Evidence map›Paper›PMID 34536913›Full record

ReviewCurrent opinion in structural biology2022

Unifying coarse-grained force fields for folded and disordered proteins.

Andrew P Latham, Bin Zhang

Open access · greenAbstract readReview
In one paragraph

Review in Current opinion in structural biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed
3.2field-weighted citation impact, top 7% 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

26 citing papers in PubMed, 50 citations in OpenAlex.

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  7. Integrative spatiotemporal modeling of biomolecular processes: Application to the assembly of the nuclear pore complex.Proceedings of the National Academy of Sciences of the United States of America · 2025
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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 at 1 institution in 1 country.

Andrew P LathamDepartment of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA.
Bin ZhangDepartment of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA. Electronic address: binz@mit.edu.
Massachusetts Institute of Technology · US

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

Liquid-liquid phase separation drives the formation of biological condensates that play essential roles in transcriptional regulation and signal sensing. Computational modeling could provide high-resolution structural characterizations of these condensates and help uncover physicochemical interactions that dictate their stability. However, many protein molecules involved in phase separation often contain multiple ordered domains connected with flexible, structureless linkers. Simulating such proteins necessitates force fields with consistent accuracy for both folded and disordered proteins. We provide a critical review of existing coarse-grained force fields for disordered proteins and highlight the challenges in their application to folded proteins. After discussing existing algorithms for force field parameterization, we propose an optimization strategy that should lead to computer models with improved transferability across protein types.

Indexed as

Intrinsically Disordered ProteinsAlgorithmsComputer SimulationIntrinsically Disordered ProteinsCoarse grainingDisordered proteinsForce field parameterizationLiquid–liquid phase separationProtein folding

Identifiers

PMID34536913
PMCPMC9057422
OpenAlexW3200440667

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.