Electrostatic features for nucleocapsid proteins of SARS-CoV and SARS-CoV-2

Wenhan Guo; Yixin Xie; Alan E Lopez-Hernandez; Shengjie Sun; Lin Li; Wenhan Guo; Yixin Xie; Alan E Lopez-Hernandez; Shengjie Sun; Lin Li

doi:10.3934/mbe.2021120

Mathematical Biosciences and Engineering

2021, Volume 18, Issue 3: 2372-2383. doi: 10.3934/mbe.2021120

Previous Article Next Article

Research article Special Issues

Electrostatic features for nucleocapsid proteins of SARS-CoV and SARS-CoV-2

1.
Computational Science Program, University of Texas at El Paso, El Paso, TX 79968, USA
2.
Department of Physics, University of Texas at El Paso, El Paso, TX 79968, USA

Received: 30 January 2021 Accepted: 05 March 2021 Published: 09 March 2021

COVID-19 is increasingly affecting human health and global economy. Understanding the fundamental mechanisms of Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2) is highly demanded to develop treatments for COVID-19. SARS-CoV and SARS-CoV-2 share 92.06% identity in their N protein RBDs' sequences, which results in very similar structures. However, the SARS-CoV-2 is more easily to spread. Utilizing multi-scale computational approaches, this work studied the fundamental mechanisms of the nucleocapsid (N) proteins of SARS-CoV and SARS-CoV-2, including their stabilities and binding strengths with RNAs at different pH values. Electrostatic potential on the surfaces of N proteins show that both the N proteins of SARS-CoV and SARS-CoV-2 have dominantly positive potential to attract RNAs. The binding forces between SARS-CoV N protein and RNAs at different distances are similar to that of SARS-CoV-2, both in directions and magnitudes. The electric filed lines between N proteins and RNAs are also similar for both SARS-CoV and SARS-CoV-2. The folding energy and binding energy dependence on pH revealed that the best environment for N proteins to perform their functions with RNAs is the weak acidic environment.
- SARS-CoV,
- SARS-CoV-2,
- COVID-19,
- protein-protein interactions,
- protein-RNA/DNA interactions,
- electrostatic force,
- DelPhi,
- DelPhiForce
Citation: Wenhan Guo, Yixin Xie, Alan E Lopez-Hernandez, Shengjie Sun, Lin Li. Electrostatic features for nucleocapsid proteins of SARS-CoV and SARS-CoV-2[J]. Mathematical Biosciences and Engineering, 2021, 18(3): 2372-2383. doi: 10.3934/mbe.2021120

Related Papers:

Abstract

COVID-19 is increasingly affecting human health and global economy. Understanding the fundamental mechanisms of Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2) is highly demanded to develop treatments for COVID-19. SARS-CoV and SARS-CoV-2 share 92.06% identity in their N protein RBDs' sequences, which results in very similar structures. However, the SARS-CoV-2 is more easily to spread. Utilizing multi-scale computational approaches, this work studied the fundamental mechanisms of the nucleocapsid (N) proteins of SARS-CoV and SARS-CoV-2, including their stabilities and binding strengths with RNAs at different pH values. Electrostatic potential on the surfaces of N proteins show that both the N proteins of SARS-CoV and SARS-CoV-2 have dominantly positive potential to attract RNAs. The binding forces between SARS-CoV N protein and RNAs at different distances are similar to that of SARS-CoV-2, both in directions and magnitudes. The electric filed lines between N proteins and RNAs are also similar for both SARS-CoV and SARS-CoV-2. The folding energy and binding energy dependence on pH revealed that the best environment for N proteins to perform their functions with RNAs is the weak acidic environment.

References

[1]	P. Zhou, X. L. Yang, X. G. Wang, B. Hu, L. Zhang, W. Zhang, et al. , A pneumonia outbreak associated with a new coronavirus of probable bat origin, Nature, 579 (2020), 270-273. doi: 10.1038/s41586-020-2012-7
[2]	M. A. Marra, S. J. Jones, C. R. Astell, R. A. Holt, A. Brooks-Wilson, Y. S. Butterfield, et al. , The genome sequence of the SARS-associated coronavirus, Science, 300 (5624), 1399-404
[3]	S. Kang, M. Yang, Z. Hong, L. Zhang, S. Chen, Crystal structure of SARS-CoV-2 nucleocapsid protein RNA binding domain reveals potential unique drug targeting sites, Acta Pharm. Sin. B, 10 (2020), 1228-1238. doi: 10.1016/j.apsb.2020.04.009
[4]	N. K. Dutta, K. Mazumdar, J. T. Gordy, The nucleocapsid protein of SARS-CoV-2: a target for vaccine development, J. Virol. , 94 (2020), e00647-20.
[5]	D. C. Dinesh, D. Chalupska, J. Silhan, E. Koutna, R. Nencka, V. Veverka, et al. , Structural basis of RNA recognition by the SARS-CoV-2 nucleocapsid phosphoprotein, PLoS Pathog. , 16 (2020), e1009100. doi: 10.1371/journal.ppat.1009100
[6]	K. S. Saikatendu, J. S. Joseph, V. Subramanian, B. W. Neuman, M. J. Buchmeier, R. C. Stevens, et al. , Ribonucleocapsid formation of severe acute respiratory syndrome coronavirus through molecular action of the N-Terminal domain of N protein, J. Virol. , 81 (2007), 3913-3921. doi: 10.1128/JVI.02236-06
[7]	Y. Xie, D. Du, C. B. Karki, W. Guo, A. E. Lopez-Hernandez, S. Sun, et al. , Revealing the mechanism of SARS-CoV-2 spike protein binding with ACE2, Comput. Sci. Eng. , 22 (2020), 21-29. doi: 10.1109/MCSE.2020.3015511
[8]	Y. Xie, C. B. Karki, D. Du, H. Li, J. Wang, A. Sobitan, et al. , Spike proteins of SARS-CoV and SARS-CoV-2 utilize different mechanisms to bind with human ACE2, Front. Mol. Biosci. , 7 (2020), e591873. doi: 10.3389/fmolb.2020.591873
[9]	W. Dai, B. Zhang, X. Jiang, H. Su, J. Li, Y. Zhao, et al. , Structure-based design of antiviral drug candidates targeting the SARS-CoV-2 main protease, Science, 368 (2020), 1331-1335. doi: 10.1126/science.abb4489
[10]	D. D. Nguyen, K. Gao, J. Chen, R. Wang, G. Wei, Unveiling the molecular mechanism of SARS-CoV-2 main protease inhibition from 137 crystal structures using algebraic topology and deep learning, Chem. Sci. , 11 (2020), 12036-12046. doi: 10.1039/D0SC04641H
[11]	K. Gao, D. D. Nguyen, J. Chen, R. Wang, G. Wei, Repositioning of 8565 existing drugs for COVID-19, J. Phys. Chem. Lett. , 11 (2020), 5373-5382. doi: 10.1021/acs.jpclett.0c01579
[12]	L. Li, C. Li, Z. Zhang, E. Alexov, On the dielectric "constant" of proteins: smooth dielectric function for macromolecular modeling and its implementation in DelPhi, J. Chem. Theory Comput. , 9 (2013), 2126-2136. doi: 10.1021/ct400065j
[13]	L. Li, J. Alper, E. Alexov, Multiscale method for modeling binding phenomena involving large objects: application to kinesin motor domains motion along microtubules, Sci Rep. , 6 (2016), 1-12. doi: 10.1038/s41598-016-0001-8
[14]	A. W. Senior, R. Evans, J. Jumper, J. Kirkpatrick, L. Sifre, T. Green, et al. , Improved protein structure prediction using potentials from deep learning, Nature, 577 (2020), 706-710. doi: 10.1038/s41586-019-1923-7
[15]	J. Wang, C. Karki, Y. Xiao, L. Li, Electrostatics of prokaryotic ribosome and its biological implication, Biophys J. , 118 (2020), 1205-1212. doi: 10.1016/j.bpj.2020.01.014
[16]	M. F. Lensink, S. Velankar, S. J. Wodak, Modeling protein-protein and protein-peptide complexes: CAPRI 6th edition, Proteins, 85 (2017), 359-377. doi: 10.1002/prot.25215
[17]	L. Li, L. Wang, E. Alexov, On the energy components governing molecular recognition in the framework of continuum approaches, Front Mol Biosci. , 2 (2015), e00005.
[18]	L. Li, D. Guo, Y. Huang, S. Liu, Y. Xiao, ASPDock: protein-protein docking algorithm using atomic solvation parameters model, BMC Bioinform. , 12 (2011), 1-9. doi: 10.1186/1471-2105-12-1
[19]	Y. Peng, E. Alexov, Computational investigation of proton transfer, pKa shifts and pH-optimum of protein-DNA and protein-RNA complexes, Proteins, 85 (2017), 282-295. doi: 10.1002/prot.25221
[20]	L. Wang, S. Witham, Z. Zhang, L. Li, M. Hodsdon, E. Alexov, In silico investigation of pH-dependence of prolactin and human growth hormone binding to human prolactin receptor, Commun. Comput. Phys. , 13 (2013), 207-222. doi: 10.4208/cicp.170911.131011s
[21]	J. Wang, Fast identification of possible drug treatment of Coronavirus Disease-19 (COVID-19) through computational drug repurposing study, J. Chem. Inf. Model. , 60 (2020), 3277-3286. doi: 10.1021/acs.jcim.0c00179
[22]	G. Eraslan, Ž. Avsec, J. Gagneur, F. J. Theis, Deep learning: new computational modelling techniques for genomics, Nat Rev Genet. , 20 (2019), 389-403.
[23]	L. Li, C. Li, S. Sarkar, J. Zhang, S. Witham, Z. Zhang, et al. , DelPhi: a comprehensive suite for DelPhi software and associated resources, BMC Biophys. , 5 (2012), 1-11. doi: 10.1186/2046-1682-5-1
[24]	L. Li, Z. Jia, Y. Peng, A. Chakravorty, L. Sun, E. Alexov, DelPhiForce web server: electrostatic forces and energy calculations and visualization, Bioinformatics, 33 (2017), 3661-3663. doi: 10.1093/bioinformatics/btx495
[25]	L. Li, A. Chakravorty, E. Alexov, DelPhiForce, a tool for electrostatic force calculations: Applications to macromolecular binding, J. Comput. Chem. , 38 (2017), 584-593. doi: 10.1002/jcc.24715
[26]	L. Wang, L. Li, E. Alexov, pKa predictions for proteins, RNAs, and DNAs with the Gaussian dielectric function using DelPhi pKa, Proteins, 83 (2015), 2186-2197.
[27]	L. Wang, M. Zhang, E. Alexov, DelPhiPKa web server: predicting pKa of proteins, RNAs and DNAs, Bioinformatics, 32 (2015), 614-615.
[28]	K. Talley, E. Alexov, On the pH‐optimum of activity and stability of proteins, Proteins, 78 (2010), 2699-2706.
[29]	R. C. Mitra, Z. Zhang, E. Alexov, In silico modeling of pH‐optimum of protein-protein binding, Proteins, 79 (2011), 925-936. doi: 10.1002/prot.22931
[30]	A. V. Onufriev, E. Alexov, Protonation and pK changes in protein-ligand binding, Q. Rev. Biophys. , 46 (2013), 181-209. doi: 10.1017/S0033583513000024
[31]	E. F. Pettersen, T. D. Goddard, C. C. Huang, G. S. Couch, D. M. Greenblatt, E. C. Meng, et al. , UCSF Chimera—a visualization system for exploratory research and analysis, J. Comput. Chem. , 25 (2004), 1605-1612. doi: 10.1002/jcc.20084
[32]	Y. Xian, Y. Xie, S. M. Silva, C. B. Karki, W. Qiu, L. Li, Structure manipulation tool structureMan: A structure manipulation tool to study large scale biomolecular interactions, Front. Mol. Biosci. , 7 (2020), 476.
[33]	W. Humphrey, A. Dalke, K. Schulten, VMD: visual molecular dynamics, J. Mol. Graph, 14 (1996), 33-38. doi: 10.1016/0263-7855(96)00018-5
[34]	F. Sievers, A. Wilm, D. Dineen, T. J. Gibson, K. Karplus, W. Li, et al. , Fast, scalable generation of high‐quality protein multiple sequence alignments using Clustal Omega, Mol. Syst. Biol. , 7 (2011), 539. doi: 10.1038/msb.2011.75
[35]	Y. Xian, C. B. Karki, S. M. Silva, L. Li, C. Xiao, The roles of electrostatic interactions in capsid assembly mechanisms of giant viruses. Int. J. Mol. Sci. , 20 (2019), 1876. doi: 10.3390/ijms20081876

mbe-18-03-120-Supplementary.pdf

Reader Comments

Your name:*

Email:*
© 2021 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)