Evidence map›Paper›PMID 40172489›Full record

ReviewBiochemistry2025

Toward Predictive Coarse-Grained Simulations of Biomolecular Condensates.

Shuming Liu, Cong Wang, Bin Zhang

Abstract readReview
In one paragraph

Review in Biochemistry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Review
  2. Article
  3. Condensates as Conformation Editors of Disordered Client Proteins.Journal of the American Chemical Society · 2026
    Article
  4. Article
  5. Article
  6. Article
  7. Molecular basis for thermoresponsive protein condensation in plants.bioRxiv : the preprint server for biology · 2025
    Article
  8. Article
  9. Article
  10. 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.

Shuming LiuDepartment of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.
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

Phase separation is a fundamental process that enables cellular organization by forming biomolecular condensates. These assemblies regulate diverse functions by creating distinct environments, influencing reaction kinetics, and facilitating processes such as genome organization, signal transduction, and RNA metabolism. Recent studies highlight the complexity of condensate properties, shaped by intrinsic molecular features and external factors such as temperature and pH. Molecular simulations serve as an effective approach to establishing a comprehensive framework for analyzing these influences, offering high-resolution insights into condensate stability, dynamics, and material properties. This review evaluates recent advancements in biomolecular condensate simulations, with a particular focus on coarse-grained 1-bead-per-amino-acid (1BPA) protein models, and emphasizes OpenABC, a tool designed to simplify and streamline condensate simulations. OpenABC supports the implementation of various coarse-grained force fields, enabling their performance evaluation. Our benchmarking identifies inconsistencies in phase behavior predictions across force fields, even though these models accurately capture single-chain statistics. This finding underscores the need for enhanced force field accuracy, achievable through enriched training data sets, many-body potentials, and advanced optimization techniques. Such refinements could significantly improve the predictive capacity of coarse-grained models, bridging molecular details with emergent condensate behaviors.

Indexed as

Biomolecular CondensatesMolecular Dynamics SimulationProteinsHumansProteinsbiomolecular condensatescoarse-grained simulationsforce field accuracyOpenABCphase separation

Identifiers

PMID40172489
PMCPMC12860963

What OpenQuestion holds

Textmetadata
LicenceTDM
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.