Evidence map›Paper›PMID 41137856›Full record

ArticleJournal of chemical theory and computation2025

Sequence-Dependent Conformational Landscapes of Intrinsically Disordered Proteins Reveal Asymmetric Chain Compaction.

Cong Wang, Bin Zhang

Abstract read
In one paragraph

Article in Journal of chemical theory and computation, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

2 authors.

Cong WangDepartment of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.ORCID 0000-0001-5998-3740
Bin ZhangDepartment of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.ORCID 0000-0002-3685-7503

Funding

Probing and Perturbing Transcriptional Condensates with Multiscale Modeling and Deep LearningR35GM133580 · NIGMS · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI Bin Zhang · 2019 to 2026
$3.1M
NIGMS NIH HHS R35 GM133580
6 · The paper itself

Abstract

Intrinsically disordered proteins (IDPs) exhibit highly dynamic and heterogeneous conformational ensembles that are strongly influenced by sequence features. While global properties such as chain compaction and scaling behavior have been widely studied, they often fail to resolve the fine-grained, sequence-specific structural variation that underlies IDP function. Here, we perform long-time scale atomistic simulations of 47 representative IDP sequences from the yeast proteome to systematically investigate the relationship between sequence composition and conformational ensemble. To analyze the high-dimensional structural data, we apply uniform manifold approximation and projection (UMAP), a nonlinear dimensionality reduction technique that preserves local structural relationships. The resulting low-dimensional embeddings effectively differentiate IDP ensembles and reveal a novel descriptor─local compactness asymmetry─that quantifies directional differences in chain organization. This metric, denoted

Indexed as

Intrinsically Disordered ProteinsAmino Acid SequenceMolecular Dynamics SimulationProtein ConformationSaccharomyces cerevisiaeIntrinsically Disordered Proteins

Identifiers

PMID41137856
PMCPMC12914743

What OpenQuestion holds

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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.