Evidence map›Paper›PMID 38978607›Full record

ArticleResearch square2024

Sampling Conformational Ensembles of Highly Dynamic Proteins via Generative Deep Learning.

Talant Ruzmetov, Ta I Hung, Saisri Padmaja Jonnalagedda, Si-Han Chen, Parisa Fasihianifard, Zhefeng Guo, Bir Bhanu, Chia-En A Chang

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In one paragraph

Article in Research square, 2024. 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.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

Talant RuzmetovDepartment of Chemistry, University of California, Riverside, CA92521.
Ta I HungDepartment of Chemistry, University of California, Riverside, CA92521.
Saisri Padmaja JonnalageddaDepartment of Electrical and Computer Engineering, University of California, Riverside, CA92521.
Si-Han ChenDepartment of Chemistry, University of California, Riverside, CA92521.
Parisa FasihianifardDepartment of Chemistry, University of California, Riverside, CA92521.
Zhefeng GuoDepartment of Neurology, Brain Research Institute, University of California, Los Angeles, CA 90095.ORCID 0000-0003-1992-7255
Bir BhanuDepartment of Bioengineering, University of California, Riverside, CA92521.ORCID 0000-0001-8971-6416
Chia-En A ChangDepartment of Chemistry, University of California, Riverside, CA92521.ORCID 0000-0002-6504-8529

Funding

Next-Generation GPU Computing Resource for Simulating Ligand-Protein Binding Kinetics/MechanismR01GM109045 · NIGMS · UNIVERSITY OF CALIFORNIA RIVERSIDE · PI CHANG, CHIA-EN · 2014 to 2023
$2.8M
Structural studies of amyloid beta globulomers with EPR spectroscopyR01AG050687 · NIA · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI GUO, ZHEFENG · 2017 to 2021
$1.9M
Combining electron and nuclear magnetic resonance to track Alzheimer's amyloid-beta oligomer-to-fibril conversionR21AG082041 · NIA · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI GUO, ZHEFENG, PARAVASTU, ANANT KRISHNA · 2023 to 2024
$427k
NIA NIH HHS R01 AG050687NIA NIH HHS R21 AG082041NIGMS NIH HHS R01 GM109045
6 · The paper itself

Abstract

Proteins are inherently dynamic, and their conformational ensembles are functionally important in biology. Large-scale motions may govern protein structure-function relationship, and numerous transient but stable conformations of intrinsically disordered proteins (IDPs) can play a crucial role in biological function. Investigating conformational ensembles to understand regulations and disease-related aggregations of IDPs is challenging both experimentally and computationally. In this paper first an unsupervised deep learning-based model, termed Internal Coordinate Net (ICoN), is developed that learns the physical principles of conformational changes from molecular dynamics (MD) simulation data. Second, interpolating data points in the learned latent space are selected that rapidly identify novel synthetic conformations with sophisticated and large-scale sidechains and backbone arrangements. Third, with the highly dynamic amyloid-β

Identifiers

PMID38978607
PMCPMC11230488

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.