Evidence map›Paper›PMID 38098231›Full record

ArticleBiophysical journal2024

Data-driven prediction of α

Siva Dasetty, Tamara C Bidone, Andrew L Ferguson

Open access · bronzeAbstract read
In one paragraph

Article in Biophysical journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.9field-weighted citation impact, top 24% 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

5 citing papers in PubMed, 6 citations in OpenAlex.

  1. Prediction of αBiophysical journal · 2025
    Article
  2. Article
  3. Generating Protein Structures for Pathway Discovery Using Deep Learning.Journal of chemical theory and computation · 2024
    Article
  4. Article
  5. Contributions of the individual domains of αCytoskeleton (Hoboken, N.J.) · 2024
    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

3 authors at 2 institutions in 1 country.

Siva DasettyPritzker School of Molecular Engineering, University of Chicago, Chicago, Illinois.
Tamara C BidoneDepartment of Biomedical Engineering, University of Utah, Salt Lake City, Utah; Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, Utah.
Andrew L FergusonPritzker School of Molecular Engineering, University of Chicago, Chicago, Illinois. Electronic address: andrewferguson@uchicago.edu.
University of Chicago · USUniversity of Utah · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The integrin heterodimer is a transmembrane protein critical for driving cellular process and is a therapeutic target in the treatment of multiple diseases linked to its malfunction. Activation of integrin involves conformational transitions between bent and extended states. Some of the conformations that are intermediate between bent and extended states of the heterodimer have been experimentally characterized, but the full activation pathways remain unresolved both experimentally due to their transient nature and computationally due to the challenges in simulating rare barrier crossing events in these large molecular systems. An understanding of the activation pathways can provide new fundamental understanding of the biophysical processes associated with the dynamic interconversions between bent and extended states and can unveil new putative therapeutic targets. In this work, we apply nonlinear manifold learning to coarse-grained molecular dynamics simulations of bent, extended, and two intermediate states of α

Indexed as

Integrin alpha2Integrin beta3Molecular Dynamics SimulationDeep LearningPlatelet Glycoprotein GPIIb-IIIa ComplexProtein MultimerizationIntegrin alpha2Integrin beta3Platelet Glycoprotein GPIIb-IIIa Complex

Identifiers

PMID38098231
PMCPMC11393677
OpenAlexW4389736978

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.