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Computational Lock and Key and Dynamic Trajectory Analysis of Natural Biophors Against COVID-19 Spike Protein to Identify Effective Lead Molecules.

ABSTRACT: New pandemic infection of coronaviridae family virus spread to more than 210 countries with total infection of 1,136,851 and 62,955 (4.6%) deaths until 5th April 2020. Which stopped the regular cycle of humankind but the nature is consistently running. There is no micro molecule remedy found yet to restore the regular life of people. Hence, we decided to work on natural biophores against the COVID proteins. As a first step, major phytoconstituents of antiviral herbs like Leucas aspera, Morinda citrifolia, Azadirachta indica, Curcuma longa, Piper nigrum, Ocimum tenuiflorum, and Corallium rubrum collected and performed the lock and key analysis with major spike protein of COVID-19 to find the best fitting lead biophore using computational drug design platform. The results of protocol run showed, phytoconstituents of Morinda citrifolia and Leucas aspera were found lower binding energy range of - 55.18 to - 25.34 kcal/mol, respectively and compared with Hydroxychloroquine (HCQ) (- 24.29 kcal/mol) and Remdesivir (- 25.38 kcal/mol). The results conclude that, core skeletons chromen, anthracene 9, 11 dione and long-chain alkyl acids/ester-containing biophores showen high stable antagonistic affinity with S-protein. Which leads the breakdown of spike protein and ACE2 receptor complex formation and host mechanism of corono virus. In addition, the dynamic trajectory analysis confirmed the complete denaturation of spike protein by the molecule 4-(24-hydroxy-1-oxo-5-n-propyltetracosanyl)-phenol from Leucas aspera and stability of spike-ligand complex. These biophores will aid the researcher to fabricate new promising analogue and being recommended to assess its COVID-19 treatment.

SUBMITTER: Navabshan I

PROVIDER: S-EPMC8219180 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature

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Computational Lock and Key and Dynamic Trajectory Analysis of Natural Biophors Against COVID-19 Spike Protein to Identify Effective Lead Molecules.

Navabshan Irfan I Sakthivel Balasubramaniyan B Pandiyan Rajesh R Antoniraj Mariya Gover MG Dharmaraj Selvakumar S Ashokkumar Veeramuthu V Khoo Kuan Shiong KS Chew Kit Wayne KW Sugumaran Abimanyu A Show Pau Loke PL

Molecular biotechnology 20210622 10

New pandemic infection of coronaviridae family virus spread to more than 210 countries with total infection of 1,136,851 and 62,955 (4.6%) deaths until 5th April 2020. Which stopped the regular cycle of humankind but the nature is consistently running. There is no micro molecule remedy found yet to restore the regular life of people. Hence, we decided to work on natural biophores against the COVID proteins. As a first step, major phytoconstituents of antiviral herbs like Leucas aspera, Morinda c ...[more]

PMID: 34159564

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Project description:BackgroundA plethora of prognostic biomarkers for esophageal squamous cell carcinoma (ESCC) that have hitherto been reported are challenged with low reproducibility due to high molecular heterogeneity of ESCC. The purpose of this study was to identify the optimal biomarkers for ESCC using machine learning algorithms.MethodsBiomarkers related to clinical survival, recurrence or therapeutic response of patients with ESCC were determined through literature database searching. Forty-eight biomarkers linked to recurrence or prognosis of ESCC were used to construct a molecular interaction network based on NetBox and then to identify the functional modules. Publicably available mRNA transcriptome data of ESCC downloaded from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) datasets included GSE53625 and TCGA-ESCC. Five machine learning algorithms, including logical regression (LR), support vector machine (SVM), artificial neural network (ANN), random forest (RF) and XGBoost, were used to develop classifiers for prognostic classification for feature selection. The area under ROC curve (AUC) was used to evaluate the performance of the prognostic classifiers. The importances of identified molecules were ranked by their occurrence frequencies in the prognostic classifiers. Kaplan-Meier survival analysis and log-rank test were performed to determine the statistical significance of overall survival.ResultsA total of 48 clinically proven molecules associated with ESCC progression were used to construct a molecular interaction network with 3 functional modules comprising 17 component molecules. The 131,071 prognostic classifiers using these 17 molecules were built for each machine learning algorithm. Using the occurrence frequencies in the prognostic classifiers with AUCs greater than the mean value of all 131,071 AUCs to rank importances of these 17 molecules, stratifin encoded by SFN was identified as the optimal prognostic biomarker for ESCC, whose performance was further validated in another 2 independent cohorts.ConclusionThe occurrence frequencies across various feature selection approaches reflect the degree of clinical importance and stratifin is an optimal prognostic biomarker for ESCC.

Dataset Information

Computational Lock and Key and Dynamic Trajectory Analysis of Natural Biophors Against COVID-19 Spike Protein to Identify Effective Lead Molecules.

Publications

Computational Lock and Key and Dynamic Trajectory Analysis of Natural Biophors Against COVID-19 Spike Protein to Identify Effective Lead Molecules.

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