Evidence map›Paper›PMID 30558883›Full record

ArticleBiophysical journal2019

GROmaρs: A GROMACS-Based Toolset to Analyze Density Maps Derived from Molecular Dynamics Simulations.

Rodolfo Briones, Christian Blau, Carsten Kutzner, Bert L de Groot, Camilo Aponte-Santamaría

Open access · bronzeAbstract read
In one paragraph

Article in Biophysical journal, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.

0numbers the graph read from it
0cells of the map it votes in
42citing papers in PubMed
3.4field-weighted citation impact, top 6% 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

42 citing papers in PubMed, 115 citations in OpenAlex.

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  13. Protein ensemble modeling and analysis with MMMx.Protein science : a publication of the Protein Society · 2024
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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

5 authors at 4 institutions in 3 countries.

Rodolfo BrionesComputational Neurophysiology Group, Institute of Complex Systems 4, Forschungszentrum Jülich, Jülich, Germany.
Christian BlauDepartment of Biochemistry and Biophysics, Science for Life Laboratory, Stockholms Universitet, Stockholm, Sweden.
Carsten KutznerDepartment of Theoretical and Computational Biophysics, Max Planck Institute for Biophysical Chemistry, Göttingen, Germany.
Bert L de GrootDepartment of Theoretical and Computational Biophysics, Max Planck Institute for Biophysical Chemistry, Göttingen, Germany.
Camilo Aponte-SantamaríaMax Planck Tandem Group in Computational Biophysics, University of Los Andes, Bogotá, Colombia; Interdisciplinary Center for Scientific Computing, Heidelberg University, Heidelberg, Germany. Electronic address: ca.aponte@uniandes.edu.co.
Max Planck Institute for Biophysical Chemistry · DEForschungszentrum Jülich · DEHeidelberg University · DEScience for Life Laboratory · SE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We introduce a computational toolset, named GROmaρs, to obtain and compare time-averaged density maps from molecular dynamics simulations. GROmaρs efficiently computes density maps by fast multi-Gaussian spreading of atomic densities onto a three-dimensional grid. It complements existing map-based tools by enabling spatial inspection of atomic average localization during the simulations. Most importantly, it allows the comparison between computed and reference maps (e.g., experimental) through calculation of difference maps and local and time-resolved global correlation. These comparison operations proved useful to quantitatively contrast perturbed and control simulation data sets and to examine how much biomolecular systems resemble both synthetic and experimental density maps. This was especially advantageous for multimolecule systems in which standard comparisons like RMSDs are difficult to compute. In addition, GROmaρs incorporates absolute and relative spatial free-energy estimates to provide an energetic picture of atomistic localization. This is an open-source GROMACS-based toolset, thus allowing for static or dynamic selection of atoms or even coarse-grained beads for the density calculation. Furthermore, masking of regions was implemented to speed up calculations and to facilitate the comparison with experimental maps. Beyond map comparison, GROmaρs provides a straightforward method to detect solvent cavities and average charge distribution in biomolecular systems. We employed all these functionalities to inspect the localization of lipid and water molecules in aquaporin systems, the binding of cholesterol to the G protein coupled chemokine receptor type 4, and the identification of permeation pathways through the dermicidin antimicrobial channel. Based on these examples, we anticipate a high applicability of GROmaρs for the analysis of molecular dynamics simulations and their comparison with experimentally determined densities.

Indexed as

Molecular Dynamics SimulationSoftwareAnimalsAquaporinsBacterial Outer Membrane ProteinsHumansProtein ConformationReceptors, CXCR4AquaporinsBacterial Outer Membrane ProteinsCXCR4 protein, humanReceptors, CXCR4

Identifiers

PMID30558883
PMCPMC6342704
OpenAlexW2903004900

What OpenQuestion holds

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Registered trials

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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.