OpenABC enables flexible, simplified, and efficient GPU accelerated simulations of biomolecular condensates.

IF 4.3 2区 生物学 PLoS Computational Biology Pub Date : 2023-09-11 eCollection Date: 2023-09-01 DOI:10.1371/journal.pcbi.1011442
Shuming Liu, Cong Wang, Andrew P Latham, Xinqiang Ding, Bin Zhang
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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 performance on a single GPU that rivals 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 OpenABC will significantly facilitate the adoption of in silico simulations by a broader community to investigate the structural and dynamical properties of condensates.

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OpenABC实现了灵活、简化和高效的GPU加速的生物分子缩合物模拟。
生物分子缩合物是各种细胞过程中的重要结构,但使用传统的实验技术进行研究具有挑战性。使用残渣级粗粒度模型的计算机模拟在计算效率和化学精度之间取得了平衡。通过将这些复杂系统的涌现特性与分子序列联系起来,他们可以提供有价值的见解。然而,现有的粗粒度模型通常缺乏易于遵循的教程,并且在不适合冷凝物模拟的软件中实现。为了解决这些问题,我们引入了OpenABC,这是一个软件包,它使用Python脚本极大地简化了具有多个力场的粗粒度冷凝物模拟的设置和执行。OpenABC与OpenMM分子动力学引擎无缝集成,在单个GPU上实现高效模拟,其性能可与数百个CPU的速度相媲美。我们还提供了将粗粒度配置转换为所有原子结构的工具,用于原子模拟。我们预计,OpenABC将大大促进更广泛的社区采用计算机模拟来研究冷凝物的结构和动力学特性。
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来源期刊
PLoS Computational Biology
PLoS Computational Biology 生物-生化研究方法
CiteScore
7.10
自引率
4.70%
发文量
820
期刊介绍: PLOS Computational Biology features works of exceptional significance that further our understanding of living systems at all scales—from molecules and cells, to patient populations and ecosystems—through the application of computational methods. Readers include life and computational scientists, who can take the important findings presented here to the next level of discovery. Research articles must be declared as belonging to a relevant section. More information about the sections can be found in the submission guidelines. Research articles should model aspects of biological systems, demonstrate both methodological and scientific novelty, and provide profound new biological insights. Generally, reliability and significance of biological discovery through computation should be validated and enriched by experimental studies. Inclusion of experimental validation is not required for publication, but should be referenced where possible. Inclusion of experimental validation of a modest biological discovery through computation does not render a manuscript suitable for PLOS Computational Biology. Research articles specifically designated as Methods papers should describe outstanding methods of exceptional importance that have been shown, or have the promise to provide new biological insights. The method must already be widely adopted, or have the promise of wide adoption by a broad community of users. Enhancements to existing published methods will only be considered if those enhancements bring exceptional new capabilities.
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