Homology modeling and mutation prediction of ACE2 from COVID-19
DOI:
https://doi.org/10.12928/pharmaciana.v11i2.19089Keywords:
ACE2, SARS-CoV-2, homology modeling, mutation predictionAbstract
SARS-CoV-2 has become a pandemic in the world. The virus binds to the Angiotensin-Converting Enzyme 2 (ACE2) receptor, which is found in epithelial cells such as in the lungs, to generate the pathology of COVID-19. It is essential to analyze the characteristics of ACE2 in understanding the development of the disease and study potential new drugs. The analysis was carried out using computer simulations to speed up protein analysis that utilized Artificial Intelligence technology, databases, and big data. Homology modeling is a method to exhibit homologous of protein families, hence the model and arrangement of protein sequences modeled are established. This research aims to determine the possibility of mutations in ACE2 by performing the mutation prediction. The result shows reliable homologous modeling with the score of GA341, MPQS, Z-DOPE, and TSVMod NO35 were 1; 1.28252; -0.47; and 0.793, respectively. Moreover, Gene Ontology (GO) analysis describes that ACE2 has a molecular transport function in cells while there are no mutations found occurred in ACE2 analyzed using SIFT and PROVEAN.
References
Ali, A., & Vijayan, R. (2020). Dynamics of the ACE2–SARS-CoV-2/SARS-CoV spike protein interface reveal unique mechanisms. Scientific Reports, 10(1). https://doi.org/10.1038/s41598-020-71188-3
Barros, E. P., Casalino, L., Gaieb, Z., Dommer, A. C., Wang, Y., Fallon, L., Raguette, L., Belfon, K., Simmerling, C., & Amaro, R. E. (2021). The flexibility of ACE2 in the context of SARS-CoV-2 infection. Biophysical Journal, 120(6), 1072–1084. https://doi.org/10.1016/j.bpj.2020.10.036
Benkert, P., Tosatto, S. C. E., & Schomburg, D. (2008). QMEAN: A comprehensive scoring function for model quality assessment. Proteins: Structure, Function and Genetics, 71(1), 261–277. https://doi.org/10.1002/prot.21715
Biasini, M., Schmidt, T., Bienert, S., Mariani, V., Studer, G., Haas, J., Johner, N., Schenk, A. D., Philippsen, A., & Schwede, T. (2013). OpenStructure: An integrated software framework for computational structural biology. Acta Crystallographica Section D: Biological Crystallography, 69(5), 701–709. https://doi.org/10.1107/S0907444913007051
Choi, Y., & Chan, A. P. (2015). Provean web server: A tool to predict the functional effect of amino acid substitutions and indels. Bioinformatics, 31(16), 2745–2747. https://doi.org/10.1093/bioinformatics/btv195
Choudhuri, S. (2014). Additional bioinformatic analyses involving protein sequences. Bioinformatics for Beginners, 183–207. https://doi.org/10.1016/b978-0-12-410471-6.00008-6
Dessimoz, C., & Å kunca, N. (2017). The Gene Ontology Handbook (Vol. 1446). Humana Press. https://doi.org/10.1007/978-1-4939-3743-1
Gromiha, M. M., Nagarajan, R., & Selvaraj, S. (2018). Protein structural bioinformatics: An overview. Encyclopedia of Bioinformatics and Computational Biology: ABC of Bioinformatics, 1–3, 445–459. https://doi.org/10.1016/B978-0-12-809633-8.20278-1
Hussain, M., Jabeen, N., Raza, F., Shabbir, S., Baig, A. A., Amanullah, A., & Aziz, B. (2020). Structural variations in human ACE2 may influence its binding with SARS-CoV-2 spike protein. Journal of Medical Virology, 92(9), 1580–1586. https://doi.org/10.1002/jmv.25832
Idakwo, G., Luttrell, J., Chen, M., Hong, H., Zhou, Z., Gong, P., & Zhang, C. (2018). A review on machine learning methods for in silico toxicity prediction. Journal of Environmental Science and Health - Part C Environmental Carcinogenesis and Ecotoxicology Reviews, 36(4), 169–191. https://doi.org/10.1080/10590501.2018.1537118
Li, Y. Y. (2012). Lack of association of ACE2 G8790A gene mutation with essential hypertension in the Chinese Population: A meta-analysis involving 5260 subjects. Frontiers in Physiology, 3 SEP. https://doi.org/10.3389/fphys.2012.00364
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. The Lancet, 395(10224), 565–574. https://doi.org/10.1016/S0140-6736(20)30251-8
Luan, J., Lu, Y., Jin, X., & Zhang, L. (2020). Spike protein recognition of mammalian ACE2 predicts the host range and an optimized ACE2 for SARS-CoV-2 infection. Biochemical and Biophysical Research Communications, 526(1), 165–169. https://doi.org/10.1016/j.bbrc.2020.03.047
Ozono, S., Zhang, Y., Ode, H., Sano, K., Tan, T. S., Imai, K., Miyoshi, K., Kishigami, S., Ueno, T., Iwatani, Y., Suzuki, T., & Tokunaga, K. (2021). SARS-CoV-2 D614G spike mutation increases entry efficiency with enhanced ACE2-binding affinity. Nature Communications, 12(1). https://doi.org/10.1038/s41467-021-21118-2
Piplani, S., Singh, P. K., Winkler, D. A., & Petrovsky, N. (2020). In silico comparison of spike protein-ACE2 binding affinities across species; significance for the possible origin of the SARS-CoV-2 virus. 2.
Sakkiah, S., Guo, W., Pan, B., Ji, Z., Yavas, G., Azevedo, M., Hawes, J., Patterson, T. A., & Hong, H. (2021). Elucidating interactions between SARS-CoV-2 trimeric spike protein and ACE2 using homology modeling and molecular dynamics simulations. Frontiers in Chemistry, 8. https://doi.org/10.3389/fchem.2020.622632
Sensoy, O., Almeida, J. G., Shabbir, J., Moreira, I. S., & Morra, G. (2017). Computational studies of G protein-coupled receptor complexes: Structure and dynamics. Methods in Cell Biology, 142, 205–245. https://doi.org/10.1016/bs.mcb.2017.07.011
Sim, N. L., Kumar, P., Hu, J., Henikoff, S., Schneider, G., & Ng, P. C. (2012). SIFT web server: Predicting effects of amino acid substitutions on proteins. Nucleic Acids Research, 40(W1). https://doi.org/10.1093/nar/gks539
Skariyachan, S., & Garka, S. (2018). Exploring the binding potential of carbon nanotubes and fullerene towards major drug targets of multidrug resistant bacterial pathogens and their utility as novel therapeutic agents. Fullerenes, Graphenes and Nanotubes: A Pharmaceutical Approach, 1–29. https://doi.org/10.1016/B978-0-12-813691-1.00001-4
Wan, Y., Shang, J., Graham, R., Baric, R. S., & Li, F. (2020). Receptor recognition by the novel Coronavirus from Wuhan: an analysis based on decade-long structural studies of SARS Coronavirus. Journal of Virology, 94(7). https://doi.org/10.1128/jvi.00127-20
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 Research, 46(W1), W296–W303. https://doi.org/10.1093/nar/gky427
Webb, B, Eswar, N., Fan, H., Khuri, N., Pieper, U., Dong, G. Q., Sali, A., Francisco, S., & Francisco, S. (2014). Author ’ s personal copy comparative modeling of drug target proteins ☆. In Chemistry, Molecular Sciences and Chemical Engineering. Elsevier Inc. https://doi.org/10.1016/B978-0-12-409547-2.11133-3
Webb, Benjamin, & Sali, A. (2016). Comparative protein structure modeling using modeller. Current Protocols in Bioinformatics, 2016, 5.6.1-5.6.37. https://doi.org/10.1002/cpbi.3
Wiltgen, M. (2018). Algorithms for structure comparison and analysis: Homology modelling of proteins. Encyclopedia of Bioinformatics and Computational Biology: ABC of Bioinformatics, 1–3, 38–61. https://doi.org/10.1016/B978-0-12-809633-8.20484-6
Yan, R., Zhang, Y., Li, Y., Xia, L., Guo, Y., & Zhou, Q. (2020). Structural basis for the recognition of SARS-CoV-2 by full-length human ACE2. Science, 367(6485), 1444–1448. https://doi.org/10.1126/science.abb2762
Ali, A., & Vijayan, R. (2020). Dynamics of the ACE2–SARS-CoV-2/SARS-CoV spike protein interface reveal unique mechanisms. Scientific Reports, 10(1). https://doi.org/10.1038/s41598-020-71188-3
Barros, E. P., Casalino, L., Gaieb, Z., Dommer, A. C., Wang, Y., Fallon, L., Raguette, L., Belfon, K., Simmerling, C., & Amaro, R. E. (2021). The flexibility of ACE2 in the context of SARS-CoV-2 infection. Biophysical Journal, 120(6), 1072–1084. https://doi.org/10.1016/j.bpj.2020.10.036
Benkert, P., Tosatto, S. C. E., & Schomburg, D. (2008). QMEAN: A comprehensive scoring function for model quality assessment. Proteins: Structure, Function and Genetics, 71(1), 261–277. https://doi.org/10.1002/prot.21715
Biasini, M., Schmidt, T., Bienert, S., Mariani, V., Studer, G., Haas, J., Johner, N., Schenk, A. D., Philippsen, A., & Schwede, T. (2013). OpenStructure: An integrated software framework for computational structural biology. Acta Crystallographica Section D: Biological Crystallography, 69(5), 701–709. https://doi.org/10.1107/S0907444913007051
Choi, Y., & Chan, A. P. (2015). Provean web server: A tool to predict the functional effect of amino acid substitutions and indels. Bioinformatics, 31(16), 2745–2747. https://doi.org/10.1093/bioinformatics/btv195
Choudhuri, S. (2014). Additional bioinformatic analyses involving protein sequences. Bioinformatics for Beginners, 183–207. https://doi.org/10.1016/b978-0-12-410471-6.00008-6
Dessimoz, C., & Å kunca, N. (2017). The Gene Ontology Handbook (Vol. 1446). Humana Press. https://doi.org/10.1007/978-1-4939-3743-1
Gromiha, M. M., Nagarajan, R., & Selvaraj, S. (2018). Protein structural bioinformatics: An overview. Encyclopedia of Bioinformatics and Computational Biology: ABC of Bioinformatics, 1–3, 445–459. https://doi.org/10.1016/B978-0-12-809633-8.20278-1
Hussain, M., Jabeen, N., Raza, F., Shabbir, S., Baig, A. A., Amanullah, A., & Aziz, B. (2020). Structural variations in human ACE2 may influence its binding with SARS-CoV-2 spike protein. Journal of Medical Virology, 92(9), 1580–1586. https://doi.org/10.1002/jmv.25832
Idakwo, G., Luttrell, J., Chen, M., Hong, H., Zhou, Z., Gong, P., & Zhang, C. (2018). A review on machine learning methods for in silico toxicity prediction. Journal of Environmental Science and Health - Part C Environmental Carcinogenesis and Ecotoxicology Reviews, 36(4), 169–191. https://doi.org/10.1080/10590501.2018.1537118
Li, Y. Y. (2012). Lack of association of ACE2 G8790A gene mutation with essential hypertension in the Chinese Population: A meta-analysis involving 5260 subjects. Frontiers in Physiology, 3 SEP. https://doi.org/10.3389/fphys.2012.00364
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. The Lancet, 395(10224), 565–574. https://doi.org/10.1016/S0140-6736(20)30251-8
Luan, J., Lu, Y., Jin, X., & Zhang, L. (2020). Spike protein recognition of mammalian ACE2 predicts the host range and an optimized ACE2 for SARS-CoV-2 infection. Biochemical and Biophysical Research Communications, 526(1), 165–169. https://doi.org/10.1016/j.bbrc.2020.03.047
Ozono, S., Zhang, Y., Ode, H., Sano, K., Tan, T. S., Imai, K., Miyoshi, K., Kishigami, S., Ueno, T., Iwatani, Y., Suzuki, T., & Tokunaga, K. (2021). SARS-CoV-2 D614G spike mutation increases entry efficiency with enhanced ACE2-binding affinity. Nature Communications, 12(1). https://doi.org/10.1038/s41467-021-21118-2
Piplani, S., Singh, P. K., Winkler, D. A., & Petrovsky, N. (2020). In silico comparison of spike protein-ACE2 binding affinities across species; significance for the possible origin of the SARS-CoV-2 virus. 2.
Sakkiah, S., Guo, W., Pan, B., Ji, Z., Yavas, G., Azevedo, M., Hawes, J., Patterson, T. A., & Hong, H. (2021). Elucidating interactions between SARS-CoV-2 trimeric spike protein and ACE2 using homology modeling and molecular dynamics simulations. Frontiers in Chemistry, 8. https://doi.org/10.3389/fchem.2020.622632
Sensoy, O., Almeida, J. G., Shabbir, J., Moreira, I. S., & Morra, G. (2017). Computational studies of G protein-coupled receptor complexes: Structure and dynamics. Methods in Cell Biology, 142, 205–245. https://doi.org/10.1016/bs.mcb.2017.07.011
Sim, N. L., Kumar, P., Hu, J., Henikoff, S., Schneider, G., & Ng, P. C. (2012). SIFT web server: Predicting effects of amino acid substitutions on proteins. Nucleic Acids Research, 40(W1). https://doi.org/10.1093/nar/gks539
Skariyachan, S., & Garka, S. (2018). Exploring the binding potential of carbon nanotubes and fullerene towards major drug targets of multidrug resistant bacterial pathogens and their utility as novel therapeutic agents. Fullerenes, Graphenes and Nanotubes: A Pharmaceutical Approach, 1–29. https://doi.org/10.1016/B978-0-12-813691-1.00001-4
Wan, Y., Shang, J., Graham, R., Baric, R. S., & Li, F. (2020). Receptor recognition by the novel Coronavirus from Wuhan: an analysis based on decade-long structural studies of SARS Coronavirus. Journal of Virology, 94(7). https://doi.org/10.1128/jvi.00127-20
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 Research, 46(W1), W296–W303. https://doi.org/10.1093/nar/gky427
Webb, B, Eswar, N., Fan, H., Khuri, N., Pieper, U., Dong, G. Q., Sali, A., Francisco, S., & Francisco, S. (2014). Author ’ s personal copy comparative modeling of drug target proteins ☆. In Chemistry, Molecular Sciences and Chemical Engineering. Elsevier Inc. https://doi.org/10.1016/B978-0-12-409547-2.11133-3
Webb, Benjamin, & Sali, A. (2016). Comparative protein structure modeling using modeller. Current Protocols in Bioinformatics, 2016, 5.6.1-5.6.37. https://doi.org/10.1002/cpbi.3
Wiltgen, M. (2018). Algorithms for structure comparison and analysis: Homology modelling of proteins. Encyclopedia of Bioinformatics and Computational Biology: ABC of Bioinformatics, 1–3, 38–61. https://doi.org/10.1016/B978-0-12-809633-8.20484-6
Yan, R., Zhang, Y., Li, Y., Xia, L., Guo, Y., & Zhou, Q. (2020). Structural basis for the recognition of SARS-CoV-2 by full-length human ACE2. Science, 367(6485), 1444–1448. https://doi.org/10.1126/science.abb2762
Zhang, C., Freddolino, P. L., & Zhang, Y. (2017). Cofactor: Improved protein function prediction by combining structure, sequence and protein-protein interaction information. Nucleic Acids Research, 45(W1), W291–W299. https://doi.org/10.1093/nar/gkx366
Zhao, Y., Zhao, Z., Wang, Y., Zhou, Y., Ma, Y., & Zuo, W. (2020). Single-cell RNA expression profiling of ACE2, the putative receptor of Wuhan 2019-nCov. BioRxiv, 2020.01.26.919985. https://doi.org/10.1101/2020.01.26.919985
Zhou, P., Yang, X. Lou, Wang, X. G., Hu, B., Zhang, L., Zhang, W., Si, H. R., Zhu, Y., Li, B., Huang, C. L., Chen, H. D., Chen, J., Luo, Y., Guo, H., Jiang, R. Di, Liu, M. Q., Chen, Y., Shen, X. R., Wang, X., … Shi, Z. L. (2020). A pneumonia outbreak associated with a new coronavirus of probable bat origin. Nature, 579(7798), 270–273. https://doi.org/10.1038/s41586-020-2012-7
Downloads
Published
Issue
Section
License
Authors who publish with Pharmaciana agree to the following terms:
- Authors retain copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC BY-SA 4.0) that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.