Predictions in Clinical Efficiency of SARS-CoV-2 RNA-Dependent RNA Polymerase (RdRp) Inhibitors by Molecular Docking  

作　　者：Pui-Jen Tsai Pui-Jen Tsai(Department of Statistical Information and Actuarial Science, Aletheia University, New Taipei)

机构地区：[1]Department of Statistical Information and Actuarial Science, Aletheia University, New Taipei

出　　处：《Journal of Biosciences and Medicines》2024年第10期178-196,共19页生物科学与医学（英文）

摘　　要：This study utilizes the enzyme-substrate complex theory to predict the clinical efficacy of COVID-19 treatments at the biological systems level, using molecular docking stability indicators. Experimental data from the Protein Data Bank and molecular structures generated by AlphaFold 3 were used to create macromolecular complex templates. Six templates were developed, including the holo nsp7-nsp8-nsp12 (RNA-dependent RNA polymerase) complex with dsRNA primers (holo-RdRp-RNA). The study evaluated several ligands—Favipiravir-RTP, Remdesivir, Abacavir, Ribavirin, and Oseltamivir—as potential viral RNA polymerase inhibitors. Notably, the first four of these ligands have been clinically employed in the treatment of COVID-19, allowing for comparative analysis. Molecular docking simulations were performed using AutoDock 4, and statistical differences were assessed through t-tests and Mann-Whitney U tests. A review of the literature on COVID-19 treatment outcomes and inhibitors targeting RNA polymerase enzymes was conducted, and the inhibitors were ranked according to their clinical efficacy: Remdesivir > Favipiravir-RTP > Oseltamivir. Docking results obtained from the second and third templates aligned with clinical observations. Furthermore, Abacavir demonstrated a predicted efficacy comparable to Favipiravir-RTP, while Ribavirin exhibited a predicted efficacy similar to that of Remdesivir. This research, focused on inhibitors of SARS-CoV-2 RNA-dependent RNA polymerase, establishes a framework for screening AI-generated drug templates based on clinical outcomes. Additionally, it develops a drug screening platform based on molecular docking binding energy, enabling the evaluation of novel or repurposed drugs and potentially accelerating the drug development process.This study utilizes the enzyme-substrate complex theory to predict the clinical efficacy of COVID-19 treatments at the biological systems level, using molecular docking stability indicators. Experimental data from the Protein Data Bank and molecular structures generated by AlphaFold 3 were used to create macromolecular complex templates. Six templates were developed, including the holo nsp7-nsp8-nsp12 (RNA-dependent RNA polymerase) complex with dsRNA primers (holo-RdRp-RNA). The study evaluated several ligands—Favipiravir-RTP, Remdesivir, Abacavir, Ribavirin, and Oseltamivir—as potential viral RNA polymerase inhibitors. Notably, the first four of these ligands have been clinically employed in the treatment of COVID-19, allowing for comparative analysis. Molecular docking simulations were performed using AutoDock 4, and statistical differences were assessed through t-tests and Mann-Whitney U tests. A review of the literature on COVID-19 treatment outcomes and inhibitors targeting RNA polymerase enzymes was conducted, and the inhibitors were ranked according to their clinical efficacy: Remdesivir > Favipiravir-RTP > Oseltamivir. Docking results obtained from the second and third templates aligned with clinical observations. Furthermore, Abacavir demonstrated a predicted efficacy comparable to Favipiravir-RTP, while Ribavirin exhibited a predicted efficacy similar to that of Remdesivir. This research, focused on inhibitors of SARS-CoV-2 RNA-dependent RNA polymerase, establishes a framework for screening AI-generated drug templates based on clinical outcomes. Additionally, it develops a drug screening platform based on molecular docking binding energy, enabling the evaluation of novel or repurposed drugs and potentially accelerating the drug development process.

关 键 词：AlphaFold 3 RNA-Dependent RNA Polymerase Anti-Viral Drugs Molecular Docking 

分 类 号：R73[医药卫生—肿瘤]