Benchmark of Popular Free Energy Approaches Revealing the Inhibitors Binding to SARS-CoV-2 Mpro

ROC-guided virtual screening, molecular dynamics simulation, and bioactivity validation assessment Z195914464 as a 3CL Mpro inhibitor

Discovering novel class anti-SARS-CoV-2 compounds with novel backbones is essential for preventing and controlling SARS-CoV-2 transmission, which poses a substantial threat to the health and social sustainable development of the global population because of its high pathogenicity and high transmissibility. Although the potential mutation of SARS-CoV-2 might diminish the therapeutic efficacy of drugs, 3CL Mpro is the target highly conservative in contrast with other targets. It is an essential enzyme for coronavirus replication. Based on this, this study utilized the drug discovery strategy of Knime molecular filtering framework, ROC-guided virtual screening, clustering analysis, binding mode analysis, and activity evaluation approaches to identify compound Z195914464 (IC50: 7.19 μM) is a novel class inhibitor of anti-SARS-CoV-2 against the 3CL Mpro target. In addition, based on molecular dynamics simulations and MMPBSA analyses, discovered that compound Z195914464 can interact with more key residues and lower bonding energies, which explains why it exhibited more activity than the other three compounds. In summary, this study developed a method for the rapid and accurate discovery of active compounds and can also be applied in the discovery of active compounds in other targets.

Design, synthesis, anti-inflammatory evaluation, and molecular docking studies of novel quinazoline-4(3H)-one-2-carbothioamide derivatives

In this paper, a series of novel quinazoline-4(3H)-one-2-carbothioamide derivatives (8a-p) were designed and synthesized via the Wilgerodt-Kindler reaction between 2-methylquinazoline-4-one 10 and amines using S8/DMSO as the oxidizing system. Their characteristics were confirmed by IR, NMR, HRMS spectra, and their melting point. These novel derivatives (8a-p) were evaluated for their anti-inflammatory activity by inhibiting NO production in lipopolysaccharide (LPS)-activated RAW 264.7 macrophage cells. Compounds 8d (IC50 = 2.99 μM), 8g (IC50 = 3.27 μM), and 8k (IC50 = 1.12 μM) exhibited potent inhibition of NO production compared to the standard drug dexamethasone (IC50 = 14.20 μM). Compound 8a (IC50 = 13.44 μM) exhibited NO inhibition comparable to dexamethasone. Structure-activity relationship (SAR) studies indicated that the presence of both the thioamide functional group (NH-C[double bond, length as m-dash]S) directly attached to the phenyl ring containing halogen substituents (4-Cl, 8d), (4-Br, 8g) and (4-CF3, 8k), is responsible for the potent anti-inflammatory activity of these novel quinazolinone derivatives. Computational modeling studies revealed that compounds 8d, 8g, and 8k are potent inhibitors of TLR4 signaling through the formation of hydrophobic interactions and are stabilized by hydrogen bonds. Replacing the thioamide (8k) with an amide (8q) resulted in an 83-fold decrease in NO inhibitory potency. This highlights the important role of H-bonding involving the thioamide group. The structural shape difference results in favorable interactions of quinazolinones containing thioamide linkers compared to amide linkers to the target receptor. Furthermore, the ADMET profiles and physicochemical properties of these three lead compounds were predicted to meet the criteria for drug-like properties. Therefore, these compounds may be potential candidates for the treatment of many inflammatory diseases associated with immune disorders.

Multiple Topology Replica Exchange of Expanded Ensembles for Multidimensional Alchemical Calculations

Relative free energy (RFE) calculations are now widely used in academia and the industry, but their accuracy is often limited by poor sampling of the complexes' conformational ensemble. To help address conformational sampling problems when simulating many relative binding free energies, we developed a novel method termed multiple topology replica exchange of expanded ensembles (MT-REXEE). This method enables parallel expanded ensemble calculations, facilitating iterative RFE computations while allowing conformational exchange between parallel transformations. These iterative transformations can be adaptable to any set of systems with a common backbone or central substructure. We demonstrate that the MT-REXEE method maintains thermodynamic cycle closure to the same extent as standard expanded ensemble calculations for both solvation free energy and relative binding free energy calculations. The transformations tested involve systems that incorporate diverse heavy atoms and multisite perturbations of a small molecule core resembling multisite λ dynamics, without necessitating modifications to the MD code. Our initial implementation is in GROMACS. We outline a systematic approach for the topology setup and provide instructions on how to perform inter-replica coordinate modifications. This work shows that MT-REXEE can be used to perform accurate and reproducible free energy estimates and prompts expansion to more complex test systems and other molecular dynamics simulation infrastructures.

Molecular insights and inhibitory dynamics of flavonoids in targeting Pim-1 kinase for cancer therapy

Pim-1 kinase, a serine/threonine kinase, is often overexpressed in various cancers, contributing to disease progression and poor prognosis. In this study, we explored the potential of flavonoids as inhibitors of Pim-1 kinase using a combination of molecular docking and steered molecular dynamics (SMD) simulations. Our docking studies revealed two main binding orientations for the flavonoid molecules. The SMD simulations showed that the binding mode with higher pulling forces was linked to stronger inhibitory activity, with a strong positive correlation (R 2 ≈ 0.92) between pulling forces and IC50 values. Quercetin stood out as the most potent inhibitor, showing a pulling force of about 820 pN and an IC_(5) 0 of less than 6 µM. Further dynamic simulations indicated that quercetin's hydroxyl groups at the C3, C-5 and C-7 positions formed stable hydrogen bonds with key residues GLU-121, Leu-44 and Val-126, respectively enhancing its binding stability and effectiveness. Our results emphasized the critical role of the hydroxyl group at the C-3 position, which plays a pivotal function in effectively anchoring these molecules in the active site of Pim-1 kinase. Principal component analysis (PCA) of Pim-1 kinase's conformational changes revealed that potent inhibitors like quercetin, galangin, and kaempferol significantly restricted the enzyme's flexibility, suggesting potential inhibitory effect. These findings provide insights into the structural interactions between flavonoids and Pim-1 kinase, offering a foundation for future experimental investigations. However, further studies, including in vitro and in vivo validation, are necessary to assess the pharmacological relevance and specificity of flavonoids in cancer therapy.

Discovery of a novel Xanthone derivative P24 for anti-AD via targeting sTGFBR3

Soluble transforming growth factor beta receptor 3 (sTGFBR3) antagonist is a new focus in the research and development of Alzheimer's disease (AD) drugs. Our previous studies have identified sTGFBR3 as a promising new target for AD, with few targeted antagonists identified. In this study, we performed structural modeling of sTGFBR3 using AlphaFold2, followed by high-throughput virtual screening and surface plasmon resonance assays. which collectively identified Xanthone as potential compounds for targeting sTGFBR3. After optimizing the sTGFBR3-Xanthone complex using molecular dynamics (MD) simulations, we prepared a series of novel Xanthone derivatives and evaluated their anti-inflammatory activity, toxicity, and structure-activity relationship in BV2 cell model induced by lipopolysaccharides (LPS) or APP/PS1/tau mouse brain extract (BE). Several derivatives with the most potent anti-inflammatory activity were tested for blood-brain barrier permeability and sTGFBR3 affinity. Derivative P24, selected for its superior properties, was further evaluated in vitro. The results indicated that P24 increased the activation of TGF-β signaling and decreased the activation of IκBα/NF-κB signaling by targeting sTGFBR3, thereby regulating the inflammation-phagocytosis balance in microglia. Moreover, the low acute toxicity, long half-life, and low plasma clearance of P24 suggest that it can be sustained in vivo. This property may render P24 a more effective treatment modality for chronic diseases, particularly AD. The study demonstrates P24 serve as potential novel candidates for the treatment of AD via antagonizing sTGFBR3.

Searching for potential acetylcholinesterase inhibitors: a combined approach of multi-step similarity search, machine learning and molecular dynamics simulations

Targeting acetylcholinesterase is one of the most important strategies for developing therapeutics against Alzheimer's disease. In this work, we have employed a new approach that combines machine learning models, a multi-step similarity search of the PubChem library and molecular dynamics simulations to investigate potential inhibitors for acetylcholinesterase. Our search strategy has been shown to significantly enrich the set of compounds with strong predicted binding affinity to acetylcholinesterase. Both machine learning prediction and binding free energy calculation, based on linear interaction energy, suggest that the compound CID54414454 would bind strongly to acetylcholinesterase and hence is a promising inhibitor.

MedChemExpress compounds prevent neuraminidase N1 via physics- and knowledge-based methods

Influenza A viruses spread out worldwide, causing several global concerns. Hence, discovering neuraminidase inhibitors to prevent the influenza A virus is of great interest. In this work, a machine learning model was employed to evaluate the ligand-binding affinity of ca. 10 000 compounds from the MedChemExpress (MCE) database for inhibiting neuraminidase. Atomistic simulations, including molecular docking and molecular dynamics simulations, then confirmed the ligand-binding affinity. Furthermore, we clarified the physical insights into the binding process of ligands to neuraminidase. It was found that five compounds, including micronomicin, didesmethyl cariprazine, argatroban, Kgp-IN-1, and AY 9944, are able to inhibit neuraminidase N1 of the influenza A virus. Ten residues, including Glu119, Asp151, Arg152, Trp179, Gln228, Glu277, Glu278, Arg293, Asn295, and Tyr402, may be very important in controlling the ligand-binding process to N1.

SARS-CoV-2 omicron RBD forms a weaker binding affinity to hACE2 compared to Delta RBD in in-silico studies

The COVID-19 pandemic sparked an unprecedented race in biotechnology in a search for effective therapies and a preventive vaccine. The continued appearance of SARS-CoV-2 variants of concern (VoCs) further swept the world. The entry of SARS-CoV-2 into cells is mediated by binding the receptor-binding domain (RBD) of the S protein to the cell-surface receptor, human angiotensin-converting enzyme 2 (hACE2). In this study, using a coarse-grained force field to parameterize the system, we employed steered-molecular dynamics (SMD) simulations to reveal the binding of SARS-CoV-2 Delta/Omicron RBD to hACE2. Our benchmarked results demonstrate a good correlation between computed rupture force and experimental binding free energy for known protein-protein systems. Moreover, our findings show that the Omicron RBD has a weaker binding affinity to hACE2, consistent with the respective experimental results. This indicates that our method can effectively be applied to other emerging SARS-CoV-2 strains.Communicated by Ramaswamy H. Sarma.

Machine learning combines atomistic simulations to predict SARS-CoV-2 Mpro inhibitors from natural compounds

To date, the COVID-19 pandemic has still been infectious around the world, continuously causing social and economic damage on a global scale. One of the most important therapeutic targets for the treatment of COVID-19 is the main protease (Mpro) of SARS-CoV-2. In this study, we combined machine-learning (ML) model with atomistic simulations to computationally search for highly promising SARS-CoV-2 Mpro inhibitors from the representative natural compounds of the National Cancer Institute (NCI) Database. First, the trained ML model was used to scan the library quickly and reliably for possible Mpro inhibitors. The ML output was then confirmed using atomistic simulations integrating molecular docking and molecular dynamic simulations with the linear interaction energy scheme. The results turned out to show that there was evidently good agreement between ML and atomistic simulations. Ten substances were proposed to be able to inhibit SARS-CoV-2 Mpro. Seven of them have high-nanomolar affinity and are very potential inhibitors. The strategy has been proven to be reliable and appropriate for fast prediction of SARS-CoV-2 Mpro inhibitors, benefiting for new emerging SARS-CoV-2 variants in the future accordingly.

Prediction of Binding Pose and Affinity of Nelfinavir, a SARS-CoV-2 Main Protease Repositioned Drug, by Combining Docking, Molecular Dynamics, and Fragment Molecular Orbital Calculations

A novel in silico drug design procedure is described targeting the Main protease (Mpro) of the SARS-CoV-2 virus. The procedure combines molecular docking, molecular dynamics (MD), and fragment molecular orbital (FMO) calculations. The binding structure and properties of Mpro were predicted for Nelfinavir (NFV), which had been identified as a candidate compound through drug repositioning, targeting Mpro. Several poses of the Mpro and NFV complexes were generated by docking, from which four docking poses were selected by scoring with FMO energy. Then, each pose was subjected to MD simulation, 100 snapshot structures were sampled from each of the generated MD trajectories, and the structures were evaluated by FMO calculations to rank the pose based on binding energy. Several residues were found to be important in ligand recognition, including Glu47, Asp48, Glu166, Asp187, and Gln189, all of which interacted strongly with NFV. Asn142 is presumably regarded to form hydrogen bonds or CH/π interaction with NFV; however, in the present calculation, their interactions were transient. Moreover, the tert-butyl group of NFV had no interaction with Mpro. Identifying such strong and weak interactions provides candidates for maintaining and substituting ligand functional groups and important suggestions for drug discovery using drug repositioning. Besides the interaction between NFV and the amino acid residues of Mpro, the desolvation effect of the binding pocket also affected the ranking order. A similar procedure of drug design was applied to Lopinavir, and the calculated interaction energy and experimental inhibitory activity value trends were consistent. Our approach provides a new guideline for structure-based drug design starting from a candidate compound whose complex crystal structure has not been obtained.

Natural compounds inhibit Monkeypox virus methyltransferase VP39 in silico studies

VP39, an essential 2'-O-RNA methyltransferase enzyme discovered in Monkeypox virus (MPXV), plays a vital role in viral RNA replication and transcription. Inhibition of the enzyme may prevent viral replication. In this context, using a combination of molecular docking and molecular dynamics (MDs) simulations, the inhibitory ability of NCI Diversity Set VII natural compounds to VP39 protein was investigated. It should be noted that the computed binding free energy of ligand via molecular docking and linear interaction energy (LIE) approaches are in good agreement with the corresponding experiments with coefficients of R = 0.72 and 0.75, respectively. NSC 319990, NSC 196515 and NSC 376254 compounds were demonstrated that can inhibit MPVX methyltransferase VP39 protein with the similar affinity compared to available inhibitor sinefungin. Moreover, nine residues involving Gln39, Gly68, Gly72, Asp95, Arg97, Val116, Asp138, Arg140 and Asn156 may be argued that they play an important role in binding process of inhibitors to VP39.Communicated by Ramaswamy H. Sarma.

Harnessing Brazilian biodiversity database: identification of flavonoids as potential inhibitors of SARS-CoV-2 main protease using computational approaches and all-atom molecular dynamics simulation

SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2) is the etiological agent responsible for the global outbreak of COVID-19 (Coronavirus Disease 2019). The main protease of SARS-CoV-2, Mpro, is a key enzyme that plays a vital role in mediating viral replication and transcription. In this study, a comprehensive computational approach was employed to investigate the binding affinity, selectivity, and stability of natural product candidates as potential new antivirals acting on the viral polyprotein processing mediated by SARS-CoV-2 Mpro. A library of 288 flavonoids extracted from Brazilian biodiversity was screened to select potential Mpro inhibitors. An initial filter based on Lipinski's rule of five was applied, and 204 compounds that did not violate any of the Lipinski rules were selected. The compounds were then docked into the active site of Mpro using the GOLD program, and the poses were subsequently re-scored using MM-GBSA (Molecular Mechanics Generalized Born Surface Area) binding free energy calculations performed by AmberTools23. The top five flavonoids with the best MM-GBSA binding free energy values were selected for analysis of their interactions with the active site residues of the protein. Next, we conducted a toxicity and drug-likeness analysis, and non-toxic compounds were subjected to molecular dynamics simulation and free energy calculation using the MM-PBSA (Molecular Mechanics Poisson-Boltzmann Surface Area) method. It was observed that the five selected flavonoids had lower MM-GBSA binding free energy with Mpro than the co-crystal ligand. Furthermore, these compounds also formed hydrogen bonds with two important residues, Cys145 and Glu166, in the active site of Mpro. Two compounds that passed the drug-likeness filter showed stable conformations during the molecular dynamics simulations. Among these, NuBBE_867 exhibited the best MM-PBSA binding free energy value compared to the crystallographic inhibitor. Therefore, this study suggests that NuBBE_867 could be a potential inhibitor against the main protease of SARS-CoV-2 and may be further examined to confirm our results.

In-silico guided design, screening, and molecular dynamic simulation studies for the identification of potential SARS-CoV-2 main protease inhibitors for the targeted treatment of COVID-19

COVID-19, the disease responsible for the recent pandemic, is caused by a novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The main protease (Mpro) of SARS-CoV-2 is an essential proteolytic enzyme that plays a number of important roles in the replication of the virus in human host cells. Blocking the function of SARS-CoV-2 Mpro offers a promising and targeted, therapeutic option for the treatment of the COVID-19 infection. Such an inhibitory strategy is currently successful in treating COVID-19 under FDA's emergency use authorization, although with limited benefit to the immunocompromised along with an unfortunate number of side effects and drug-drug interactions. Current COVID vaccines protect against severe disease and death but are mostly ineffective toward long COVID which has been seen in 5-36% of patients. SARS-CoV-2 is a rapidly mutating virus and is here to stay endemically. Hence, alternate therapeutics to treat SARS-CoV-2 infections are still needed. Moreover, because of the high degree of conservation of Mpro among different coronaviruses, any newly developed antiviral agents should better prepare us for potential future epidemics or pandemics. In this paper, we first describe the design and computational docking of a library of novel 188 first-generation peptidomimetic protease inhibitors using various electrophilic warheads with aza-peptide epoxides, α-ketoesters, and β-diketones identified as the most effective. Second-generation designs, 192 compounds in total, focused on aza-peptide epoxides with drug-like properties, incorporating dipeptidyl backbones and heterocyclic ring motifs such as proline, indole, and pyrrole groups, yielding 8 hit candidates. These novel and specific inhibitors for SARS-CoV-2 Mpro can ultimately serve as valuable alternate and broad-spectrum antivirals against COVID-19.Communicated by Ramaswamy H. Sarma.

Bakuchiol inhibits Pseudomonas aeruginosa's quorum sensing-dependent biofilm formation by selectively inhibiting its transcriptional activator protein LasR

In the present study, we characterized Bakuchiol (Bak) as a new potent quorum sensing (QS) inhibitor against Pseudomonas aeruginosa biofilm formation. Upon extensive in vitro investigations, Bak was found to suppress the P. aeruginosa biofilm formation (75.5 % inhibition) and its associated virulence factor e.g., pyocyanin and rhamnolipids (% of inhibition = 71.5 % and 66.9 %, respectively). Upon LuxR-type receptors assay, Bak was found to selectively inhibit P. aeruginosa's LasR in a dose-dependent manner. Further in-depth molecular investigations (e.g., sedimentation velocity and thermal shift assays) revealed that Bak destabilized LasR upon binding and disrupted its functioning quaternary structure (i.e., the functioning dimeric form). The subsequent modeling and molecular dynamics (MD) simulations explained in more molecular detail how Bak interacts with LasR and how it can induce its dimeric form disruption. In conclusion, our study identified Bak as a potent and specific LasR antagonist that should be widely used as a chemical probe of QS in P. aeruginosa, offering new insights into LasR antagonism processes. The new findings shed light on the cryptic world of LuxR-type QS in this important opportunistic pathogen.

A phenothiazine urea derivative broadly inhibits coronavirus replication via viral protease inhibition

Coronavirus (CoV) replication requires efficient cleavage of viral polyproteins into an array of non-structural proteins involved in viral replication, organelle formation, viral RNA synthesis, and host shutoff. Human CoVs (HCoVs) encode two viral cysteine proteases, main protease (Mpro) and papain-like protease (PLpro), that mediate polyprotein cleavage. Using a structure-guided approach, a phenothiazine urea derivative that inhibits both SARS-CoV-2 Mpro and PLpro protease activity was identified. In silico docking studies also predicted the binding of the phenothiazine urea to the active sites of structurally similar Mpro and PLpro proteases from distantly related alphacoronavirus, HCoV-229 E (229 E), and the betacoronavirus, HCoV-OC43 (OC43). The lead phenothiazine urea derivative displayed broad antiviral activity against all three HCoVs tested in cellulo. It was further demonstrated that the compound inhibited 229 E and OC43 at an early stage of viral replication, with diminished formation of viral replication organelles, and the RNAs that are made within them, as expected following viral protease inhibition. These observations suggest that the phenothiazine urea derivative readily inhibits viral replication and may broadly inhibit proteases of diverse coronaviruses.

Upgrading nirmatrelvir to inhibit SARS-CoV-2 Mpro via DeepFrag and free energy calculations

The first oral drug for the treatment of COVID-19, Paxlovid, has been authorized; however, nirmatrelvir, a major component of the drug, is reported to be associated with some side effects. Moreover, the appearance of many novel variants raises concerns about drug resistance, and designing new potent inhibitors to prevent viral replication is thus urgent. In this context, using a hybrid approach combining machine learning (ML) and free energy simulations, 6 compounds obtained by modifying nirmatrelvir were proposed to bind strongly to SARS-CoV-2 Mpro. The structural modification of nirmatrelvir significantly enhances the electrostatic interaction free energy between the protein and ligand and slightly decreases the vdW term. However, the vdW term is the most important factor in controlling the ligand-binding affinity. In addition, the modified nirmatrelvir might be less toxic to the human body than the original inhibitor.

Development and Evaluation of a Self-Nanoemulsifying Drug Delivery System for Sinapic Acid with Improved Antiviral Efficacy against SARS-CoV-2

This study aimed to develop a self-nanoemulsifying drug delivery system (SNE) for sinapic acid (SA) to improve its solubility and antiviral activity. Optimal components for the SA-SNE formulation were selected, including Labrafil as the oil, Cremophor EL as the surfactant, and Transcutol as the co-surfactant. The formulation was optimized using surface response design, and the optimized SA-SNE formulation exhibited a small globule size of 83.6 nm, high solubility up to 127.1 ± 3.3, and a 100% transmittance. In vitro release studies demonstrated rapid and high SA release from the formulation. Pharmacokinetic analysis showed improved bioavailability by 2.43 times, and the optimized SA-SNE formulation exhibited potent antiviral activity against SARS-CoV-2. The developed SA-SNE formulation can enhance SA's therapeutic efficacy by improving its solubility, bioavailability, and antiviral activity. Further in silico, modeling, and Gaussian accelerated molecular dynamics (GaMD)-based studies revealed that SA could interact with and inhibit the viral main protease (Mpro). This research contributes to developing effective drug delivery systems for poorly soluble drugs like SA, opening new possibilities for their application via nebulization in SARS-CoV-2 therapy.

Norlobaridone Inhibits Quorum Sensing-Dependent Biofilm Formation and Some Virulence Factors in Pseudomonas aeruginosa by Disrupting Its Transcriptional Activator Protein LasR Dimerization

In the present study, norlobaridone (NBD) was isolated from Parmotrema and then evaluated as a new potent quorum sensing (QS) inhibitor against Pseudomonas aeruginosa biofilm development. This phenolic natural product was found to reduce P. aeruginosa biofilm formation (64.6% inhibition) and its related virulence factors, such as pyocyanin and rhamnolipids (% inhibition = 61.1% and 55%, respectively). In vitro assays inhibitory effects against a number of known LuxR-type receptors revealed that NBD was able to specifically block P. aeruginosa's LasR in a dose-dependent manner. Further molecular studies (e.g., sedimentation velocity and thermal shift assays) demonstrated that NBD destabilized LasR upon binding and damaged its functional quaternary structure (i.e., the functional dimeric form). The use of modelling and molecular dynamics (MD) simulations also allowed us to further understand its interaction with LasR, and how this can disrupt its dimeric form. Finally, our findings show that NBD is a powerful and specific LasR antagonist that should be widely employed as a chemical probe in QS of P. aeruginosa, providing new insights into LasR antagonism processes. The new discoveries shed light on the mysterious world of LuxR-type QS in this key opportunistic pathogen.

COVID infection in 4 steps: Thermodynamic considerations reveal how viral mucosal diffusion, target receptor affinity and furin cleavage act in concert to drive the nature and degree of infection in human COVID-19 disease

We have developed a mechanistic model of SARS-CoV-2 and SARS-CoV infection, exploring the relationship between the viral diffusion in the mucosa and viral affinity for the angiotensin converting enzyme 2 (ACE2) target. Utilising the structural similarity of SARS-CoV and SARS-CoV-2 and a shared viral target receptor (ACE2), but a dramatic difference in upper or lower respiratory tract infectivity, we were able to generate insights into the linkage of mucosal diffusion and target receptor affinity in determining the pathophysiological pathways of these two viruses. Our analysis reveals that for SARS-CoV-2 the higher affinity of ACE2 binding, the faster and more complete the mucosal diffusion in its transport from the upper airway to the region of the ACE2 target on the epithelium. This diffusional process is essential for the presentation of this virus to the furin catalysed highly efficient entry and infection process in the upper respiratory tract epithelial cells. A failure of SARS-CoV to follow this path is associated with lower respiratory tract infection and decreased infectivity. Thus, our analysis supports the view that through tropism SARS-CoV-2 has evolved a highly efficient membrane entry process that can act in concert with a high binding affinity of this virus and its variants for its ACE2 which in turn promotes enhanced movement of the virus from airway to epithelium. In this way ongoing mutations yielding higher affinities of SARS-CoV-2 for the ACE2 target becomes the basis for higher upper respiratory tract infectivity and greater viral spread. It is concluded that SARS-CoV-2 is constrained in the extent of its activities by the fundamental laws of physics and thermodynamics. Laws that describe diffusion and molecular binding. Moreover it can be speculated that the very earliest contact of this virus with the human mucosa defines the pathogenesis of this infection.

Blackberry-Loaded AgNPs Attenuate Hepatic Ischemia/Reperfusion Injury via PI3K/Akt/mTOR Pathway

Liver ischemia-reperfusion injury (IRI) is a pathophysiological insult that often occurs during liver surgery. Blackberry leaves are known for their anti-inflammatory and antioxidant activities. Aims: To achieve site-specific delivery of blackberry leaves extract (BBE) loaded AgNPs to the hepatocyte in IRI and to verify possible molecular mechanisms. Methods: IRI was induced in male Wister rats. Liver injury, hepatic histology, oxidative stress markers, hepatic expression of apoptosis-related proteins were evaluated. Non-targeted metabolomics for chemical characterization of blackberry leaves extract was performed. Key findings: Pre-treatment with BBE protected against the deterioration caused by I/R, depicted by a significant improvement of liver functions and structure, as well as reduction of oxidative stress with a concomitant increase in antioxidants. Additionally, BBE promoted phosphorylation of antiapoptotic proteins; PI3K, Akt and mTOR, while apoptotic proteins; Bax, Casp-9 and cleaved Casp-3 expressions were decreased. LC-HRMS-based metabolomics identified a range of metabolites, mainly flavonoids and anthocyanins. Upon comprehensive virtual screening and molecular dynamics simulation, the major annotated anthocyanins, cyanidin and pelargonidin glucosides, were suggested to act as PLA2 inhibitors. Significance: BBE can ameliorate hepatic IRI augmented by BBE-AgNPs nano-formulation via suppressing, oxidative stress and apoptosis as well as stimulation of PI3K/Akt/mTOR signaling pathway.

Korupensamine A, but not its atropisomer, korupensamine B, inhibits SARS-CoV-2 in vitro by targeting its main protease (Mpro)

By combining docking and molecular dynamics simulations, we explored a library of 65 mostly axially chiral naphthylisoquinoline alkaloids and their analogues, with most different molecular architectures and structural analogues, for their activity against SARS-CoV-2. Although natural biaryls are often regarded without consideration of their axial chirality, they can bind to protein targets in an atroposelective manner. By combining docking results with steered molecular dynamics simulations, we identified one alkaloid, korupensamine A, that atropisomer-specifically inhibited the main protease (M^pro) activity of SARS-CoV-2 significantly in comparison to the reference covalent inhibitor GC376 (IC₅₀ = 2.52 ± 0.14 and 0.88 ± 0.15 μM, respectively) and reduced viral growth by five orders of magnitude in vitro (EC₅₀ = 4.23 ± 1.31 μM). To investigate the binding pathway and mode of interaction of korupensamine A within the active site of the protease, we utilized Gaussian accelerated molecular dynamics simulations, which reproduced the docking pose of korupensamine A inside the active site of the enzyme. The study presents naphthylisoquinoline alkaloids as a new class of potential anti-COVID-19 agents.

Lessons Learnt from COVID-19: Computational Strategies for Facing Present and Future Pandemics

Since its outbreak in December 2019, the COVID-19 pandemic has caused the death of more than 6.5 million people around the world. The high transmissibility of its causative agent, the SARS-CoV-2 virus, coupled with its potentially lethal outcome, provoked a profound global economic and social crisis. The urgency of finding suitable pharmacological tools to tame the pandemic shed light on the ever-increasing importance of computer simulations in rationalizing and speeding up the design of new drugs, further stressing the need for developing quick and reliable methods to identify novel active molecules and characterize their mechanism of action. In the present work, we aim at providing the reader with a general overview of the COVID-19 pandemic, discussing the hallmarks in its management, from the initial attempts at drug repurposing to the commercialization of Paxlovid, the first orally available COVID-19 drug. Furthermore, we analyze and discuss the role of computer-aided drug discovery (CADD) techniques, especially those that fall in the structure-based drug design (SBDD) category, in facing present and future pandemics, by showcasing several successful examples of drug discovery campaigns where commonly used methods such as docking and molecular dynamics have been employed in the rational design of effective therapeutic entities against COVID-19.

Identification of and Mechanistic Insights into SARS-CoV-2 Main Protease Non-Covalent Inhibitors: An In-Silico Study

The indispensable role of the SARS-CoV-2 main protease (Mpro) in the viral replication cycle and its dissimilarity to human proteases make Mpro a promising drug target. In order to identify the non-covalent Mpro inhibitors, we performed a comprehensive study using a combined computational strategy. We first screened the ZINC purchasable compound database using the pharmacophore model generated from the reference crystal structure of Mpro complexed with the inhibitor ML188. The hit compounds were then filtered by molecular docking and predicted parameters of drug-likeness and pharmacokinetics. The final molecular dynamics (MD) simulations identified three effective candidate inhibitors (ECIs) capable of maintaining binding within the substrate-binding cavity of Mpro. We further performed comparative analyses of the reference and effective complexes in terms of dynamics, thermodynamics, binding free energy (BFE), and interaction energies and modes. The results reveal that, when compared to the inter-molecular electrostatic forces/interactions, the inter-molecular van der Waals (vdW) forces/interactions are far more important in maintaining the association and determining the high affinity. Given the un-favorable effects of the inter-molecular electrostatic interactions-association destabilization by the competitive hydrogen bond (HB) interactions and the reduced binding affinity arising from the un-compensable increase in the electrostatic desolvation penalty-we suggest that enhancing the inter-molecular vdW interactions while avoiding introducing the deeply buried HBs may be a promising strategy in future inhibitor optimization.

Aloin A inhibits SARS CoV-2 replication by targeting its binding with ACE2 - Evidence from modeling-supported molecular dynamics simulation

The current study aimed to expand on the recently published results and assess the inhibitory efficacy of aloin A against SARS CoV-2. In vitro testing of aloin A against SARS CoV-2 proteases (i.e., MPro and PLPro) showed weak to moderate activity (IC50 = 68.56 ± 1.13 µM and 24.77 ± 1.57 µM, respectively). However, aloin A was able to inhibit the replication of SARS CoV-2 in Vero E6 cells efficiently with an IC50 of 0.095 ± 0.022 µM. Depending on the reported poor permeability of aloin A alongside its insignificant protease inhibitory activities presented in this study, we ran a number of extensive virtual screenings and physics-based simulations to determine the compound's potential mode of action. As a result, RBD-ACE2 was identified as a key target for aloin A. Results from 600 ns-long molecular dynamics (MD) simulation experiments pointed to aloin A's role as an RBD-ACE2 destabilizer. Therefore, the results of this work may pave the way for further development of this scaffold and the eventual production of innovative anti-SARS CoV-2 medicines with several mechanisms of action.Communicated by Ramaswamy H. Sarma.

Prioritizing Small Sets of Molecules for Synthesis through in-silico Tools: A Comparison of Common Ranking Methods

Prioritizing molecules for synthesis is a key role of computational methods within medicinal chemistry. Multiple tools exist for ranking molecules, from the cheap and popular molecular docking methods to more computationally expensive molecular-dynamics (MD)-based methods. It is often questioned whether the accuracy of the more rigorous methods justifies the higher computational cost and associated calculation time. Here, we compared the performance on ranking the binding of small molecules for seven scoring functions from five docking programs, one end-point method (MM/GBSA), and two MD-based free energy methods (PMX, FEP+). We investigated 16 pharmaceutically relevant targets with a total of 423 known binders. The performance of docking methods for ligand ranking was strongly system dependent. We observed that MD-based methods predominantly outperformed docking algorithms and MM/GBSA calculations. Based on our results, we recommend the application of MD-based free energy methods for prioritization of molecules for synthesis in lead optimization, whenever feasible.

Searching for potential inhibitors of SARS-COV-2 main protease using supervised learning and perturbation calculations

Inhibiting the biological activity of SARS-CoV-2 Mpro can prevent viral replication. In this context, a hybrid approach using knowledge- and physics-based methods was proposed to characterize potential inhibitors for SARS-CoV-2 Mpro. Initially, supervised machine learning (ML) models were trained to predict a ligand-binding affinity of ca. 2 million compounds with the correlation on a test set of R = 0.748 ± 0.044 . Atomistic simulations were then used to refine the outcome of the ML model. Using LIE/FEP calculations, nine compounds from the top 100 ML inhibitors were suggested to bind well to the protease with the domination of van der Waals interactions. Furthermore, the binding affinity of these compounds is also higher than that of nirmatrelvir, which was recently approved by the US FDA to treat COVID-19. In addition, the ligands altered the catalytic triad Cys145 - His41 - Asp187, possibly disturbing the biological activity of SARS-CoV-2.

Characterizing the ligand-binding affinity toward SARS-CoV-2 Mpro via physics- and knowledge-based approaches

Computational approaches, including physics- and knowledge-based methods, have commonly been used to determine the ligand-binding affinity toward SARS-CoV-2 main protease (Mpro or 3CLpro). Strong binding ligands can thus be suggested as potential inhibitors for blocking the biological activity of the protease. In this context, this paper aims to provide a short review of computational approaches that have recently been applied in the search for inhibitor candidates of Mpro. In particular, molecular docking and molecular dynamics (MD) simulations are usually combined to predict the binding affinity of thousands of compounds. Quantitative structure-activity relationship (QSAR) is the least computationally demanding and therefore can be used for large chemical collections of ligands. However, its accuracy may not be high. Moreover, the quantum mechanics/molecular mechanics (QM/MM) method is most commonly used for covalently binding inhibitors, which also play an important role in inhibiting the activity of SARS-CoV-2. Furthermore, machine learning (ML) models can significantly increase the searching space of ligands with high accuracy for binding affinity prediction. Physical insights into the binding process can then be confirmed via physics-based calculations. Integration of ML models into computational chemistry provides many more benefits and can lead to new therapies sooner.

Computational molecular interaction between SARS-CoV-2 main protease and theaflavin digallate using free energy perturbation and molecular dynamics

Our objective was to identify the molecule which can inhibit SARS-CoV-2 main protease and can be easily procured. Natural products may provide such molecules and can supplement the current custom chemical synthesis-based drug discovery for this objective. A combination of docking approaches, scoring functions, classical molecular dynamic simulation, binding pose metadynamics, and free energy perturbation calculations have been employed in this study. Theaflavin digallate has been observed in top-scoring compounds after the three independent virtual screening simulations of 598435 compounds (unique 27256 chemical entities). The main protease-theaflavin digallate complex interacts with critical active site residues of the main protease in molecular dynamics simulation independent of the explored computational framework, simulation time, initial structure, and force field used. Theaflavin digallate forms approximately three hydrogen bonds with Glutamate166 of main protease, primarily through hydroxyl groups in the benzene ring of benzo(7)annulen-6-one, along with other critical residues. Glu166 is the most critical amino acid for main protease dimerization, which is necessary for catalytic activity. The estimated binding free energy, calculated by Amber and Schrodinger MMGBSA module, reflects a high binding free energy between theaflavin digallate and main protease. Binding pose metadynamics simulation shows the highly persistent H-bond and a stable pose for the theaflavin digallate-main protease complex. Using method control, experimental controls, and test set, alchemical transformation studies confirm high relative binding free energy of theaflavin digallate with the main protease. Computational molecular interaction suggests that theaflavin digallate can inhibit the main protease of SARS-CoV-2.

A Comprehensive In Silico Study of New Metabolites from Heteroxenia fuscescens with SARS-CoV-2 Inhibitory Activity

Chemical investigation of the total extract of the Egyptian soft coral Heteroxenia fuscescens, led to the isolation of eight compounds, including two new metabolites, sesquiterpene fusceterpene A (1) and a sterol fuscesterol A (4), along with six known compounds. The structures of 1–8 were elucidated via intensive studies of their 1D, 2D-NMR, and HR-MS analyses, as well as a comparison of their spectral data with those mentioned in the literature. Subsequent comprehensive in-silico-based investigations against almost all viral proteins, including those of the new variants, e.g., Omicron, revealed the most probable target for these isolated compounds, which was found to be M^pro. Additionally, the dynamic modes of interaction of the putatively active compounds were highlighted, depending on 50-ns-long MDS. In conclusion, the structural information provided in the current investigation highlights the antiviral potential of H. fuscescens metabolites with 3β,5α,6β-trihydroxy steroids with different nuclei against SARS-CoV-2, including newly widespread variants.

Anti-Inflammatory and Antioxidant Properties of Malapterurus electricus Skin Fish Methanolic Extract in Arthritic Rats: Therapeutic and Protective Effects

The protective and therapeutic anti-inflammatory and antioxidant potency of Malapterurus electricus (F. Malapteruridae) skin fish methanolic extract (FE) (300 mg/kg.b.wt/day for 7 days, orally) was tested in monosodium urate(MSU)-induced arthritic Wistar albino male rats' joints. Serum uric acid, TNF-α, IL-1β, NF-𝜅B, MDA, GSH, catalase, SOD, and glutathione reductase levels were all measured. According to the findings, FE significantly reduced uric acid levels and ankle swelling in both protective and therapeutic groups. Furthermore, it has anti-inflammatory effects by downregulating inflammatory cytokines, primarily through decreased oxidative stress and increased antioxidant status. All the aforementioned lesions were significantly improved in protected and treated rats with FE, according to histopathological findings. iNOS immunostaining revealed that protected and treated arthritic rats with FE had weak positive immune-reactive cells. Phytochemical analysis revealed that FE was high in fatty and amino acids. The most abundant compounds were vaccenic (24.52%), 9-octadecenoic (11.66%), palmitic (34.66%), stearic acids (14.63%), glycine (0.813 mg/100 mg), and alanine (1.645 mg/100 mg). Extensive molecular modelling and dynamics simulation experiments revealed that compound 4 has the potential to target and inhibit COX isoforms with a higher affinity for COX-2. As a result, we contend that FE could be a promising protective and therapeutic option for arthritis, aiding in the prevention and progression of this chronic inflammatory disease.

¹H-NMR Metabolic Profiling, Antioxidant Activity, and Docking Study of Common Medicinal Plant-Derived Honey

The purpose of this investigation was to determine ¹H-NMR profiling and antioxidant activity of the most common types of honey, namely, citrus honey (HC1) (Morcott tangerine L. and Jaffa orange L.), marjoram honey (HM1) (Origanum majorana L.), and clover honey (HT1) (Trifolium alexandrinum L.), compared to their secondary metabolites (HC2, HM2, HT2, respectively). By using a ¹H-NMR-based metabolomic technique, PCA, and PLS-DA multivariate analysis, we found that HC2, HM2, HC1, and HM1 were clustered together. However, HT1 and HT2 were quite far from these and each other. This indicated that HC1, HM1, HC2, and HM2 have similar chemical compositions, while HT1 and HT2 were unique in their chemical profiles. Antioxidation potentials were determined colorimetrically for scavenging activities against DPPH, ABTS, ORAC, 5-LOX, and metal chelating activity in all honey extract samples and their secondary metabolites. Our results revealed that HC2 and HM2 possessed more antioxidant activities than HT2 in vitro. HC2 demonstrated the highest antioxidant effect in all assays, followed by HM2 (DPPH assay: IC₅₀ 2.91, 10.7 μg/mL; ABTS assay: 431.2, 210.24 at 50 ug/mL Trolox equivalent; ORAC assay: 259.5, 234.8 at 50 ug/mL Trolox equivalent; 5-LOX screening assay/IC₅₀: 2.293, 6.136 ug/mL; and metal chelating activity at 50 ug/mL: 73.34526%, 63.75881% inhibition). We suggest that the presence of some secondary metabolites in HC and HM, such as hesperetin, linalool, and caffeic acid, increased the antioxidant activity in citrus and marjoram compared to clover honey.

Computation of the Binding Energies between Human ACE2 and Spike RBDs of the Original Strain, Delta and Omicron Variants of the SARS-CoV-2: A DFT Simulation Approach

The receptor binding domain (RBD) of SARS-CoV-2 binds to human ACE2 leading to infection. In this study, the complexes that are formed by the attachment of the SARS-CoV-2 spike RBDs of the original strain, delta and omicron variants to the human ACE2 are investigated via density functional theory (DFT) simulations to obtain binding energies. The DFT computations are performed without fragmenting the interfaces to involve longer-range interactions for improved accuracy, which is one of the primary features of the approach used in this study. Basis set superposition error corrections and van der Waals dispersions are also included in the DFT simulations. The binding energies of the SARS-CoV-2 spike RBDs of the original strain, delta and omicron variants to the human ACE2 are computed as -4.76, -6.68, and -11.77 eV, respectively. These binding energy values indicate that the binding of the omicron variant to the ACE2 is much more favorable than the binding of the original strain and the delta variant, which constitute a molecular reason for the takeover of the omicron variant. The binding energies and the decomposition of these energies found in this study are expected to aid in the development of neutralizing agents.

Integrated bioinformatics-cheminformatics approach toward locating pseudo-potential antiviral marine alkaloids against SARS-CoV-2-Mpro

The emergence of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) with the most contagious variants, alpha (B.1.1.7), beta (B.1.351), delta (B.1.617.2), and Omicron (B.1.1.529) has continuously added a higher number of morbidity and mortality, globally. The present integrated bioinformatics-cheminformatics approach was employed to locate potent antiviral marine alkaloids that could be used against SARS-CoV-2. Initially, 57 antiviral marine alkaloids and two repurposing drugs were selected from an extensive literature review. Then, the putative target enzyme SARS-CoV-2 main protease (SARS-CoV-2-Mpro) was retrieved from the protein data bank and carried out a virtual screening-cum-molecular docking study with all candidates using PyRx 0.8 and AutoDock 4.2 software. Further, the molecular dynamics (MD) simulation of the two most potential alkaloids and a drug docking complex at 100 ns (with two ligand topology files from PRODRG and ATB server, separately), the molecular mechanics/Poisson-Boltzmann surface area (MM/PBSA) free energy, and contributions of entropy were investigated. Then, the physicochemical-toxicity-pharmacokinetics-drug-likeness profiles, the frontier molecular orbitals energies (highest occupied molecular orbital, lowest unoccupied molecular orbital, and ΔE), and structural-activity relationship were assessed and analyzed. Based on binding energy, 8-hydroxymanzamine (-10.5 kcal/mol) and manzamine A (-10.1 kcal/mol) from all alkaloids with darunavir (-7.9 kcal/mol) and lopinavir (-7.4 kcal/mol) against SARS-CoV-2-Mpro were recorded. The MD simulation (RMSD, RMSF, Rg, H-bond, MM/PBSA binding energy) illustrated that the 8-hydroxymanzamine exhibits a static thermodynamic feature than the other two complexes. The predicted physicochemical, toxicity, pharmacokinetics, and drug-likeness profiles also revealed that the 8-hydroxymanzamine could be used as a potential lead candidate individually and/or synergistically with darunavir or lopinavir to combat SARS-CoV-2 infection after some pharmacological validation.

Mechanistic study of the antibacterial potential of the prenylated flavonoid auriculasin against Escherichia coli

Bacterial resistance is spreading in an alarming manner, outpacing the rate of development of new antibacterial agents and surging the need for effective alternatives. Prenylated flavonoids are a promising class of natural antibiotics with reported activity against a wide range of resistant pathogens. Here, a large library of natural flavonoids (1718 structures) was virtually screened for potential candidates inhibiting the B-subunit of gyrase (Gyr-B). Twenty-eight candidates, predominated by prenylated flavonoids, appeared as promising hits. Six of them were selected for further in vitro antibacterial and Gyr-B enzyme inhibitory activities. Auriculasin is presented as the most potent antibacterial candidate, with a MIC ranging from 2 to 4 µg/ml against two clinically isolated multidrug-resistant Escherichia coli strains. Mechanistic antibacterial analysis revealed auriculasin inhibitory activity towards the Gyr-B enzyme on the micromolar scale (IC₅₀  = 0.38 ± 0.15 µM). Gyr-B interaction was further detailed by conducting an isothermal titration calorimetric experiment, which revealed a competitive inhibition with a high affinity for the Gyr-B active site, achieved mostly through enthalpic interactions (ΔG_binding  = -10.69 kcal/mol). Molecular modeling and physics-based simulations demonstrated the molecule's manner of fitting inside the Gyr-B active site, indicating a very potential nucleus for the future generation of more potent derivatives. To conclude, prenylated flavonoids are interesting antibacterial candidates with anti-Gyr-B mechanism of action that can be obtained from a plant-derived flavonoid.

The Wound-Healing Potential of Olea europaea L. Cv. Arbequina Leaves Extract: An Integrated In Vitro, In Silico, and In Vivo Investigation

Olea europaea L. Cv. Arbequina (OEA) (Oleaceae) is an olive variety species that has received little attention. Besides our previous work for the chemical profiling of OEA leaves using LC−HRESIMS, an additional 23 compounds are identified. An excision wound model is used to measure wound healing action. Wounds are provided with OEA (2% w/v) or MEBO® cream (marketed treatment). The wound closure rate related to vehicle-treated wounds is significantly increased by OEA. Comparing to vehicle wound tissues, significant levels of TGF-β in OEA and MEBO® (p < 0.05) are displayed by gene expression patterns, with the most significant levels in OEA-treated wounds. Proinflammatory TNF-α and IL-1β levels are substantially reduced in OEA-treated wounds. The capability of several lignan-related compounds to interact with MMP-1 is revealed by extensive in silico investigation of the major OEA compounds (i.e., inverse docking, molecular dynamics simulation, and ΔG calculation), and their role in the wound-healing process is also characterized. The potential of OEA as a potent MMP-1 inhibitor is shown in subsequent in vitro testing (IC50 = 88.0 ± 0.1 nM). In conclusion, OEA is introduced as an interesting therapeutic candidate that can effectively manage wound healing because of its anti-inflammatory and antioxidant properties.

Strain Wars: Competitive interactions between SARS-CoV-2 strains are explained by Gibbs energy of antigen-receptor binding

Since the beginning of the COVID-19 pandemic, SARS-CoV-2 has mutated several times into new strains, with an increased infectivity. Infectivity of SARS-CoV-2 strains depends on binding affinity of the virus to its host cell receptor. In this paper, we quantified the binding affinity using Gibbs energy of binding and analyzed the competition between SARS-CoV-2 strains as an interference phenomenon. Gibbs energies of binding were calculated for several SARS-SoV-2 strains, including Hu-1 (wild type), B.1.1.7 (alpha), B.1.351 (beta), P.1 (Gamma), B.1.36 and B.1.617 (Delta). The least negative Gibbs energy of binding is that of Hu-1 strain, -37.97 kJ/mol. On the other hand, the most negative Gibbs energy of binding is that of the Delta strain, -49.50 kJ/mol. We used the more negative Gibbs energy of binding to explain the increased infectivity of newer SARS-CoV-2 strains compared to the wild type. Gibbs energies of binding was found to decrease chronologically, with appearance of new strains. The ratio of Gibbs energies of binding of mutated strains and wild type was used to define a susceptibility coefficient, which is an indicator of viral interference, where a virus can prevent or partially inhibit infection with another virus.

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

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

Possible neuroprotective effects of amide alkaloids from Bassia indica and Agathophora alopecuroides: in vitro and in silico investigations

In Alzheimer's disease (AD), the accumulation of amyloid-β plaques, overactivity of MAO-B, and phosphorylated tau protein in the central nervous system result in neuroinflammation and cognitive impairments. Therefore, the multi-targeting of these therapeutic targets has emerged as a promising strategy for the development of AD treatments. The current study reports the isolation and identification of seven amide alkaloids, namely, N-trans-feruloyl-3-methoxytyramine (1), N-trans-feruloyltyramine (2), S-(-)-N-trans-feruloylnormetanephrine (3), S-(-)-N-trans-feruloyloctopamine (4), R-(+)-N-trans-feruloyloctopamine (5), N-trans-caffeoyltyramine (6), and S-(-)-3-(4-hydroxy-3-methoxyphenyl)-N-[2-(4-hydroxyphenyl)-methoxyethyl]acrylamide (7), from B. indica and A. alopecuroides, which are halophytic plants that have been reported to contain diverse phytochemicals. Additionally, the study explores the potential inhibition effects of the isolates on β-secretase, monoamine oxidase enzymes, and phosphorylated tau protein, and their anti-aggregation effects on amyloid-β fibrils. Compounds 1, 2, and 7 showed potent inhibitory activity against BACE1, MAO-B, and phosphorylated tau protein, as well as anti-aggregation activity against Aβ-peptides. Additionally, compound 6 displayed promising inhibition activity against MAO-B enzyme. Further in-depth in silico and modeling analyses (i.e., docking, absolute binding free energy calculations, and molecular dynamics simulations) were carried out to reveal the binding mode of each active compound inside the corresponding enzyme (i.e., MAO-B and BACE1). The results indicate that B. indica, A. alopecuroides, and the isolated amide alkaloids might be useful in the development of lead compounds for the prevention of neurodegenerative diseases, especially AD.

Identifying Possible AChE Inhibitors from Drug-like Molecules via Machine Learning and Experimental Studies

Acetylcholinesterase (AChE) is one of the most important drug targets for Alzheimer's disease (AD) treatment. In this work, a machine learning model was trained to rapidly and accurately screen large chemical databases for the potential inhibitors of AChE. The obtained results were then validated via in vitro enzyme assay. Moreover, atomistic simulations including molecular docking and molecular dynamics simulations were then used to understand molecular insights into the binding process of ligands to AChE. In particular, two compounds including benzyl trifluoromethyl ketone and trifluoromethylstyryl ketone were indicated as highly potent inhibitors of AChE because they established IC₅₀ values of 0.51 and 0.33 μM, respectively. The obtained IC₅₀ of two compounds is significantly lower than that of galantamine (2.10 μM). The predicted log(BB) suggests that the compounds may be able to traverse the blood-brain barrier. A good agreement between computational and experimental studies was observed, indicating that the hybrid approach can enhance AD therapy.

Bioactive Phytochemicals of Citrus reticulata Seeds—An Example of Waste Product Rich in Healthy Skin Promoting Agents

Phytochemical investigation of Egyptian mandarin orange (Citrus reticulata Blanco, F. Rutaceae) seeds afforded thirteen known compounds, 1–13. The structures of isolated compounds were assigned using 1D and 2D NMR and HRESIMS analyses. To characterize the pharmacological activity of these compounds, several integrated virtual screening-based and molecular dynamics simulation-based experiments were applied. As a result, compounds 2, 3 and 5 were putatively identified as hyaluronidase, xanthine oxidase and tyrosinase inhibitors. The subsequent in vitro testing was done to validate the in silico-based experiments to highlight the potential of these flavonoids as promising hyaluronidase, xanthine oxidase and tyrosinase inhibitors with IC50 values ranging from 6.39 ± 0.36 to 73.7 ± 2.33 µM. The present study shed light on the potential of Egyptian mandarin orange’s waste product (i.e., its seeds) as a skin health-promoting natural agent. Additionally, it revealed the applicability of integrated inverse docking-based virtual screening and MDS-based experiments in efficiently predicting the biological potential of natural products.

501Y.V2 spike protein resists the neutralizing antibody in atomistic simulations

SARS-CoV-2 outbreaks worldwide caused COVID-19 pandemic, which is related to several million deaths. In particular, SARS-CoV-2 Spike (S) protein is a major biological target for COVID-19 vaccine design. Unfortunately, recent reports indicated that Spike (S) protein mutations can lead to antibody resistance. However, understanding the process is limited, especially at the atomic scale. The structural change of S protein and neutralizing antibody fragment (FAb) complexes was thus probed using molecular dynamics (MD) simulations. In particular, the backbone RMSD of the 501Y.V2 complex was significantly larger than that of the wild-type one implying a large structural change of the mutation system. Moreover, the mean of Rg, CCS, and SASA are almost the same when compared two complexes, but the distributions of these values are absolutely different. Furthermore, the free energy landscape of the complexes was significantly changed when the 501Y.V2 variant was induced. The binding pose between S protein and FAb was thus altered. The FAb-binding affinity to S protein was thus reduced due to revealing over steered-MD (SMD) simulations. The observation is in good agreement with the respective experiment that the 501Y.V2 SARS-CoV-2 variant can escape from neutralizing antibody (NAb).

Target Design of Novel Histone Deacetylase 6 Selective Inhibitors with 2-Mercaptoquinazolinone as the Cap Moiety

Epigenetic alterations found in all human cancers are promising targets for anticancer therapy. In this sense, histone deacetylase inhibitors (HDACIs) are interesting anticancer agents that play an important role in the epigenetic regulation of cancer cells. Here, we report 15 novel hydroxamic acid-based histone deacetylase inhibitors with quinazolinone core structures. Five compounds exhibited antiproliferative activity with IC50 values of 3.4-37.8 µM. Compound 8 with a 2-mercaptoquinazolinone cap moiety displayed the highest antiproliferative efficacy against MCF-7 cells. For the HDAC6 target selectivity study, compound 8 displayed an IC50 value of 2.3 µM, which is 29.3 times higher than those of HDAC3, HDAC4, HDAC8, and HDAC11. Western blot assay proved that compound 8 strongly inhibited tubulin acetylation, a substrate of HDAC6. Compound 8 also displayed stronger inhibition activity against HDAC11 than the control drug Belinostat. The inhibitory mechanism of action of compound 8 on HDAC enzymes was then explored using molecular docking study. The data revealed a high binding affinity (-7.92 kcal/mol) of compound 8 toward HDAC6. In addition, dock pose analysis also proved that compound 8 might serve as a potent inhibitor of HDAC11.

Classical and Machine Learning Methods for Protein - Ligand Binding Free Energy Estimation

Binding free energy estimation of drug candidates to their biomolecular target is one of the best quantitative estimators in computer-aided drug discovery. Accurate binding free energy estimation is still a challengeable task even after decades of research, along with the complexity of the algorithm, time-consuming procedures, and reproducibility issues. In this review, we have discussed the advantages and disadvantages of diverse free energy methods like Thermodynamic Integration (TI), Bennett's Acceptance Ratio (BAR), Free Energy Perturbation (FEP), alchemical methods. Moreover, we discussed the possible application of the machine learning method in protein-ligand binding free energy estimation.

Synthesis, spectroscopic, and computational studies on molecular charge-transfer complex of 2-((2-hydroxybenzylidene) amino)-2-(hydroxymethyl) propane-1, 3-diol with chloranilic acid: Potential antiviral activity simulation of CT-complex against SARS-CoV-2

An innovative charge-transfer complex between the Schiff base 2-((2-hydroxybenzylidene) amino)-2-(hydroxymethyl) propane-1,3-diol [SAL-THAM] and the π-acceptor, chloranilic acid (CLA) within the mole ratio (1:1) was synthesized and characterized aiming to investigate its electronic transition spectra in acetonitrile (ACN), methanol (MeOH) and ethanol (EtOH) solutions. Applying Job`s method in the three solvents supported the 1:1 (CLA: SAL-THAM) mole ratio complex formation. The formation of stable CT- complex was shown by the highest values of charge-transfer complex formation constants, K_CT, calculated using minimum-maximum absorbance method, with the sequence, acetonitrile > ethanol > methanol DFT study on the synthesized CT complex was applied based on the B3LYP method to evaluate the optimized structure and extract geometrical and reactivity parameters. Based on TD-DFT theory, the electronic properties, ¹H and ¹³C NMR, IR, and UV-Vis spectra of the studied system in different solvents showing good agreement with the experimental studies. MEP map described the possibility of hydrogen bonding and charge transfer in the studied system. Finally, a computational approach for screening the antiviral activity of CT - complex towards SARS-CoV-2 coronavirus protease via molecular docking simulation was conducted and confirmed with molecular dynamic (MD) simulation.