Evidence map›Paper›PMID 24748852›Full record

ArticleJournal of chemical theory and computation2013

Variational Optimization of an All-Atom Implicit Solvent Force Field to Match Explicit Solvent Simulation Data.

Sandro Bottaro, Kresten Lindorff-Larsen, Robert B Best

Erratum issuedAbstract read
In one paragraph

Article in Journal of chemical theory and computation, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed
3.3field-weighted citation impact, top 8% of its field
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

22 citing papers in PubMed, 52 citations in OpenAlex.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors at 3 institutions in 4 countries.

Sandro BottaroDepartment of Biology, University of Copenhagen, Copenhagen, Denmark ; Laboratory of Chemical Physics, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, 9000 Rockville Pike, U.S.A. ; SISSA-Scuola Internazionale Superiore di Studi Avanzati,Trieste, Italy.
Kresten Lindorff-LarsenDepartment of Biology, University of Copenhagen, Copenhagen, Denmark ; Laboratory of Chemical Physics, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, 9000 Rockville Pike, U.S.A.
Robert B BestDepartment of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom ; Laboratory of Chemical Physics, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, 9000 Rockville Pike, U.S.A.
National Institutes of Health · USScuola Internazionale Superiore di Studi Avanzati · ITUniversity of Copenhagen · DK

Funding

Understanding protein folding, evolution and function via molecular simulationZIADK075104 · NIDDK · NATIONAL INSTITUTE OF DIABETES AND DIGESTIVE AND KIDNEY DISEASES · PI BEST, ROBERT · 2013 to 2025
$13.7M
Intramural NIH HHS Z99 DK999999
6 · The paper itself

Abstract

The development of accurate implicit solvation models with low computational cost is essential for addressing many large-scale biophysical problems. Here, we present an efficient solvation term based on a Gaussian solvent-exclusion model (EEF1) for simulations of proteins in aqueous environment, with the primary aim of having a good overlap with explicit solvent simulations, particularly for unfolded and disordered states - as would be needed for multiscale applications. In order to achieve this, we have used a recently proposed coarse-graining procedure based on minimization of an entropy-related objective function to train the model to reproduce the equilibrium distribution obtained from explicit water simulations. Via this methodology, we have optimized both a charge screening parameter and a backbone torsion term against explicit solvent simulations of an

Identifiers

PMID24748852
PMCPMC3987920
OpenAlexW2018437246

What OpenQuestion holds

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Registered trials

None linked

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.