Evidence map›Paper›PMID 38074468›Full record

ArticleComputational and structural biotechnology journal2023

Coevolution combined with molecular dynamics simulations provides structural and mechanistic insights into the interactions between the integrator complex subunits.

Bernard Fongang, Yannick N Wadop, Yingjie Zhu, Eric J Wagner, Andrzej Kudlicki, Maga Rowicka

Open access · goldAbstract read
In one paragraph

Article in Computational and structural biotechnology journal, 2023. 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
0.3field-weighted citation impact, top 52% 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

3 citing papers in PubMed, 3 citations in OpenAlex.

  1. Article
  2. Review
  3. 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

6 authors at 3 institutions in 1 country.

Bernard FongangGlenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, The University of Texas Health Science Center at San Antonio, San Antonio, TX, United States.
Yannick N WadopGlenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, The University of Texas Health Science Center at San Antonio, San Antonio, TX, United States.
Yingjie ZhuDepartment of Biochemistry and Molecular Biology, The University of Texas Medical Branch, Galveston, TX, United States.
Eric J WagnerDepartment of Biochemistry and Molecular Biology, The University of Texas Medical Branch, Galveston, TX, United States.
Andrzej KudlickiDepartment of Biochemistry and Molecular Biology, The University of Texas Medical Branch, Galveston, TX, United States.
Maga RowickaDepartment of Biochemistry and Molecular Biology, The University of Texas Medical Branch, Galveston, TX, United States.
The University of Texas Medical Branch at Galveston · USThe University of Texas Health Science Center at San Antonio · USUniversity of Rochester Medical Center · US

Funding

UTMB Clinical and Translational Science AwardUL1TR001439 · NCATS · UNIVERSITY OF TEXAS MED BR GALVESTON · PI URBAN, RANDALL J · 2015 to 2024
$40.0M
South Texas Alzheimer's Disease Research CenterP30AG066546 · NIA · UNIVERSITY OF TEXAS HLTH SCIENCE CENTER · PI Tiffany Faye Kautz · 2021 to 2026
$24.0M
Probing the specificity and activity of the metazoan Integrator complexR01GM134539 · NIGMS · HARVARD MEDICAL SCHOOL · PI Karen L Adelman, ERIC J WAGNER · 2019 to 2026
$4.7M
Decoding Genome Instability by Combining Accurate Mapping and Predictive ModelingR01GM112131 · NIGMS · UNIVERSITY OF TEXAS MED BR GALVESTON · PI ROWICKA, MAGA MALGORZATA · 2014 to 2023
$3.2M
Delineating the role of the gut microbiota and its derived metabolites in the development of dementia in multi-ethnic populationsK01NS126489 · NINDS · UNIVERSITY OF TEXAS HLTH SCIENCE CENTER · PI Bernard Fongang · 2023 to 2026
$882k
NCATS NIH HHS UL1 TR001439NIA NIH HHS P30 AG066546NIGMS NIH HHS R01 GM112131NIGMS NIH HHS R01 GM134539NINDS NIH HHS K01 NS126489
6 · The paper itself

Abstract

Finding the 3D structure of large, multi-subunit complexes is difficult, despite recent advances in cryo-EM technology, due to remaining challenges to expressing and purifying subunits. Computational approaches that predict protein-protein interactions, including Direct Coupling Analysis (DCA), represent an attractive alternative for dissecting interactions within protein complexes. However, they are readily applicable only to small proteins due to high computational complexity and a high number of false positives. To solve this problem, we proposed a modified DCA approach, a powerful tool to predict the most likely interfaces of protein complexes. Since our modified approach cannot provide structural and mechanistic details of interacting peptides, we combine it with Molecular Dynamics (MD) simulations. To illustrate this novel approach, we predict interacting domains and structural details of interactions of two Integrator complex subunits, INTS9 and INTS11. Our predictions of interacting residues of INTS9/INTS11 are highly consistent with crystallographic structure. We then expand our procedure to two complexes whose structures are not well-studied: 1) The heterodimer formed by the Cleavage and Polyadenylation Specificity Factor 100-kD (CPSF100) and 73-kD (CPSF73); 2) The heterotrimer formed by INTS4/INTS9/INTS11. Experimental data supports our predictions of interactions within these two complexes, demonstrating that combining DCA and MD simulations is a powerful approach to revealing structural insights of large protein complexes.

Indexed as

CPSF100CPSF73DCAGaussian convolutionINTS11INTS4INTS9Molecular dynamicsResidue coevolution

Identifiers

PMID38074468
PMCPMC10700540
OpenAlexW4388802323

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.