The deadly coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has gone out of control globally. Despite much effort by scientists, medical experts, and society in general, the slow progress on drug discovery and antibody therapeutic development, the unknown possible side effects of the existing vaccines, and the high transmission rate of the SARS-CoV-2, remind us of the sad reality that our current understanding of the transmission, infectivity, and evolution of SARS-CoV-2 is unfortunately very limited. The major limitation is the lack of mechanistic understanding of viral-host cell interactions, the viral regulation, protein-protein interactions, including antibody-antigen binding, protein-drug binding, host immune response, etc. This limitation will likely haunt the scientific community for a long time and have a devastating consequence in combating COVID-19 and other pathogens. Notably, compared to the long-cycle, highly cost, and safety-demanding molecular-level experiments, the theoretical and computational studies are economical, speedy, and easy to perform. There exists a tsunami of the literature on molecular modeling, simulation, and prediction of SARS-CoV-2 that has become impossible to fully be covered in a review. To provide the reader a quick update about the status of molecular modeling, simulation, and prediction of SARS-CoV-2, we present a comprehensive and systematic methodology-centered narrative in the nick of time. Aspects such as molecular modeling, Monte Carlo (MC) methods, structural bioinformatics, machine learning, deep learning, and mathematical approaches are included in this review. This review will be beneficial to researchers who are looking for ways to contribute to SARS-CoV-2 studies and those who are assessing the current status in the field.

Methodology-centered review of molecular modeling, simulation, and prediction of SARS-CoV-2

Despite tremendous efforts in the past two years, our understanding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), virus-host interactions, immune response, virulence, transmission, and evolution is still very limited. This limitation calls for further in-depth investigation. Computational studies have become an indispensable component in combating coronavirus disease 2019 (COVID-19) due to their low cost, their efficiency, and the fact that they are free from safety and ethical constraints. Additionally, the mechanism that governs the global evolution and transmission of SARS-CoV-2 cannot be revealed from individual experiments and was discovered by integrating genotyping of massive viral sequences, biophysical modeling of protein-protein interactions, deep mutational data, deep learning, and advanced mathematics. There exists a tsunami of literature on the molecular modeling, simulations, and predictions of SARS-CoV-2 and related developments of drugs, vaccines, antibodies, and diagnostics. To provide readers with a quick update about this literature, we present a comprehensive and systematic methodology-centered review. Aspects such as molecular biophysics, bioinformatics, cheminformatics, machine learning, and mathematics are discussed. This review will be beneficial to researchers who are looking for ways to contribute to SARS-CoV-2 studies and those who are interested in the status of the field. 

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

Coronavirus illness (COVID-19) is an infectious pathology generated by intense severe respiratory syndrome coronavirus 2 (SARS-CoV-2). This infectious disease has emerged in 2019. The COVID-19-associated pandemic has considerably affected the way of life and the economy in the world. It is consequently crucial to find solutions allowing remedying or alleviating the effects of this infectious disease. Natural products have been in perpetual application from immemorial time given that they are attested to be efficient towards several illnesses without major side effects. Various studies have shown that plant extracts or purified molecules have a promising inhibiting impact towards coronavirus. In addition, it is substantial to understand the characteristics, susceptibility and impact of diet on patients infected with COVID-19. In this review, we recapitulate the influence of extracts or pure molecules from medicinal plants on COVID-19. We approach the possibilities of plant treatment/co-treatment and feeding applied to COVID-19. We also show coronavirus susceptibility and complications associated with nutrient deficiencies and then discuss the major food groups efficient on COVID-19 pathogenesis. Then, we covered emerging technologies using plant-based SARS-CoV-2 vaccine. We conclude by giving nutrient and plants curative therapy recommendations which are of potential interest in the COVID-19 infection and could pave the way for pharmacological treatments or co-treatments of COVID-19.

/pdf/role-of-diet-and-nutrients-in-sars-cov-2-infection-incidence-l38yvjzw.pdf

Role of Diet and Nutrients in SARS-CoV-2 Infection: Incidence on Oxidative Stress, Inflammatory Status and Viral Production

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative pathogen of COVID-19, is spreading rapidly and has caused hundreds of millions of infections and millions of deaths worldwide. Due to the lack of specific vaccines and effective treatments for COVID-19, there is an urgent need to identify effective drugs. Traditional Chinese medicine (TCM) is a valuable resource for identifying novel anti-SARS-CoV-2 drugs based on the important contribution of TCM and its potential benefits in COVID-19 treatment. Herein, we aimed to discover novel anti-SARS-CoV-2 compounds and medicinal plants from TCM by establishing a prediction method of anti-SARS-CoV-2 activity using machine learning methods. We first constructed a benchmark dataset from anti-SARS-CoV-2 bioactivity data collected from the ChEMBL database. Then, we established random forest (RF) and support vector machine (SVM) models that both achieved satisfactory predictive performance with AUC values of 0.90. By using this method, a total of 1011 active anti-SARS-CoV-2 compounds were predicted from the TCMSP database. Among these compounds, six compounds with highly potent activity were confirmed in the anti-SARS-CoV-2 experiments. The molecular fingerprint similarity analysis revealed that only 24 of the 1011 compounds have high similarity to the FDA-approved antiviral drugs, indicating that most of the compounds were structurally novel. Based on the predicted anti-SARS-CoV-2 compounds, we identified 74 anti-SARS-CoV-2 medicinal plants through enrichment analysis. The 74 plants are widely distributed in 68 genera and 43 families, 14 of which belong to antipyretic detoxicate plants. In summary, this study provided several medicinal plants with potential anti-SARS-CoV-2 activity, which offer an attractive starting point and a broader scope to mine for potentially novel anti-SARS-CoV-2 drugs.

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In Silico Identification of Anti-SARS-CoV-2 Medicinal Plants Using Cheminformatics and Machine Learning

In Silico Molecular Docking of Phytochemical Compounds of Ocimum bacilicum Against Mutated SARS-CoV-2 Virus

Abstract COVID-19, a disease caused by the SARS-CoV-2 virus, is responsible for a pandemic since March 2020 and it has no cure. Therefore, herein, different theoretical methods were used to obtain potential candidates from herbal compounds to inhibit the SARS-CoV-2 main protease (Mpro). Initially, the 16 best-scored compounds were selected from a library containing 4066 ligands using virtual screening by molecular docking. Among them, six molecules (physalin B 5,6-epoxide (PHY), methyl amentoflavone (MAM), withaphysalin C (WPC), daphnoline or trilobamine (TRI), cepharanoline (CEP) and tetrandrine (TET)) were selected based on Lipinski’s rule and ADMET analysis as criteria. These compounds complexed with the Mpro were submitted to triplicate 100 ns molecular dynamics simulations. RMSD, RMSF, and radius of gyration results show that the overall protein structure is preserved along the simulation time. The average ΔGbinding values, calculated by the MM/PBSA method, were −41.7, −55.8, −45.2, −38.7, −49.3, and −57.9 kcal/mol for the PHY-Mpro, MAM-Mpro, WPC-Mpro, CEP-Mpro, TRI-Mpro, and TET-Mpro complexes, respectively. Pairwise decomposition analyses revealed that the binding pocket is formed by His41-Val42, Met165-Glu166-Leu167, Asp187, and Gln189. The PLS regression model generated by QSPR analysis indicated that non-polar and polar groups with the presence of hydrogen bond acceptors play an important role in the herbal compounds-Mpro interactions. Overall, we found six potential candidates to inhibit the SARS-CoV-2 Mpro and highlighted key residues from the binding pocket that can be used for future drug design. Communicated by Ramaswamy H. Sarma

/pdf/traditional-herbal-compounds-as-candidates-to-inhibit-the-2ww3kjpr.pdf

Traditional herbal compounds as candidates to inhibit the SARS-CoV-2 main protease: an in silico study

Theoretical methods were used to investigate the encapsulation of two vitamin E isoforms, α-tocopherol (α-TOC) and α-tocotrienol (α-TCT), into the macrocycle cucurbit[7]uril (CB[7]). The [email protected][7] complexes were stable along 100 ns molecular dynamics simulation with similar conformation. The binding free energy obtained from the MM/PBSA method is ∼ –25 kcal.mol−1 for both complexes. Also, DFT-D3/B3LYP/6-31G(d,p) calculations indicate a favorable encapsulation process with bind energies of –58.25 (α[email protected][7]) and –61.08 kcal.mol−1 (α[email protected][7]). Moreover, the calculated solvation enthalpies show a solubility enhancement of complexed vitamins compared to their free forms. Finally, CB[7] may be a promising macrocyclic host for encapsulating vitamins E. 

Rafael Giordano Viegas

Papers

Traditional herbal compounds as candidates to inhibit the SARS-CoV-2 main protease: an in silico study

In silico study of the encapsulation of α-tocopherol and α-tocotrienol vitamins E into cucurbit[7]uril

Computational studies of the encapsulation of ibuprofen and paracetamol into cucurbit[7]uril