Evidence map›Paper›PMID 37131742›Full record

ArticlebioRxiv : the preprint server for biology2023

OpenABC Enables Flexible, Simplified, and Efficient GPU Accelerated Simulations of Biomolecular Condensates.

Shuming Liu, Cong Wang, Andrew Latham, Xinqiang Ding, Bin Zhang

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2023. 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

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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

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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

5 authors.

Shuming LiuDepartment of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA.
Cong WangDepartment of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA.
Andrew LathamDepartment of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA.
Xinqiang DingDepartment of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA.
Bin ZhangDepartment of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA.

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

Biomolecular condensates are important structures in various cellular processes but are challenging to study using traditional experimental techniques. In silico simulations with residue-level coarse-grained models strike a balance between computational efficiency and chemical accuracy. They could offer valuable insights by connecting the emergent properties of these complex systems with molecular sequences. However, existing coarse-grained models often lack easy-to-follow tutorials and are implemented in software that is not optimal for condensate simulations. To address these issues, we introduce OpenABC, a software package that greatly simplifies the setup and execution of coarse-grained condensate simulations with multiple force fields using Python scripting. OpenABC seamlessly integrates with the OpenMM molecular dynamics engine, enabling efficient simulations with performances on a single GPU that rival the speed achieved by hundreds of CPUs. We also provide tools that convert coarse-grained configurations to all-atom structures for atomistic simulations. We anticipate that Open-ABC will significantly facilitate the adoption of in silico simulations by a broader community to investigate the structural and dynamical properties of condensates. Open-ABC is available at https://github.com/ZhangGroup-MITChemistry/OpenABC.

Identifiers

PMID37131742
PMCPMC10153273

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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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.