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Insights on 3D Structures of Potential Drug-targeting Proteins of SARS-CoV-2: Application of Cavity Search and Molecular Docking.

ABSTRACT: The emergence of the COVID-19 has caused public health problems worldwide and there is no effective pharmacological treatment for this disease. Research on 3D models of proteins and the search for active molecular sites are important tools to assist in the discovery of effective antiviral drugs to combat COVID-19. To address this problem, the 3D protein structures of SARS-CoV 2 were analyzed and submitted to cavities research, evaluation of their druggabillity and liganbility, and applied to molecular docking studies with potential ligand candidates actually assayed against COVID-19. Eight druggable potential cavity sites were determined in model structures' PDB code, 6W4B, 6VWW, 6W01, 6M3M, and 6VYO, and these are the good alternatives to be characterized as targets for antiviral compounds. The good cavity model of the protease 3D structure was used in molecular docking, and this allowed verifying the theoric interactions of this protein and lopinavir and ritonavir antiviral drugs. These results may assist in the use of 3D protein models in drug design studies aiming to develop drugs against the COVID-19 pandemic.

SUBMITTER: Fernandes MS

PROVIDER: S-EPMC7436870 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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Insights on 3D Structures of Potential Drug-targeting Proteins of SARS-CoV-2: Application of Cavity Search and Molecular Docking.

Fernandes Mariana S MS da Silva Francielly S FS Freitas Ana Carolina S G ACSG de Melo Eduardo B EB Trossini Gustavo H G GHG Paula Fávero R FR

Molecular informatics 20201020 2

The emergence of the COVID-19 has caused public health problems worldwide and there is no effective pharmacological treatment for this disease. Research on 3D models of proteins and the search for active molecular sites are important tools to assist in the discovery of effective antiviral drugs to combat COVID-19. To address this problem, the 3D protein structures of SARS-CoV 2 were analyzed and submitted to cavities research, evaluation of their druggabillity and liganbility, and applied to mol ...[more]

PMID: 32750187

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Project description:Comparing and classifying the three-dimensional (3D) structures of proteins is of crucial importance to molecular biology, from helping to determine the function of a protein to determining its evolutionary relationships. Traditionally, 3D structures are classified into groups of families that closely resemble the grouping according to their primary sequence. However, significant structural similarities exist at multiple levels between proteins that belong to these different structural families. In this study, we propose a new algorithm, CLICK, to capture such similarities. The method optimally superimposes a pair of protein structures independent of topology. Amino acid residues are represented by the Cartesian coordinates of a representative point (usually the C(α) atom), side chain solvent accessibility, and secondary structure. Structural comparison is effected by matching cliques of points. CLICK was extensively benchmarked for alignment accuracy on four different sets: (i) 9537 pair-wise alignments between two structures with the same topology; (ii) 64 alignments from set (i) that were considered to constitute difficult alignment cases; (iii) 199 pair-wise alignments between proteins with similar structure but different topology; and (iv) 1275 pair-wise alignments of RNA structures. The accuracy of CLICK alignments was measured by the average structure overlap score and compared with other alignment methods, including HOMSTRAD, MUSTANG, Geometric Hashing, SALIGN, DALI, GANGSTA(+), FATCAT, ARTS and SARA. On average, CLICK produces pair-wise alignments that are either comparable or statistically significantly more accurate than all of these other methods. We have used CLICK to uncover relationships between (previously) unrelated proteins. These new biological insights include: (i) detecting hinge regions in proteins where domain or sub-domains show flexibility; (ii) discovering similar small molecule binding sites from proteins of different folds and (iii) discovering topological variants of known structural/sequence motifs. Our method can generally be applied to compare any pair of molecular structures represented in Cartesian coordinates as exemplified by the RNA structure superimposition benchmark.

Dataset Information

Insights on 3D Structures of Potential Drug-targeting Proteins of SARS-CoV-2: Application of Cavity Search and Molecular Docking.

Publications

Insights on 3D Structures of Potential Drug-targeting Proteins of SARS-CoV-2: Application of Cavity Search and Molecular Docking.

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