Coronavirus SARS-CoV-2 enters the host cell via binding with the angiotensin-converting enzyme 2 (ACE2), and here we used computational modelling to study the molecular recognition pattern of this interaction. The fragment of the N-terminal part of the enzyme containing amino acids 19–45 was used as the lead peptide in this study. The structure of this peptide was systematically modified by successive replacement of its amino acids with alanine, serine, glycine, and phenylalanine. Then docking energies were calculated for all these mutant peptides. These docking energies were correlated with physical descriptors, proposed for the modelling of peptide–protein interactions, characterizing hydrophilicity and volume-related properties of amino acid side chains. From these correlations the corresponding specificity factors were obtained for all amino acid positions, and thus the full description of the molecular recognition pattern of the ACE2 α1 domain by the virus S1 protein binding site was obtained.
1. Shang, J., Wan, Y., Luo, C., Ye, G., Geng, Q., Auerbach, A., and Li, F. Cell entry mechanisms of SARS-CoV-2. PNAS, 2020, 117, 11727–11734.
https://doi.org/10.1073/pnas.2003138117
2. Shang, J., Ye, G., Shi, K., Wan, Y., Luo, C., Aihara, H., Geng, Q., Auerbach, A., and Li, F. Structural basis of receptor recognition by SARS-CoV-2. Nature, 2020, 581, 221–224.
https://doi.org/10.1038/s41586-020-2179-y
3. Wang, Q., Zhang, Y., Wu, L., Niu, S., Song, C., Zhang, Z., Lu, G., Qiao, C., Hu, Y., Yuen, K. Y., Wang, Q., Zhou, H., Yan, J., and Qi, J. Structural and functional basis of SARS-CoV-2 entry by using human ACE2. Cell, 2020, 181, 894–904.
https://doi.org/10.1016/j.cell.2020.03.045
4. Yan, R., Zhang, Y., Li, Y., Xia, L., Guo, Y., and Zhou, Q. Structural basis for the recognition of SARS-CoV-2 by full-length human ACE2. Science, 2020, 367, 1444–1448.
https://doi.org/10.1126/science.abb2762
5. Peter, E. and Schug, A. The inhibitory effect of a Corona virus spike protein fragment with ACE2. bioRxiv 2020.06.03.132506.
https://doi.org/10.1101/2020.06.03.132506
6. Han, Y. and Král, P. Computational design of ACE2-based peptide inhibitors of SARS-CoV-2. ASC Nano, 2020, 5143–5147.
https://doi.org/10.1021/acsnano.0c02857
7. Kuznetsov, A. and Järv, J. Mapping of ACE2 binding site on SARS-CoV-2 spike protein S1: docking study with peptides. Proc. Estonian Acad. Sci., 2020, 69, 228–234.
https://doi.org/10.3176/proc.2020.3.06
8. VanPatten, S., He, M., Altiti, A., Cheng, K. F., Ghanem, M. H., and Al-Abed, Y. Evidence supporting the use of peptides and peptidomimetics as potential SARS-CoV-2 (COVID-19) therapeutics. Future Med. Chem., 2020. Advance online publication.
https://doi.org/10.4155/fmc-2020-0180
9. Barh, D., Tiwari, S., Andrade, B., Giovanetti, M., Costa, E., Kumavath, R., Ghosh, P., Góes-Neto, A., Alcantara, L., and Azevedo, V. Potential chimeric peptides to block the SARS-CoV-2 spike receptor-binding domain. F1000Research, 2020, 9, 576.
https://doi.org/10.12688/f1000research.24074.1
10. Baig, M., Alagumuthu, M., Rajpoot, S., and Saqib, U. Identification of a potential peptide inhibitor of SARS-CoV-2 targeting its entry into the host cells. Drugs R&D, 2020, 20, 161–169.
https://doi.org/10.1007/s40268-020-00312-5
11. Kortemme, T., Kim, D. E., and Baker, D. Computational alanine scanning of protein-protein interfaces. Science’s STKE, 2004, 2004, pl2.
https://doi.org/10.1126/stke.2192004pl2
12. Trott, O. and Olson, A. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J. Comput. Chem., 2010, 31(2), 455–461.
https://doi.org/10.1002/jcc.21334
13. Hess, B., Kutzner, C., van der Spoel, D., and Lindahl, E. GROMACS 4: algorithms for highly efficient, load-balanced, and scalable molecular simulation. J. Chem. Theory Comput., 2008, 4(3), 435–447.
https://doi.org/10.1021/ct700301q
14. PyMOL. The PyMOL Molecular Graphics System. Version 1.8.0.4. Schrödinger, LLC.
https://www.pymol.org
15. Humphrey, W., Dalke, A., and Schulten, K. VMD: visual molecular dynamics. J. Molec. Graphics, 1996, 14(1), 33–38.
https://doi.org/10.1016/0263-7855(96)00018-5
16. Lan, J., Ge, J., Yu, J. Shan, S., Zhou, H., Fan, S., Zhang, Q., Shi, X., Wang, Q., Zhang, L., and Wang, X. Structure of the SARS-CoV-2 spike receptor-binding domain bound to the ACE2 receptor. Nature, 2020, 581, 215–220.
https://doi.org/10.1038/s41586-020-2180-5
17. Laurini, E., Marson, D., Aulic, S., Fermeglia, M., and Pricl, S. Computational alanine scanning and structural analysis of the SARS-CoV-2 spike protein/angiotensin-converting enzyme 2 complex. ACS Nano, 2020, 14, 11821–11830.
https://doi.org/10.1021/acsnano.0c04674
18. Sneath, P. Relations between chemical structure and biological activity in peptides. J. Theor. Biol., 1966, 12, 157–195.
https://doi.org/10.1016/0022-5193(66)90112-3
19. Barley, M., Turner, N., and Goodacre, R. Improved descriptors for the quantitative structure–activity relationship modeling of peptides and proteins. J. Chem. Inf. Model., 2018, 58, 234–243.
https://doi.org/10.1021/acs.jcim.7b00488
20. Järv, J. and Ragrarsson, U. Linear free energy relationships in cAMP-dependent protein kinase reactions with synthetic substrates. Bioorg. Chem., 1991, 19, 77–87.
https://doi.org/10.1016/0045-2068(91)90045-Q
21. Hansch, C. Recent advances in biochemical QSAR. In Correlation Analysis in Chemistry (Chapman, N. B. and Shorter, J., eds). Springer, Boston, MA, 1978, 397–438.
https://doi.org/10.1007/978-1-4615-8831-3_9
