Dataset Information

Prediction of infectivity of SARS-CoV2: Mathematical model with analysis of docking simulation for spike proteins and angiotensin-converting enzyme 2.

ABSTRACT:

Objectives

Variants of a coronavirus (SARS-CoV-2) have been spreading in a global pandemic. Improved understanding of the infectivity of future new variants is important so that effective countermeasures against them can be quickly undertaken. In our research reported here, we aimed to predict the infectivity of SARS-CoV-2 by using a mathematical model with molecular simulation analysis, and we used phylogenetic analysis to determine the evolutionary distance of the spike protein gene (S gene) of SARS-CoV-2.

Methods

We subjected the six variants and the wild type of spike protein and human angiotensin-converting enzyme 2 (ACE2) to molecular docking simulation analyses to understand the binding affinity of spike protein and ACE2. We then utilized regression analysis of the correlation coefficient of the mathematical model and the infectivity of SARS-CoV-2 to predict infectivity.

Results

The evolutionary distance of the S gene correlated with the infectivity of SARS-CoV-2 variants. The calculated biding affinity for the mathematical model obtained with results of molecular docking simulation also correlated with the infectivity of SARS-CoV-2 variants. These results suggest that the data from the docking simulation for the receptor binding domain of variant spike proteins and human ACE2 were valuable for prediction of SARS-CoV-2 infectivity.

Conclusion

We developed a mathematical model for prediction of SARS-CoV-2 variant infectivity by using binding affinity obtained via molecular docking and the evolutionary distance of the S gene.

SUBMITTER: Takaoka Y

PROVIDER: S-EPMC9212987 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature

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Publications

Prediction of infectivity of SARS-CoV2: Mathematical model with analysis of docking simulation for spike proteins and angiotensin-converting enzyme 2.

Takaoka Yutaka Y Sugano Aki A Morinaga Yoshitomo Y Ohta Mika M Miura Kenji K Kataguchi Haruyuki H Kumaoka Minoru M Kimura Shigemi S Maniwa Yoshimasa Y

Microbial risk analysis 20220616

<h4>Objectives</h4>Variants of a coronavirus (SARS-CoV-2) have been spreading in a global pandemic. Improved understanding of the infectivity of future new variants is important so that effective countermeasures against them can be quickly undertaken. In our research reported here, we aimed to predict the infectivity of SARS-CoV-2 by using a mathematical model with molecular simulation analysis, and we used phylogenetic analysis to determine the evolutionary distance of the spike protein gene (S ...[more]

PMID: 35756961

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Project description:Angiotensin converting enzyme (ACE) is a key enzyme and mediator in the aetiology of high blood pressure (HBP) and hypertension. As one of the leading cause of untimely death worldwide, there is a lot of research and studies on the management and treatment of hypertension. The usage of medicinal plants in the management of hypertension as alternative to synthetic allopathic drugs is a common practice in folkloric and traditional medicine. Therefore, this study was aimed to investigate the ACE inhibitory activity of some medicinal plants which are commonly used in the treatment of HBP in southwestern part of Nigeria using extensive in-silico approach. Compounds identified in the plants through GC-MS technique, together with Lisinopril were docked against ACE protein. It was observed that only 40 of the compounds had binding affinity ≥ - 6.8 kcal/mol which was demonstrated by the standard drug (lisinopril). Interaction between the compounds and ACE was via conventional hydrogen, carbon hydrogen, alkyl, pi-alkyl, pi-carbon, and Van Der Wall bonds among others. Most of these compounds exhibited drug like properties, without violating majority of the physicochemical descriptors and Lipinski rule of 5. The ADMET evaluation revealed that only 2 compounds (cyclopentadecanone and oxacycloheptadecan-2-one) which were identified in Bacopa florinbunda plant were predicted non-toxic and thus were subjected to molecular dynamics and simulation with ACE. From the molecular dynamics and mechanics analysis, both cyclopentadecanone and oxacycloheptadecan-2-one showed high stability and inhibitory potentials when bound to ACE. Oxacycloheptadecan-2-one was more stable than lisinopril and cyclopentadecanone in the ligand-ACE complex; we therefore suggested its experimental and clinical validation as drug candidates for the treatment of hypertension.Supplementary informationThe online version contains supplementary material available at 10.1007/s40203-022-00135-z.

Project description:The angiotensin-converting enzyme (ACE) is a two-domain dipeptidylcarboxypeptidase, which has a direct involvement in the control of blood pressure by performing the hydrolysis of angiotensin I to produce angiotensin II. At the same time, ACE hydrolyzes other substrates such as the vasodilator peptide bradykinin and the anti-inflammatory peptide N-acetyl-SDKP. In this sense, ACE inhibitors are bioactive substances with potential use as medicinal products for treatment or prevention of hypertension, heart failures, myocardial infarction, and other important diseases. This review examined the most recent literature reporting ACE inhibitors with the help of molecular modeling. The examples exposed here demonstrate that molecular modeling methods, including docking, molecular dynamics (MD) simulations, quantitative structure-activity relationship (QSAR), etc, are essential for a complete structural picture of the mode of action of ACE inhibitors, where molecular docking has a key role. Examples show that too many works identified ACE inhibitory activities of natural peptides and peptides obtained from hydrolysates. In addition, other works report non-peptide compounds extracted from natural sources and synthetic compounds. In all these cases, molecular docking was used to provide explanation of the chemical interactions between inhibitors and the ACE binding sites. For docking applications, most of the examples exposed here do not consider that: (i) ACE has two domains (nACE and cACE) with available X-ray structures, which are relevant for the design of selective inhibitors, and (ii) nACE and cACE binding sites have large dimensions, which leads to non-reliable solutions during docking calculations. In support of the solution of these problems, the structural information found in Protein Data Bank (PDB) was used to perform an interaction fingerprints (IFPs) analysis applied on both nACE and cACE domains. This analysis provides plots that identify the chemical interactions between ligands and both ACE binding sites, which can be used to guide docking experiments in the search of selective natural components or novel drugs. In addition, the use of hydrogen bond constraints in the S2 and S2' subsites of nACE and cACE are suggested to guarantee that docking solutions are reliable.

Dataset Information

Prediction of infectivity of SARS-CoV2: Mathematical model with analysis of docking simulation for spike proteins and angiotensin-converting enzyme 2.

Objectives

Methods

Results

Conclusion

Publications

Prediction of infectivity of SARS-CoV2: Mathematical model with analysis of docking simulation for spike proteins and angiotensin-converting enzyme 2.

Similar Datasets

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