Omicron variants bind to human angiotensin-converting enzyme 2 (ACE2) much stronger due to higher number of charged-charged interactions

Abstract

Since the start of COVID-19 pandemic, several mutant variants of SARS-CoV-2 have emerged with different virulence and transmissibility patterns. Some of these variants have been labeled as variants of concern (VOC). There are mainly five strain clades with VOC status: Alpha, Beta, Gamma, Delta, and Omicron. Omicron sub-variants have been currently in circulation around the world, and they show faster transmissibility and lower virulence compared to others. Receptor binding domain (RBD) of SARS-CoV-2 spike protein is the region where it binds to human angiotensin-converting enzyme 2 (hACE2) on the host cell. Mutations on RBD might have direct or indirect effects on differential disease patterns of these variants. In this study, we analyzed sequence and structures of SARS-CoV-2 variants’ RBD domains and documented their predicted affinities and contact interactions with hACE2. We found that Omicron sub-variants have much higher hACE2 affinities compared to other VOC strains. To understand reasons behind this, we checked biophysical characteristics of RBD-hACE2 contacts. Surprisingly, number of charged-charged interactions of Omicron sub-variants were on average 4-fold higher. These higher charged residue mutations on epitope region of Omicron sub-variants leading to stronger affinity for hACE2 might shed light onto why Omicron has less severe disease symptoms.

Keywords

• Coronavirus disease 2019 (COVID-19)
• Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)
• Omicron
• Receptor binding domain (RBD)
• Angiotensin-converting enzyme 2 (ACE2)

References

1. Aleem, A., Akbar Samad, A. B., & Slenker, A. K. (2022). Emerging Variants of SARS-CoV-2 And Novel Therapeutics Against Coronavirus (COVID-19). In StatPearls. https://www.ncbi.nlm.nih.gov/pubmed/34033342
2. Are, E. B., Song, Y., Stockdale, J. E., Tupper, P., & Colijn, C. (2023). COVID-19 endgame: From pandemic to endemic? Vaccination, reopening and evolution in low- and high-vaccinated populations. J Theor Biol, 559, 111368. https://doi.org/10.1016/j.jtbi.2022.111368
3. Barton, M. I., MacGowan, S. A., Kutuzov, M. A., Dushek, O., Barton, G. J., & van der Merwe, P. A. (2021). Effects of common mutations in the SARS-CoV-2 Spike RBD and its ligand, the human ACE2 receptor on binding affinity and kinetics. Elife, 10. https://doi.org/10.7554/eLife.70658
4. Chatterjee, S., Bhattacharya, M., Nag, S., Dhama, K., & Chakraborty, C. (2023). A Detailed Overview of SARS-CoV-2 Omicron: Its Sub-Variants, Mutations and Pathophysiology, Clinical Characteristics, Immunological Landscape, Immune Escape, and Therapies. Viruses, 15(1). https://doi.org/10.3390/v15010167
5. Das, S., Samanta, S., Banerjee, J., Pal, A., Giri, B., Kar, S. S., & Dash, S. K. (2022). Is Omicron the end of pandemic or start of a new innings? Travel Med Infect Dis, 48, 102332. https://doi.org/10.1016/j.tmaid.2022.102332
6. Dong, E., Du, H., & Gardner, L. (2020). An interactive web-based dashboard to track COVID-19 in real time. Lancet Infect Dis, 20(5), 533-534.https://doi.org/10.1016/S1473-3099(20)30120-1
7. Elbe, S., & Buckland-Merrett, G. (2017). Data, disease and diplomacy: GISAID's innovative contribution to global health. Glob Chall, 1(1), 33-46. https://doi.org/10.1002/gch2.1018
8. Eyre, D. W., Taylor, D., Purver, M., Chapman, D., Fowler, T., Pouwels, K. B., Walker, A. S., & Peto, T. E. A. (2022). Effect of Covid-19 Vaccination on Transmission of Alpha and Delta Variants. N Engl J Med, 386(8), 744-756. https://doi.org/10.1056/NEJMoa2116597

9. Jackson, C. B., Farzan, M., Chen, B., & Choe, H. (2022). Mechanisms of SARS-CoV-2 entry into cells. Nat Rev Mol Cell Biol, 23(1), 3-20. https://doi.org/10.1038/s41580-021-00418-x
10. Jawad, B., Adhikari, P., Podgornik, R., & Ching, W. Y. (2021). Key Interacting Residues between RBD of SARS-CoV-2 and ACE2 Receptor: Combination of Molecular Dynamics Simulation and Density Functional Calculation. J Chem Inf Model, 61(9), 4425-4441. https://doi.org/10.1021/acs.jcim.1c00560
11. Kalyoncu, S., Yilmaz, S., Kuyucu, A. Z., Sayili, D., Mert, O., Soyturk, H., Gullu, S., Akinturk, H., Citak, E., Arslan, M., Taskinarda, M. G., Tarman, I. O., Altun, G. Y., Ozer, C., Orkut, R., Demirtas, A., Tilmensagir, I., Keles, U., Ulker, C., Inan, M. (2023). Process development for an effective COVID-19 vaccine candidate harboring recombinant SARS-CoV-2 delta plus receptor binding domain produced by Pichia pastoris. Sci Rep, 13(1), 5224. https://doi.org/10.1038/s41598-023-32021-9
12. Kastritis, P. L., & Bonvin, A. M. (2013). On the binding affinity of macromolecular interactions: daring to ask why proteins interact. J R Soc Interface, 10(79), 20120835. https://doi.org/10.1098/rsif.2012.0835
13. Kim, S., Liu, Y., Ziarnik, M., Cao, Y., Zhang, X. F., & Im, W. (2022). Binding of Human ACE2 and RBD of Omicron Enhanced by Unique Interaction Patterns Among SARS-CoV-2 Variants of Concern. bioRxiv. https://doi.org/10.1101/2022.01.24.477633
14. Letko, M., Marzi, A., & Munster, V. (2020). Functional assessment of cell entry and receptor usage for SARS-CoV-2 and other lineage B betacoronaviruses. Nat Microbiol, 5(4), 562-569. https://doi.org/10.1038/s41564-020-0688-y
15. Li, Q., Guan, X., Wu, P., Wang, X., Zhou, L., Tong, Y., Ren, R., Leung, K. S. M., Lau, E. H. Y., Wong, J. Y., Xing, X., Xiang, N., Wu, Y., Li, C., Chen, Q., Li, D., Liu, T., Zhao, J., Liu, M., Feng, Z. (2020). Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus-Infected Pneumonia. N Engl J Med, 382(13), 1199-1207. https://doi.org/10.1056/NEJMoa2001316
16. Liu, H., Zhang, Q., Wei, P., Chen, Z., Aviszus, K., Yang, J., Downing, W., Jiang, C., Liang, B., Reynoso, L., Downey, G. P., Frankel, S. K., Kappler, J., Marrack, P., & Zhang, G. (2021). The basis of a more contagious 501Y.V1 variant of SARS-CoV-2. Cell Res, 31(6), 720-722. https://doi.org/10.1038/s41422-021-00496-8
17. Liu, J., Li, Y., Liu, Q., Yao, Q., Wang, X., Zhang, H., Chen, R., Ren, L., Min, J., Deng, F., Yan, B., Liu, L., Hu, Z., Wang, M., & Zhou, Y. (2021). SARS-CoV-2 cell tropism and multiorgan infection. Cell Discov, 7(1), 17. https://doi.org/10.1038/s41421-021-00249-2
18. Lu, R., Zhao, X., Li, J., Niu, P., Yang, B., Wu, H., Wang, W., Song, H., Huang, B., Zhu, N., Bi, Y., Ma, X., Zhan, F., Wang, L., Hu, T., Zhou, H., Hu, Z., Zhou, W., Zhao, L., . . . Tan, W. (2020). Genomic characterisation and epidemiology of 2019 novel coronavirus: implications for virus origins and receptor binding. Lancet, 395(10224), 565-574. https://doi.org/10.1016/S0140-6736(20)30251-8
19. Madeira, F., Pearce, M., Tivey, A. R. N., Basutkar, P., Lee, J., Edbali, O., Madhusoodanan, N., Kolesnikov, A., & Lopez, R. (2022). Search and sequence analysis tools services from EMBL-EBI in 2022. Nucleic Acids Res, 50(W1), W276-279. https://doi.org/10.1093/nar/gkac240
20. Markov, P. V., Ghafari, M., Beer, M., Lythgoe, K., Simmonds, P., Stilianakis, N. I., & Katzourakis, A. (2023). The evolution of SARS-CoV-2. Nat Rev Microbiol. https://doi.org/10.1038/s41579-023-00878-2
21. Miller, B. R., 3rd, McGee, T. D., Jr., Swails, J. M., Homeyer, N., Gohlke, H., & Roitberg, A. E. (2012). MMPBSA.py: An Efficient Program for End-State Free Energy Calculations. J Chem Theory Comput, 8(9), 3314-3321. https://doi.org/10.1021/ct300418h
22. Rambaut, A., Holmes, E. C., O'Toole, A., Hill, V., McCrone, J. T., Ruis, C., du Plessis, L., & Pybus, O. G. (2020). A dynamic nomenclature proposal for SARS-CoV-2 lineages to assist genomic epidemiology. Nat Microbiol, 5(11), 1403-1407. https://doi.org/10.1038/s41564-020-0770-5
23. Rana, R., Kant, R., Huirem, R. S., Bohra, D., & Ganguly, N. K. (2022). Omicron variant: Current insights and future directions. Microbiol Res, 265, 127204. https://doi.org/10.1016/j.micres.2022.127204
24. Vangone, A., & Bonvin, A. M. (2015). Contacts-based prediction of binding affinity in protein-protein complexes. Elife, 4, e07454. https://doi.org/10.7554/eLife.07454
25. Wang, C., Greene, D., Xiao, L., Qi, R., & Luo, R. (2017). Recent Developments and Applications of the MMPBSA Method. Front Mol Biosci, 4, 87. https://doi.org/10.3389/fmolb.2017.00087
26. Wang, Y., Yan, J., & Goult, B. T. (2019). Force-Dependent Binding Constants. Biochemistry, 58(47), 4696-4709. https://doi.org/10.1021/acs.biochem.9b00453
27. Waterhouse, A., Bertoni, M., Bienert, S., Studer, G., Tauriello, G., Gumienny, R., Heer, F. T., de Beer, T. A. P., Rempfer, C., Bordoli, L., Lepore, R., & Schwede, T. (2018). SWISS-MODEL: homology modelling of protein structures and complexes. Nucleic Acids Res, 46(W1), W296-W303. https://doi.org/10.1093/nar/gky427
28. Xue, L. C., Rodrigues, J. P., Kastritis, P. L., Bonvin, A. M., & Vangone, A. (2016). PRODIGY: a web server for predicting the binding affinity of protein-protein complexes. Bioinformatics, 32(23), 3676-3678. https://doi.org/10.1093/bioinformatics/btw514