1. WHO Director-General’s opening remarks at the media briefing on COVID-19 - 11 March 2020. (2021).
2. Wu, F. et al. A new coronavirus associated with human respiratory disease in China. Nature 579, 265–269 (2020).
3. Wang, M.-Y. et al. SARS-CoV-2: Structure, Biology, and Structure-Based Therapeutics Development. Front. Cell. Infect. Microbiol. 10, 587269 (2020).
4. GISAID - Clade and lineage nomenclature aids in genomic epidemiology of active hCoV-19 viruses. (2021).
5. SeyedAlinaghi, S. et al. Characterization of SARS-CoV-2 different variants and related morbidity and mortality: a systematic review. Eur. J. Med. Res. 26, 51 (2021).
6. Hamed, S. M., Elkhatib, W. F., Khairalla, A. S. & Noreddin, A. M. Global dynamics of SARS-CoV-2 clades and their relation to COVID-19 epidemiology. Sci. Rep. 11, 8435 (2021).
7. Khailany, R. A., Safdar, M. & Ozaslan, M. Genomic characterization of a novel SARS-CoV-2. Gene Rep 19, 100682 (2020).
8. Bianchi, M. et al. Sars-CoV-2 Envelope and Membrane Proteins: Structural Differences Linked to Virus Characteristics? Biomed Res. Int. 2020, 4389089 (2020).
9. Arndt, A. L., Larson, B. J. & Hogue, B. G. A conserved domain in the coronavirus membrane protein tail is important for virus assembly. J. Virol. 84, 11418–11428 (2010).
10. Tseng, Y.-T., Chang, C.-H., Wang, S.-M., Huang, K.-J. & Wang, C.-T. Identifying SARS-CoV membrane protein amino acid residues linked to virus-like particle assembly. PLoS One 8, e64013 (2013).
11. Satarker, S. & Nampoothiri, M. Structural Proteins in Severe Acute Respiratory Syndrome Coronavirus-2. Arch. Med. Res. 51, 482–491 (2020).
12. Neuman, B. W. et al. A structural analysis of M protein in coronavirus assembly and morphology. J. Struct. Biol. 174, 11–22 (2011).
13. Carpenter, E. P., Beis, K., Cameron, A. D. & Iwata, S. Overcoming the challenges of membrane protein crystallography. Curr. Opin. Struct. Biol. 18, 581–586 (2008).
14. Mariano, G., Farthing, R. J., Lale-Farjat, S. L. M. & Bergeron, J. R. C. Structural Characterization of SARS-CoV-2: Where We Are, and Where We Need to Be. Front Mol Biosci 7, 605236 (2020).
15. Kuhlman, B. & Bradley, P. Advances in protein structure prediction and design. Nat. Rev. Mol. Cell Biol. 20, 681–697 (2019).
16. Aslam, B., Basit, M., Nisar, M. A., Khurshid, M. & Rasool, M. H. Proteomics: Technologies and Their Applications. J. Chromatogr. Sci. 55, 182–196 (2017).
17. Senior, A. W. et al. Improved protein structure prediction using potentials from deep learning. Nature 577, 706–710 (2020).
18. Lomize, M. A., Pogozheva, I. D., Joo, H., Mosberg, H. I. & Lomize, A. L. OPM database and PPM web server: resources for positioning of proteins in membranes. Nucleic Acids Res. 40, D370-6 (2012).
19. K. Hofmann, W. S. TMbase-a database of membrane spanning proteins segments. Biol. Chem. Hoppe Seyler 374, 166 (1993).
20. Sonnhammer, E. L., von Heijne, G. & Krogh, A. A hidden Markov model for predicting transmembrane helices in protein sequences. Proc. Int. Conf. Intell. Syst. Mol. Biol. 6, 175–182 (1998).
21. Krogh, A., Larsson, B., von Heijne, G. & Sonnhammer, E. L. Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. J. Mol. Biol. 305, 567–580 (2001).
22. Buchan, D. W. A. & Jones, D. T. The PSIPRED Protein Analysis Workbench: 20 years on. Nucleic Acids Res. 47, W402–W407 (2019).
23. Jones, D. T. Protein secondary structure prediction based on position-specific scoring matrices. J. Mol. Biol. 292, 195–202 (1999).
24. Dobson, L., Reményi, I. & Tusnády, G. E. The human transmembrane proteome. Biol. Direct 10, 31 (2015).
25. Dobson, L., Reményi, I. & Tusnády, G. E. CCTOP: a Consensus Constrained TOPology prediction web server. Nucleic Acids Res. 43, W408–W412 (2015).
26. Jones, D. T., Taylor, W. R. & Thornton, J. M. A model recognition approach to the prediction of all-helical membrane protein structure and topology. Biochemistry 33, 3038–3049 (1994).
27. Jo, S., Kim, T., Iyer, V. G. & Im, W. CHARMM-GUI: A web-based graphical user interface for CHARMM. J. Comput. Chem. 29, 1859–1865 (2008).
28. Bekker, H. et al. Gromacs-a parallel computer for molecular-dynamics simulations. 4th International Conference on Computational Physics (PC 92) 252–256 (1993).
29. Berendsen, H. J. C., van der Spoel, D. & van Drunen, R. GROMACS: A message-passing parallel molecular dynamics implementation. Comput. Phys. Commun. 91, 43–56 (1995).
30. van Zundert, G. C. P. P. et al. The HADDOCK2.2 Web Server: User-Friendly Integrative Modeling of Biomolecular Complexes. J. Mol. Biol. 428, 720–725 (2016).
31. Xing, Y., Li, X., Gao, X. & Dong, Q. MicroGMT: A Mutation Tracker for SARS-CoV-2 and Other Microbial Genome Sequences. Front. Microbiol. 11, 1502 (2020).
32. Rahman, M. S. et al. Comprehensive annotations of the mutational spectra of SARS-CoV-2 spike protein: a fast and accurate pipeline. Transbound. Emerg. Dis. (2020) doi:10.1111/tbed.13834.
33. Schymkowitz, J. et al. The FoldX web server: an online force field. Nucleic Acids Res. 33, W382-8 (2005).
34. Elbe, S. & Buckland-Merrett, G. Data, disease and diplomacy: GISAID’s innovative contribution to global health. Glob. Challenges 1, 33–46 (2017).
35. Shu, Y. & McCauley, J. GISAID: Global initiative on sharing all influenza data - from vision to reality. Euro Surveill. 22, (2017).
36. Preto, A. J. & Moreira, I. S. SPOTONE: Hot Spots on Protein Complexes with Extremely Randomized Trees via Sequence-Only Features. Int. J. Mol. Sci. 21, (2020).
37. Moreira, I. S. The Role of Water Occlusion for the Definition of a Protein Binding Hot-Spot. Curr. Top. Med. Chem. 15, 2068–2079 (2015).
38. Munteanu, C. R. et al. Solvent Accessible Surface Area-Based Hot-Spot Detection Methods for Protein–Protein and Protein–Nucleic Acid Interfaces. Journal of Chemical Information and Modeling vol. 55 1077–1086 (2015).
39. Martins, J. M., Ramos, R. M., Pimenta, A. C. & Moreira, I. S. Solvent-accessible surface area: How well can be applied to hot-spot detection? Proteins 82, 479–490 (2014).
40. Moreira, I. S., Ramos, R. M., Martins, J. M., Fernandes, P. A. & Ramos, M. J. Are hot-spots occluded from water? J. Biomol. Struct. Dyn. 32, 186–197 (2014).
41. Bogan, A. A. & Thorn, K. S. Anatomy of hot spots in protein interfaces. J. Mol. Biol. 280, 1–9 (1998).
42. Majumdar, P. & Niyogi, S. SARS-CoV-2 mutations: the biological trackway towards viral fitness. Epidemiol. Infect. 149, e110 (2021).
43. AlQuraishi, M. Machine learning in protein structure prediction. Curr. Opin. Chem. Biol. 65, 1–8 (2021).
44. Huang, J. & Mackerell, A. D. CHARMM36 all-atom additive protein force field: Validation based on comparison to NMR data. J. Comput. Chem. 34, 2135–2145 (2013).
45. O’Donnell, V. B. et al. Potential Role of Oral Rinses Targeting the Viral Lipid Envelope in SARS-CoV-2 Infection. Function 1, (2020).
46. Berendsen, H. J. C., Postma, J. P. M., van Gunsteren, W. F., DiNola, A. & Haak, J. R. Molecular dynamics with coupling to an external bath. J. Chem. Phys. 81, 3684–3690 (1984).
47. Darden, T., York, D. & Pedersen, L. Particle mesh Ewald: An N⋅log(N) method for Ewald sums in large systems. J. Chem. Phys. 98, 10089–10092 (1993).
48. Thomas, S. The Structure of the Membrane Protein of SARS-CoV-2 Resembles the Sugar Transporter SemiSWEET. Pathog Immun 5, 342–363 (2020).
49. Blundell, T. L. & Srinivasan, N. Symmetry, stability, and dynamics of multidomain and multicomponent protein systems. Proc. Natl. Acad. Sci. U. S. A. 93, 14243–14248 (1996).
50. de Vries, S. J. & Bonvin, A. M. J. J. J. J. Cport: A consensus interface predictor and its performance in prediction-driven docking with HADDOCK. PLoS One 6, e17695 (2011).
51. Krissinel, E. & Henrick, K. Inference of macromolecular assemblies from crystalline state. J. Mol. Biol. 372, 774–797 (2007).
52. Vangone, A. & Bonvin, A. M. Contacts-based prediction of binding affinity in protein-protein complexes. Elife 4, e07454 (2015).
53. Xue, L. C., Rodrigues, J. P., Kastritis, P. L., Bonvin, A. M. & Vangone, A. PRODIGY: a web server for predicting the binding affinity of protein-protein complexes. Bioinformatics 32, 3676–3678 (2016).
54. Grant, B. J., Rodrigues, A. P. C., ElSawy, K. M., McCammon, J. A. & Caves, L. S. D. Bio3d: an R package for the comparative analysis of protein structures. Bioinformatics 22, 2695–2696 (2006).
55. Tomasello, G., Armenia, I. & Molla, G. The Protein Imager: a full-featured online molecular viewer interface with server-side HQ-rendering capabilities. Bioinformatics 36, 2909–2911 (2020).
56. Wilkinson, L. ggplot2: Elegant Graphics for Data Analysis by WICKHAM, H. Biometrics vol. 67 678–679 (2011).