Evidence map›Paper›PMID 39893583›Full record

ReviewJournal of chemical information and modeling2025

MELD in Action: Harnessing Data to Accelerate Molecular Dynamics.

Jokent Gaza, Emiliano Brini, Justin L MacCallum, Ken A Dill, Alberto Perez

Abstract readReview
In one paragraph

Review in Journal of chemical information and modeling, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Allosteric Protein Chemical Shift Perturbations are Ubiquitous.bioRxiv : the preprint server for biology · 2026
    Article
  2. Article
  3. Beyond Classical Force Fields: Physics-Driven Assessment of the Grappa Machine-Learned Force Field on the FoldBind Dataset.Chemphyschem : a European journal of chemical physics and physical chemistry · 2026
    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

5 authors.

Jokent GazaDepartment of Chemistry, University of Florida, Gainesville, Florida 32611, United States.ORCID 0000-0002-7836-4539
Emiliano BriniSchool of Chemistry and Materials Science, 85 Lomb Memorial Drive, Rochester, New York 14623, United States.ORCID 0000-0002-1314-8405
Justin L MacCallumDepartment of Chemistry, University of Calgary, Calgary, Alberta T2N 1N4, Canada.ORCID 0000-0001-7917-7068
Ken A DillLaufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York 11794, United States.ORCID 0000-0002-2390-2002
Alberto PerezDepartment of Chemistry, University of Florida, Gainesville, Florida 32611, United States.ORCID 0000-0002-5054-5338

Funding

Enabling Rational Design of Drug Targeting Protein-Protein Interactions with Physics-based Computational ModelingR16GM150512 · NIGMS · ROCHESTER INSTITUTE OF TECHNOLOGY · PI Emiliano Brini · 2023 to 2026
$740k
Uncovering the role of a new DNA sequence pattern in nucleosome-protein interactionsR15GM149587 · NIGMS · ROCHESTER INSTITUTE OF TECHNOLOGY · PI CUI, FENG · 2023 to 2025
$491k
NIGMS NIH HHS R15 GM149587NIGMS NIH HHS R16 GM150512
6 · The paper itself

Abstract

We review MELD, an accelerator of Molecular Dynamics simulations of biomolecules. MELD (Modeling Employing Limited Data) integrates molecular dynamics (MD) with a variety of types of structural information through Bayesian inference, generating ensembles of protein and DNA structures having proper Boltzmann populations. MELD minimizes the computational sampling of irrelevant regions of phase space by applying energetic penalties to areas that conflict with the available data. MELD is effective in refining protein structures using NMR or cryo-EM data or predicting protein-ligand binding poses. As a plugin for OpenMM, MELD is interoperable with other enhanced sampling methods, offering a versatile tool for structural determination in computational chemistry and biophysics.

Indexed as

Molecular Dynamics SimulationProteinsBayes TheoremDNALigandsProtein ConformationDNALigandsProteins

Identifiers

PMID39893583
PMCPMC12290800

What Socratic holds

Textmetadata
LicenceTDM
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 Socratic graph.