Calculation of Electrostatic Potential Field of Coronavirus S Proteins for Brownian Dynamics Simulations

Q2 Computer Science
E. P. Vasyuchenko, V. Fedorov, E. G. Kholina, S. Khruschev, I. Kovalenko, M. Strakhovskaya
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引用次数: 0

Abstract

The Brownian dynamics method can give insight into the initial stages of the interaction of antiviral drug molecules with the structural components of bacteria or viruses. RAM of conventional personal computer allows calculation of Brownian dynamics of interaction of antiviral drugs with individual coronavirus S protein. However, scaling up this approach for modeling the interaction of antiviral drugs with the whole virion consisting of thousands of proteins and lipids is difficult due to high requirements for computing resources. In the case of the Brownian dynamics method, the main amount of RAM in the calculations is occupied by an array of values of the virion electrostatic potential field. When the system is increased from one S protein to the whole virion, the volume of data increases significantly. The standard protocol for calculating Brownian dynamics uses a three-dimensional grid with a spatial step of 1°A to calculate the electrostatic potential field. In this work, we consider the possibility of increasing the grid spacing parameter for calculating the electrostatic potential field of individual coronavirus S proteins. In this case, the amount of RAM occupied by the electrostatic potential field is reduced, which makes it possible to use personal computers for calculations. We performed Brownian dynamics simulations of interaction of an antiviral photosensitizer molecule with S proteins of three coronaviruses SARS-CoV, MERS-CoV, and SARS-CoV-2, and demonstrated that reduction of detalization of electrostatic potential field does not influence the results of Brownian dynamics much © The Authors 2022. This paper is published with open access at SuperFri.org
基于布朗动力学模拟的冠状病毒S蛋白静电势场计算
布朗动力学方法可以深入了解抗病毒药物分子与细菌或病毒结构组分相互作用的初始阶段。传统个人计算机的RAM允许计算抗病毒药物与单个冠状病毒S蛋白相互作用的布朗动力学。然而,由于对计算资源的高要求,扩大这种方法来模拟抗病毒药物与由数千种蛋白质和脂质组成的整个病毒粒子的相互作用是困难的。在布朗动力学方法中,计算中的RAM主要由一组病毒粒子静电势场的值所占用。当系统从一个S蛋白增加到整个病毒粒子时,数据量显著增加。计算布朗动力学的标准方案使用空间步长为1°a的三维网格来计算静电势场。在这项工作中,我们考虑了增加网格间距参数用于计算单个冠状病毒S蛋白静电势场的可能性。在这种情况下,静电势场占用的RAM量减少了,这使得使用个人计算机进行计算成为可能。我们对抗病毒光敏剂分子与三种冠状病毒SARS-CoV、MERS-CoV和SARS-CoV-2的S蛋白相互作用进行了布朗动力学模拟,并证明静电势场去离子化的减少对布朗动力学结果影响不大©the Authors 2022。这篇论文发表在SuperFri.org上
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来源期刊
Supercomputing Frontiers and Innovations
Supercomputing Frontiers and Innovations Computer Science-Computational Theory and Mathematics
CiteScore
1.60
自引率
0.00%
发文量
7
审稿时长
12 weeks
期刊介绍: The Journal of Supercomputing Frontiers and Innovations (JSFI) is a new peer reviewed publication that addresses the urgent need for greater dissemination of research and development findings and results at the leading edge of high performance computing systems, highly parallel methods, and extreme scaled applications. Key topic areas germane include, but not limited to: Enabling technologies for high performance computing Future generation supercomputer architectures Extreme-scale concepts beyond conventional practices including exascale Parallel programming models, interfaces, languages, libraries, and tools Supercomputer applications and algorithms Distributed operating systems, kernels, supervisors, and virtualization for highly scalable computing Scalable runtime systems software Methods and means of supercomputer system management, administration, and monitoring Mass storage systems, protocols, and allocation Energy and power minimization for very large deployed computers Resilience, reliability, and fault tolerance for future generation highly parallel computing systems Parallel performance and correctness debugging Scientific visualization for massive data and computing both external and in situ Education in high performance computing and computational science.
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