GC-MS analysis of phytoconstituents from Ruellia prostrata and Senna tora and identification of potential anti-viral activity against SARS-CoV-2

Neuropharmacological assessment and identification of possible lead compound (apomorphine) from Hygrophila spinosa through in-vivo and in-silico approaches

The aim of this research is to examine possible neurological activity of methanol, ethyl acetate, and aqueous extracts of Hygrophila spinosa and identify possible lead compounds through in silico analysis. In vivo, neuropharmacological activity was evaluated by using four distinct neuropharmacological assessment assays. Previously reported GC-MS data and earlier literature were utilized to identify the phytochemicals present in Hygrophila spinosa. Computational studies notably molecular docking and molecular dynamic simulations were conducted with responsible receptors to assess the stability of the best interacting compound. Pharmacokinetics properties like absorption, distribution, metabolism, excretion, and toxicity were considered to evaluate the drug likeliness properties of the identified compounds. All the in vivo results support the notion that different extracts (methanol, ethyl acetate, and aqueous) of Hygrophila spinosa have significant (*p = 0.05) sedative-hypnotic, anxiolytic, and anti-depressant activity. Among all the extracts, specifically methanol extracts of Hygrophila spinosa (MHS 400 mg/kg.b.w.) showed better sedative, anxiolytic and antidepressant activity than aqueous and ethyl acetate extracts. In silico molecular docking analysis revealed that among 53 compounds 7 compounds showed good binding affinities and one compound, namely apomorphine (CID: 6005), surprisingly showed promising binding affinity to all the receptors . An analysis of molecular dynamics simulations confirmed that apomorphine (CID: 6005) had a high level of stability at the protein binding site. Evidence suggests that Hygrophila spinosa has significant sedative, anxiolytic, and antidepressant activity. In silico analysis revealed that a particular compound (apomorphine) is responsible for this action. Further research is required in order to establish apomorphine as a drug for anxiety, depression, and sleep disorders.Communicated by Ramaswamy H. Sarma.

Structural, functional, molecular docking analysis of a hypothetical protein from Talaromyces marneffei and its molecular dynamic simulation: an in-silico approach

Telaromyces marneffei (formerly Penicillium marneffei) is an endemic pathogenic fungus in Southern China and Southeast Asia. It can cause disease in patients with travel-related exposure to this organism and high morbidity and mortality in acquired immune deficiency syndrome (AIDS). In this study, we analyzed the structure and function of a hypothetical protein from T. marneffei using several bioinformatics tools and servers to unveil novel pharmacological targets and design a peptide vaccine against specific epitopes. A total of seven functional epitopes were screened on the protein, and 'STGVDMWSV' was the most antigenic, non-allergenic and non-toxic. Molecular docking showed stronger affinity between the CTL epitope 'STGVDMWSV' and the MHC I allele HLA-A*02:01, a higher docking score -234.98 kcal/mol, revealed stable interactions during a 100 ns molecular dynamic simulation. Overall, the results of this study revealed that this hypothetical protein is crucial for comprehending biochemical, physiological pathways and identifying novel therapeutic targets for human health. Communicated by Ramaswamy H. Sarma.

Natural defense against multi-drug resistant Pseudomonas aeruginosa: Cassia occidentalis L. in vitro and in silico antibacterial activity

Cassia occidentalis L. is widely used in indigenous and traditional medicine, but its impact on multi-drug resistant (MDR) bacterial infections mostly remains unknown. Therefore, this study aimed to evaluate the in vitro antibacterial efficiency of methanol and ethyl acetate extracts of C. occidentalis L. leaves (MECOL and EAECOL) against multi-drug resistant Pseudomonas aeruginosa and to identify potential antibacterial agents through computational studies targeting the LasR protein. Initially, 82 compounds were identified using GC-MS analysis, and the functional groups were determined through FT-IR analysis. Both extracts of the plant exhibited dose-dependent antibacterial activity, with MICs of 104.16 ± 36.08 μg mL-1 for MECOL and 83.33 ± 36.08 μg mL-1 for EAECOL, and an MBC of 125 μg mL-1. Among the 82 compounds, 12 potential compounds were identified based on binding scores using molecular docking with the LasR protein and MM-GBSA analysis. Furthermore, screening for ADME properties, including physicochemical features, water solubility, lipophilicity, RO5 compliance, and toxicity, identified the top three compounds: methyl dihydrojasmonate, methyl benzoate, and 4a-methyl-4,4a,5,6,7,8-hexahydro-2(3H)-naphthalenone, which also demonstrated binding affinity with the active site residues of the LpxC protein of the bacteria. Additionally, molecular dynamics (MD) simulations confirmed the binding reliability of these three phytochemicals to LasR's active pocket, comparable to the protein native inhibitory ligands (C12-HSL). The study offers scientific support for the traditional use of C. occidentalis in treating bacterial infections, highlighting the potential of the three compounds as leads for developing LasR inhibitors to combat multi-drug resistant P. aeruginosa.

Computational formulation of a multiepitope vaccine unveils an exceptional prophylactic candidate against Merkel cell polyomavirus

Merkel cell carcinoma (MCC) is a rare neuroendocrine skin malignancy caused by human Merkel cell polyomavirus (MCV), leading to the most aggressive skin cancer in humans. MCV has been identified in approximately 43%-100% of MCC cases, contributing to the highly aggressive nature of primary cutaneous carcinoma and leading to a notable mortality rate. Currently, no existing vaccines or drug candidates have shown efficacy in addressing the ailment caused by this specific pathogen. Therefore, this study aimed to design a novel multiepitope vaccine candidate against the virus using integrated immunoinformatics and vaccinomics approaches. Initially, the highest antigenic, immunogenic, and non-allergenic epitopes of cytotoxic T lymphocytes, helper T lymphocytes, and linear B lymphocytes corresponding to the virus whole protein sequences were identified and retrieved for vaccine construction. Subsequently, the selected epitopes were linked with appropriate linkers and added an adjuvant in front of the construct to enhance the immunogenicity of the vaccine candidates. Additionally, molecular docking and dynamics simulations identified strong and stable binding interactions between vaccine candidates and human Toll-like receptor 4. Furthermore, computer-aided immune simulation found the real-life-like immune response of vaccine candidates upon administration to the human body. Finally, codon optimization was conducted on the vaccine candidates to facilitate the in silico cloning of the vaccine into the pET28+(a) cloning vector. In conclusion, the vaccine candidate developed in this study is anticipated to augment the immune response in humans and effectively combat the virus. Nevertheless, it is imperative to conduct in vitro and in vivo assays to evaluate the efficacy of these vaccine candidates thoroughly. These evaluations will provide critical insights into the vaccine's effectiveness and potential for further development.

Halting aberrant DNA methylation via in silico Identification of potent inhibitors of DNMT3B enzyme: Atomistic insights

To date, Cancer remains a global threat due to its impact on growing life expectancy. With the many efforts and methods of combating the disease, complete success remains a challenge owing to several limitations including cancer cells developing resistance through mutations, off-target effect of some cancer drugs resulting in toxicities, among many others. Aberrant DNA methylation is understood to be the primary reason for improper gene silence, which can result in neoplastic transformation, carcinogenesis, and tumour progression. DNA methyltransferase B (DNMT3B) enzyme is considered a potential target for the treatment of several cancers due to its important role in DNA methylation. However, only a few DNMT3B inhibitors have been reported to date. Herein, in silico molecular recognition techniques such as Molecular docking, Pharmacophore-based virtual screen and MD simulation were employed to identify potential inhibitors of DNMT3B that can halt aberrancy in DNA methylation. Findings initially identified 878 hit compounds based on a designed pharmacophore model from the reference compound Hypericin. Molecular docking was used to rank the hits by testing their efficiency when bound to the target enzyme and the top three (3) selected. All three (3) of the top hits showed excellent pharmacokinetic properties but two (2) (Zinc33330198 and Zinc77235130) were identified to be non-toxic. Molecular dynamic simulation of the final two hits showed good stability, flexibility, and structural rigidity of the compounds on DNMT3B. Finally, thermodynamic energy estimations show both compounds had favourable free energies comprising - 26.04 kcal/mol for Zinc77235130 and - 15.73 kcal/mol for Zinc33330198. Amongst the final two hits, Zinc77235130 showed consistency in favourable results across all the tested parameters and was thus selected as the lead compound for further experimental validation. The identification of this lead compound will form important basis for the inhibition of aberrant DNA methylation in cancer therapy.

In silico formulation of a next-generation multiepitope vaccine for use as a prophylactic candidate against Crimean-Congo hemorrhagic fever

BACKGROUND

Crimean-Congo hemorrhagic fever (CCHF) is a widespread disease transmitted to humans and livestock animals through the bite of infected ticks or close contact with infected persons' blood, organs, or other bodily fluids. The virus is responsible for severe viral hemorrhagic fever outbreaks, with a case fatality rate of up to 40%. Despite having the highest fatality rate of the virus, a suitable treatment option or vaccination has not been developed yet. Therefore, this study aimed to formulate a multiepitope vaccine against CCHF through computational vaccine design approaches.

METHODS

The glycoprotein, nucleoprotein, and RNA-dependent RNA polymerase of CCHF were utilized to determine immunodominant T- and B-cell epitopes. Subsequently, an integrative computational vaccinology approach was used to formulate a multi-epitopes vaccine candidate against the virus.

RESULTS

After rigorous assessment, a multiepitope vaccine was constructed, which was antigenic, immunogenic, and non-allergenic with desired physicochemical properties. Molecular dynamics (MD) simulations of the vaccine-receptor complex show strong stability of the vaccine candidates to the targeted immune receptor. Additionally, the immune simulation of the vaccine candidates found that the vaccine could trigger real-life-like immune responses upon administration to humans.

CONCLUSIONS

Finally, we concluded that the formulated multiepitope vaccine candidates would provide excellent prophylactic properties against CCHF.

Interaction of Bioactive Compounds of Moringa oleifera Leaves with SARS-CoV-2 Proteins to Combat COVID-19 Pathogenesis: a Phytochemical and In Silico Analysis

Novel SARS-CoV-2 claimed a large number of human lives. The main proteins for viral entry into host cells are SARS-CoV-2 spike glycoprotein (PDB ID: 6VYB) and spike receptor-binding domain bound with ACE2 (spike RBD-ACE2; PDB ID: 6M0J). Currently, specific therapies are lacking globally. This study was designed to investigate the bioactive components from Moringa oleifera leaf (MOL) extract by gas chromatography-mass spectroscopy (GC–MS) and their binding interactions with spike glycoprotein and spike RBD-ACE2 protein through computational analysis. GC–MS-based analysis unveiled the presence of thirty-seven bioactive components in MOL extract, viz. polyphenols, fatty acids, terpenes/triterpenes, phytosterols/steroids, and aliphatic hydrocarbons. These bioactive phytoconstituents showed potential binding with SARS-CoV-2 spike glycoprotein and spike RBD-ACE2 protein through the AutoDock 4.2 tool. Further by using AutoDock 4.2 and AutoDock Vina, the top sixteen hits (binding energy ≥  − 6.0 kcal/mol) were selected, and these might be considered as active biomolecules. Moreover, molecular dynamics simulation was determined by the Desmond module. Interestingly two biomolecules, namely β-tocopherol with spike glycoprotein and β-sitosterol with spike RBD-ACE2, displayed the best interacting complexes and low deviations during 100-ns simulation, implying their strong stability and compactness. Remarkably, both β-tocopherol and β-sitosterol also showed the drug- likeness with no predicted toxicity. In conclusion, these findings suggested that both compounds β-tocopherol and β-sitosterol may be developed as anti-SARS-CoV-2 drugs. The current findings of in silico approach need to be optimized using in vitro and clinical studies to prove the effectiveness of phytomolecules against SARS-CoV-2.

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.

Application of Mathematical Modeling and Computational Tools in the Modern Drug Design and Development Process

The conventional drug discovery approach is an expensive and time-consuming process, but its limitations have been overcome with the help of mathematical modeling and computational drug design approaches. Previously, finding a small molecular candidate as a drug against a disease was very costly and required a long time to screen a compound against a specific target. The development of novel targets and small molecular candidates against different diseases including emerging and reemerging diseases remains a major concern and necessitates the development of novel therapeutic targets as well as drug candidates as early as possible. In this regard, computational and mathematical modeling approaches for drug development are advantageous due to their fastest predictive ability and cost-effectiveness features. Computer-aided drug design (CADD) techniques utilize different computer programs as well as mathematics formulas to comprehend the interaction of a target and drugs. Traditional methods to determine small-molecule candidates as a drug have several limitations, but CADD utilizes novel methods that require little time and accurately predict a compound against a specific disease with minimal cost. Therefore, this review aims to provide a brief insight into the mathematical modeling and computational approaches for identifying a novel target and small molecular candidates for curing a specific disease. The comprehensive review mainly focuses on biological target prediction, structure-based and ligand-based drug design methods, molecular docking, virtual screening, pharmacophore modeling, quantitative structure-activity relationship (QSAR) models, molecular dynamics simulation, and MM-GBSA/MM-PBSA approaches along with valuable database resources and tools for identifying novel targets and therapeutics against a disease. This review will help researchers in a way that may open the road for the development of effective drugs and preventative measures against a disease in the future as early as possible.

25 (S)-Hydroxycholesterol acts as a possible dual enzymatic inhibitor of SARS-CoV-2 Mpro and RdRp-: an insight from molecular docking and dynamics simulation approaches

The coronavirus disease (COVID-19) pandemic has rapidly extended globally and killed approximately 5.83 million people all over the world. But, to date, no effective therapeutic against the disease has been developed. The disease is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and enters the host cell through the spike glycoprotein (S protein) of the virus. Subsequently, RNA-dependent RNA polymerase (RdRp) and main protease (M^pro) of the virus mediate viral transcription and replication. Mechanistically inhibition of these proteins can hinder the transcription as well as replication of the virus. Recently oxysterols and its derivative, such as 25 (S)-hydroxycholesterol (25-HC) has shown antiviral activity against SARS-CoV-2. But the exact mechanisms and their impact on RdRp and M^pro have not been explored yet. Therefore, the study aimed to identify the inhibitory activity of 25-HC against the viral enzymes RdRp and M^pro simultaneously. Initially, a molecular docking simulation was carried out to evaluate the binding activity of the compound against the two proteins. The pharmacokinetics (PK) and toxicity parameters were analyzed to observe the 'drug-likeness' properties of the compound. Additionally, molecular dynamics (MD) simulation was performed to confirm the binding stability of the compound to the targeted protein. Furthermore, molecular mechanics generalized Born surface area (MM-GBSA) was used to predict the binding free energies of the compound to the targeted protein. Molecular docking simulation identified low glide energy -51.0 kcal/mol and -35.0 kcal/mol score against the RdRp and M^pro, respectively, where MD simulation found good binding stability of the compound to the targeted proteins. In addition, the MM/GBSA approach identified a good value of binding free energies (ΔG bind) of the compound to the targeted proteins. Therefore, the study concludes that the compound 25-HC could be developed as a treatment and/or prevention option for SARS-CoV-2 disease-related complications. Although, experimental validation is suggested for further evaluation of the work.Communicated by Ramaswamy H. Sarma.

Toward the Identification of Natural Antiviral Drug Candidates against Merkel Cell Polyomavirus: Computational Drug Design Approaches

Merkel cell carcinoma (MCC) is a rare form of aggressive skin cancer mainly caused by Merkel cell polyomavirus (MCPyV). Most MCC tumors express MCPyV large T (LT) antigens and play an important role in the growth-promoting activities of oncoproteins. Truncated LT promotes tumorigenicity as well as host cell proliferation by activating the viral replication machinery, and inhibition of this protein in humans drastically lowers cellular growth linked to the corresponding cancer. Our study was designed with the aim of identifying small molecular-like natural antiviral candidates that are able to inhibit the proliferation of malignant tumors, especially those that are aggressive, by blocking the activity of viral LT protein. To identify potential compounds against the target protein, a computational drug design including molecular docking, ADME (absorption, distribution, metabolism, and excretion), toxicity, molecular dynamics (MD) simulation, and molecular mechanics generalized Born surface area (MM-GBSA) approaches were applied in this study. Initially, a total of 2190 phytochemicals isolated from 104 medicinal plants were screened using the molecular docking simulation method, resulting in the identification of the top five compounds having the highest binding energy, ranging between −6.5 and −7.6 kcal/mol. The effectiveness and safety of the selected compounds were evaluated based on ADME and toxicity features. A 250 ns MD simulation confirmed the stability of the selected compounds bind to the active site (AS) of the target protein. Additionally, MM-GBSA analysis was used to determine the high values of binding free energy (ΔG bind) of the compounds binding to the target protein. The five compounds identified by computational approaches, Paulownin (CID: 3084131), Actaealactone (CID: 11537736), Epigallocatechin 3-O-cinnamate (CID: 21629801), Cirsilineol (CID: 162464), and Lycoricidine (CID: 73065), can be used in therapy as lead compounds to combat MCPyV-related cancer. However, further wet laboratory investigations are required to evaluate the activity of the drugs against the virus.