en عمومى General پژوهشي orginal article <span style="font-size:12pt"><span style="line-height:16.0pt"><span new="" roman="" style="font-family:" times=""><b><i><span style="font-size:9.0pt">Background & Aims</span></i></b><b><span style="font-size:9.0pt">: </span></b><span lang="EN-IN" style="font-size:9.0pt">There is inadequate information available about the genomics and proteomics characterization of SARS-CoV-2 isolates reported from India and other part of the globe. This characterization is important for the <i>in-silico</i> drug designing, as there are no approved medications available to treat SARS-CoV-2 infection. The present study based on the characterization of SARS-CoV-2 (MZ558159) isolate reported from India using homology modeling, validation, and in silico drug designing methods.</span></span></span></span><br> <span style="font-size:12pt"><span style="line-height:16.0pt"><span new="" roman="" style="font-family:" times=""><b><i><span style="font-size:9.0pt">Materials & Methods</span></i></b><b><span style="font-size:9.0pt">:</span></b> <span lang="EN-IN" style="font-size:9.0pt">Genome sequence of SARS-CoV-2 (MZ558159) was retrieved from NCBI, and four protein sequences e.g., QXN18496, QXN18498, QXN18504, and QXN18497 were selected for the homology modeling, validation, and in silico drug designing. SWISS-MODEL and UCLA-DOE server were used for homology modeling. Validation for structure model performed using PROCHECK and molecular docking using MCULE-1-Click server.</span></span></span></span><br> <span style="font-size:12pt"><span style="line-height:16.0pt"><span new="" roman="" style="font-family:" times=""><b><i><span style="font-size:9.0pt">Results</span></i></b><b><span style="font-size:9.0pt">:</span></b> <span lang="EN-IN" style="font-size:9.0pt">The surface glycoprotein (QXN18496) model corresponding to probability conformation with 93.6%, envelope protein (QXN18498) with 88.9%, nucleocapsid phosphoprotein (QXN18504) with 93.6%, and ORF3a protein (QXN18497) with 91.8% residues in core section of &phi;-&psi; plot that specifies accuracy of prediction models. The corresponding ProSA Z-score score -12.67, -0.01, -4.4, and -2.87 indicates the good quality of the models. Molecular dynamic simulation and docking studies revealed that inhibitor binds effectively at the SARS-CoV-2 (MZ558159) proteins. Predicted inhibitor 2-acetamido-2-deoxy-beta-D-glucopyranose exhibited effective binding affinity against surface glycoprotein (QXN18496).</span></span></span></span><br> <span style="font-size:12pt"><span style="line-height:16.0pt"><span new="" roman="" style="font-family:" times=""><b><i><span style="font-size:9.0pt">Conclusion</span></i></b><b><span style="font-size:9.0pt">: </span></b><span lang="EN-IN" style="font-size:9.0pt">The results of this study established inhibitor 2-Acetamido-2-deoxy-beta-D-glucopyranose as valuable lead molecule with great potential for surface glycoprotein (QXN18496).</span><b><span style="font-size:9.0pt"></span></b></span></span></span><br> &nbsp; SARS-CoV-2, COVID-19, φ-ψ plot, ProSA 175 188 http://ijrabms.umsu.ac.ir/browse.php?a_code=A-10-246-1&slc_lang=en&sid=1 Rajneesh Prajapat rajneesh030041@gmail.com 10031947532846002609 10031947532846002609 Yes Ph.D Pacific Institute of Medical Sciences, Sai Tirupati University, Udaipur, Rajasthan, India Suman Jain sumannemijain@gmail.com 10031947532846002610 10031947532846002610 No Ph.D Pacific Institute of Medical Sciences, Sai Tirupati University, Udaipur, Rajasthan, India