Methodology-Centered Review of Molecular Modeling, Simulation, and Prediction of SARS-CoV-2

Gao, Kun; Wang, Rui; Chen, J; Cheng, Linfang; Frishcosy, J; Huzumi, Y; Qiu, Yong; Schluckbier, T; Wei, Guo-Wei

doi:10.1021/acs.chemrev.1c00965

Cited by 54 publications

(44 citation statements)

References 1,274 publications

(2,046 reference statements)

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“…There were several inspiring insights from previous studies, which motivated the current research. Firstly, as we know, a comprehensive ML includes regression, classification, clustering, and dimensionality reduction [ 40 ]. Additionally, molecular subtypes contribute to the treatment of COVID-19 [ 41 , 42 , 43 ].…”

Section: Discussionmentioning

confidence: 99%

A Novel 3-Gene Signature for Identifying COVID-19 Patients Based on Bioinformatics and Machine Learning

Lai

Liu

Deng

et al. 2022

Genes

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Although many biomarkers associated with coronavirus disease 2019 (COVID-19) were found, a novel signature relevant to immune cells has not been developed. In this work, the “CIBERSORT” algorithm was used to assess the fraction of immune infiltrating cells in GSE152641 and GSE171110. Key modules associated with important immune cells were selected by the “WGCNA” package. The “GO” enrichment analysis was used to reveal the biological function associated with COVID-19. The “Boruta” algorithm was used to screen candidate genes, and the “LASSO” algorithm was used for collinearity reduction. A novel gene signature was developed based on multivariate logistic regression analysis. Subsequently, M0 macrophages (PRAUC = 0.948 in GSE152641 and PRAUC = 0.981 in GSE171110) and neutrophils (PRAUC = 0.892 in GSE152641 and PRAUC = 0.960 in GSE171110) were considered as important immune cells. Forty-three intersected genes from two modules were selected, which mainly participated in some immune-related activities. Finally, a three-gene signature comprising CLEC4D, DUSP13, and UNC5A that can accurately distinguish COVID-19 patients and healthy controls in three datasets was constructed. The ROCAUC was 0.974 in the training set, 0.946 in the internal test set, and 0.709 in the external test set. In conclusion, we constructed a three-gene signature to identify COVID-19, and CLEC4D, DUSP13, and UNC5A may be potential biomarkers for COVID-19 patients.

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Section: Discussionmentioning

confidence: 99%

A Novel 3-Gene Signature for Identifying COVID-19 Patients Based on Bioinformatics and Machine Learning

Lai

Liu

Deng

et al. 2022

Genes

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“…At the same time, it is expected that such coarse-grained (CG) simulations of biomolecules should accurately describe the electrostatic interactions and pH effects. Among possible available tools, [80][81][82] the FORTE (Fast cOarse-grained pRotein-proTein modEl) 10,21,23 approach was a suitable choice to fulfill all these previous issues and also due to its record of successful applications in other biophysical systems 67,[83][84][85] (including viruses 9,10,21,23,32,51,86 ) where electrostatic interactions are quite important. This was the main computational tool used in the present work.…”

Section: Model and Methodologymentioning

confidence: 99%

Electrostatic features for the Receptor binding domain of SARS-COV-2 wildtype and its variants. Compass to the severity of the future variants with the charge-rule

Silva

Giron

Laaksonen

2022

Preprint

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Electrostatic intermolecular interactions are important in many aspects of biology. We have studied the main electrostatic features involved in the interaction of the receptor-binding domain (RBD) of the SARS-CoV-2 spike protein with the human receptor Angiotensin-converting enzyme 2 (ACE2). As the principal computational tool, we have used the FORTE approach, capable to model proton fluctuations and computing free energies for a very large number of protein-protein systems under different physical-chemical conditions, here focusing on the RBD-ACE2 interactions. Both the wild-type and all critical variants are included in this study. From our large ensemble of extensive simulations, we obtain, as a function of pH, the binding affinities, charges of the proteins, their charge regulation capacities, and their dipole moments. In addition, we have calculated the pKas for all ionizable residues and mapped the electrostatic coupling between them. We are able to present a simple predictor for the RBD-ACE2 binding based on the data obtained for Alpha, Beta, Gamma, Delta, and Omicron variants, as a linear correlation between the total charge of the RBD and the corresponding binding affinity. This RBD charge rule should work as a quick test of the degree of severity of the coming SARS-CoV-2 variants in the future.

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“…The virion comprises a single positive-strand RNA enveloped by a lipid bilayer with sphere-like shape. Among various virial proteins, the so-called spike (S) protein protruding from the lipid bilayer plays an important role in the invasion of host cells and in antibody binding. − Binding of the receptor binding domain (RBD) of the S protein to the human angiotensin-converting enzyme 2 (hACE2) protein initiates entry of the virus to the host cell. , Therefore, over the past two years, a lot of experimental , and computational − studies on the RBD-hACE2 interaction have been performed.…”

Section: Introductionmentioning

confidence: 99%

SARS-CoV-2 Omicron Variant Binds to Human Cells More Strongly than the Wild Type: Evidence from Molecular Dynamics Simulation

et al. 2022

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The emergence of the variant of concern Omicron (B.1.1.529) of the severe acute respiratory syndrome coronavirus 2 has aggravated the Covid-19 pandemic due to its very contagious ability. The high infection rate may be due to the high binding affinity of Omicron to human cells, but both experimental and computational studies have yielded conflicting results on this issue. Some studies have shown that the Omicron variant binds to human angiotensin-converting enzyme 2 (hACE2) more strongly than the wild type (WT), but other studies have reported comparable binding affinities. To shed light on this open problem, in this work, we calculated the binding free energy of the receptor binding domain (RBD) of the WT and Omicron spike protein to hACE2 using all-atom molecular dynamics simulation and the molecular mechanics Poisson–Boltzmann surface area method. We showed that Omicron binds to human cells more strongly than the WT due to increased RBD charge, which enhances electrostatic interaction with negatively charged hACE2. N440K, T478K, E484A, Q493R, and Q498R mutations in the RBD have been found to play a critical role in the stability of the RBD-hACE2 complex. The effect of homogeneous and heterogeneous models of glycans coating the viral RBD and the peptidyl domain of hACE2 was examined. Although the total binding free energy is not sensitive to the glycan model, the distribution of per-residue interaction energies depends on it. In addition, glycans have a little effect on the binding affinity of the WT RBD to hACE2.

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Methodology-Centered Review of Molecular Modeling, Simulation, and Prediction of SARS-CoV-2

Cited by 54 publications

References 1,274 publications

A Novel 3-Gene Signature for Identifying COVID-19 Patients Based on Bioinformatics and Machine Learning

A Novel 3-Gene Signature for Identifying COVID-19 Patients Based on Bioinformatics and Machine Learning

Electrostatic features for the Receptor binding domain of SARS-COV-2 wildtype and its variants. Compass to the severity of the future variants with the charge-rule

SARS-CoV-2 Omicron Variant Binds to Human Cells More Strongly than the Wild Type: Evidence from Molecular Dynamics Simulation

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