In Silico Approach to Construct the 3D Structures of Spike Glycoproteins of Novel Variants of Severe Acute Respiratory Syndrome Coronavirus 2

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Published: Apr 19, 2023
DOI: https://doi.org/10.47191/ijpbms/v3-i4-04
Keywords:
SARS CoV-2, SWISS homology modeling, PMDB, Ramachandran Plot, Variants of concern, Spike glycoproteins, 3D structure.

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Shalini Swami
Department of Microbiology, Ram Lal Anand College, University of Delhi, Benito Jaurez Marg, South Campus, New Delhi

Abstract

Objective: This study aims in constructing a three-dimensional modeled Spike glycoprotein  structure of novel variants of SARS CoV-2.


Methods: The protein models were constructed using SWISS-Model online tool. The constructed protein models were submitted in online database called Protein Model Database (PMDB) for public access to the structures.


Results: A total of 70 protein sequences of Spike glycoprotein of novel variants of SARS CoV-2 were retrieved from NCBI virus database and were subjected for sequence similarity search and homology model construction. The constructed models were subjected for Ramachandran plot analysis to validate the quality of the structures. A total of 40 structures were considered to be of significant quality and were submitted to the online database PMDB.


Conclusion: These predicted structures would help greatly in identification and drug design. This would greatly help in drug development and personalized drug treatment against different variants of the pathogen. This database would significantly support the structure-based computational drug design applications toward personalized medicine against the variants of concern of SARS CoV-2.

Article Details

How to Cite
Swami, S. . (2023). In Silico Approach to Construct the 3D Structures of Spike Glycoproteins of Novel Variants of Severe Acute Respiratory Syndrome Coronavirus 2. International Journal of Pharmaceutical and Bio Medical Science, 3(04), 173–177. https://doi.org/10.47191/ijpbms/v3-i4-04
Issue
Vol. 3 No. 04 (2023): Volume 03 Issue 04 April 2023
Section
Articles
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This work is licensed under a Creative Commons Attribution 4.0 International License.

References

I. Anderson AC. The process of structure-based drug design. Chem. Biol. 2003; 10:787–797

II. Sreenivas KA, Imran S, Ravi L. Elucidation of computational 3D Models of protein drug targets for Colletotrichum falcatum a fungal plant pathogen causing red rod of sugarcane. Biomed Pharmacol J 2020; 13:627-33.

III. Jindam D, Ravi L, Krishnan K. Construction of computational protein structure data base by Homology Modeling for the aquatic pathogen Perkinsus marinus for targeted drug design and development. Research J. Pharm. and Tech 2018; 11: 2203-2208.

IV. Feolo M, Helmberg W, Sherry ST, Maglott DR. NCBI genetic resources supporting immunogenetic research. Rev Immunogenet 2000; 2: 461-7.

V. Patel N, Prajapati N, Patel K, Patel R, Kalasariya H. Sequence Homology, Primer Designing and Homology Modeling Prediction by In Silico Pursuit. New Delhi: Microbiology in Services of Mankind; 2019.

VI. Kopp J. The SWISS-MODEL repository of annotated three-dimensional protein structure homology models. Nucleic Acids Res 2004; 32:230-4.

VII. Callaway E. Delta coronavirus variant: scientists brace for impact. Nature 2021; 595:17-

VIII. Salvatore M, Bhattacharyya R, Soumik Purkayastha S, et al. Resurgence of SARS-CoV-2 in India: Potential role of the B.1.617.2 (Delta) variant and delayed interventions. medRxiv 2021;25: 9405.

IX. Shu Y and McCauley J. Gisaid: global initiative on sharing all influenza data—from vision to reality. Euro Surveill. 2017; 22:3.

X. Khateeb J, Li Y and Zhang H. Emerging SARS-CoV-2 variants of concern and potential intervention approaches. Crit. Care. 2021; 25: 244.

XI. Koyama T, Platt D, and Parida L. (2020). Variant Analysis of SARS-CoV-2 Genomes. Bull. World Health Organ. 2020; 98: 495–504.

XII. Teruel N, Mailhot O, Najmanovich RJ. Modelling conformational state dynamics and its role on infection for SARS-CoV-2 Spike protein variants. PLoS Comput. Biol. 2021; 17:8.

XIII. Noh JY, Jeong HW, Shin EC. SARS-CoV-2 mutations, vaccines, and immunity: implication of variants of concern. Sig Transduct Target Ther. 2021; 6: 203.

XIV. Chen SC and Bahar I. Mining Frequent Patterns in Protein Structures: a Study of Protease Families. Bioinformatics. 2004; 20: 77–85.

XV. Gurung AB. In silico structure modelling of SARS-CoV-2 Nsp13 helicase and Nsp14 and repurposing of FDA approved antiviral drugs as dual inhibitors. Gene Rep. 2020; 21:100860.

XVI. Kiefer F, Arnold K, Künzli M, Bordoli L, Schwede T. The SWISSMODEL repository and associated resources. Nucleic Acids Res 2009;37: 387-92

XVII. Wu F, Zhao S, Yu B, et al. A New Coronavirus Associated with Human Respiratory Disease in China. Nature 2020; 579: 265-269.

XVIII. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. J Mol Biol 1990; 215:403-10.

XIX. Ye J, McGinnis S, Madden TL. BLAST: improvements for better sequence analysis. Nucleic Acids Res. 2006; 34: W6‐W9.

XX. Johnson M, Zaretskaya I, Raytselis Y, Merezhuk Y, McGinnis S, Madden TL. NCBI BLAST: a better web interface. Nucleic Acids Res. 2008; 36: W5‐W9

XXI. Guex N, Peitsch MC, Schwede T. Automated comparative protein structure modeling with SWISS‐MODEL and Swiss‐PdbViewer: a historical perspective. Electrophoresis. 2009; 30: 162‐173.

XXII. Waterhouse A, Bertoni M, Bienert S, et al. SWISS‐MODEL: homology modelling of protein structures and complexes. Nucleic Acids Res. 2018; 46: W296‐W303

XXIII. Gasteiger, E., et al., Protein Identification and Analysis Tools on the ExPASy Server, J.M. Walker, Editor. 2005, Humana Press. U.S.: U.S. p.571-607

XXIV. Arg E. MolProbity Ramachandran analysis. Proteins 2003; 437:180.

XXV. Hooft RW, Sander C, Vriend G. Objectively judging the quality of a protein structure from a Ramachandran plot. Bioinformatics 1997; 13:425-30.

XXVI. Hollingsworth SA, Karplus PA. A fresh look at the Ramachandran plot and the occurrence of standard structures in proteins. Biomol Concepts 2010; 1:271-83.

XXVII. Studer G, Rempfer C, Waterhouse AM, Gumienny R, Haas J, Schwede T. QMEANDisCo - distance constraints applied on model quality estimation. Bioinformatics 2020; 36: 1765-1771.

XXVIII. Bertoni M, Kiefer F, Biasini M, Bordoli L, Schwede T. Modeling protein quaternary structure of homo- and hetero-oligomers beyond binary interactions by homology. Scientific Reports 7 2017.

XXIX. Benkert P, Biasini M, Schwede T. Toward the estimation of the absolute quality of individual protein structure models. Bioinformatics 2011; 27: 343-350.

XXX. Laskowski R A, MacArthur MW, Moss DS. PROCHECK - a Program to Check the Stereochemical Quality of Protein Structures. J. App. Cryst 1993; 26:283-291.

XXXI. Wiederstein M & Sippl MJ. ProSA-web: Interactive Web Service for the Recognition of Errors in Three-dimensional Structures of Proteins. Nucleic Acids Research 2007; 35: 407-410.

XXXII. Sippl MJ. Recognition of errors in three-dimensional structures of proteins. Proteins 1993; 4:355-62.

XXXIII. Mirza MU, Froeyen M. Structural elucidation of SARS-CoV-2 vital proteins: Computational methods reveal potential drug candidates against main protease, Nsp12 polymerase and Nsp13 helicase. J Pharm Anal. 2020; 4:320-328.