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拥抱核酸模拟中的百亿亿次级计算。

Embracing exascale computing in nucleic acid simulations.

机构信息

Department of Physics, Department of Biochemistry and Institute for Data Science and Informatics, University of Missouri, 223 Physics Bldg., Columbia, 65211, MO, USA.

Department of Physics, Department of Biochemistry and Institute for Data Science and Informatics, University of Missouri, 223 Physics Bldg., Columbia, 65211, MO, USA.

出版信息

Curr Opin Struct Biol. 2024 Aug;87:102847. doi: 10.1016/j.sbi.2024.102847. Epub 2024 May 29.

Abstract

This mini-review reports the recent advances in biomolecular simulations, particularly for nucleic acids, and provides the potential effects of the emerging exascale computing on nucleic acid simulations, emphasizing the need for advanced computational strategies to fully exploit this technological frontier. Specifically, we introduce recent breakthroughs in computer architectures for large-scale biomolecular simulations and review the simulation protocols for nucleic acids regarding force fields, enhanced sampling methods, coarse-grained models, and interactions with ligands. We also explore the integration of machine learning methods into simulations, which promises to significantly enhance the predictive modeling of biomolecules and the analysis of complex data generated by the exascale simulations. Finally, we discuss the challenges and perspectives for biomolecular simulations as we enter the dawning exascale computing era.

摘要

本文综述了生物分子模拟的最新进展,特别是核酸方面的进展,并探讨了新兴的百亿亿次级计算对核酸模拟的潜在影响,强调了需要先进的计算策略来充分利用这一技术前沿。具体而言,我们介绍了大规模生物分子模拟的计算机体系结构的最新突破,并综述了核酸模拟的模拟方案,包括力场、增强采样方法、粗粒化模型以及与配体的相互作用。我们还探讨了机器学习方法在模拟中的整合,这有望极大地提高生物分子的预测建模能力,并分析百亿亿次级计算产生的复杂数据。最后,我们讨论了在进入百亿亿次级计算时代之际,生物分子模拟所面临的挑战和展望。

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Embracing exascale computing in nucleic acid simulations.拥抱核酸模拟中的百亿亿次级计算。
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本文引用的文献

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OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials.OpenMM 8:基于机器学习势的分子动力学模拟。
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Trends Biotechnol. 2024 May;42(5):517-521. doi: 10.1016/j.tibtech.2023.11.005. Epub 2023 Dec 1.
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