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An in-silico study of the mutation-associated effects on the spike protein of SARS-CoV-2, Omicron variant
Open accessPreprint — published as 10.1371/journal.pone.0266844
Abstract
The emergence of Omicron (B.1.1.529), a new Variant of Concern in the COVID-19 outbreak, while accompanied by the ongoing Delta variant infection, has once again fueled fears of a new infection wave and global health concern. In the Omicron variant, the receptor-binding domain (RBD) of its spike glycoprotein is heavily mutated, a feature critical for the transmission rate of the virus by interacting with hACE2. In this study, we used a combination of conventional and advanced neural network-based in silico approaches to predict how these mutations would affect the spike protein. The results demonstrated a decrease in the electrostatic potentials of residues corresponding to receptor recognition sites, an increase in the alkalinity of the protein, a change in hydrophobicity, variations in functional residues, and an increase in the percentage of alpha-helix structure. Our next step was to predict the structural changes of the spike protein using the AI-based tool Alphafold2 and dock it with hACE2. The results revealed that the RBD of the Omicron variant had a higher affinity than the reference. Moreover, all-atom molecular dynamics simulations concluded that the RBD of the Omicron variant exhibits a more dispersed interaction network since mutations resulted in an increased number of hydrophobic interactions and hydrogen bonds with hACE2 compared to the reference RBD. In summary, our current study highlighted the potential structural basis for the enhanced transmissibility and pathogenicity of the Omicron variant, although further research is needed to investigate its epidemiological and biological implications.
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Cites
VMD: visual molecular dynamics
Humphrey, W., Dalke, A., & Schulten, K. (1996) · Journal of Molecular Graphics · doi:10.1016/0263-7855(96)00018-5 · Cited by 66,952
Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764
GROMACS: Fast, flexible, and free
Van Der Spoel, D., Lindahl, E., Hess, B., Groenhof, G., Mark, A. E., & Berendsen, H. J. C. (2005) · Journal of Computational Chemistry · doi:10.1002/jcc.20291 · Cited by 19,182
EMBOSS: the European molecular biology open software suite.
Rice, P., Longden, I., & Bleasby, A. (2000) · Trends in Genetics · doi:10.1016/s0168-9525(00)02024-2 · Cited by 9,466
UniProt: the universal protein knowledgebase in 2021
The UniProt Consortium, Bateman, A., Martin, M.-J., Orchard, S., Magrane, M., Agivetova, R., Ahmad, S., Alpi, E., Bowler-Barnett, E. H., Britto, R., et al. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkaa1100 · Cited by 6,372
TM-align: a protein structure alignment algorithm based on the TM-score
Zhang, Y. (2005) · Nucleic Acids Research · doi:10.1093/nar/gki524 · Cited by 3,774
SARS-CoV-2 variants, spike mutations and immune escape
Harvey, W. T., Carabelli, A. M., Jackson, B., Gupta, R. K., Thomson, E. C., Harrison, E. M., Ludden, C., Reeve, R., Rambaut, A., COVID-19 Genomics UK (COG-UK) Consortium, et al. (2021) · Nature Reviews Microbiology · doi:10.1038/s41579-021-00573-0 · Cited by 3,528
The ClusPro web server for protein–protein docking
Kozakov, D., Hall, D. R., Xia, B., Porter, K. A., Padhorny, D., Yueh, C., Beglov, D., & Vajda, S. (2017) · Nature Protocols · doi:10.1038/nprot.2016.169 · Cited by 3,310
Ashkenazy, H., Abadi, S., Martz, E., Chay, O., Mayrose, I., Pupko, T., & Ben-Tal, N. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw408 · Cited by 3,163
PROVEAN web server: a tool to predict the functional effect of amino acid substitutions and indels
Choi, Y., & Chan, A. P.-Y. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv195 · Cited by 2,560
SIFT web server: predicting effects of amino acid substitutions on proteins
Sim, N.-L., Kumar, P. N., Hu, J., Henikoff, S., Schneider, G., & Ng, P. C. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks539 · Cited by 2,476
ExPASy: SIB bioinformatics resource portal
Artimo, P., Jonnalagedda, M., Arnold, K., Baratin, D., Csardi, G., de Castro, E., Duvaud, S., Flegel, V., Fortier, A., Gasteiger, E., et al. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks400 · Cited by 2,266
Clustal Omega for making accurate alignments of many protein sequences
Sievers, F., & Higgins, D. G. (2018) · Protein Science · doi:10.1002/pro.3290 · Cited by 2,137
A global database of COVID-19 vaccinations
Mathieu, E., Ritchie, H., Ortiz-Ospina, E., Roser, M., Hasell, J., Appel, C., Giattino, C., & Rodés-Guirao, L. (2021) · Nature Human Behaviour · doi:10.1038/s41562-021-01122-8 · Cited by 1,974
The HDOCK server for integrated protein–protein docking
Yan, Y., Tao, H., He, J., & Huang, S.-Y. (2020) · Nature Protocols · doi:10.1038/s41596-020-0312-x · Cited by 1,857
The outbreak of COVID-19: An overview
Wu, Y.-C., Chen, C.-S., & Chan, Y.-J. (2020) · Journal of the Chinese Medical Association · doi:10.1097/jcma.0000000000000270 · Cited by 1,768
JPred4: a protein secondary structure prediction server
Drozdetskiy, A., Cole, C., Procter, J., & Barton, G. J. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv332 · Cited by 1,744
PRODIGY: a web server for predicting the binding affinity of protein–protein complexes
Xue, L. C., Rodrigues, J. P., Kastritis, P. L., Bonvin, A. M., & Vangone, A. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw514 · Cited by 1,413
Spillover of COVID-19: Impact on the Global Economy
Ozili, P. K., & Arun, T. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3562570 · Cited by 1,400
SARS-CoV-2 spike-protein D614G mutation increases virion spike density and infectivity
Zhang, L., Jackson, C. B., Mou, H., Ojha, A., Peng, H., Quinlan, B. D., Rangarajan, E. S., Pan, A., Vanderheiden, A., Suthar, M. S., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-19808-4 · Cited by 1,387
Burley, S. K., Berman, H. M., Bhikadiya, C., Bi, C., Chen, L., Di Costanzo, L., Christie, C., Dalenberg, K., Duarte, J. M., Dutta, S., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gky1004 · Cited by 1,243
Omicron SARS-CoV-2 variant: a new chapter in the COVID-19 pandemic
Karim, S. S. A., & Karim, Q. A. (2021) · The Lancet · doi:10.1016/s0140-6736(21)02758-6 · Cited by 1,226
Tregoning, J. S., Flight, K. E., Higham, S. L., Wang, Z., & Pierce, B. F. (2021) · Nature reviews. Immunology · doi:10.1038/s41577-021-00592-1 · Cited by 1,151
PIC: Protein Interactions Calculator
Tina, K. G., Bhadra, R., & Srinivasan, N. (2007) · Nucleic Acids Research · doi:10.1093/nar/gkm423 · Cited by 867
Rodrigues, C. H. M., Pires, D. E. V., & Ascher, D. B. (2020) · Protein Science · doi:10.1002/pro.3942 · Cited by 711
NetSurfP‐2.0: Improved prediction of protein structural features by integrated deep learning
Klausen, M. S., Jespersen, M. C., Nielsen, H., Jensen, K. K., Jurtz, V. I., Sønderby, C. K., Sommer, M. O. A., Winther, O., Nielsen, M., Petersen, B., et al. (2019) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.25674 · Cited by 574
Heavily mutated Omicron variant puts scientists on alert
Callaway, E. (2021) · Nature · doi:10.1038/d41586-021-03552-w · Cited by 553
SARS-CoV-2 spike D614G change enhances replication and transmission
Zhou, B., Thao, T. T. N., Hoffmann, D., Taddeo, A., Ebert, N., Labroussaa, F., Pohlmann, A., King, J., Steiner, S., Kelly, J. N., et al. (2021) · Nature · doi:10.1038/s41586-021-03361-1 · Cited by 538
ACE2: The Major Cell Entry Receptor for SARS-CoV-2
Scialo, F., Daniele, A., Amato, F., Pastore, L., Matera, M. G., Cazzola, M., Castaldo, G., & Bianco, A. (2020) · Lung · doi:10.1007/s00408-020-00408-4 · Cited by 481
STRUCTURAL AND GENETIC ANALYSIS OF PROTEIN STABILITY
Matthews, B. W. (1993) · Annual Review of Biochemistry · doi:10.1146/annurev.bi.62.070193.001035 · Cited by 451
SARS-CoV-2 Variants of Concern
Choi, J. Y., & Smith, D. M. (2021) · Yonsei Medical Journal · doi:10.3349/ymj.2021.62.11.961 · Cited by 293
flDPnn: Accurate intrinsic disorder prediction with putative propensities of disorder functions
Hu, G., Katuwawala, A., Wang, K., Wu, Z., Ghadermarzi, S., Gao, J., & Kurgan, L. (2021) · Nature Communications · doi:10.1038/s41467-021-24773-7 · Cited by 292
Principles of protein stability derived from protein engineering experiments
Fersht, A. R., & Serrano, L. G. (1993) · Current Opinion in Structural Biology · doi:10.1016/0959-440x(93)90205-y · Cited by 260
DeepDDG: Predicting the Stability Change of Protein Point Mutations Using Neural Networks
Cao, H., Wang, J., He, L., Qi, Y., & Zhang, J. Z. H. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00697 · Cited by 245
PredictProtein - Predicting Protein Structure and Function for 29 Years
Bernhofer, M., Dallago, C., Karl, T., Satagopam, V., Heinzinger, M., Littmann, M., Olenyi, T., Qiu, J., Schütze, K., Yachdav, G., et al. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab354 · Cited by 233
Ultrapotent antibodies against diverse and highly transmissible SARS-CoV-2 variants
Wang, L., Zhou, T., Zhang, Y., Yang, E. S., Schramm, C. A., Shi, W., Pegu, A., Oloniniyi, O. K., Henry, A. R., Darko, S. W., et al. (2021) · Science · doi:10.1126/science.abh1766 · Cited by 195
Transmission event of SARS-CoV-2 delta variant reveals multiple vaccine breakthrough infections
Farinholt, T., Doddapaneni, H., Qin, X., Menon, V. K., Meng, Q., Metcalf, G., Chao, H., Gingras, M., Avadhanula, V., Farinholt, P., et al. (2021) · BMC Medicine · doi:10.1186/s12916-021-02103-4 · Cited by 187
CMView: Interactive contact map visualization and analysis
Vehlow, C., Stehr, H., Winkelmann, M., Duarte, J. M., Petzold, L., Dinse, J., & Lappé, M. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr163 · Cited by 141
Pattern Formation inside Bacteria: Fluctuations due to the Low Copy Number of Proteins
Howard, M., & Rutenberg, A. D. (2003) · Physical Review Letters · doi:10.1103/physrevlett.90.128102 · Cited by 124
VarSite: Disease variants and protein structure
Laskowski, R. A., Stephenson, J. D., Sillitoe, I., Orengo, C. A., & Thornton, J. M. (2019) · Protein Science · doi:10.1002/pro.3746 · Cited by 116
PredyFlexy: flexibility and local structure prediction from sequence
de Brevern, A. G., Bornot, A., Craveur, P., Etchebest, C., & Gelly, J. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks482 · Cited by 102
The great escape? SARS-CoV-2 variants evading neutralizing responses
Prévost, J., & Finzi, A. (2021) · Cell Host & Microbe · doi:10.1016/j.chom.2021.02.010 · Cited by 80
Covid-19: South Africa’s surge in cases deepens alarm over omicron variant
Dyer, O. (2021) · BMJ · doi:10.1136/bmj.n3013 · Cited by 78
SARS-CoV-2 variants and effectiveness of vaccines: a review of current evidence
Tatsi, E., Filippatos, F., & Michos, A. G. (2021) · Epidemiology and Infection · doi:10.1017/s0950268821002430 · Cited by 68
Helix formation and stability in membranes
McKay, M. J., Afrose, F., Koeppe, R. E., & Greathouse, D. V. (2018) · Biochimica et Biophysica Acta (BBA) - Biomembranes · doi:10.1016/j.bbamem.2018.02.010 · Cited by 52
Update of the CHARMM36 United Atom Chain Model for Hydrocarbons and Phospholipids
Yu, Y., & Klauda, J. B. (2020) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.0c04795 · Cited by 38
SARS‐CoV‐2: Mechanism of infection and emerging technologies for future prospects
Li, S., Li, S., Disoma, C., Zheng, R., Zhou, M. L., Razzaq, A., Liu, P., Zhou, Y., Dong, Z., Du, A., et al. (2020) · Reviews in Medical Virology · doi:10.1002/rmv.2168 · Cited by 32
Bonnal, R. J. P., Aerts, J. A., Githinji, G., Goto, N., MacLean, D., Miller, C., Mishima, H., Pagani, M., Ramirez-Gonzalez, R. H., Smant, G., et al. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts080 · Cited by 31
Liu, B., Liu, K., Zhang, H., Zhang, L., Bian, Y., & Huang, L. (2020) · Journal of Medical Internet Research · doi:10.2196/22299 · Cited by 31
Lindstrøm, J. C., Engebretsen, S., Kristoffersen, A. B., Rø, G., Palomares, A. D.-L., Engø‐Monsen, K., Madslien, E. H., Forland, F., Nygård, K. M., Hagen, F., et al. (2021) · Scandinavian Journal of Infectious Diseases · doi:10.1080/23744235.2021.1977382 · Cited by 27
Lista, M. J., Winstone, H., Wilson, H., Dyer, A. S., Pickering, S., Galão, R. P., Lorenzo, G. D., Cowton, V. M., Furnon, W., Suárez, N. M., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.11.09.467693 · Cited by 20
Flexibility and mobility of SARS-CoV-2-related protein structures
Römer, R. A., Römer, N. S., & Wallis, A. K. (2021) · Scientific Reports · doi:10.1038/s41598-021-82849-2 · Cited by 17
Bartonek, L., & Žagrović, B. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz407 · Cited by 14
Mogus, A. T., Connors, N. K., Middelberg, A. P. J., & Lua, L. H. L. (2016) · Protein Science · doi:10.1002/pro.2953 · Cited by 13
Stability of alpha-helical and beta-structural blocks in proteins of four structural classes
Poboinev, V. V., Khrustalev, V. V., Khrustaleva, T. A., & Stozharov, A. N. (2018) · Proceedings of the National Academy of Sciences of Belarus Biological Series · doi:10.29235/1029-8940-2018-63-4-391-400 · Cited by 3
Structure and stability of the ecosystem
Slater, K. (2003) · Elsevier eBooks · doi:10.1533/9781855738645.1 · Cited by 2
Research works printed but not linked: 1
- GISAID, 2020, GISAID Initiative, Adv. Virus Res, 2008, 1
Metadata
- Authors
- Shishir, T. A., Jannat, T., & Naser, I. B.
- Affiliations
- BRAC University (BD)
- Corresponding author
- Naser, Iftekhar Bin
- Venue
- bioRxiv (Cold Spring Harbor Laboratory)
- Publisher
- openRxiv
- Published
- 2022-02-23
- OpenAlex
- W4214775475
- Cited by
- 5
- References
- 56
- Citations by year
- 2025: 1 · 2023: 2 · 2022: 2
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Locations
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Health Sciences › Medicine › Infectious Diseases › SARS-CoV-2 and COVID-19 Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › vaccines and immunoinformatics approaches
- Keywords
- Omicron variant, spike protein, receptor binding domain, genetic mutations, hACE2, protein-protein docking, electrostatic potential, hydrophobic interactions, hydrogen bonding, protein structure prediction, AlphaFold2, molecular dynamics simulation, viral transmissibility, COVID-19 pandemic
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