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原子分辨率细胞样蛋白质环境的高度优化模拟

Highly Optimized Simulation of Atomic Resolution Cell-Like Protein Environment.

作者信息

Tytarenko Andrii M, Singh Amar, Ambati Vineeth Kumar, Copeland Matthew M, Kundrotas Petras J, Halfmann Randal, Kasyanov Pavlo O, Feinberg Eugene A, Vakser Ilya A

机构信息

Institute for Applied System Analysis at the Igor Sikorsky Kyiv Polytechnic Institute, Kyiv 03056, Ukraine.

Computational Biology Program, The University of Kansas, Lawrence, Kansas 66045, United States.

出版信息

J Phys Chem B. 2025 Mar 27;129(12):3183-3190. doi: 10.1021/acs.jpcb.4c07769. Epub 2025 Mar 12.

DOI:10.1021/acs.jpcb.4c07769
PMID:40077832
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11956777/
Abstract

Computational approaches can provide details of molecular mechanisms in a crowded environment inside cells. Protein docking predicts stable configurations of molecular complexes, which correspond to deep energy minima. Systematic docking approaches, such as those based on fast Fourier transform (FFT), also map the entire intermolecular energy landscape by determining the position and depth of the full spectrum of the energy minima. Such mapping allows speeding up simulations by precalculating the intermolecular energy values. Our earlier study combined FFT docking with the Monte Carlo protocol, enabling simulation of cell-size, crowded protein systems with seconds, and longer trajectories at atomic resolution, several orders of magnitude longer than those achievable by alternative approaches. In this study, we present a further drastic extension of the modeling capabilities by parallelized implementation of the simulation protocol. The procedure was applied to a panel of Death Fold Domains that form nucleated polymers in human innate immune signaling, recapitulating their homooligomerization tendencies and providing insights into the molecular mechanisms of polymer nucleation. The parallelized protocol allows extension of the simulation trajectories by orders of magnitude beyond the previously reported implementation, reaching into the uncharted territory of atomic resolution simulation of cell-sized systems.

摘要

计算方法能够提供细胞内拥挤环境中分子机制的细节。蛋白质对接可预测分子复合物的稳定构型,这些构型对应着能量深度最小值。系统对接方法,如基于快速傅里叶变换(FFT)的方法,还可通过确定能量最小值全谱的位置和深度来描绘整个分子间能量态势。这种描绘能通过预先计算分子间能量值来加速模拟。我们早期的研究将FFT对接与蒙特卡罗协议相结合,能够在数秒内模拟细胞大小、蛋白质拥挤的系统,并以原子分辨率获得比其他方法长得多几个数量级的更长轨迹。在本研究中,我们通过模拟协议的并行化实现进一步大幅扩展了建模能力。该程序应用于一组在人类先天免疫信号传导中形成成核聚合物的死亡折叠结构域,概括了它们的同源寡聚化趋势,并为聚合物成核的分子机制提供了见解。并行化协议使模拟轨迹能够比之前报道的实现方式扩展几个数量级,进入到细胞大小系统原子分辨率模拟的未知领域。

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本文引用的文献

1
Structure-based prediction of protein-nucleic acid binding using graph neural networks.使用图神经网络基于结构的蛋白质-核酸结合预测。
Biophys Rev. 2024 Jun 26;16(3):297-314. doi: 10.1007/s12551-024-01201-w. eCollection 2024 Jun.
2
Diffusion of proteins in crowded solutions studied by docking-based modeling.基于对接建模研究拥挤溶液中蛋白质的扩散。
J Chem Phys. 2024 Sep 7;161(9). doi: 10.1063/5.0220545.
3
Accurate structure prediction of biomolecular interactions with AlphaFold 3.利用 AlphaFold 3 进行生物分子相互作用的精确结构预测。
Nature. 2024 Jun;630(8016):493-500. doi: 10.1038/s41586-024-07487-w. Epub 2024 May 8.
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Computational Approaches to Predict Protein-Protein Interactions in Crowded Cellular Environments.计算方法在拥挤细胞环境中预测蛋白质-蛋白质相互作用。
Chem Rev. 2024 Apr 10;124(7):3932-3977. doi: 10.1021/acs.chemrev.3c00550. Epub 2024 Mar 27.
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GRAMM Web Server for Protein Docking.GRAMM 蛋白质对接网络服务器。
Methods Mol Biol. 2024;2714:101-112. doi: 10.1007/978-1-0716-3441-7_5.
6
Intracellular environment can change protein conformational dynamics in cells through weak interactions.细胞内环境可以通过弱相互作用改变细胞内蛋白质构象动力学。
Sci Adv. 2023 Jul 21;9(29):eadg9141. doi: 10.1126/sciadv.adg9141.
7
Effects of Homogeneous and Heterogeneous Crowding on Translational Diffusion of Rigid Bovine Serum Albumin and Disordered Alfa-Casein.同质和异质拥挤对刚性牛血清白蛋白和无序α-酪蛋白的翻译扩散的影响。
Int J Mol Sci. 2023 Jul 6;24(13):11148. doi: 10.3390/ijms241311148.
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Size of the protein-protein energy funnel in crowded environment.拥挤环境中蛋白质-蛋白质能量漏斗的大小。
Front Mol Biosci. 2022 Nov 8;9:1031225. doi: 10.3389/fmolb.2022.1031225. eCollection 2022.
9
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Proc Natl Acad Sci U S A. 2022 Oct 11;119(41):e2210249119. doi: 10.1073/pnas.2210249119. Epub 2022 Oct 3.
10
What the protein data bank tells us about the evolutionary conservation of protein conformational diversity.蛋白质数据库告诉我们蛋白质构象多样性的进化保守性。
Protein Sci. 2022 Jul;31(7):e4325. doi: 10.1002/pro.4325.