Evidence map›Paper›PMID 26723628›Full record

ReviewThe Journal of chemical physics2015

Constraint methods that accelerate free-energy simulations of biomolecules.

Alberto Perez, Justin L MacCallum, Evangelos A Coutsias, Ken A Dill

Abstract readReview
In one paragraph

Review in The Journal of chemical physics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

1 citing paper in PubMed.

  1. Consistent Force Field Captures Homologue-Resolved HP1 Phase Separation.Journal of chemical theory and computation · 2021
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Alberto PerezLaufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York 11794, USA.
Justin L MacCallumDepartment of Chemistry, University of Calgary, Calgary, Alberta T2N 1N4, Canada.
Evangelos A CoutsiasLaufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York 11794, USA.
Ken A DillLaufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York 11794, USA.

Funding

New Solvent Models, Sampling Methods and Maintenance of Amber SoftwareR01GM107104 · NIGMS · STATE UNIVERSITY NEW YORK STONY BROOK · PI SIMMERLING, CARLOS · 2013 to 2020
$2.4M
New Mathematical Methods for Protein Loop ModelingR01GM090205 · NIGMS · UNIVERSITY OF NEW MEXICO · PI COUTSIAS, EVANGELOS A. · 2009 to 2012
$1.3M
NIGMS NIH HHS GM107104NIGMS NIH HHS R01 GM090205NIGMS NIH HHS R01GM090205NIGMS NIH HHS R01 GM107104
6 · The paper itself

Abstract

Atomistic molecular dynamics simulations of biomolecules are critical for generating narratives about biological mechanisms. The power of atomistic simulations is that these are physics-based methods that satisfy Boltzmann's law, so they can be used to compute populations, dynamics, and mechanisms. But physical simulations are computationally intensive and do not scale well to the sizes of many important biomolecules. One way to speed up physical simulations is by coarse-graining the potential function. Another way is to harness structural knowledge, often by imposing spring-like restraints. But harnessing external knowledge in physical simulations is problematic because knowledge, data, or hunches have errors, noise, and combinatoric uncertainties. Here, we review recent principled methods for imposing restraints to speed up physics-based molecular simulations that promise to scale to larger biomolecules and motions.

Indexed as

Molecular Dynamics SimulationThermodynamicsNuclear Magnetic Resonance, BiomolecularProtein ConformationProteinsProteins

Identifiers

PMID26723628
PMCPMC4684272

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.