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Revista Politécnica

versão On-line ISSN 2477-8990versão impressa ISSN 1390-0129


GONZALEZ-PAZ, Lenin; PAZ, José Luis; VERA-VILLALOBOS, Joan  e  ALVARADO, Ysaias J.. Phytochemical Compounds Targeted for Viral Polymerase Blocking of SARS-CoV-2 Causing COVID-19: A Comparative Analysis of Scoring Functions for Docking with Biomedical Interest. Rev Politéc. (Quito) [online]. 2020, vol.46, n.1, pp.7-20. ISSN 2477-8990.

The worldwide pandemic of COVID-19 caused by SARS-CoV-2 has made it necessary to search for treatment alternatives. The WHO has recommended the FDA-approved drug Remdesivir targeting viral RNA polymerase. Additionally, natural compounds with antiviral properties have been computationally evaluated. However, these studies focus on using the AutoDock Vina (ADV) algorithm scoring function to predict candidates. We propose to evaluate the phytochemicals Piperina_ID_638024, EPGG_ID_65064, Curcumina_ID_969516, and Capsaicina_ID_1548943 against the RNA polymerase of SARS-CoV-2 (PDB_ID_6NUR), using Remdesivir_ID_121304016 as control, through computational, comparative and multivariate analysis of the scoring functions ADV, PLANTS, MolDock, Rerank and DockT considering the solubility of ligands and hydrophobicity of the cavities involved in the interactions, to increase the precision in predicting the best docking of natural compounds in front of COVID-19. We found that 4/5 of the scoring functions except ADV predicted that the most thermodynamically favorable docking occurs with Piperine, outperforming Remdesivir. We also observe that the scores of the PLANTS, ADV and DockT functions are affected by the solubility of the ligand and the hydrophobicity of cavities. Therefore, under the conditions of this study, we conclude by proposing the MolDock and Rerank algorithms for rapid screening and reorganization of couplings, respectively, when working with soluble ligands (Rp=0.70), regardless of their polarity, and targeting hydrophobic cavities (Rp=0.95 and Rp=0.90, respectively) of the SARS-CoV-2 RNA polymerase, especially for computational approaches in the context of drug research versus COVID-19.

Palavras-chave : Remdesivir; natural compounds; bioinformatics; RNA polymerase; molecular docking.

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