Computer-aided screening for potential TMPRSS2 inhibitors: a combination of pharmacophore modeling, molecular docking and molecular dynamics simulation approaches

Quercetin and taxifolin inhibits TMPRSS2 activity and its interaction with EGFR in paclitaxel-resistant breast cancer cells: An in silico and in vitro study

Transmembrane protease/serine (TMPRSS2), a type II transmembrane serine protease, plays a crucial role in different stages of cancer. Recent studies have reported that the triggering epidermal growth factor receptor (EGFR) activation through protease action promotes metastasis. However, there are no reports on the interaction of TMPRSS2 with EGFR, especially in triple-negative triple negative (TNBC). The current study investigates the unexplored interaction between TMPRSS2 and EGFR, which are key partners mediating metastasis. This interaction is explored for potential targeting using quercetin (QUE) and taxifolin (TAX). TMPRSS2 expression patterns in breast cancer (BC) tissues and subtypes have been predicted, with the prognostic significance assessed using the GENT2.0 database. Validation of TMPRSS2 expression was performed in normal and TNBC tissues, including drug-resistant cell lines, utilizing GEO datasets. TMPRSS2 was further validated as a predictive biomarker for FDA-approved chemotherapeutics through transcriptomic data from BC patients. The study demonstrated the association of TMPRSS2 with EGFR through in silico analysis and validates the findings in TNBC cohorts using the TIMER2.0 web server and the TCGA dataset through C-Bioportal. Molecular docking and molecular dynamic simulation studies identified QUE and TAX as best leads targeting TMPRSS2. They inhibited cell-free TMPRSS2 activity like clinical inhibitor of TMPRSS2, Camostat mesylate. In cell-based assays focused on paclitaxel-resistant TNBC (TNBC/PR), QUE and TAX demonstrated potent inhibitory activity against extracellular and membrane-bound TMPRSS2, with low IC50 values. Furthermore, ELISA and cell-based AlphaLISA assays demonstrated that QUE and TAX inhibit the interaction of TMPRSS2 with EGFR. Additionally, QUE and TAX exhibited significant inhibition of proliferation and cell cycle accompanied by notable alterations in the morphology of TNBC/PR cells. This study provides valuable insights into potential of QUE and TAX targeting TMPRSS2 overexpressing TNBC.

Investigation of the MDM2-binding potential of de novo designed peptides using enhanced sampling simulations

The tumor suppressor p53 plays a crucial role in cellular responses to various stresses, regulating key processes such as apoptosis, senescence, and DNA repair. Dysfunctional p53, prevalent in approximately 50 % of human cancers, contributes to tumor development and resistance to treatment. This study employed deep learning-based protein design and structure prediction methods to identify novel high-affinity peptide binders (Pep1 and Pep2) targeting MDM2, with the aim of disrupting its interaction with p53. Extensive all-atom molecular dynamics simulations highlighted the stability of the designed peptide in complex with the target, supported by several structural analyses, including RMSD, RMSF, Rg, SASA, PCA, and free energy landscapes. Using the steered molecular dynamics and umbrella sampling simulations, we elucidate the dissociation dynamics of p53, Pep1, and Pep2 from MDM2. Notable differences in interaction profiles were observed, emphasizing the distinct dissociation patterns of each peptide. In conclusion, the results of our umbrella sampling simulations suggest Pep1 as a higher-affinity MDM2 binder compared to p53 and Pep2, positioning it as a potential inhibitor of the MDM2-p53 interaction. Using state-of-the-art protein design tools and advanced MD simulations, this study provides a comprehensive framework for rational in silico design of peptide binders with therapeutic implications in disrupting MDM2-p53 interactions for anticancer interventions.

In silico study of alkaloids with quercetin nucleus for inhibition of SARS-CoV-2 protease and receptor cell protease

Covid-19 disease caused by the deadly SARS-CoV-2 virus is a serious and threatening global health issue declared by the WHO as an epidemic. Researchers are studying the design and discovery of drugs to inhibit the SARS-CoV-2 virus due to its high mortality rate. The main Covid-19 virus protease (Mpro) and human transmembrane protease, serine 2 (TMPRSS2) are attractive targets for the study of antiviral drugs against SARS-2 coronavirus. Increasing consumption of herbal medicines in the community and a serious approach to these drugs have increased the demand for effective herbal substances. Alkaloids are one of the most important active ingredients in medicinal plants that have wide applications in the pharmaceutical industry. In this study, seven alkaloid ligands with Quercetin nucleus for the inhibition of Mpro and TMPRSS2 were studied using computational drug design including molecular docking and molecular dynamics simulation (MD). Auto Dock software was used to evaluate molecular binding energy. Three ligands with the most negative docking score were selected to be entered into the MD simulation procedure. To evaluate the protein conformational changes induced by tested ligands and calculate the binding energy between the ligands and target proteins, GROMACS software based on AMBER03 force field was used. The MD results showed that Phyllospadine and Dracocephin-A form stable complexes with Mpro and TMPRSS2. Prolinalin-A indicated an acceptable inhibitory effect on Mpro, whereas it resulted in some structural instability of TMPRSS2. The total binding energies between three ligands, Prolinalin-A, Phyllospadine and Dracocephin-A and two proteins MPro and TMRPSS2 are (-111.235 ± 15.877, - 75.422 ± 11.140), (-107.033 ± 9.072, -84.939 ± 10.155) and (-102.941 ± 9.477, - 92.451 ± 10.539), respectively. Since the binding energies are at a minimum, this indicates confirmation of the proper binding of the ligands to the proteins. Regardless of some Prolinalin-A-induced TMPRSS2 conformational changes, it may properly bind to TMPRSS2 binding site due to its acceptable binding energy. Therefore, these three ligands can be promising candidates for the development of drugs to treat infections caused by the SARS-CoV-2 virus.

Polymeric membrane potentiometric antibiotic sensors using computer-aided screening of supramolecular macrocyclic carriers

Carrier-based polymeric membrane potentiometric sensors are an ideal tool for detecting ionic species. However, in the fabrication of these sensors, the screening of carriers still relies on empirical trial- and error-based optimization, which requires tedious and time-consuming experimental verification. In this work, computer-aided screening of carriers is applied in the preparation of polymeric membrane potentiometric sensors. Molecular docking is used to study the host-guest interactions between receptors and targets. Binding energies are employed as the standard to screen the appropriate carrier. As a proof-of-concept experiment, the antibiotic ciprofloxacin is selected as the target model. A series of supramolecular macrocyclic receptors including cyclodextrins, cucurbiturils and calixarenes are chosen as potential receptors. The proposed sensor based on the receptor calix[4]arene screened by molecular docking shows a lower detection limit of 0.5 μmol L-1 for ciprofloxacin. It can be expected that the proposed computer-aided screening technique of carriers can provide a simple but highly efficient method for the fabrication of carrier-based electrochemical and optical sensors.

Inhibitor design for TMPRSS2: insights from computational analysis of its backbone hydrogen bonds using a simple descriptor

Transmembrane protease serine 2 (TMPRSS2) is an important drug target due to its role in the infection mechanism of coronaviruses including SARS-CoV-2. Current understanding regarding the molecular mechanisms of known inhibitors and insights required for inhibitor design are limited. This study investigates the effect of inhibitor binding on the intramolecular backbone hydrogen bonds (BHBs) of TMPRSS2 using the concept of hydrogen bond wrapping, which is the phenomenon of stabilization of a hydrogen bond in a solvent environment as a result of being surrounded by non-polar groups. A molecular descriptor which quantifies the extent of wrapping around BHBs is introduced for this. First, virtual screening for TMPRSS2 inhibitors is performed by molecular docking using the program DOCK 6 with a Generalized Born surface area (GBSA) scoring function. The docking results are then analyzed using this descriptor and its relationship to the solvent-accessible surface area term ΔGsa of the GBSA score is demonstrated with machine learning regression and principal component analysis. The effect of binding of the inhibitors camostat, nafamostat, and 4-guanidinobenzoic acid (GBA) on the wrapping of important BHBs in TMPRSS2 is also studied using molecular dynamics. For BHBs with a large increase in wrapping groups due to these inhibitors, the radial distribution function of water revealed that certain residues involved in these BHBs, like Gln438, Asp440, and Ser441, undergo preferential desolvation. The findings offer valuable insights into the mechanisms of these inhibitors and may prove useful in the design of new inhibitors.

Virtual screening, activity evaluation, and stability of pancreatic lipase inhibitors in the gastrointestinal degradation of nattokinase

Nattokinase is an alkaline serine protease secreted by natto during fermentation. Despite its good thrombolytic effect, it is intolerant to gastrointestinal conditions and is easily digested and degraded into polypeptides, oligopeptides, and amino acids. However, whether these peptides inhibit fat-digesting enzymes and other biological activities remains unknown. To explore the bioactivity of peptides produced through nattokinase degradation, nattokinase was subjected to simulated digestion in the gastrointestinal tract, and 41 small peptides were obtained through the enzymolysis of gastric enzymes, pancreases, and chymotrypsin. Four pancreatic lipase (PL) inhibitory peptides (SW, ASF, GAY, and PGGTY) were selected based on their activity scores, water solubility, and toxicity predictions. The molecular docking results revealed that hydrogen bonds and electrostatic interactions were the main forces for inhibiting PL activity. The results of enzyme activity verification revealed that all four peptides inhibited PL activity. Among them, GAY exhibited the strongest inhibitory effect, with an inhibitory rate of 10.93 % at a concentration of 1 mg/mL. Molecular dynamics simulations confirmed that the GAY-1ETH complex demonstrated good stability. Natto foods containing nattokinase own the activity of inhibiting fat-digesting enzymes and show antiobesity potentials.

Advances in Drug Discovery and Design using Computer-aided Molecular Modeling

Computer-aided molecular modeling is a rapidly emerging technology that is being used to accelerate the discovery and design of new drug therapies. It involves the use of computer algorithms and 3D structures of molecules to predict interactions between molecules and their behavior in the body. This has drastically improved the speed and accuracy of drug discovery and design. Additionally, computer-aided molecular modeling has the potential to reduce costs, increase the quality of data, and identify promising targets for drug development. Through the use of sophisticated methods, such as virtual screening, molecular docking, pharmacophore modeling, and quantitative structure-activity relationships, scientists can achieve higher levels of efficacy and safety for new drugs. Moreover, it can be used to understand the activity of known drugs and simplify the process of formulating, optimizing, and predicting the pharmacokinetics of new and existing drugs. In conclusion, computer-aided molecular modeling is an effective tool to rapidly progress drug discovery and design by predicting the interactions between molecules and anticipating the behavior of new drugs in the body.

A combined in silico approaches of 2D-QSAR, molecular docking, molecular dynamics and ADMET prediction of anti-cancer inhibitor activity for actinonin derivatives

Inhibition of human mitochondrial peptide deformylase (HsPDF) plays a major role in reducing growth, proliferation, and cellular cancer survival. In this work, a series of 32 actinonin derivatives for HsPDF (PDB: 3G5K) inhibitor's anticancer activity was computationally analyzed for the first time, using an in silico study considering 2D-QSAR modeling, and molecular docking studies, and validated by molecular dynamics and ADMET properties. The results of multilinear regression (MLR) and artificial neural networks (ANN) statistical analysis reveal a good correlation between pIC50 activity and the seven (7) descriptors. The developed models were highly significant with cross-validation, the Y-randomization test and their applicability range. In addition, all considered data sets show that the AC30 compound, exhibits the best binding affinity (docking score = -212.074 kcal/mol and H-bonding energy = -15.879 kcal/mol). Furthermore, molecular dynamics simulations were performed at 500 ns, confirming the stability of the studied complexes under physiological conditions and validating the molecular docking results. Five selected actinonin derivatives (AC1, AC8, AC15, AC18 and AC30), exhibiting best docking score, were rationalized as potential leads for HsPDF inhibition, in well agreement with experimental outcomes. Furthermore, based on the in silico study, new six molecules (AC32, AC33, AC34, AC35, AC36 and AC37) were suggested as HsPDF inhibition candidates, which would be combined with in-vitro and in-vivo studies to perspective validation of their anticancer activity. Indeed, the ADMET predictions indicate that these six new ligands have demonstrated a fairly good drug-likeness profile.

CSC01 shows promise as a potential inhibitor of the oncogenic G13D mutant of KRAS: an in silico approach

About 30% of malignant tumors include KRAS mutations, which are frequently required for the development and maintenance of malignancies. KRAS is now a top-priority cancer target as a result. After years of research, it is now understood that the oncogenic KRAS-G12C can be targeted. However, many other forms, such as the G13D mutant, are yet to be addressed. Here, we used a receptor-based pharmacophore modeling technique to generate potential inhibitors of the KRAS-G13D oncogenic mutant. Using a comprehensive virtual screening workflow model, top hits were selected, out of which CSC01 was identified as a promising inhibitor of the oncogenic KRAS mutant (G13D). The stability of CSC01 upon binding the switch II pocket was evaluated through an exhaustive molecular dynamics simulation study. The several post-simulation analyses conducted suggest that CSC01 formed a stable complex with KRAS-G13D. CSC01, through a dynamic protein-ligand interaction profiling analysis, was also shown to maintain strong interactions with the mutated aspartic acid residue throughout the simulation. Although binding free energy analysis through the umbrella sampling approach suggested that the affinity of CSC01 with the switch II pocket of KRAS-G13D is moderate, our DFT analysis showed that the stable interaction of the compound might be facilitated by the existence of favorable molecular electrostatic potentials. Furthermore, based on ADMET predictions, CSC01 demonstrated a satisfactory drug likeness and toxicity profile, making it an exemplary candidate for consideration as a potential KRAS-G13D inhibitor.

Design of new Mcl-1 inhibitors for cancer using fragments hybridization, molecular docking, and molecular dynamics studies

Apoptosis is a critical process that regulates cell survival and death and plays an essential role in cancer development. The Bcl-2 protein family, including myeloid leukemia 1 (Mcl-1), is a key regulator of the intrinsic apoptosis pathway, and its overexpression in many human cancers has prompted efforts to develop Mcl-1 inhibitors as potential anticancer agents. In this study, we aimed to design new Mcl-1 inhibitors using various computational techniques. First, we used the Mcl-1 receptor-ligand complex to build an e-pharmacophore hypothesis and screened a library of 567,000 fragments from the Enamine database. We obtained 410 fragments and used them to design 92,384 novel compounds, which we then docked into the Mcl-1 binding cavity using HTVS, SP, and XP docking modes of Glide. To assess their suitability as drug candidates, we conducted MM-GBSA calculations and ADME prediction, leading to the identification of 10 compounds with excellent binding affinity and favorable pharmacokinetic properties. To further investigate the interaction strength, we performed molecular dynamics simulations on the top three Mcl-1 receptor-ligand complexes to study their interaction stability. Overall, our findings suggest that these compounds have promising potential as anticancer agents, pending further experimental validation such as Mcl-1 apoptosis Assay. By combining experimental methods with various in silico approaches, these techniques prove to be invaluable for identifying novel drug candidates with distinct therapeutic applications using fragment-based drug design. This methodology has the potential to expedite the drug discovery process while also reducing its costs.Communicated by Ramaswamy H. Sarma.

Nuclear receptor superfamily structural diversity in pacific oyster: In silico identification of estradiol binding candidates

The increasing presence of anthropogenic contaminants in aquatic environments poses challenges for species inhabiting contaminated sites. Due to their structural binding characteristics to ligands that inhibit or activate gene transcription, these xenobiotic compounds frequently target the nuclear receptor superfamily. The present work aims to understand the potential interaction between the hormone 17-β-estradiol, an environmental contaminant, and the nuclear receptors of Crassostrea gigas, the Pacific oyster. This filter-feeding, sessile oyster species is subject to environmental changes and exposure to contaminants. In the Pacific oyster, the estrogen-binding nuclear receptor is not able to bind this hormone as it does in vertebrates. However, another receptor may exhibit responsiveness to estrogen-like molecules and derivatives. We employed high-performance in silico methodologies, including three-dimensional modeling, molecular docking and atomistic molecular dynamics to identify likely binding candidates with the target moecule. Our approach revealed that among the C. gigas nuclear receptor superfamily, candidates with the most favorable interaction with the molecule of interest belonged to the NR1D, NR1H, NR1P, NR2E, NHR42, and NR0B groups. Interestingly, NR1H and NR0B were associated with planktonic/larval life cycle stages, while NR1P, NR2E, and NR0B were associated with sessile/adult life stages. The application of this computational methodological strategy demonstrated high performance in the virtual screening of candidates for binding with the target xenobiotic molecule and can be employed in other studies in the field of ecotoxicology in non-model organisms.

Herbal combinations against COVID-19: A network pharmacology, molecular docking and dynamics study

OBJECTIVE

The aim of this study is to identify molecules from traditional Chinese medicine (TCM) with potential activity against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its variants.

METHODS

We applied the Apriori algorithm to identify important combinations of herbs in the TCM prescriptions for the treatment of coronavirus disease 2019 (COVID-19). Then, we explored the active components and core targets using network pharmacology. In addition, the molecular docking approach was performed to investigate the interaction of these components with the main structural and non-structural proteins, as well as the mutants. Furthermore, their stability in the binding pockets was further evaluated with the molecular dynamics approach.

RESULTS

A combination of Amygdalus Communis Vas., Ephedra Herba and Scutellaria baicalensis Georgi was selected as the important herbal combination, and 11 main components and 20 core targets against COVID-19 were obtained. These components, including luteolin, naringenin, stigmasterol, baicalein, and so on, were the potentially active compounds against COVID-19. The binding affinity of these compounds with the potential targets was as high as the positive controls. Among them, baicalein could interfere with multiple targets simultaneously, and it also interfered with the interaction between spike protein and angiotensin-converting enzyme 2 receptor. Additionally, almost all the systems reached stability during dynamics simulation.

CONCLUSION

The combination of A. communis, Ephedra Herba and S. baicalensis was the most important herbal combination for the treatment of COVID-19. Baicalein may be a potential candidate against SARS-CoV-2 and its variants. Please cite this article as: Song JB, Zhao LQ, Wen HP, Li YP. Herbal combinations against COVID-19: A network pharmacology, molecular docking and dynamics study. J Integr Med. 2023;21(6):593-604.

Computer-assisted drug discovery of potential natural inhibitors of the SARS-CoV-2 RNA-dependent RNA polymerase through a multi-phase in silico approach

BACKGROUND

The COVID-19 pandemic has led to significant loss of life and economic disruption worldwide. Currently, there are limited effective treatments available for this disease. SARS-CoV-2 RNA-dependent RNA polymerase (SARS-CoV-2 RdRp) has been identified as a potential target for drug development against COVID-19. Natural products have been shown to possess antiviral properties, making them a promising source for developing drugs against SARS-CoV-2.

OBJECTIVES

The objective of this study is to identify the most effective natural inhibitors of SARS-CoV-2 RdRp among a set of 4924 African natural products using a multi-phase in silico approach.

METHODS

The study utilized remdesivir (RTP), the co-crystallized ligand of RdRp, as a starting point to select compounds that have the most similar chemical structures among the examined set of compounds. Molecular fingerprints and structure similarity studies were carried out in the first part of the study. The second part of the study included molecular docking against SARS-CoV-2 RdRp (PDB ID: 7BV2) and Molecular Dynamics (MD) simulations including the calculation of RMSD, RMSF, Rg, SASA, hydrogen bonding, and PLIP. Moreover, the calculations of Molecular mechanics with generalised Born and surface area solvation (MM-GBSA) Lennard-Jones and Columbic electrostatic interaction energies have been conducted. Additionally, in silico ADMET and toxicity studies were performed to examine the drug likeness degrees of the selected compounds.

RESULTS

Eight compounds were identified as the most effective natural inhibitors of SARS-CoV-2 RdRp. These compounds are kaempferol 3-galactoside, kaempferol 3-O-β-D-glucopyranoside, mangiferin methyl ether, luteolin 7-O-β-D-glucopyranoside, quercetin-O-β-D-3-glucopyranoside, 1-methoxy-3-indolylmethyl glucosinolate, naringenin, and asphodelin A 4'-O-β-D-glucopyranoside.

CONCLUSION

The results of this study provide valuable information for the development of natural product-based drugs against COVID-19. However, the elected compounds should be further studied in vitro and in vivo to confirm their efficacy in treating COVID-19.

CHAPERONg: A tool for automated GROMACS-based molecular dynamics simulations and trajectory analyses

Molecular dynamics (MD) simulation is a powerful computational tool used in biomolecular studies to investigate the dynamics, energetics, and interactions of a wide range of biological systems at the atomic level. GROMACS is a widely used free and open-source biomolecular MD simulation software recognized for its efficiency, accuracy, and extensive range of simulation options. However, the complexity of setting up, running, and analyzing MD simulations for diverse systems often poses a significant challenge, requiring considerable time, effort, and expertise. Here, we introduce CHAPERONg, a tool that automates the GROMACS MD simulation pipelines for protein and protein-ligand systems. CHAPERONg also integrates seamlessly with GROMACS modules and third-party tools to provide comprehensive analyses of MD simulation trajectories, offering up to 20 post-simulation processing and trajectory analyses. It also streamlines and automates established pipelines for conducting and analyzing biased MD simulations via the steered MD-umbrella sampling workflow. Thus, CHAPERONg makes MD simulations more accessible to beginner GROMACS users whilst empowering experts to focus on data interpretation and other less programmable aspects of MD simulation workflows. CHAPERONg is written in Bash and Python, and the source code is freely available at https://github.com/abeebyekeen/CHAPERONg. Detailed documentation and tutorials are available online at dedicated web pages accessible via https://abeebyekeen.com/chaperong-online.

Promising disruptors of p53-MDM2 dimerization from some medicinal plant phytochemicals: a molecular modeling study

Cancer is a major global health issue that has a high mortality rate. p53, which functions as a tumor suppressor, is critical in preventing tumor development by regulating the cell cycle and inducing apoptosis in damaged cells. However, the tumor suppressor function of p53 is effectively inhibited by its direct interaction with the hydrophobic cleft of MDM2 protein via multiple mechanisms As a result, restoring p53 activity by blocking the p53-MDM2 protein-protein interaction has been proposed as a compelling therapeutic strategy for cancer treatment. The use of molecular docking and phytochemical screening procedures are appraised to inhibit MDM2's hydrophobic cleft and disrupt the p53-MDM2 interaction. For this purpose, a library of 51 bioactive compounds from 10 medicinal plants was compiled and subjected to structure-based virtual screening. Out of these, only 3 compounds (Atalantoflavone, Cudraxanthone 1, and Ursolic acid) emerged as promising inhibitors of MDM2-p53 based on their binding affinities (-9.1 kcal/mol, -8.8 kcal/mol, and -8.8 kcal/mol respectively) when compared to the standard (-8.8 kcal/mol). Moreover, these compounds showed better pharmacokinetic and drug-like profiling than the standard inhibitor (Chromonotriazolopyrimidine 1). Finally, the 100 ns MD simulation analysis confirmed no significant perturbation in the conformational dynamics of the simulated binary complexes when compared to the standard. In particular, Ursolic acid was found to satisfy the molecular enumeration the most compared to the other inhibitors. Our overall molecular modeling finding shows why these compounds may emerge as potent arsenals for cancer therapeutics. Nonetheless, extensive experimental and clinical research is needed to augment their use in clinics.Communicated by Ramaswamy H. Sarma.

Evaluation of therapeutic potentials of selected phytochemicals against Nipah virus, a multi-dimensional in silico study

The current study attempted to evaluate the potential of fifty-three (53) natural compounds as Nipah virus attachment glycoprotein (NiV G) inhibitors through in silico molecular docking study. Pharmacophore alignment of the four (4) selected compounds (Naringin, Mulberrofuran B, Rutin and Quercetin 3-galactoside) through Principal Component Analysis (PCA) revealed that common pharmacophores, namely four H bond acceptors, one H bond donor and two aromatic groups were responsible for the residual interaction with the target protein. Out of these four compounds, Naringin was found to have the highest inhibitory potential ( - 9.19 kcal mol^-1) against the target protein NiV G, when compared to the control drug, Ribavirin ( - 6.95 kcal mol^-1). The molecular dynamic simulation revealed that Naringin could make a stable complex with the target protein in the near-native physiological condition. Finally, MM-PBSA (Molecular Mechanics-Poisson-Boltzmann Solvent-Accessible Surface Area) analysis in agreement with our molecular docking result, showed that Naringin ( - 218.664 kJ mol^-1) could strongly bind with the target protein NiV G than the control drug Ribavirin ( - 83.812 kJ mol^-1).

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-023-03595-y.

Identification of Potential TMPRSS2 Inhibitors for COVID-19 Treatment in Chinese Medicine by Computational Approaches and Surface Plasmon Resonance Technology

BACKGROUND

Coronavirus disease-19 (COVID-19) pneumonia continues to spread in the entire globe with limited medication available. In this study, the active compounds in Chinese medicine (CM) recipes targeting the transmembrane serine protease 2 (TMPRSS2) protein for the treatment of COVID-19 were explored.

METHODS

The conformational structure of TMPRSS2 protein (TMPS2) was built through homology modeling. A training set covering TMPS2 inhibitors and decoy molecules was docked to TMPS2, and their docking poses were re-scored with scoring schemes. A receiver operating characteristic (ROC) curve was applied to select the best scoring function. Virtual screening of the candidate compounds (CCDs) in the six highly effective CM recipes against TMPS2 was conducted based on the validated docking protocol. The potential CCDs after docking were subject to molecular dynamics (MD) simulations and surface plasmon resonance (SPR) experiment.

RESULTS

A training set of 65 molecules were docked with modeled TMPS2 and LigScore2 with the highest area under the curve, AUC, value (0.886) after ROC analysis selected to best differentiate inhibitors from decoys. A total of 421 CCDs in the six recipes were successfully docked into TMPS2, and the top 16 CCDs with LigScore2 higher than the cutoff (4.995) were screened out. MD simulations revealed a stable binding between these CCDs and TMPS2 due to the negative binding free energy. Lastly, SPR experiments validated the direct combination of narirutin, saikosaponin B1, and rutin with TMPS2.

CONCLUSIONS

Specific active compounds including narirutin, saikosaponin B1, and rutin in CM recipes potentially target and inhibit TMPS2, probably exerting a therapeutic effect on COVID-19.

MasitinibL shows promise as a drug-like analog of masitinib that elicits comparable SARS-Cov-2 3CLpro inhibition with low kinase preference

SARS-CoV-2 infection has led to several million deaths worldwide and ravaged the economies of many countries. Hence, developing therapeutics against SARS-CoV-2 remains a core priority in the fight against COVID-19. Most of the drugs that have received emergency use authorization for treating SARS-CoV-2 infection exhibit a number of limitations, including side effects and questionable efficacy. This challenge is further compounded by reinfection after vaccination and the high likelihood of mutations, as well as the emergence of viral escape mutants that render SARS-CoV-2 spike glycoprotein-targeting vaccines ineffective. Employing de novo drug synthesis or repurposing to discover broad-spectrum antivirals that target highly conserved pathways within the viral machinery is a focus of current research. In a recent drug repurposing study, masitinib, a clinically safe drug against the human coronavirus OC43 (HCoV-OC43), was identified as an antiviral agent with effective inhibitory activity against the SARS-CoV-2 3CLpro. Masitinib is currently under clinical trial in combination with isoquercetin in hospitalized patients (NCT04622865). Nevertheless, masitinib has kinase-related side effects; hence, the development of masitinib analogs with lower anti-tyrosine kinase activity becomes necessary. In this study, in an attempt to address this limitation, we executed a comprehensive virtual workflow in silico to discover drug-like compounds matching selected pharmacophore features in the SARS-CoV-2 3CLpro-bound state of masitinib. We identified a novel lead compound, "masitinibL", a drug-like analog of masitinib that demonstrated strong inhibitory properties against the SARS-CoV-2 3CLpro. In addition, masitinibL further displayed low selectivity for tyrosine kinases, which strongly suggests that masitinibL is a highly promising therapeutic that is preferable to masitinib.

Abdallah M, M. Mukhtar R, Moutasim F, Rafie Ahmed A, Edris A, Ibraheem W, Makki AA, M. Elshamly E, Elhag R, Osman W, A. Mothana R, Alzain AA. Identification of Novel Natural Dual HDAC and Hsp90 Inhibitors for Metastatic TNBC Using e-Pharmacophore Modeling, Molecular Docking, and Molecular Dynamics Studies

Breast cancer (BC) is one of the main types of cancer that endangers women's lives. The characteristics of triple-negative breast cancer (TNBC) include a high rate of recurrence and the capacity for metastasis; therefore, new therapies are urgently needed to combat TNBC. Dual targeting HDAC6 and Hsp90 has shown good synergistic effects in treating metastatic TNBC. The goal of this study was to find potential HDAC6 and Hsp90 dual inhibitors. Therefore, several in silico approaches have been used. An e-pharmacophore model generation based on the HDAC6-ligand complex and subsequently a pharmacophore-based virtual screening on 270,450 natural compounds from the ZINC were performed, which resulted in 12,663 compounds that corresponded to the obtained pharmacophoric hypothesis. These compounds were docked into HDAC6 and Hsp90. This resulted in the identification of three compounds with good docking scores and favorable free binding energy against the two targets. The top three compounds, namely ZINC000096116556, ZINC000020761262, and ZINC000217668954, were further subjected to ADME prediction and molecular dynamic simulations, which showed promising results in terms of pharmacokinetic properties and stability. As a result, these three compounds can be considered potential HDAC6 and Hsp90 dual inhibitors and are recommended for experimental evaluation.

Exploration of Computational Aids for Effective Drug Designing and Management of Viral Diseases: A Comprehensive Review

BACKGROUND

Microbial diseases, specifically originating from viruses are the major cause of human mortality all over the world. The current COVID-19 pandemic is a case in point, where the dynamics of the viral-human interactions are still not completely understood, making its treatment a case of trial and error. Scientists are struggling to devise a strategy to contain the pandemic for over a year and this brings to light the lack of understanding of how the virus grows and multiplies in the human body.

METHODS

This paper presents the perspective of the authors on the applicability of computational tools for deep learning and understanding of host-microbe interaction, disease progression and management, drug resistance and immune modulation through in silico methodologies which can aid in effective and selective drug development. The paper has summarized advances in the last five years. The studies published and indexed in leading databases have been included in the review.

RESULTS

Computational systems biology works on an interface of biology and mathematics and intends to unravel the complex mechanisms between the biological systems and the inter and intra species dynamics using computational tools, and high-throughput technologies developed on algorithms, networks and complex connections to simulate cellular biological processes.

CONCLUSION

Computational strategies and modelling integrate and prioritize microbial-host interactions and may predict the conditions in which the fine-tuning attenuates. These microbial-host interactions and working mechanisms are important from the aspect of effective drug designing and fine- tuning the therapeutic interventions.

Molecular docking-based virtual screening: Challenges in hits identification for Anti-SARS-Cov-2 activity

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) requires finding new drugs or repurposing drugs for clinical use. Molecular docking belongs to structure-based drug design providing a fast method for identifying the hit compounds with antiviral activity against SARS-Cov-2. However, the weakness of the docking method is compounded by the limited crystallographic information and comparison drugs due to the novelty of this virus can present challenges in identifying hits of anti-SARS-Cov-2. In the current review, we highlighted several aspects, especially those related to the target structure, docking validation, and virtual hit selection, that need to be considered to obtain reliable docking results. Here, we discussed several cases pertaining to the issue highlighted and approaches that could be used to solve them.

Discovery of novel potential inhibitors of TMPRSS2 and Mpro of SARS-CoV-2 using E-pharmacophore and docking-based virtual screening combined with molecular dynamic and quantum mechanics

The pandemic of coronavirus disease is caused by the SARS-CoV-2 which is considered a global health issue. The main protease of COVID 19 (Mpro) has an important role in viral multiplication in the host cell. Inhibiting Mpro is a novel approach to drug discovery and development. Also, transmembrane serine proteases (TMPSS2) facilitate viral activation by cleavage S glycoproteins, thus considered one of the essential host factors for COVID-19 pathogenicity. Computational tools were widely used to reduce time and costs in search of effective inhibitors. A chemical library that contains over two million molecules was virtually screened against TMPRSS2. Also, XP docking for the top hits was screened against (Mpro) to identify dual-target inhibitors. Furthermore, MM-GBSA and predictive ADMET were performed. The top hits were further studied through density functional theory (DFT) calculation and showed good binding to the active sites. Moreover, molecular dynamics (MD) for the top hits were performed which gave information about the stability of the protein-ligand complex during the simulation period. This study has led to the discovery of potential dual-target inhibitors Z751959696, Z751954014, and Z56784282 for COVID-19 with acceptable pharmacokinetic properties. The outcome of this study can participate in the development of novel inhibitors to defeat SARS-CoV-2.Communicated by Ramaswamy H. Sarma.

Computer-aided molecular modeling and structural analysis of the human centromere protein–HIKM complex

Background

Protein–peptide and protein–protein interactions play an essential role in different functional and structural cellular organizational aspects. While Cryo-EM and X-ray crystallography generate the most complete structural characterization, most biological interactions exist in biomolecular complexes that are neither compliant nor responsive to direct experimental analysis. The development of computational docking approaches is therefore necessary. This starts from component protein structures to the prediction of their complexes, preferentially with precision close to complex structures generated by X-ray crystallography.

Results

To guarantee faithful chromosomal segregation, there must be a proper assembling of the kinetochore (a protein complex with multiple subunits) at the centromere during the process of cell division. As an important member of the inner kinetochore, defects in any of the subunits making up the CENP-HIKM complex lead to kinetochore dysfunction and an eventual chromosomal mis-segregation and cell death. Previous studies in an attempt to understand the assembly and mechanism devised by the CENP-HIKM in promoting the functionality of the kinetochore have reconstituted the protein complex from different organisms including fungi and yeast. Here, we present a detailed computational model of the physical interactions that exist between each component of the human CENP-HIKM, while validating each modeled structure using orthologs with existing crystal structures from the protein data bank.

Conclusions

Results from this study substantiate the existing hypothesis that the human CENP-HIK complex shares a similar architecture with its fungal and yeast orthologs, and likewise validate the binding mode of CENP-M to the C-terminus of the human CENP-I based on existing experimental reports.

Graphical abstract

De Novo Design of Cathepsin B1 Inhibitors as Potential Anti-Schistosomal Agents Using Computational Studies

Background

Schistosomiasis is the world’s second most devastating disease after malaria and the leading cause of disease and mortality for more than 200 million people in developing countries. Cysteine proteases, in particular SmCB1, are the most well-researched biological targets for this disorder.

Objective

To apply computational techniques to design new antischistosomal agents against SmCB1 protein with favorable pharmacokinetic properties.

Methods

The smCB1 receptor-based pharmacophore model was created and used to screen 567,000 fragments from the Enamine library. The best scoring fragments have been linked to build novel compounds that were subjected to molecular docking, MM-GBSA free energy estimation, ADME prediction, and molecular dynamics.

Results

A seven-point pharmacophore hypothesis ADDDRRR was created. The developed hypothesis was used to screen 1.3 M fragment conformations. Among them, 23,732 fragments matched the hypothesis and screened against the protein. The top 50 fragments were used to design new 7745 compounds using the Breed ligand panel which were subjected to docking and MMGBSA binding energy. This led to the identification of 10 compounds with better docking scores (−8.033– −7.483 kcal/mol) and lower-bound free energies (−58.49 – −40.02 kcal/mol) compared to the reference bound ligand. Most of the designed compounds demonstrated good drug-like properties. Concerning Molecular dynamics (MD) simulation results, a low root mean square deviation (RMSD) range (0.25–1.2 Å) was found for the top 3 complexes which indicated their stability.

Conclusion

We identified compounds that could be potential candidates in the search for novel Schistosoma mansoni inhibitors by targeting SmCB1 utilizing various computational tools. Three newly designed compounds namely breed 1, 2, and 3 showed promising affinity to the target as well as favorable drug-like properties which might be considered potential anti-schistosomal agents.

Discovery of novel natural products as dual MNK/PIM inhibitors for acute myeloid leukemia treatment: Pharmacophore modeling, molecular docking, and molecular dynamics studies

MNK-2 and PIM-2 kinases play an indispensable role in cell proliferation signaling pathways linked to tyrosine kinase inhibitors resistance. In this study, pharmacophore modeling studies have been conducted on the co-crystalized ligands of MNK-2 and PIM-2 enzyme crystal structures to determine the essential features required for the identification of potential dual inhibitors. The obtained pharmacophore features were then screened against a library of 270,540 natural products from the ZINC database. The matched natural molecules were docked into the binding sites of MNK-2 and PIM-2 enzymes. The compounds with high docking scores with the two enzymes were further subjected to MM-GBSA calculations and ADME prediction. This led to the identification of compound 1 (ZINC000085569211), compound 2 (ZINC000085569178), and compound 3 (ZINC000085569190), with better docking scores compared to the reference co-crystallized ligands of MNK-2 and PIM-2. Moreover, compounds 1‒3 displayed better MM-GBSA binding free energies compared to the reference ligands. Finally, molecular dynamics (MD) study was used to assess the interaction stability of the compounds with MNK-2. To this end, compounds 1 and 3 bound strongly to the target during the whole period of MD simulation. The findings of the current study may further help the researchers in the discovery of novel molecules against MNK-2 and PIM-2.

SperoPredictor: An Integrated Machine Learning and Molecular Docking-Based Drug Repurposing Framework With Use Case of COVID-19

The global spread of the SARS coronavirus 2 (SARS-CoV-2), its manifestation in human hosts as a contagious disease, and its variants have induced a pandemic resulting in the deaths of over 6,000,000 people. Extensive efforts have been devoted to drug research to cure and refrain the spread of COVID-19, but only one drug has received FDA approval yet. Traditional drug discovery is inefficient, costly, and unable to react to pandemic threats. Drug repurposing represents an effective strategy for drug discovery and reduces the time and cost compared to de novo drug discovery. In this study, a generic drug repurposing framework (SperoPredictor) has been developed which systematically integrates the various types of drugs and disease data and takes the advantage of machine learning (Random Forest, Tree Ensemble, and Gradient Boosted Trees) to repurpose potential drug candidates against any disease of interest. Drug and disease data for FDA-approved drugs (n = 2,865), containing four drug features and three disease features, were collected from chemical and biological databases and integrated with the form of drug-disease association tables. The resulting dataset was split into 70% for training, 15% for testing, and the remaining 15% for validation. The testing and validation accuracies of the models were 99.3% for Random Forest and 99.03% for Tree Ensemble. In practice, SperoPredictor identified 25 potential drug candidates against 6 human host-target proteomes identified from a systematic review of journals. Literature-based validation indicated 12 of 25 predicted drugs (48%) have been already used for COVID-19 followed by molecular docking and re-docking which indicated 4 of 13 drugs (30%) as potential candidates against COVID-19 to be pre-clinically and clinically validated. Finally, SperoPredictor results illustrated the ability of the platform to be rapidly deployed to repurpose the drugs as a rapid response to emergent situations (like COVID-19 and other pandemics).

Ligand and Structure-Based In Silico Determination of the Most Promising SARS-CoV-2 nsp16-nsp10 2'-o-Methyltransferase Complex Inhibitors among 3009 FDA Approved Drugs

As a continuation of our earlier work against SARS-CoV-2, seven FDA-approved drugs were designated as the best SARS-CoV-2 nsp16-nsp10 2'-o-methyltransferase (2'OMTase) inhibitors through 3009 compounds. The in silico inhibitory potential of the examined compounds against SARS-CoV-2 nsp16-nsp10 2'-o-methyltransferase (PDB ID: (6W4H) was conducted through a multi-step screening approach. At the beginning, molecular fingerprints experiment with SAM (S-Adenosylmethionine), the co-crystallized ligand of the targeted enzyme, unveiled the resemblance of 147 drugs. Then, a structural similarity experiment recommended 26 compounds. Therefore, the 26 compounds were docked against 2'OMTase to reveal the potential inhibitory effect of seven promising compounds (Protirelin, (1187), Calcium folinate (1913), Raltegravir (1995), Regadenoson (2176), Ertapenem (2396), Methylergometrine (2532), and Thiamine pyrophosphate hydrochloride (2612)). Out of the docked ligands, Ertapenem (2396) showed an ideal binding mode like that of the co-crystallized ligand (SAM). It occupied all sub-pockets of the active site and bound the crucial amino acids. Accordingly, some MD simulation experiments (RMSD, RMSF, Rg, SASA, and H-bonding) have been conducted for the 2'OMTase-Ertapenem complex over 100 ns. The performed MD experiments verified the correct binding mode of Ertapenem against 2'OMTase exhibiting low energy and optimal dynamics. Finally, MM-PBSA studies indicated that Ertapenem bonded advantageously to the targeted protein with a free energy value of -43 KJ/mol. Furthermore, the binding free energy analysis revealed the essential amino acids of 2'OMTase that served positively to the binding. The achieved results bring hope to find a treatment for COVID-19 via in vitro and in vivo studies for the pointed compounds.

Computer-Aided Drug Design of Anti-inflammatory Agents Targeting Microsomal Prostaglandin E2 Synthase-1 (mPGES-1)

Inflammation is a natural process in response to external stimuli associated with organism protection. However, this reaction could be exaggerated, leading to severe damages related to physiopathological processes, such as rheumatoid arthritis, cancer, diabetes, allergies, infections, among others. Inflammation is mainly characterized by pain, increased temperature, flushing, and edema, which can be controlled using anti-inflammatory drugs. In this context, prostaglandin E2 (PGE2) inhibition has been targeted for designing new compounds with anti-inflammatory properties. It is a bioactive lipid overproduced during an inflammatory process, in which its increased production is carried out mainly by COX-1, COX-2, and microsomal prostaglandin E2 synthase-1 (mPGES-1). Recently, studies have demonstrated that mPGES-1 inhibition is a safe strategy to develop anti-inflammatory agents, which could protect against pain, acute inflammation, arthritis, autoimmune diseases, and different types of cancers. To decrease production costs and increase the probability of discovering active substances, computer-aided drug design (CADD) approaches have been increasingly used for designing new inhibitors. Thus, this review will cover all aspects involving high-throughput virtual screening, molecular docking, dynamics, fragment-based drug design, quantitative structure-activity relationship in seeking new promising mPGES-1 inhibitors.

Discovery of novel TMPRSS2 inhibitors for COVID-19 using in silico fragment-based drug design, molecular docking, molecular dynamics, and quantum mechanics studies

The global expansion of COVID-19 and the mutations of severe acute respiratory syndrome coronavirus necessitate quick development of treatment and vaccination. Because the androgen-responsive serine protease TMPRSS2 is involved in cleaving the SARS-CoV-2 spike protein allowing the virus to enter the cell, therefore, direct TMPRSS2 inhibition will inhibit virus activation and disease progression which make it an important target for drug discovery. In this study, a homology model of TMPRSS2 protein was initially developed. Then, we used the fragment-based drug design (FBDD) technique to develop effective TMPRSS2 inhibitors. Over a half-million fragments from the enamine database were screened for their binding ability to target protein, and then best-scoring fragments were linked to building new molecules with a good binding affinity. XP docking and MM-GBSA studies revealed 10 new formed molecules with docking score ≤ -14.982 kcal/mol compared to ambroxol (control) with a docking score of -6.464 kcal/mol. Finally, molecular dynamics (MD) and density functional theory (DFT) were calculated for the top 3 molecules.

Active site-specific quantum tunneling of hACE2 receptor to assess its complexing poses with selective bioactive compounds in co-suppressing SARS-CoV-2 influx and subsequent cardiac injury

Objective:

This research aims to study the target specificity of selective bioactive compounds in complexing with the human angiotensin-converting enzyme (hACE2) receptor to impede the severe acute respiratory syndrome coronavirus 2 influx mechanism resulting in cardiac injury and depending on the receptor’s active site properties and quantum tunneling.

Materials and Methods:

A library of 120 phytochemical ligands was prepared, from which 5 were selected considering their absorption, distribution, metabolism, and excretion (ADMET) and quantitative structure–activity relationship (QSAR) profiles. The protein active sites and belonging quantum tunnels were defined to conduct supramolecular docking of the aforementioned ligands. The hydrogen bond formation and hydrophobic interactions between the ligand–receptor complexes were studied following the molecular docking steps. A comprehensive molecular dynamic simulation (MDS) was conducted for each of the ligand–receptor complexes to figure out the values – root mean square deviation (RMSD) (Å), root mean square fluctuation (RMSF) (Å), H-bonds, Cα, solvent accessible surface area (SASA) (Ų), molecular surface area (MolSA) (Ų), Rg (nm), and polar surface area (PSA) (Å). Finally, computational programming and algorithms were used to interpret the dynamic simulation outputs into their graphical quantitative forms.

Results:

ADMET and QSAR profiles revealed that the most active candidates from the library to be used were apigenin, isovitexin, piperolactam A, and quercetin as test ligands, whereas serpentine as the control. Based on the binding affinities of supramolecular docking and the parameters of molecular dynamic simulation, the strength of the test ligands can be classified as isovitexin > quercetin > piperolactam A > apigenin when complexed with the hACE2 receptor. Surprisingly, serpentine showed lower affinity (−8.6 kcal/mol) than that of isovitexin (−9.9 kcal/mol) and quercetin (−8.9 kcal/mol). The MDS analysis revealed all ligands except isovitexin having a value lower than 2.5 Ǻ. All the test ligands exhibited acceptable fluctuation ranges of RMSD (Å), RMSF (Å), H-bonds, Cα, SASA (Ų), MolSA (Ų), Rg (nm), and PSA (Å) values.

Conclusion:

Considering each of the parameters of molecular optimization, docking, and dynamic simulation interventions, all of the test ligands can be suggested as potential targeted drugs in blocking the hACE2 receptor.

Repurposing FDA-approved Drugs Targeting SARS-CoV2 3CLpro: a study by applying Virtual Screening, Molecular Dynamics, MM-PBSA Calculations and Covalent Docking

Background:

Since the end of 2019, the etiologic agent SAR-CoV-2 responsible for one of the most significant epidemics in history has caused severe global economic, social, and health damages. The drug repurposing approach and application of Structure-based Drug Discovery (SBDD) using in silico techniques are increasingly frequent, leading to the identification of several molecules that may represent promising potential.

Method:

In this context, here we use in silico methods of virtual screening (VS), pharmacophore modeling (PM), and fragment-based drug design (FBDD), in addition to molecular dynamics (MD), molecular mechanics/Poisson-Boltzmann surface area (MM -PBSA) calculations, and covalent docking (CD) for the identification of potential treatments against SARS-CoV-2. We initially validated the docking protocol followed by VS in 1,613 FDA-approved drugs obtained from the ZINC database. Thus, we identified 15 top hits, of which three of them were selected for further simulations. In parallel, for the compounds with a fit score value ≤ of 30, we performed the FBDD protocol, where we designed 12 compounds

Result:

By applying a PM protocol in the ZINC database, we identified three promising drug candidates. Then, the 9 top hits were evaluated in simulations of MD, MM-PBSA, and CD. Subsequently, MD showed that all identified hits showed stability at the active site without significant changes in the protein's structural integrity, as evidenced by the RMSD, RMSF, Rg, SASA graphics. They also showed interactions with the catalytic dyad (His41 and Cys145) and other essential residues for activity (Glu166 and Gln189) and high affinity for MM-PBSA, with possible covalent inhibition mechanism.

Conclution:

Finally, our protocol helped identify potential compounds wherein ZINC896717 (Zafirlukast), ZINC1546066 (Erlotinib), and ZINC1554274 (Rilpivirine) were more promising and could be explored in vitro, in vivo, and clinical trials to prove their potential as antiviral agents.

Computational screening of phytochemicals from three medicinal plants as inhibitors of transmembrane protease serine 2 implicated in SARS-CoV-2 infection

Background

SARS-CoV-2 infection or COVID-19 is a major global public health issue that requires urgent attention in terms of drug development. Transmembrane Protease Serine 2 (TMPRSS2) is a good drug target against SARS-CoV-2 because of the role it plays during the viral entry into the cell. Virtual screening of phytochemicals as potential inhibitors of TMPRSS2 can lead to the discovery of drug candidates for the treatment of COVID-19.

Purpose

The study was designed to screen 132 phytochemicals from three medicinal plants traditionally used as antivirals; Zingiber officinalis Roscoe (Zingiberaceae), Artemisia annua L. (Asteraceae), and Moringa oleifera Lam. (Moringaceae), as potential inhibitors of TMPRSS2 for the purpose of finding therapeutic options to treat COVID-19.

Methods

Homology model of TMPRSS2 was built using the ProMod3 3.1.1 program of the SWISS-MODEL. Binding affinities and interaction between compounds and TMPRSS2 model was examined using molecular docking and molecular dynamics simulation. The drug-likeness and ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties of potential inhibitors of TMPRSS2 were also assessed using admetSAR web tool.

Results

Three compounds, namely, niazirin, quercetin, and moringyne from M. oleifera demonstrated better molecular interactions with binding affinities ranging from -7.1 to -8.0 kcal/mol compared to -7.0 kcal/mol obtained for camostat mesylate (a known TMPRSS2 inhibitor), which served as a control. All the three compounds exhibited good drug-like properties by not violating the Lipinski rule of 5. Niazirin and moringyne possessed good ADMET properties and were stable in their interactions with the TMPRSS2 based on the molecular dynamics simulation. However, the ADMET tool predicted the potential hepatotoxic and mutagenic effects of quercetin.

Conclusion

This study demonstrated the potentials of niazirin, quercetin, and moringyne from M. oleifera, to inhibit the activities of human TMPRSS2, thus probably being good candidates for further development as new drugs for the treatment or management of COVID-19.

Identification of novel TMPRSS2 inhibitors for COVID-19 using e-pharmacophore modelling, molecular docking, molecular dynamics and quantum mechanics studies

SARS coronavirus 2 (SARS-CoV-2) has spread rapidly around the world and continues to have a massive global health effect, contributing to an infectious respiratory illness called coronavirus infection-19 (COVID-19). TMPRSS2 is an emerging molecular target that plays a role in the early stages of SARS-CoV-2 infection; hence, inhibiting its activity might be a target for COVID-19. This study aims to use many computational approaches to provide compounds that could be optimized into clinical candidates. As there is no experimentally derived protein information, initially we develop the TMPRSS2 model. Then, we generate a pharmacophore model from TMPRSS2 active site consequently, and the developed models were employed for the screening of one million molecules from the Enamine database. The created model was then screened using e-pharmacophore-based screening, molecular docking, free energy estimation and molecular dynamic simulation. Also, ADMET prediction and density functional theory calculations were performed. Three potential molecules (Z126202570, Z46489368, and Z422255982) exhibited promising stable binding interactions with the target. In conclusion, these findings empower further in vitro and clinical assessment for these compounds as novel anti-COVID19 agents.

Virtual Screening on Marine Natural Products for Discovering TMPRSS2 Inhibitors

Although SARS-CoV-2 entry to cells strictly depends on angiotensin-converting enzyme 2 (ACE2), the virus also needs transmembrane serine protease 2 (TMPRSS2) for its spike protein priming. It has been shown that the entrance of SARS-CoV-2 through ACE2 can be blocked by cellular TMPRSS2 blockers. The main aim of this study was to find potential inhibitor(s) of TMPRSS2 through virtual screening against a homology model of TMPRSS2 using the library of marine natural products (MNPs). The homology modeling technique for generating a three-dimensional structure of TMPRSS2 was applied. Molecular docking, MM-GBSA and absorption, distribution, metabolism, excretion (ADME) evaluations were performed to investigate the inhibitory activity of marine natural products (MNPs) against TMPRSS2 and their pharmacokinetic properties. Camostat and nafamostat mesylate were used as the standard inhibitory molecules. Seven MNPs were able to inhibit TMPRSS2 better than the standard compounds. MNP 10 with CAS number 107503-09-3, called Watasenia β-D- Preluciferyl glucopyrasoiuronic acid, was found to be the best inhibitor of TMPRSS2 with acceptable pharmacokinetic properties. Herein, for the first time, a new marine natural product was introduced with potent inhibitory effects against TMPRSS2. MNP 10 exhibited favorable drug-like pharmacokinetic properties and it promises a novel TMPRSS2 blocker to combat SARS-CoV-2.

Identification of novel transmembrane Protease Serine Type 2 drug candidates for COVID-19 using computational studies

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) emergence has resulted in a global health crisis. As a consequence, discovering an effective therapy that saves lives and slows the spread of the pandemic is a global concern currently. In silico drug repurposing is highly regarded as a precise computational method for obtaining fast and reliable results. Transmembrane serine-type 2 (TMPRSS2) is a SARS CoV-2 enzyme that is essential for viral fusion with the host cell. Inhibition of TMPRSS2 may block or lessen the severity of SARS-CoV-2 infection. In this study, we aimed to perform an in silico drug repurposing to identify drugs that can effectively inhibit SARS-CoV-2 TMPRSS2. As there is no 3D structure of TMPRSS2 available, homology modeling was performed to build the 3D structure of human TMPRSS2. 3848 world-approved drugs were screened against the target. Based on docking scores and visual outcomes, the best-fit drugs were chosen. Molecular dynamics (MD) and density functional theory (DFT) studies were also conducted. Five potential drugs (Amikacin, isepamicin, butikacin, lividomycin, paromomycin) exhibited promising binding affinities. In conclusion, these findings empower purposing these agents.

Understanding the molecular interaction of SARS-CoV-2 spike mutants with ACE2 (angiotensin converting enzyme 2)

Covid-19 is a viral disease caused by the virus SARS-CoV-2 that spread worldwide and caused more than 4.3 million deaths. Moreover, SARS-CoV-2 still continues to evolve, and specifically the E484K, N501Y, and South Africa triple (K417N + E484K + N501Y) spike protein mutants remain as the 'escape' phenotypes. The aim of this study was to compare the interaction between the receptor binding domain (RBD) of the E484K, N501Y and South Africa triple spike variants and ACE2 with the interaction between wild-type spike RBD-ACE2 and to show whether the obtained binding affinities and conformations corraborate clinical findings. The structures of the RBDs of the E484K, N501Y and South Africa triple variants were generated with DS Studio v16 and energetically minimized using the CHARMM22 force field. Protein-protein dockings were performed in the HADDOCK server and the obtained wild-type and mutant spike-ACE2 complexes were submitted to 200-ns molecular dynamics simulations with subsequent free energy calculations using GROMACS. Based on docking binding affinities and free energy calculations the E484K, N501Y and triple mutant variants were found to interact stronger with the ACE2 than the wild-type spike. Interestingly, molecular dynamics and MM-PBSA results showed that E484K and spike triple mutant complexes were more stable than the N501Y one. Moreover, the E484K and South Africa triple mutants triggered greater conformational changes in the spike glycoprotein than N501Y. The E484K variant alone, or the combination of K417N + E484K + N501Y mutations induce significant conformational transitions in the spike glycoprotein, while increasing the spike-ACE2 binding affinity.Communicated by Ramaswamy H. Sarma.

Molecular Modeling Targeting Transmembrane Serine Protease 2 (TMPRSS2) as an Alternative Drug Target Against Coronaviruses

Since November 2019, the new Coronavirus disease (COVID-19) caused by the etiological agent SARS-CoV-2 has been responsible for several cases worldwide, becoming pandemic in March 2020. Pharmaceutical industries and academics have joined their efforts to discover new therapies to control the disease, since there are no specific drugs to combat this emerging virus. Thus, several targets have been explored, among them the transmembrane protease serine 2 (TMPRSS2) has gained greater interest in the scientific community. In this context, this review will describe the importance of TMPRSS2 protease and the significant advances in virtual screening focused on discovering new inhibitors. In this review, it was observed that molecular modeling methods could be powerful tools in identifying new molecules against SARS-CoV-2. Thus, this review could be used to guide researchers worldwide to explore the biological and clinical potential of compounds that could be promising drug candidates against SARS-CoV-2, acting by inhibition of TMPRSS2 protein.

Targeting p53-MDM2 interactions to identify small molecule inhibitors for cancer therapy: beyond "Failure to rescue"

At present, disrupting p53-MDM2 interactions through small molecule ligands is a promising approach to safe treatment and management of human cancer. Tumor cells unlike the normal cells, are rapidly evolving affecting the efficacy of many approved anti-cancer agents due to drug resistance. Therefore, identifying a potential anticancer compound is crucial. Pharmacophore based virtual screening, followed by molecular docking, ADMET evaluation, and molecular dynamics studies against MDM2 protein was investigated to identify potential ligands that may act as inhibitors. The model (AHRR_1) with survival score (4.176) was selected among the top ranked generated Pharmacophore hypothesis. Validation of the model hypothesis by an external dataset of actives and inactive compounds produced significant validation attributes including; AUC = 0.85, BEDROC = 0.56 at α = 20.0, RIE = 8.18, AUAC = 0.88, and EF of 6.2 at the top 2% of the dataset. The model was use for screening the ZINC database, and the top 1375 hits satisfying the model hypothesis were subjected to molecular docking studies to understand the molecular and structural basis of selectivity of compounds for MDM2 protein. A sub-set of 25 compounds with binding energy lower than the reference inhibitors were evaluated for pharmacokinetic properties. Four compounds (ZINC02639178, ZINC06752762, ZINC38933175, and ZINC77969611) showed the most desired pharmacokinetic profile. Lastly, investigation of the dynamic behaviour of leads-protein complexes through MD simulation showed similar RMSD, RMSF, and H-bond occupancy profile compared to a reference inhibitor, suggesting stability throughout the simulation time. However, ZINC02639178 was found to satisfy the molecular enumeration the most compared to the other three leads. It may emerge as potential treatment option after extensive experimental studies. Communicated by Ramaswamy H. Sarma.

Computer-aided design of 1,4-naphthoquinone-based inhibitors targeting cruzain and rhodesain cysteine proteases

Chagas disease and Human African Trypanosomiasis (HAT) are caused by Trypanosoma cruzi and T. brucei parasites, respectively. Cruzain (CRZ) and Rhodesain (RhD) are cysteine proteases that share 70% of identity and play vital functions in these parasites. These macromolecules represent promising targets for designing new inhibitors. In this context, 26 CRZ and 5 RhD 3D-structures were evaluated by molecular redocking to identify the most accurate one to be utilized as a target. Posteriorly, a virtual screening of a library containing 120 small natural and nature-based compounds was performed on both of them. In total, 14 naphthoquinone-based analogs were identified, synthesized, and biologically evaluated. In total, five compounds were active against RhD, being three of them also active on CRZ. A derivative of 1,4-naphthoquinonepyridin-2-ylsulfonamide was found to be the most active molecule, exhibiting IC₅₀ values of 6.3 and 1.8 µM for CRZ and RhD, respectively. Dynamic simulations at 100 ns demonstrated good stability and do not alter the targets' structures. MM-PBSA calculations revealed that it presents a higher affinity for RhD (-25.3 Kcal mol^-1) than CRZ, in which van der Waals interactions were more relevant. A mechanistic hypothesis (via C3-Michael-addition reaction) involving a covalent mode of inhibition for this compound towards RhD was investigated by covalent molecular docking and DFT B3LYP/6-31 + G* calculations, exhibiting a low activation energy (ΔG^‡) and providing a stable product (ΔG), with values of 7.78 and - 39.72 Kcal mol^-1, respectively; similar to data found in the literature. Nevertheless, a reversibility assay by dilution revealed that JN-11 is a time-dependent and reversible inhibitor. Finally, this study applies modern computer-aided techniques to identify promising inhibitors from a well-known chemical class of natural products. Then, this work could inspire other future studies in the field, being useful for designing potent naphthoquinones as RhD inhibitors.

Therapeutic approaches for SARS-CoV-2 infection

Therapeutic approaches to COVID-19 treatment require appropriate inhibitors to target crucial proteins of SARS-CoV-2 replication machinery. It’s been approximately 12 months since the pandemic started, yet no known specific drugs are available. However, research progresses with time in terms of high throughput virtual screening (HTVS) and rational design of repurposed, novel synthetic and natural products discovery by understanding the viral life cycle, immuno-pathological and clinical outcomes in patients based on host’s nutritional, metabolic, and lifestyle status. Further, complementary and alternative medicine (CAM) approaches have also improved resiliency and immune responses. In this article, we summarize all the therapeutic antiviral strategies for COVID-19 drug discovery including computer aided virtual screening, repurposed drugs, immunomodulators, vaccines, plasma therapy, various adjunct therapies, and phage technology to unravel insightful mechanistic pathways of targeting SARS-CoV-2 and host’s intrinsic, innate immunity at multiple checkpoints that aid in the containment of the disease.

Pharmacoinformatics-based identification of transmembrane protease serine-2 inhibitors from Morus Alba as SARS-CoV-2 cell entry inhibitors

Transmembrane protease serine-2 (TMPRSS2) is a cell-surface protein expressed by epithelial cells of specific tissues including those in the aerodigestive tract. It helps the entry of novel coronavirus (n-CoV) or Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) in the host cell. Successful inhibition of the TMPRSS2 can be one of the crucial strategies to stop the SARS-CoV-2 infection. In the present study, a set of bioactive molecules from Morus alba Linn. were screened against the TMPRSS2 through two widely used molecular docking engines such as Autodock vina and Glide. Molecules having a higher binding affinity toward the TMPRSS2 compared to Camostat and Ambroxol were considered for in-silico pharmacokinetic analyses. Based on acceptable pharmacokinetic parameters and drug-likeness, finally, five molecules were found to be important for the TMPRSS2 inhibition. A number of bonding interactions in terms of hydrogen bond and hydrophobic interactions were observed between the proposed molecules and ligand-interacting amino acids of the TMPRSS2. The dynamic behavior and stability of best-docked complex between TRMPRSS2 and proposed molecules were assessed through molecular dynamics (MD) simulation. Several parameters from MD simulation have suggested the stability between the protein and ligands. Binding free energy of each molecule calculated through MM-GBSA approach from the MD simulation trajectory suggested strong affection toward the TMPRSS2. Hence, proposed molecules might be crucial chemical components for the TMPRSS2 inhibition.

Screening strategy of TMPRSS2 inhibitors by FRET-based enzymatic activity for TMPRSS2-based cancer and COVID-19 treatment

Transmembrane serine protease (TMPRSS2) plays an oncogenic role in prostate cancer as the fusion gene with ERG, and has also been demonstrated to be essential for the cellular entry of severe acute respiratory syndrome coronaviruses (SARS-CoV). Thus, targeting TMPRSS2 is a promising strategy for therapies against both prostate cancer and coronavirus infection. Although Nafamostat and Camostat have been identified as TMPRSS2 inhibitors, severe side effects such as cerebral hemorrhage, anaphylactoid reaction, and cardiac arrest shock greatly hamper their clinical use. Therefore, more potent and safer drugs against this serine protease should be further developed. In this study, we developed a fluorescence resonance energy transfer (FRET)-based platform for effectively screening of inhibitors against TMPRSS2 protease activity. The disruption of FRET between green and red fluorescent proteins conjugated with the substrate peptide, which corresponds to the cleavage site of SARS-CoV-2 Spike protein, was measured to determine the enzymatic activity of TMPRSS2. Through an initiate pilot screening with around 100 compounds, Flupirtine, a selective neuronal potassium channel opener, was identified as a potential TMPRSS2 inhibitor from an FDA-approved drug library by using this screening platform, and showed inhibitory effect on the TMPRSS-dependent infection of SARS-CoV-2 Spike-pseudotyped lentiviral particles. This study describes a platform proven effective for rapidly screening of TMPRSS2 inhibitors, and suggests that Flupirtine may be worthy of further consideration of repurposing to treat COVID-19 patients.

Strong Binding of Leupeptin with TMPRSS2 Protease May Be an Alternative to Camostat and Nafamostat for SARS-CoV-2 Repurposed Drug: Evaluation from Molecular Docking and Molecular Dynamics Simulations

The unprecedented coronavirus SARS-CoV-2 outbreak at Wuhan, China, caused acute respiratory infection to humans. There is no precise vaccine/therapeutic agents available to combat the COVID-19 disease. Some repurposed drugs are saving the life of diseased, but the complete cure is relatively less. Several drug targets have been reported to inhibit the SARS-CoV-2 virus infection, in that TMPRSS2 (transmembrane protease serine 2) is one of the potential targets; inhibiting this protease stops the virus entry into the host human cell. Camostat mesylate, nafamostat, and leupeptin are the drugs, in which the first two drugs are being used for COVID-19 and leupeptin also tested. To consider these drugs as the repurposed drug for COVID-19, it is essential to understand their binding affinity and stability with TMPRSS2. In the present study, we performed the molecular docking and molecular dynamics (MD) simulation of these molecules with the TMPRSS2. The docking study reveals that leupeptin molecule strongly binds with TMPRSS2 protein than the other two drug molecules. The RMSD and RMSF values of MD simulation confirm that leupeptin and the amino acids of TMPRSS2 are very stable than the other two molecules. Furthermore, leupeptin forms interactions with the key amino acids of TMPRSS2 and the same have been maintained during the MD simulations. This structural and dynamical information is useful to evaluate these drugs to be used as repurposed drugs, however, the strong binding profile of leupeptin with TMPRSS2, suggests, it may be considered as a repurposed drug for COVID-19 disease after clinical trial.

COVID-19: molecular targets, drug repurposing and new avenues for drug discovery

Coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a highly contagious infection that may break the healthcare system of several countries. Here, we aimed at presenting a critical view of ongoing drug repurposing efforts for COVID-19 as well as discussing opportunities for development of new treatments based on current knowledge of the mechanism of infection and potential targets within. Finally, we also discuss patent protection issues, cost effectiveness and scalability of synthetic routes for some of the most studied repurposing candidates since these are key aspects to meet global demand for COVID-19 treatment.