Assessment of Binding Affinity in the Complexes of CoV-S-Protein’s RBD and the ACE2 Using Convolutional Neural Networks

Cover Page

Cite item

Full Text

Open Access Open Access
Restricted Access Access granted
Restricted Access Subscription or Fee Access

Abstract

The experimentally obtained structures of 48 complexes of the ACE2 receptor with the S protein’s RBD of the coronaviruses SARS-CoV and SARS-CoV-2 (including mutant forms of the latter) were assessed and the dissociation constant was calculated for them. Prediction of binding affinity was carried out using ProBAN, a neural network algorithm, previously developed by the authors, and a number of other algorithms for estimating the Gibbs free energy such as Prodigy, FoldX, DFIRE and RosettaDock. A comparison of the evaluation results shows that ProBAN has the best prediction quality (Pearson correlation − 0.56, MAE − 0.7 kcal/mol) of all the analyzed algorithms. The results obtained suggest better quality of affinity prediction for other protein-protein complexes. Information about the complexes under study and prediction results are available in the repository at the link: https://github.com/EABogdanova/ProBAN_RBD-ACE2.

About the authors

E. A Bogdanova

Lomonosov Moscow State University

Moscow, 119991 Russia

A. V Chernukhin

D.I. Mendeleev University of Chemical Technology of Russia

Moscow, 125047 Russia

K. V Shaitan

Lomonosov Moscow State University

Moscow, 119991 Russia

V. N Novoseletsky

Shenzhen MSU-BIT University, International University Park Road

Email: novoseletsky@smbu.edu.cn
Shenzhen, 518172, People’s Republic of China

References

  1. Choudhary S., Sreenivasulu K., Mitra P., Misra S., and Sharma P. Role of genetic variants and gene expression in the susceptibility and severity of COVID-19. Ann. Lab. Med., 41 (2), 129–138 (2021). doi: 10.3343/alm.2021.41.2.129
  2. Ali A. and Vijayan R. Dynamics of the ACE2-SARSCoV-2/SARS-CoV spike protein interface reveal unique mechanisms. Sci. Rep., 10 (1), 14214 (2020). doi: 10.1038/s41598-020-71188-3
  3. Vogel M., Augusto G., Chang X., Liu X., Speiser D., Mohsen M. O., and Bachmann M. F. Molecular definition of severe acute respiratory syndrome coronavirus 2 receptor-binding domain mutations: Receptor affinity versus neutralization of receptor interaction. Allergy, 77 (1), 143–149 (2022). doi: 10.1111/all.15002
  4. Bozdaganyan M. E., Shaitan K. V., Kirpichnikov M. P., Sokolova O. S., and Orekhov P. S. Computational analysis of mutations in the receptor-binding domain of SARS-CoV-2 spike and their effects on antibody binding. Viruses, 14 (2), 295 (2022). doi: 10.3390/v14020295
  5. Lan J., He X., and Ren Y. Structural insights into the SARS-CoV-2 Omicron RBD-ACE2 interaction. Cell Res., 32, 593–595 (2022). doi: 10.1038/s41422-022-00644-8
  6. Chothia C. and Janin J. Principles of protein–protein recognition. Nature, 256, 705–708 (1975). doi: 10.1038/256705a0
  7. Archakov A. I., Govorun V. M., Dubanov A. V., Ivanov Y. D., Veselovsky A. V., Lewi P., and Janssen P. Protein-protein interactions as a target for drugs in proteomics. Proteomics, 3 (4), 380–391 (2003). doi: 10.1002/pmic.200390053
  8. Shi T. L., Li Y. X., Cai Y. D., and Chou K. C. Computational Methods for protein–protein interaction and their application. Curr. Prot. Peptide Sci., 6, 443–449 (2005). doi: 10.2174/138920305774329313
  9. Vangone A. and Bonvin A. M. Contacts-based prediction of binding affinity in protein-protein complexes. eLife, 4, e07454 (2015). doi: 10.7554/eLife.07454
  10. Purisima E. O., Corbeil C. R., Gaudreault F., Wei W., Deprez C., and Sulea T. Solvated interaction energy: from small-molecule to antibody drug design. Front. Mol. Biosci., 7 (10), 1210576 (2023). doi: 10.3389/fmolb.2023.1210576
  11. Jawad B., Adhikari P., Podgornik R., and Ching W. Y. 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 (2021). doi: 10.1021/acs.jcim.1c00560
  12. Salleh M. Z., Derrick J. P., and Deris Z. Z. Structural evaluation of the spike glycoprotein variants on SARSCoV-2 transmission and immune evasion. Int. J. Mol. Sci., 22 (14), 7425 (2021). doi: 10.3390/ijms22147425
  13. Yang Y., Zhang Y., Qu Y., Zhang C., Liu X. W., Zhao M., Mu Y., and Li W. Key residues of the receptor binding domain in the spike protein of SARS-CoV-2 mediating the interactions with ACE2: a molecular dynamics study. Nanoscale, 13 (20), 9364–9370 (2021). doi: 10.1039/d1nr01672e
  14. Zhang Z. B., Xia Y. L., Shen J. X., Du W. W., Fu Y. X., and Liu S. Q. Mechanistic origin of different binding affinities of SARS-CoV and SARS-CoV-2 spike RBDs to human ACE2. Cells, 11 (8), 1274 (2022). doi: 10.3390/cells11081274
  15. Xue L. C., Rodrigues J. P., Kastritis P. L., Bonvin A. M., and Vangone A. PRODIGY: a web server for predicting the binding affinity of protein-protein complexes. Bioinformatics, 32 (23), 3676–3678 (2016). doi: 10.1093/bioinformatics/btw514
  16. Schymkowitz J., Borg J., Stricher F., Nys R., Rousseau F., and Serrano L. The FoldX web server: an online force field. Nucl. Acids Res., 33, W382–W388 (2005). doi: 10.1093/nar/gki387
  17. Zhang C., Liu S., and Zhou Y. Accurate and efficient loop selections by the DFIRE-based all-atom statistical potential. Prot. Sci., 13 (2), 391–399 (2004). doi: 10.1110/ps.03411904
  18. Lyskov S., and Gray J. J. The RosettaDock server for local protein-protein docking. Nucl. Acids Res., 36, W233–W238 (2008). doi: 10.1093/nar/gkn216
  19. Bogdanova E. A., Novoseletsky V. N., and Shaitan K. V. Binding affinity prediction in protein-protein complexes using convolutional neural network. In Advances in Neural Computation, Machine Learning, and Cognitive Research VII (NEUROINFORMATICS 2023). Ed. by B. Kryzhanovsky, V. Dunin-Barkowski, et al. (Springer, Cham., 2023), pp. 389–397. doi: 10.1007/978-3-031-44865-2_42
  20. Bogdanova E. A. and Novoseletsky V.N. ProBAN: neural network algorithm for predicting binding affinity in protein-protein complexes. Proteins, 92 (9), 1127– 1136 (2024). doi: 10.1002/prot.26700
  21. Wang R., Fang X., Lu Y., Yang C. Y., and Wang, S. The PDBbind database: methodologies and updates. J. Med. Chem., 48 (12), 4111–4119 (2005). doi: 10.1021/jm048957q
  22. Moal I. H., Jiménez-García B., and FernándezRecio J. CCharPPI web server: computational characterization of protein-protein interactions from structure. Bioinformatics, 31(1), 123–125 (2015). doi: 10.1093/bioinformatics/btu594
  23. Pyrkov T. V., Chugunov A. O., Krylov N. A., Nolde D. E., and Efremov R. G. PLATINUM: a web tool for analysis of hydrophobic/hydrophilic organization of biomolecular complexes. Bioinformatics, 25 (9), 1201–1202 (2009). doi: 10.1093/bioinformatics/btp111
  24. Humphrey W., Dalke A., and Schulten K. VMD: visual molecular dynamics. J. Mol. Graphics, 14 (1), 33–28 (1996). doi: 10.1016/0263-7855(96)00018-5
  25. Su C., He J., Han P., Bai B., Li D., Cao J., Tian M., Hu Y., Zheng A., Niu S., Chen Q., Rong X., Zhang Y., Li W., Qi J., Zhao X., Yang M., Wang Q., and Gao G. F. Molecular basis of mink ACE2 binding to SARS-CoV-2 and its mink-derived variants. J. Virology, 96 (17), e0081422 (2022). doi: 10.1128/jvi.00814-22
  26. Han P., Su C., Zhang Y., Bai C., Zheng A., Qiao C., Wang Q., Niu S., Chen Q., Zhang Y., Li W., Liao H., Li J., Zhang Z., Cho H., Yang M., Rong X., Hu Y., Huang N., Yan J., Wang Q., Zhao X., Gao G. F., and Qi J. Molecular insights into receptor binding of recent emerging SARS-CoV-2 variants. Nature Commun., 12 (1), 6103 (2021). doi: 10.1038/s41467-021-26401-w
  27. 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, 181 (4), 894–904 (2020). doi: 10.1016/j.cell.2020.03.045
  28. Starr T. N., Greaney A. J., Hannon W. W., Loes A. N., Hauser K., et al. Shifting mutational constraints in the SARS-CoV-2 receptor-binding domain during viral evolution. Science (New York), 377 (6604), 420–424 (2022). doi: 10.1126/science.abo7896
  29. Geng Q., Shi K., Ye G., Zhang W., Aihara H., and Li F. Structural basis for human receptor recognition by SARS-CoV-2 Omicron variant BA.1. J. Virology, 96 (8), e0024922 (2022). doi: 10.1128/jvi.00249-22
  30. Nelson-Sathi S., Umasankar P. K., Sreekumar E., Nair R. R., Joseph I., Nori S. R. C., Philip J. S., Prasad R., Navyasree K. V., Ramesh S., Pillai H., Ghosh S., Santosh Kumar T. R., and Pillai M. R. Mutational landscape and in silico structure models of SARS-CoV-2 spike receptor binding domain reveal key molecular determinants for virus-host interaction. BMC Mol. Cell Biol., 23 (1), 2 (2022). doi: 10.1186/s12860-021-00403-4
  31. Kastritis P. L., Rodrigues J. P., Folkers G. E., Boelens R., and Bonvin, A. M. Proteins feel more than they see: Fine-tuning of binding affinity by properties of the non-interacting surface. J. Mol. Biol., 426 (14), 2632–2652 (2014). doi: 10.1016/j.jmb.2014.04.017
  32. Reichmann D., Phillip Y., Carmi A., and Schreiber G. On the contribution of water-mediated interactions to protein-complex stability. Biochemistry, 47 (3), 1051– 1060 (2008). doi: 10.1021/bi7019639
  33. Schweke H., Mucchielli M. H., Sacquin-Mora S., Bei W., and Lopes A. Protein interaction energy landscapes are shaped by functional and also non-functional partners. J. Mol. Biol., 432 (4), 1183–1198 (2020). doi: 10.1016/j.jmb.2019.12.047
  34. Ozden B., Şamiloğlu E., Özsan A., Erguven M., Yükrük C., Koşaca M., Oktayoğlu M., Menteş M., Arslan N., Karakülah G., Barlas A. B., Savaş B., and Karaca E. Benchmarking the accuracy of structure-based binding affinity predictors on Spike-ACE2 deep mutational interaction set. Proteins, 92 (4), 529–539 (2024). doi: 10.1002/prot.26645

Supplementary files

Supplementary Files
Action
1. JATS XML
Download

Copyright (c) 2024 Russian Academy of Sciences