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Molecular docking and machine learning affinity prediction of compounds identified upon softwood bark extraction to the main protease of the SARS-CoV-2 virus

ABSTRACT: Molecular docking of 234 unique compounds identified in the softwood bark (W set) is presented with a focus on their inhibition potential to the main protease of the SARS-CoV-2 virus 3CLpro (6WQF). The docking results are compared with the docking results of 866 COVID19-related compounds (S set). Furthermore, machine learning (ML) prediction of docking scores of the W set is presented using the S set trained TensorFlow, XGBoost, and SchNetPack ML approaches. Docking scores are evaluated with the Autodock 4.2.6 software. Four compounds in the W set achieve a docking score below −13 kcal/mol, with (+)-lariciresinol 9′-p-coumarate (CID 11497085) achieving the best docking score (−15 kcal/mol) within the W and S sets. In addition, 50% of W set docking scores are found below −8 kcal/mol and 25% below −10 kcal/mol. Therefore, the compounds identified in the softwood bark, show potential for antiviral activity upon extraction or further derivatization. The W set molecular docking studies are validated by means of molecular dynamics (five best compounds). The solubility (Log S, ESOL) and druglikeness of the best docking compounds in S and W sets are compared to evaluate the pharmacological potential of compounds identified in softwood bark. Graphical abstract Unlabelled Image

SUBMITTER: Jablonsky M

PROVIDER: S-EPMC9233873 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundDue to the migratory flow of infected people with severe acute respiratory syndrome virus (SARS COV-2), the number of confirmed cases of coronavirus disease 2019 (COVID-19) is accelerating globally; preclinical evidence of antiviral agents that can combat this pandemic is still elusive. We identified published articles on SARS-COV efficacy experiments in which some selected compounds were used to test the reduction of the virus load in mice.MethodsA systematic search of articles was conducted in PubMed, Web of Science, and Scopus. We then developed a combined model based on a systematic review, meta-analyses, and molecular docking studies to evaluate the effect size of preclinical studies of compounds that have been tested against SARS-COV. Because substantial heterogeneity was expected, random effect model meta-analyses were carried out to estimate the overall pooled disease's prevalence. All meta-analyses were performed with Stata version 15.0. Subgroup analyses on therapies were conducted as well. Molecular docking studies of the inhibitors in the active pocket of COVID-19 protease were also performed.ResultsFrom all screened articles, six studies were appropriate for ultimate meta-analysis and systematic review. The residual amount of heterogeneity was high (τ2 =0.02; heterogeneity I2 =85.5 % with heterogeneity chi-square =103.57, a degree of freedom =15, and p <0.001). The overall random pooled prevalence of infected mice treated with the selected compounds was 78.1 % [95 % Confidence Interval (CI): 14.7-17.0 %]. Prophylactic has a significantly higher pooled prevalence than therapeutic, with 21.8 % (95 % CI: 16.4 % to 28.8 %). Our results indicated that most of the SARS-COV inhibitors analyzed were less effective in reducing the lung virus titer of SARS-COV infection in animal models. The findings from molecular docking studies also identified COVID-19 inhibitors that are good for optimization and drug development to fight against COVID-19 infection.ConclusionsFindings from the review showed that studies on the preclinical compounds targeting SARS-COV and COVID-19 are limited. Furthermore, molecular docking studies and meta-analysis results substantiated three compounds, i.e., EIDD-2801, GS-5734, and amodiaquine. HIPPOKRATIA 2020, 24(3): 99-106.

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Molecular docking and machine learning affinity prediction of compounds identified upon softwood bark extraction to the main protease of the SARS-CoV-2 virus

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