac...mentioning

confidence: 99%

Section: Markov State Modelsmentioning

confidence: 99%

Model selection using replica averaging with Bayesian inference of conformational populations

Raddi

Marshall

Ge³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

An evaluation of force field accuracy for the mini-protein chignolin using Markov state models

Cited by 1 publication

References 0 publications

Model selection using replica averaging with Bayesian inference of conformational populations

Model selection using replica averaging with Bayesian inference of conformational populations

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