ArticlePLoS computational biology2018
Trajectory-based training enables protein simulations with accurate folding and Boltzmann ensembles in cpu-hours.
Article in PLoS computational biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
24 citing papers in PubMed.
- Article
- Accurate conformational ensembles of intrinsically disordered proteins using reweighting based on NMR chemical shifts.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Article
- Characterization and modulation of human insulin degrading enzyme conformational dynamics to control enzyme activity.bioRxiv : the preprint server for biology · 2025Article
- Temperature-dependent fold-switching mechanism of the circadian clock protein KaiB.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Steric trapping strategy for studying the folding of helical membrane proteins.Methods (San Diego, Calif.) · 2024Review
- Differentiable simulation to develop molecular dynamics force fields for disordered proteins.Chemical science · 2024Article
- HDX-MS finds that partial unfolding with sequential domain activation controls condensation of a cellular stress marker.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Folding of prestin's anion-binding site and the mechanism of outer hair cell electromotility.eLife · 2023Article
- AlphaFold developers Demis Hassabis and John Jumper share the 2023 Albert Lasker Basic Medical Research Award.The Journal of clinical investigation · 2023Article
- Worth the Weight: Sub-Pocket EXplorer (SubPEx), a Weighted Ensemble Method to Enhance Binding-Pocket Conformational Sampling.Journal of chemical theory and computation · 2023Article
- Predicting rare events using neural networks and short-trajectory data.Journal of computational physics · 2023Article
- Factors That Control the Force Needed to Unfold a Membrane Protein in Silico Depend on the Mode of Denaturation.International journal of molecular sciences · 2023Article
- The protein folding rate and the geometry and topology of the native state.Scientific reports · 2022Article
- Prediction and Validation of a Protein's Free Energy Surface Using Hydrogen Exchange and (Importantly) Its Denaturant Dependence.Journal of chemical theory and computation · 2022Article
- Lipid bilayer induces contraction of the denatured state ensemble of a helical-bundle membrane protein.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- A guide to machine learning for biologists.Nature reviews. Molecular cell biology · 2022Review
- Structure-based protein design with deep learning.Current opinion in chemical biology · 2021Review
- Differentiable biology: using deep learning for biophysics-based and data-driven modeling of molecular mechanisms.Nature methods · 2021Review
- Protein-structure prediction revolutionized.Nature · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
An ongoing challenge in protein chemistry is to identify the underlying interaction energies that capture protein dynamics. The traditional trade-off in biomolecular simulation between accuracy and computational efficiency is predicated on the assumption that detailed force fields are typically well-parameterized, obtaining a significant fraction of possible accuracy. We re-examine this trade-off in the more realistic regime in which parameterization is a greater source of error than the level of detail in the force field. To address parameterization of coarse-grained force fields, we use the contrastive divergence technique from machine learning to train from simulations of 450 proteins. In our procedure, the computational efficiency of the model enables high accuracy through the precise tuning of the Boltzmann ensemble. This method is applied to our recently developed Upside model, where the free energy for side chains is rapidly calculated at every time-step, allowing for a smooth energy landscape without steric rattling of the side chains. After this contrastive divergence training, the model is able to de novo fold proteins up to 100 residues on a single core in days. This improved Upside model provides a starting point both for investigation of folding dynamics and as an inexpensive Bayesian prior for protein physics that can be integrated with additional experimental or bioinformatic data.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.