Evidence map›Paper›PMID 36532662›Full record

ArticleJournal of computational physics2021

Computational modeling of protein conformational changes - Application to the opening SARS-CoV-2 spike.

Anna Kucherova, Selma Strango, Shahar Sukenik, Maxime Theillard

Abstract read
In one paragraph

Article in Journal of computational physics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. 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.

Anna KucherovaDepartment of Applied Mathematics, University of California, Merced, CA 95343, USA.
Selma StrangoDepartment of Applied Mathematics, University of California, Merced, CA 95343, USA.
Shahar SukenikDepartment of Chemistry and Chemical Biology, University of California, Merced, CA 95343, USA.
Maxime TheillardDepartment of Applied Mathematics, University of California, Merced, CA 95343, USA.

Funding

Uncovering the structural underpinnings of function in disordered transcription factor regionsR35GM137926 · NIGMS · UNIVERSITY OF CALIFORNIA, MERCED · PI Shahar Sukenik · 2020 to 2026
$2.5M
NIGMS NIH HHS R35 GM137926
6 · The paper itself

Abstract

We present a new approach to compute and analyze the dynamical electro-geometric properties of proteins undergoing conformational changes. The molecular trajectory is obtained from Markov state models, and the electrostatic potential is calculated using the continuum Poisson-Boltzmann equation. The numerical electric potential is constructed using a parallel sharp numerical solver implemented on adaptive Octree grids. We introduce novel

Indexed as

Covid-19Molecular trajectoryMultiscale modelingPoisson-BoltzmannSARS-CoV-2Spike protein

Identifiers

PMID36532662
PMCPMC9749448

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.