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




Brownian dynamics, coarse grain, spike protein, SARS-CoV-2, SARS-CoV, MERS-CoV, phthalocyanine, photosensitizer


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 1Å 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.


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How to Cite

Vasyuchenko, E. P., Fedorov, V. A., Kholina, E. G., Khruschev, S. S., Kovalenko, I. B., & Strakhovskaya, M. G. (2022). Calculation of Electrostatic Potential Field of Coronavirus S Proteins for Brownian Dynamics Simulations. Supercomputing Frontiers and Innovations, 9(3), 65–71.