Computational Approaches for Drug Discovery

Bioinformatics tools for the study of bioactive peptides from vegetal sources: evolution and future perspectives

Bioactive peptides from vegetal sources have been shown to have functional properties as anti-inflammatory, antioxidant, antihypertensive or antidiabetic capacity. For this reason, they have been proposed as an interesting and promising alternative to improve human health. In recent years, the numerous advances in the bioinformatics field for in silico prediction have speeded up the discovery of bioactive peptides, also reducing the associated costs when using an integrated approach between the classical and bioinformatics discovery. This review aims to provide an overview of the evolution, limitations and latest advances in the field of bioinformatics and computational tools, and specifically make a critical and comprehensive insight into computational techniques used to study the mechanism of interaction that allows the explanation of plant bioactive peptide functionality. In particular, molecular docking is considered key to explain the different functionalities that have been previously identified. The assumptions to simplify such a high complex environment implies a degree of uncertainty that can only be guaranteed and validated by in vitro or in vivo studies, however, the combination of databases, software and bioinformatics applications with the classical approach has become a promising procedure for the study of bioactive peptides.

Leveraging computational tools to combat malaria: assessment and development of new therapeutics

As the world grapples with the relentless challenges posed by diseases like malaria, the advent of sophisticated computational tools has emerged as a beacon of hope in the quest for effective treatments. In this study we delve into the strategies behind computational tools encompassing virtual screening, molecular docking, artificial intelligence (AI), and machine learning (ML). We assess their effectiveness and contribution to the progress of malaria treatment. The convergence of these computational strategies, coupled with the ever-increasing power of computing systems, has ushered in a new era of drug discovery, holding immense promise for the eradication of malaria. SCIENTIFIC CONTRIBUTION: Computational tools remain pivotal in drug design and development. They provide a platform for researchers to explore various treatment options and save both time and money in the drug development pipeline. It is imperative to assess computational techniques and monitor their effectiveness in disease control. In this study we examine renown computational tools that have been employed in the battle against malaria, the benefits and challenges these tools have presented, and the potential they hold in the future eradication of the disease.

Anti-virulence potential of patuletin, a natural flavone, against Staphylococcus aureus: In vitro and In silico investigations

Staphylococcus aureus is a highly prevalent and aggressive human pathogen causing a wide range of infections. This study aimed to explore the potential of Patuletin, a rare natural flavone, as an anti-virulence agent against S. aureus. At a sub-inhibitory concentration (1/4 MIC), Patuletin notably reduced biofilm formation by 27 % and 23 %, and decreased staphyloxanthin production by 53 % and 46 % in Staphylococcus aureus isolate SA25923 and clinical isolate SA1, respectively. In order to gain a more comprehensive understanding of the in vitro findings, several in silico analyses were conducted. Initially, a 3D-flexible alignment study demonstrated a favorable structural similarity between Patuletin and B70, the co-crystallized ligand of CrtM, an enzyme that plays a pivotal role in the biosynthesis of staphyloxanthin. Molecular docking highlighted the strong binding of Patuletin to the active site of CrtM, with a high affinity of -20.95 kcal/mol. Subsequent 200 ns molecular dynamics simulations, along with MM-GBSA, ProLIF, PLIP, and PCAT analyses, affirmed the stability of the Patuletin-CrtM complex, revealing no significant changes in CrtM's structure upon binding. Key amino acids crucial for binding were also identified. Collectively, this study showcased the effective inhibition of CrtM activity by Patuletin in silico and its attenuation of key virulence factors in vitro, including biofilm formation and staphyloxanthin production. These findings hint at Patuletin's potential as a valuable therapeutic agent, especially in combination with antibiotics, to counter antibiotic-resistant Staphylococcus aureus infections.

DAPredict: a database for drug action phenotype prediction

The phenotypes of drug action, including therapeutic actions and adverse drug reactions (ADRs), are important indicators for evaluating the druggability of new drugs and repositioning the approved drugs. Here, we provide a user-friendly database, DAPredict (http://bio-bigdata.hrbmu.edu.cn/DAPredict), in which our novel original drug action phenotypes prediction algorithm (Yang,J., Zhang,D., Liu,L. et al. (2021) Computational drug repositioning based on the relationships between substructure-indication. Brief. Bioinformatics, 22, bbaa348) was embedded. Our algorithm integrates characteristics of chemical genomics and pharmacogenomics, breaking through the limitations that traditional drug development process based on phenotype cannot analyze the mechanism of drug action. Predicting phenotypes of drug action based on the local active structures of drugs and proteins can achieve more innovative drug discovery across drug categories and simultaneously evaluate drug efficacy and safety, rather than traditional one-by-one evaluation. DAPredict contains 305 981 predicted relationships between 1748 approved drugs and 454 ADRs, 83 117 predicted relationships between 1478 approved drugs and 178 Anatomical Therapeutic Chemicals (ATC). More importantly, DAPredict provides an online prediction tool, which researchers can use to predict the action phenotypic spectrum of more than 110 000 000 compounds (including about 168 000 natural products) and corresponding proteins to analyze their potential effect mechanisms. DAPredict can also help researchers obtain the phenotype-corresponding active structures for structural optimization of new drug candidates, making it easier to evaluate the druggability of new drug candidates and develop more innovative drugs across drug categories. Database URL:  http://bio-bigdata.hrbmu.edu.cn/DAPredict/.

Computational Approaches: A New Frontier in Cancer Research

Cancer is a broad category of disease that can start in virtually any organ or tissue of the body when aberrant cells assault surrounding organs and proliferate uncontrollably. According to the most recent statistics, cancer will be the cause of 10 million deaths worldwide in 2020, accounting for one death out of every six worldwide. The typical approach used in anti-cancer research is highly time-consuming and expensive, and the outcomes are not particularly encouraging. Computational techniques have been employed in anti-cancer research to advance our understanding. Recent years have seen a significant and exceptional impact on anticancer research due to the rapid development of computational tools for novel drug discovery, drug design, genetic studies, genome characterization, cancer imaging and detection, radiotherapy, cancer metabolomics, and novel therapeutic approaches. In this paper, we examined the various subfields of contemporary computational techniques, including molecular docking, artificial intelligence, bioinformatics, virtual screening, and QSAR, and their applications in the study of cancer.

Screening of potential phytomolecules against MurG as drug target in nosocomial pathogen Pseudomonas aeruginosa: perceptions from computational campaign

The nosocomial infection outbreak caused by Pseudomonas aeruginosa remains a public health concern. Multi-drug resistant (MDR) strains of P. aeruginosa are rapidly spreading leading to a huge mortality rate because of the unavailability of promising antimicrobials. MurG glycotransferase [UDP-N-acetylglucosamine-N-acetylmuramyl (pentapeptide) pyrophosphoryl-undecaprenol N-acetylglucosamine transferase] is located at the plasma membrane and plays a key role in murein (peptidoglycan) biosynthesis in bacteria. Since MurG is required for bacterial cell wall synthesis and is non-homologous to Homo sapiens; it can be a potential target for the antagonist to treat P. aeruginosa infection. The discovery of high-resolution crystal structure of P. aeruginosa MurG offers an opportunity for the computational identification of its prospective inhibitors. Therefore, in the present study, the crystal structure of MurG (PDB ID: 3S2U) from P. aeruginosa was selected, and computational docking analyses were performed to search for functional inhibitors of MurG. IMPPAT (Indian medicinal plants, phytochemicals and therapeutic) phytomolecule database was screened by computational methods with MurG catalytic site. Docking results identified Theobromine (-8.881 kcal/mol), demethoxycurcumin (-8.850 kcal/mol), 2-alpha-hydroxycostic acid (-8.791 kcal/mol), aurantiamide (-8.779 kcal/mol) and petasiphenol (-8.685 kcal/mol) as a potential inhibitor of the MurG activity. Further, theobromine and demethoxycurcumin were subjected to MDS (molecular dynamics simulation) and free energy (MM/GBSA) analysis to comprehend the physiological state and structural stability of MurG-phytomolecules complexes. The outcomes suggested that these two phytomolecules could act as most favorable natural hit compounds for impeding the enzymatic action of MurG in P. aeruginosa, and thus it needs further validation by both in vitro and in vivo analysis. HIGHLIGHTSThe top phytomolecules such as theobromine, demethoxycurcumin, 2-alpha-hydroxycostic acid, aurantiamide and petasiphenol displayed promising binding with MurG catalytic domain.MurG complexed with theobromine and demethoxycurcumin showed the best interaction and stable by MD simulation at 100 ns.The outcome of MurG binding phytomolecules has expanded the possibility of hit phytomolecules validation.Communicated by Ramaswamy H. Sarma.

Molecular insights into the anti-inflammatory activity of fermented pineapple juice using multimodal computational studies

Pineapple has been recognized for its potential to enhance health and well-being. This study aimed to gain molecular insights into the anti-inflammatory properties of fermented pineapple juice using multimodal computational studies. In this study, pineapple juice was fermented using Lactobacillus paracasei, and the solution underwent liquid chromatography-mass spectrometry analysis. Network pharmacology was applied to investigate compound interactions and targets. In silico methods assessed compound bioactivities. Protein-protein interactions, network topology, and enrichment analysis identified key compounds. Molecular docking explored compound-receptor interactions in inflammation regulation. Molecular dynamics simulations were conducted to confirm the stability of interactions between the identified crucial compounds and their respective receptors. The study revealed several compounds including short-chain fatty acids, peptides, dihydroxyeicosatrienoic acids, and glycerides that exhibited promising anti-inflammatory properties. Leucyl-leucyl-norleucine and Leu-Leu-Tyr exhibited robust and stable interactions with mitogen-activated protein kinase 14 and IκB kinase β, respectively, indicating their potential as promising therapeutic agents for inflammation modulation. This proposition is grounded in the pivotal involvement of these two proteins in inflammatory signaling pathways. These findings provide valuable insights into the anti-inflammatory potential of these compounds, serving as a foundation for further experimental validation and exploration. Future studies can build upon these results to advance the development of these compounds as effective anti-inflammatory agents.

Targeting Protein-Protein Interfaces with Peptides: The Contribution of Chemical Combinatorial Peptide Library Approaches

Protein-protein interfaces play fundamental roles in the molecular mechanisms underlying pathophysiological pathways and are important targets for the design of compounds of therapeutic interest. However, the identification of binding sites on protein surfaces and the development of modulators of protein-protein interactions still represent a major challenge due to their highly dynamic and extensive interfacial areas. Over the years, multiple strategies including structural, computational, and combinatorial approaches have been developed to characterize PPI and to date, several successful examples of small molecules, antibodies, peptides, and aptamers able to modulate these interfaces have been determined. Notably, peptides are a particularly useful tool for inhibiting PPIs due to their exquisite potency, specificity, and selectivity. Here, after an overview of PPIs and of the commonly used approaches to identify and characterize them, we describe and evaluate the impact of chemical peptide libraries in medicinal chemistry with a special focus on the results achieved through recent applications of this methodology. Finally, we also discuss the role that this methodology can have in the framework of the opportunities, and challenges that the application of new predictive approaches based on artificial intelligence is generating in structural biology.

Synthesis and Antimycobacterial Activity of Isoniazid Derivatives Tethered with Aliphatic Amines

BACKGROUND

There is an urgent need for new antitubercular compounds. Modification of antimycobacterial isonicotinohydrazide at hydrazide N2 provided antimycobacterial active compounds.

OBJECTIVE

Combining this scaffold with various aliphatic amines that are also frequently present in antitubercular compounds, we have designed, synthesized, and evaluated twenty-three N- (cyclo)alkyl-2-(2-isonicotinoylhydrazineylidene)propanamides and their analogues as potential antimycobacterial compounds. By increasing lipophilicity, we intended to facilitate the penetration of mycobacteria's highly impermeable cell wall.

METHODS

The target amides were prepared via condensation of isoniazid and pyruvic acid, followed by carbodiimide-mediated coupling with yields from 35 to 98 %. The compounds were screened against Mycobacterium tuberculosis H37Rv and two nontuberculous mycobacteria (M. avium, M. kansasii).

RESULTS

All the derivatives exhibited low minimum inhibitory concentrations (MIC) from ≤0.125 and 2 μM against M. tuberculosis and nontuberculous mycobacteria, respectively. The most active molecules were substituted by a longer n-alkyl from C8 to C14. Importantly, the compounds showed comparable or even several-fold lower MIC than parent isonicotinohydrazide. Based on in silico predictions, a vast majority of the derivatives share suitable physicochemical properties and structural features for drug-likeness.

CONCLUSION

Presented amides are promising antimycobacterial agents.

A Combined QSAR and Molecular Docking Approach for Identifying Pyrimidine Derivatives as Penicillin Binding Protein Inhibitors

Background:

Antimicrobial resistance has been rising continuously in the past few years due to the overuse and exploitation of existing antimicrobials. This has motivated the search for a novel scaffold that has the capability of rapid antimicrobial action. The hybridized pyrimidines have attracted us due to their widespread biological activities, such as anti-bacterial and antifungal activities.

Objective:

The present study incorporates a series of pyrimidine-based antimicrobial agents for the 2D quantitative structure-activity relationship analysis (2D QSAR) and docking analysis.

Methods:

The exploration of the chemical structures in combination with the biological activity in CPMLR led to the detection of six descriptors (Constitutional descriptors, Topological descriptors, Modified Burden Eigenvalues and 2D autocorrelations) for modeling the activity. The resulted QSAR model has been validated using combinatorial protocol in multiple linear regression (CP-MLR) and partial least squares (PLS) analysis.

Methods:

The exploration of the chemical structures in combination with the biological activity in CPMLR led to the detection of six descriptors (Constitutional descriptors, Topological descriptors, Modified Burden Eigenvalues and 2D autocorrelations) for modeling the activity. The resulted QSAR model has been validated using a combinatorial protocol in multiple linear regression (CP-MLR) and partial least squares (PLS) analysis.

Results:

The best QSAR model displays the r2 t value of 0.594, Q2 LOO value of 0.779, Q2 L5O value of 0.767. Further docking study was executed using Autodock Vina against Penicillin-binding protein (PBP2a).

Conclusion:

From the results, Compounds 4, 11and 24 were found to possess a good binding affinity towards PBP2a.

In silico selectivity modeling of pyridine and pyrimidine based CYP11B1 and CYP11B2 inhibitors: A case study

DESIGN

of selective drug candidates for highly structural similar targets is a challenging task for researchers. The main objective of this study was to explore the selectivity modeling of pyridine and pyrimidine scaffold towards the highly homologous targets CYP11B1 and CYP11B2 enzymes by in silico (Molecular docking and QSAR) approaches. In this regard, a big dataset (n = 228) of CYP11B1 and CYP11B2 inhibitors were gathered and classified based on heterocyclic ring and the exhaustive analysis was carried out for pyridine and pyrimidinescaffolds. The LibDock algorithm was used to explore the binding pattern, screening, and identify the structural feature responsible for the selectivity of the ligands towards the studied targets. Finally, QSAR analysis was done to explore the correlation between various binding parameters and structural features responsible for the inhibitory activity and selectivity of the ligands in a quantitative way. The docking and QSAR analysis clearly revealed and distinguished the importance of structural features, functional groups attached for CYP11B2 and CYP11B1 selectivity for pyridine and pyrimidine analogs. Additionally, the docking analysis highlighted the differentiating amino acids residues for selectivity for ligands for each of the enzymes. The results obtained from this research work will be helpful in designing the selective CYP11B1/CYP11B2 inhibitors.

Inferring Therapeutic Targets in Candida albicans and Possible Inhibition through Natural Products: A Binding and Physiological Based Pharmacokinetics Snapshot

Despite being responsible for invasive infections, fungal pathogens have been underrepresented in computer aided therapeutic target mining and drug design. Excess of Candida albicans causes candidiasis, causative of thrush and vaginal infection due to off-balance. In this study, we attempted to mine drug targets (n = 46) using a subtractive proteomic approach in this pathogenic yeast and screen natural products with inhibition potential against fructose-bisphosphate aldolase (FBA) of the C. albicans. The top compound selected on the basis of best docking score from traditional Indian medicine/Ayurvedic library was (4-Hydroxybenzyl)thiocarbamic acid, from the ZINC FBA inhibitor library was ZINC13507461 (IUPAC name: [(2R)-2-hydroxy-3-phosphonooxypropyl] (9E,12E)-octadeca-9,12-dienoate), and from traditional Tibetan medicine/Sowa rigpa was Chelerythrine (IUPAC name: 1,2-Dimethoxy-12-methyl-9H-[1,3]benzodioxolo[5,6-c]phenanthridin-12-ium), compared to the control (2E)-1-(4-nitrophenyl)-2-[(4-nitrophenyl)methylidene]hydrazine. No Ames toxicity was predicted for prioritized compounds while control depicted this toxicity. (4-Hydroxybenzyl)thiocarbamic acid showed hepatotoxicity, while Chelerythrine depicted hERG inhibition, which can lead to QT syndrome, so we recommend ZINC13507461 for further testing in lab. Pharmacological based pharmacokinetic modeling revealed that it has low bioavailability and hence, absorption in healthy state. In cirrhosis and renal impairment, absorption and plasma accumulation increased so we recommend further investigation into this occurrence and recommend high dosage in further tests to increase bioavailability.

Mechanistic Insights and Docking Studies of Phytomolecules as Potential Candidates in the Management of Cancer

BACKGROUND

Cancer is a leading risk of death globally. According to the World Health Organization, it is presently the second most important disease that causes death in both developing and developed countries. Remarkable progress has been made in the war against cancer with the development of numerous novel chemotherapy agents. However, it remains an immense challenge to discover new efficient therapeutic potential candidates to combat cancer.

OBJECTIVES

The majority of the currently used anticancer drugs are of natural origins, such as curcumin, colchicine, vinca alkaloid, paclitaxel, bergenin, taxols, and combretastatin. Concerning this, this review article presents the structure of the most potent molecules along with IC50 values, structure-activity relationships, mechanistic studies, docking studies, in silico studies of phytomolecules, and important key findings on human cancer cell lines.

METHODS

A viewpoint of drug design and development of antiproliferative agents from natural phytomolecules has been established by searching peer-reviewed literature from Google Scholar, PubMed, Scopus, Springer, Science Direct, and Web of Science over the past few years.

RESULTS

Our analysis revealed that this article would assist chemical biologists and medicinal chemists in industry and academia in gaining insights into the anticancer potential of phytomolecules.

CONCLUSION

In vitro and in silico studies present phytomolecules, such as curcumin, colchicine, vinca alkaloids, colchicine, bergenin, combretastatin, and taxol encompassing anticancer agents, offerings abundant sanguinity and capacity in the arena of drug discovery to inspire the investigators towards the continual investigations on these phytomolecules. It is extremely expected that efforts in this track will strengthen and grant some budding cancer therapeutics candidates in the near future.

Robust antiviral activity of commonly prescribed antidepressants against emerging coronaviruses: in vitro and in silico drug repurposing studies

During the current coronavirus disease 2019 (COVID-19) pandemic, symptoms of depression are commonly documented among both symptomatic and asymptomatic quarantined COVID-19 patients. Despite that many of the FDA-approved drugs have been showed anti-SARS-CoV-2 activity in vitro and remarkable efficacy against COVID-19 in clinical trials, no pharmaceutical products have yet been declared to be fully effective for treating COVID-19. Antidepressants comprise five major drug classes for the treatment of depression, neuralgia, migraine prophylaxis, and eating disorders which are frequently reported symptoms in COVID-19 patients. Herein, the efficacy of eight frequently prescribed FDA-approved antidepressants on the inhibition of both SARS-CoV-2 and MERS-CoV was assessed. Additionally, the in vitro anti-SARS-CoV-2 and anti-MERS-CoV activities were evaluated. Furthermore, molecular docking studies have been performed for these drugs against the spike (S) and main protease (M^pro) pockets of both SARS-CoV-2 and MERS-CoV. Results showed that Amitriptyline, Imipramine, Paroxetine, and Sertraline had potential anti-viral activities. Our findings suggested that the aforementioned drugs deserve more in vitro and in vivo studies targeting COVID-19 especially for those patients suffering from depression.

An Overview of Antiviral Peptides and Rational Biodesign Considerations

Viral diseases have contributed significantly to worldwide morbidity and mortality throughout history. Despite the existence of therapeutic treatments for many viral infections, antiviral resistance and the threat posed by novel viruses highlight the need for an increased number of effective therapeutics. In addition to small molecule drugs and biologics, antimicrobial peptides (AMPs) represent an emerging class of potential antiviral therapeutics. While AMPs have traditionally been regarded in the context of their antibacterial activities, many AMPs are now known to be antiviral. These antiviral peptides (AVPs) have been shown to target and perturb viral membrane envelopes and inhibit various stages of the viral life cycle, from preattachment inhibition through viral release from infected host cells. Rational design of AMPs has also proven effective in identifying highly active and specific peptides and can aid in the discovery of lead peptides with high therapeutic selectivity. In this review, we highlight AVPs with strong antiviral activity largely curated from a publicly available AMP database. We then compile the sequences present in our AVP database to generate structural predictions of generic AVP motifs. Finally, we cover the rational design approaches available for AVPs taking into account approaches currently used for the rational design of AMPs.

Calendulaglycoside A showing potential activity against SARS-CoV-2 main protease: Molecular docking, molecular dynamics, and SAR studies

BACKGROUND AND AIM

The discovery of drugs capable of inhibiting SARS-CoV-2 is a priority for human beings due to the severity of the global health pandemic caused by COVID-19. To this end, natural products can provide therapeutic alternatives that could be employed as an effective safe treatment for COVID-19.

EXPERIMENTAL PROCEDURE

Twelve compounds were isolated from the aerial parts of C. officinalis L. and investigated for their inhibitory activities against SARS-CoV-2 Mpro compared to its co-crystallized N3 inhibitor using molecular docking studies. Furthermore, a 100 ns MD simulation was performed for the most active two promising compounds, Calendulaglycoside A (SAP5) and Osteosaponin-I (SAP8).

RESULTS AND CONCLUSION

At first, molecular docking studies showed interesting binding scores as compared to the N3 inhibitor. Calendulaglycoside A (SAP5) achieved a superior binding than the co-crystallized inhibitor indicating promising affinity and intrinsic activity towards the Mpro of SARS-CoV-2 as well. Moreover, findings illustrated preferential stability for SAP5 within the Mpro pocket over that of N3 beyond the 40 ns MD simulation course. Structural preferentiality for triterpene-Mpro binding highlights the significant role of 17β-glucosyl and carboxylic 3α-galactosyl I moieties through high electrostatic interactions across the MD simulation trajectories. Furthermore, this study clarified a promising SAR responsible for the antiviral activity against the SARS-CoV-2 Mpro and the design of new drug candidates targeting it as well. The above findings could be promising for fast examining the previously isolated triterpenes both pre-clinically and clinically for the treatment of COVID-19.

Interaction of Spike protein and lipid membrane of SARS-CoV-2 with Ursodeoxycholic acid, an in-silico analysis

Numerous repositioned drugs have been sought to decrease the severity of SARS-CoV-2 infection. It is known that among its physicochemical properties, Ursodeoxycholic Acid (UDCA) has a reduction in surface tension and cholesterol solubilization, it has also been used to treat cholesterol gallstones and viral hepatitis. In this study, molecular docking was performed with the SARS-CoV-2 Spike protein and UDCA. In order to confirm this interaction, we used Molecular Dynamics (MD) in “SARS-CoV-2 Spike protein-UDCA”. Using another system, we also simulated MD with six UDCA residues around the Spike protein at random, naming this “SARS-CoV-2 Spike protein-6UDCA”. Finally, we evaluated the possible interaction between UDCA and different types of membranes, considering the possible membrane conformation of SARS-CoV-2, this was named “SARS-CoV-2 membrane-UDCA”. In the “SARS-CoV-2 Spike protein-UDCA”, we found that UDCA exhibits affinity towards the central region of the Spike protein structure of − 386.35 kcal/mol, in a region with 3 alpha helices, which comprises residues from K986 to C1032 of each monomer. MD confirmed that UDCA remains attached and occasionally forms hydrogen bonds with residues R995 and T998. In the presence of UDCA, we observed that the distances between residues atoms OG1 and CG2 of T998 in the monomers A, B, and C in the prefusion state do not change and remain at 5.93 ± 0.62 and 7.78 ± 0.51 Å, respectively, compared to the post-fusion state. Next, in “SARS-CoV-2 Spike protein-6UDCA”, the three UDCA showed affinity towards different regions of the Spike protein, but only one of them remained bound to the region between the region's heptad repeat 1 and heptad repeat 2 (HR1 and HR2) for 375 ps of the trajectory. The RMSD of monomer C was the smallest of the three monomers with a value of 2.89 ± 0.32, likewise, the smallest RMSF was also of the monomer C (2.25 ± 056). In addition, in the simulation of “SARS-CoV-2 membrane-UDCA”, UDCA had a higher affinity toward the virion-like membrane; where three of the four residues remained attached once they were close (5 Å, to the centre of mass) to the membrane by 30 ns. However, only one of them remained attached to the plasma-like membrane and this was in a cluster of cholesterol molecules. We have shown that UDCA interacts in two distinct regions of Spike protein sequences. In addition, UDCA tends to stay bound to the membrane, which could potentially reduce the internalization of SARS-CoV-2 in the host cell.

Mahmoud S, Shehata M, Shama NMA, GabAllah M, Mostafa-Hedeab G, Marzouk MM. Naturally Available Flavonoid Aglycones as Potential Antiviral Drug Candidates against SARS-CoV-2

Flavonoids are important secondary plant metabolites that have been studied for a long time for their therapeutic potential in inflammatory diseases because of their cytokine-modulatory effects. Five flavonoid aglycones were isolated and identified from the hydrolyzed aqueous methanol extracts of Anastatica hierochuntica L., Citrus reticulata Blanco, and Kickxia aegyptiaca (L.) Nabelek. They were identified as taxifolin (1), pectolinarigenin (2), tangeretin (3), gardenin B (4), and hispidulin (5). These structures were elucidated based on chromatographic and spectral analysis. In this study, molecular docking studies were carried out for the isolated and identified compounds against SARS-CoV-2 main protease (Mpro) compared to the co-crystallized inhibitor of SARS-CoV-2 Mpro (α-ketoamide inhibitor (KI), IC50 = 66.72 µg/mL) as a reference standard. Moreover, in vitro screening against SARS-CoV-2 was evaluated. Compounds 2 and 3 showed the highest virus inhibition with IC50 12.4 and 2.5 µg/mL, respectively. Our findings recommend further advanced in vitro and in vivo studies of the examined isolated flavonoids, especially pectolinarigenin (2), tangeretin (3), and gardenin B (4), either alone or in combination with each other to identify a promising lead to target SARS-CoV-2 effectively. This is the first report of the activity of these compounds against SARS-CoV-2.

Pimenta dioica (L.) Merr. Bioactive Constituents Exert Anti-SARS-CoV-2 and Anti-Inflammatory Activities: Molecular Docking and Dynamics, In Vitro, and In Vivo Studies

In response to the urgent need to control Coronavirus disease 19 (COVID-19), this study aims to explore potential anti-SARS-CoV-2 agents from natural sources. Moreover, cytokine immunological responses to the viral infection could lead to acute respiratory distress which is considered a critical and life-threatening complication associated with the infection. Therefore, the anti-viral and anti-inflammatory agents can be key to the management of patients with COVID-19. Four bioactive compounds, namely ferulic acid 1, rutin 2, gallic acid 3, and chlorogenic acid 4 were isolated from the leaves of Pimenta dioica (L.) Merr (ethyl acetate extract) and identified using spectroscopic evidence. Furthermore, molecular docking and dynamics simulations were performed for the isolated and identified compounds (1-4) against SARS-CoV-2 main protease (Mpro) as a proposed mechanism of action. Furthermore, all compounds were tested for their half-maximal cytotoxicity (CC50) and SARS-CoV-2 inhibitory concentrations (IC50). Additionally, lung toxicity was induced in rats by mercuric chloride and the effects of treatment with P. dioca aqueous extract, ferulic acid 1, rutin 2, gallic acid 3, and chlorogenic acid 4 were recorded through measuring TNF-α, IL-1β, IL-2, IL-10, G-CSF, and genetic expression of miRNA 21-3P and miRNA-155 levels to assess their anti-inflammatory effects essential for COVID-19 patients. Interestingly, rutin 2, gallic acid 3, and chlorogenic acid 4 showed remarkable anti-SARS-CoV-2 activities with IC50 values of 31 µg/mL, 108 μg/mL, and 360 µg/mL, respectively. Moreover, the anti-inflammatory effects were found to be better in ferulic acid 1 and rutin 2 treatments. Our results could be promising for more advanced preclinical and clinical studies especially on rutin 2 either alone or in combination with other isolates for COVID-19 management.

In Silico Evaluation of Iranian Medicinal Plant Phytoconstituents as Inhibitors against Main Protease and the Receptor-Binding Domain of SARS-CoV-2

The novel coronavirus disease 2019 (COVID-19) is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which initially appeared in Wuhan, China, in December 2019. Elderly individuals and those with comorbid conditions may be more vulnerable to this disease. Consequently, several research laboratories continue to focus on developing drugs to treat this infection because this disease has developed into a global pandemic with an extremely limited number of specific treatments available. Natural herbal remedies have long been used to treat illnesses in a variety of cultures. Modern medicine has achieved success due to the effectiveness of traditional medicines, which are derived from medicinal plants. The objective of this study was to determine whether components of natural origin from Iranian medicinal plants have an antiviral effect that can prevent humans from this coronavirus infection using the most reliable molecular docking method; in our case, we focused on the main protease (Mpro) and a receptor-binding domain (RBD). The results of molecular docking showed that among 169 molecules of natural origin from common Iranian medicinal plants, 20 molecules (chelidimerine, rutin, fumariline, catechin gallate, adlumidine, astragalin, somniferine, etc.) can be proposed as inhibitors against this coronavirus based on the binding free energy and type of interactions between these molecules and the studied proteins. Moreover, a molecular dynamics simulation study revealed that the chelidimerine-Mpro and somniferine-RBD complexes were stable for up to 50 ns below 0.5 nm. Our results provide valuable insights into this mechanism, which sheds light on future structure-based designs of high-potency inhibitors for SARS-CoV-2.

Main protease inhibitors and drug surface hotspots for the treatment of COVID-19: A drug repurposing and molecular docking approach

Here, drug repurposing and molecular docking were employed to screen approved MPP inhibitors and their derivatives to suggest a specific therapeutic agent for the treatment of COVID-19. The approved MPP inhibitors against HIV and HCV were prioritized, while RNA dependent RNA Polymerase (RdRp) inhibitor remdesivir including Favipiravir, alpha-ketoamide were studied as control groups. The target drug surface hotspot was also investigated through the molecular docking technique. Molecular dynamics was performed to determine the binding stability of docked complexes. Absorption, distribution, metabolism, and excretion analysis was conducted to understand the pharmacokinetics and drug-likeness of the screened MPP inhibitors. The results of the study revealed that Paritaprevir (-10.9 kcal/mol) and its analog (CID 131982844) (-16.3 kcal/mol) showed better binding affinity than the approved MPP inhibitors compared in this study, including remdesivir, Favipiravir, and alpha-ketoamide. A comparative study among the screened putative MPP inhibitors revealed that the amino acids T25, T26, H41, M49, L141, N142, G143, C145, H164, M165, E166, D187, R188, and Q189 are at potentially critical positions for being surface hotspots in the MPP of SARS-CoV-2. The top 5 predicted drugs (Paritaprevir, Glecaprevir, Nelfinavir, and Lopinavir) and the topmost analog showed conformational stability in the active site of the SARS-CoV-2 MP protein. The study also suggested that Paritaprevir and its analog (CID 131982844) might be effective against SARS-CoV-2. The current findings are limited to in silico analysis and lack in vivo efficacy testing; thus, we strongly recommend a quick assessment of Paritaprevir and its analog (CID 131982844) in a clinical trial.

Elkaeed E, S. Abulkhair H, Khattab M, Al-Karmalawy AA. Computational Insights on the Potential of Some NSAIDs for Treating COVID-19: Priority Set and Lead Optimization

The discovery of drugs capable of inhibiting SARS-CoV-2 is a priority for human beings due to the severity of the global health pandemic caused by COVID-19. To this end, repurposing of FDA-approved drugs such as NSAIDs against COVID-19 can provide therapeutic alternatives that could be utilized as an effective safe treatment for COVID-19. The anti-inflammatory activity of NSAIDs is also advantageous in the treatment of COVID-19, as it was found that SARS-CoV-2 is responsible for provoking inflammatory cytokine storms resulting in lung damage. In this study, 40 FDA-approved NSAIDs were evaluated through molecular docking against the main protease of SARS-CoV-2. Among the tested compounds, sulfinpyrazone 2, indomethacin 3, and auranofin 4 were proposed as potential antagonists of COVID-19 main protease. Molecular dynamics simulations were also carried out for the most promising members of the screened NSAID candidates (2, 3, and 4) to unravel the dynamic properties of NSAIDs at the target receptor. The conducted quantum mechanical study revealed that the hybrid functional B3PW91 provides a good description of the spatial parameters of auranofin 4. Interestingly, a promising structure-activity relationship (SAR) was concluded from our study that could help in the future design of potential SARS-CoV-2 main protease inhibitors with expected anti-inflammatory effects as well. NSAIDs may be used by medicinal chemists as lead compounds for the development of potent SARS-CoV-2 (Mpro) inhibitors. In addition, some NSAIDs can be selectively designated for treatment of inflammation resulting from COVID-19.

Bioactive Polyphenolic Compounds Showing Strong Antiviral Activities against Severe Acute Respiratory Syndrome Coronavirus 2

Until now, there has been no direct evidence of the effectiveness of repurposed FDA-approved drugs against Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infections. Although curcumin, hesperidin, and quercetin have broad spectra of pharmacological properties, their antiviral activities against SARS-CoV-2 remain unclear. Our study aimed to assess the in vitro antiviral activities of curcumin, hesperidin, and quercetin against SARS-CoV-2 compared to hydroxychloroquine and determine their mode of action. In Vero E6 cells, these compounds significantly inhibited virus replication, mainly as virucidal agents primarily indicating their potential activity at the early stage of viral infection. To investigate the mechanism of action of the tested compounds, molecular docking studies were carried out against both SARS-CoV-2 spike (S) and main protease (Mpro) receptors. Collectively, the obtained in silico and in vitro findings suggest that the compounds could be promising SARS-CoV-2 Mpro inhibitors. We recommend further preclinical and clinical studies on the studied compounds to find a potential therapeutic targeting COVID-19 in the near future.

Revisiting activity of some glucocorticoids as a potential inhibitor of SARS-CoV-2 main protease: theoretical study

The global breakout of COVID-19 and raised death toll has prompted scientists to develop novel drugs capable of inhibiting SARS-CoV-2. Conducting studies on repurposing some FDA-approved glucocorticoids can be a promising prospective for finding a treatment for COVID-19. In addition, the use of anti-inflammatory drugs, such as glucocorticoids, is a pivotal step in the treatment of critical cases of COVID-19, as they can provoke an inflammatory cytokine storm, damaging lungs. In this study, 22 FDA-approved glucocorticoids were identified through in silico (molecular docking) studies as the potential inhibitors of COVID-19's main protease. From tested compounds, ciclesonide 11, dexamethasone 2, betamethasone 1, hydrocortisone 4, fludrocortisone 3, and triamcinolone 8 are suggested as the most potent glucocorticoids active against COVID-19's main protease. Moreover, molecular dynamics simulations followed by the calculations of the binding free energy using MM-GBSA were carried out for the aforementioned promising candidate-screened glucocorticoids. In addition, quantum chemical calculations revealed two electron-rich sites on ciclesonide where binding interactions with the main protease and cleavage of the prodrug to the active metabolite take place. Our results have ramifications for conducting preclinical and clinical studies on promising glucocorticoids to hasten the development of effective therapeutics against COVID-19. Another advantage is that some glucocorticoids can be prioritized over others for the treatment of inflammation accompanying COVID-19.

The global breakout of COVID-19 and raised death toll has prompted scientists to develop novel drugs capable of inhibiting SARS-CoV-2.

Taher R, Al-Karmalawy AA, El-Ebeedy D, Metwaly AG, Elkateeb NM, Ghanem A, Elghaish RA, Abd El Maksoud AI. Cordyline fruticosa (L.) A. Chev. leaves: isolation, HPLC/MS profiling and evaluation of nephroprotective and hepatoprotective activities supported by molecular docking

The metabolites profile of C. fruticosa (L.) A. Chev. leaves, 12 isolates, and its nephroprotective and hepatoprotective activities are described.

In silico Investigations of the Mode of Action of Novel Colchicine Derivatives Targeting β-Tubulin Isotypes: A Search for a Selective and Specific β-III Tubulin Ligand

The cardinal role of microtubules in cell mitosis makes them interesting drug targets for many pharmacological treatments, including those against cancer. Moreover, different expression patterns between cell types for several tubulin isotypes represent a great opportunity to improve the selectivity and specificity of the employed drugs and to design novel compounds with higher activity only on cells of interest. In this context, tubulin isotype βIII represents an excellent target for anti-tumoral therapies since it is overexpressed in most cancer cells and correlated with drug resistance. Colchicine is a well-known antimitotic agent, which is able to bind the tubulin dimer and to halt the mitotic process. However, it shows high toxicity also on normal cells and it is not specific for isotype βIII. In this context, the search for colchicine derivatives is a matter of great importance in cancer research. In this study, homology modeling techniques, molecular docking, and molecular dynamics simulations have been employed to characterize the interaction between 55 new promising colchicine derivatives and tubulin isotype βIII. These compounds were screened and ranked based on their binding affinity and conformational stability in the colchicine binding site of tubulin βIII. Results from this study point the attention on an amide of 4-chlorine thiocolchicine. This colchicine-derivative is characterized by a unique mode of interaction with tubulin, compared to all other compounds considered, which is primarily characterized by the involvement of the α-T5 loop, a key player in the colchicine binding site. Information provided by the present study may be particularly important in the rational design of colchicine-derivatives targeting drug resistant cancer phenotypes.