Evidence map›Paper›PMID 38076911›Full record

ArticlebioRxiv : the preprint server for biology2023

Topological Learning Approach to Characterizing Biological Membranes.

Andres S Arango, Hyun Park, Emad Tajkhorshid

Open access · greenAbstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed, 0 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors at 1 institution in 1 country.

Andres S ArangoTheoretical and Computational Biophysics Group, NIH Resource Center for Macromolecular Modeling and Visualization, Beckman Institute for Advanced Science and Technology, Department of Biochemistry, and Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA.
Hyun ParkTheoretical and Computational Biophysics Group, NIH Resource Center for Macromolecular Modeling and Visualization, Beckman Institute for Advanced Science and Technology, Department of Biochemistry, and Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA.
Emad TajkhorshidTheoretical and Computational Biophysics Group, NIH Resource Center for Macromolecular Modeling and Visualization, Beckman Institute for Advanced Science and Technology, Department of Biochemistry, and Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA.ORCID 0000-0001-8434-1010
University of Illinois Urbana-Champaign · US

Funding

WHOLE CELL SIMULATIONP41GM104601 · NIGMS · UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN · PI SCHULTEN, KLAUS · 2012 to 2021
$19.0M
Resource for Macromolecular Modeling and VisualizationR24GM145965 · NIGMS · UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN · PI Emad Tajkhorshid · 2022 to 2026
$6.2M
Toolkit for High-Resolution Structure and Dynamics of Functional LipidsR01GM123455 · NIGMS · UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN · PI MORRISSEY, JAMES H., RIENSTRA, CHAD M · 2016 to 2020
$5.0M
Molecular Basis of Membrane Binding and Activation of Coagulation FactorsF31HL136155 · NHLBI · UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN · PI MULLER, MELANIE · 2017 to 2020
$199k
NHLBI NIH HHS F31 HL136155NIGMS NIH HHS P41 GM104601NIGMS NIH HHS R01 GM123455NIGMS NIH HHS R24 GM145965
6 · The paper itself

Abstract

Biological membranes play key roles in cellular compartmentalization, structure, and its signaling pathways. At varying temperatures, individual membrane lipids sample from different configurations, a process that frequently leads to higher-order phase behavior and phenomena. Here we present a persistent homology-based method for quantifying the structural features of individual and bulk lipids, providing local and contextual information on lipid tail organization. Our method leverages the mathematical machinery of algebraic topology and machine learning to infer temperature-dependent structural information of lipids from static coordinates. To train our model, we generated multiple molecular dynamics trajectories of DPPC membranes at varying temperatures. A fingerprint was then constructed for each set of lipid coordinates by a persistent homology filtration, in which interactions spheres were grown around the lipid atoms while tracking their intersections. The sphere filtration formed a

Identifiers

PMID38076911
PMCPMC10705453
OpenAlexW4389151755

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.