Evidence map›Paper›PMID 40077832›Full record

ArticleThe journal of physical chemistry. B2025

Highly Optimized Simulation of Atomic Resolution Cell-Like Protein Environment.

Andrii M Tytarenko, Amar Singh, Vineeth Kumar Ambati, Matthew M Copeland, Petras J Kundrotas, Randal Halfmann, Pavlo O Kasyanov, Eugene A Feinberg, Ilya A Vakser

Abstract read
In one paragraph

Article in The journal of physical chemistry. B, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. 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

9 authors.

Andrii M TytarenkoInstitute for Applied System Analysis at the Igor Sikorsky Kyiv Polytechnic Institute, Kyiv 03056, Ukraine.
Amar SinghComputational Biology Program, The University of Kansas, Lawrence, Kansas 66045, United States.ORCID 0000-0001-9582-670X
Vineeth Kumar AmbatiComputational Biology Program, The University of Kansas, Lawrence, Kansas 66045, United States.
Matthew M CopelandComputational Biology Program, The University of Kansas, Lawrence, Kansas 66045, United States.ORCID 0000-0002-0696-4746
Petras J KundrotasComputational Biology Program, The University of Kansas, Lawrence, Kansas 66045, United States.ORCID 0000-0001-5080-1664
Randal HalfmannStowers Institute for Medical Research, Kansas City, Missouri 64110, United States.
Pavlo O KasyanovInstitute for Applied System Analysis at the Igor Sikorsky Kyiv Polytechnic Institute, Kyiv 03056, Ukraine.
Eugene A FeinbergDepartment of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York 11794, United States.
Ilya A VakserComputational Biology Program, The University of Kansas, Lawrence, Kansas 66045, United States.ORCID 0000-0002-5743-2934

Funding

Integrated Resource for Protein Recognition StudiesR01GM074255 · NIGMS · UNIVERSITY OF KANSAS LAWRENCE · PI VAKSER, ILYA · 2005 to 2024
$6.2M
Elucidating mechanisms of amyloid nucleation in vivoR01GM130927 · NIGMS · STOWERS INSTITUTE FOR MEDICAL RESEARCH · PI HALFMANN, RANDAL ARTHUR · 2020 to 2023
$1.3M
NIGMS NIH HHS R01 GM074255NIGMS NIH HHS R01 GM130927
6 · The paper itself

Abstract

Computational approaches can provide details of molecular mechanisms in a crowded environment inside cells. Protein docking predicts stable configurations of molecular complexes, which correspond to deep energy minima. Systematic docking approaches, such as those based on fast Fourier transform (FFT), also map the entire intermolecular energy landscape by determining the position and depth of the full spectrum of the energy minima. Such mapping allows speeding up simulations by precalculating the intermolecular energy values. Our earlier study combined FFT docking with the Monte Carlo protocol, enabling simulation of cell-size, crowded protein systems with seconds, and longer trajectories at atomic resolution, several orders of magnitude longer than those achievable by alternative approaches. In this study, we present a further drastic extension of the modeling capabilities by parallelized implementation of the simulation protocol. The procedure was applied to a panel of Death Fold Domains that form nucleated polymers in human innate immune signaling, recapitulating their homooligomerization tendencies and providing insights into the molecular mechanisms of polymer nucleation. The parallelized protocol allows extension of the simulation trajectories by orders of magnitude beyond the previously reported implementation, reaching into the uncharted territory of atomic resolution simulation of cell-sized systems.

Indexed as

Molecular Dynamics SimulationProteinsFourier AnalysisHumansMolecular Docking SimulationMonte Carlo MethodThermodynamicsProteins

Identifiers

PMID40077832
PMCPMC11956777

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.