Prospects of multitarget drug designing strategies by linking molecular docking and molecular dynamics to explore the protein-ligand recognition process

Quercetin and Kaempferol as Multi-Targeting Antidiabetic Agents against Mouse Model of Chemically Induced Type 2 Diabetes

Diabetes, a multifactorial metabolic disorder, demands the discovery of multi-targeting drugs with minimal side effects. This study investigated the multi-targeting antidiabetic potential of quercetin and kaempferol. The druggability and binding affinities of both compounds towards multiple antidiabetic targets were explored using pharmacokinetic and docking software (AutoDock Vina 1.1.2). Our findings showed that quercetin and kaempferol obey Lipinski's rule of five and exhibit desirable ADMET (absorption, distribution, metabolism excretion, and toxicity) profiles. Both compounds showed higher binding affinities towards C-reactive protein (CRP), interleukin-1 (IL-1), dipeptidyl peptidase-4 (DPP-IV), peroxisome proliferator-activated receptor gamma (PPARG), protein tyrosine phosphatase (PTP), and sodium-glucose co-transporter-1 (SGLT-1) compared to metformin (the positive control). Both quercetin and kaempferol inhibited α-amylase activity (in vitro) up to 20.30 ± 0.49 and 37.43 ± 0.42%, respectively. Their oral supplementation significantly reduced blood glucose levels (p < 0.001), improved lipid profile (p < 0.001), and enhanced total antioxidant status (p < 0.01) in streptozotocin-nicotinamide (STZ-NA)-induced diabetic mice. Additionally, both compounds significantly inhibited the proliferation of Huh-7 and HepG2 (cancer cells) (p < 0.0001) with no effect on the viability of Vero cell line (non-cancer). In conclusion, quercetin and kaempferol demonstrated higher binding affinities towards multiple targets than metformin. In vitro and in vivo antidiabetic potential along with the anticancer activities of both compounds suggest promise for further development in diabetes management. The combination of both drugs did not show a synergistic effect, possibly due to their same target on the receptors.

Integrated virtual screening and molecular dynamics simulation approaches revealed potential natural inhibitors for DNMT1 as therapeutic solution for triple negative breast cancer

Triple negative breast cancers (TNBC) are clinically heterogeneous but mostly aggressive malignancies devoid of expression of the estrogen, progesterone, and HER2 (ERBB2 or NEU) receptors. It accounts for 15-20% of all cases. Altered epigenetic regulation including DNA hypermethylation by DNA methyltransferase 1 (DNMT1) has been implicated as one of the causes of TNBC tumorigenesis. The antitumor effect of DNMT1 has also been explored in TNBC that currently lacks targeted therapies. However, the actual treatment for TNBC is yet to be discovered. This study is attributed to the identification of novel drug targets against TNBC. A comprehensive docking and simulation analysis was performed to optimize promising new compounds by estimating their binding affinity to the target protein. Molecular dynamics simulation of 500 ns well complemented the binding affinity of the compound and revealed strong stability of predicted compounds at the docked site. Calculation of binding free energies using MMPBSA and MMGBSA validated the strong binding affinity between compound and binding pockets of DNMT1. In a nutshell, our study uncovered that Beta-Mangostin, Gancaonin Z, 5-hydroxysophoranone, Sophoraflavanone L, and Dorsmanin H showed maximum binding affinity with the active sites of DNMT1. Furthermore, all of these compounds depict maximum drug-like properties. Therefore, the proposed compounds can be a potential candidate for patients with TNBC, but, experimental validation is needed to ensure their safety.Communicated by Ramaswamy H. Sarma.

The role and potential of computer-aided drug discovery strategies in the discovery of novel antimicrobials

Antimicrobial resistance (AMR) has become more of a concern in recent decades, particularly in infections associated with global public health threats. The development of new antibiotics is crucial to ensuring infection control and eradicating AMR. Although drug discovery and development are essential processes in the transformation of a drug candidate from the laboratory to the bedside, they are often very complicated, expensive, and time-consuming. The pharmaceutical sector is continuously innovating strategies to reduce research costs and accelerate the development of new drug candidates. Computer-aided drug discovery (CADD) has emerged as a powerful and promising technology that renews the hope of researchers for the faster identification, design, and development of cheaper, less resource-intensive, and more efficient drug candidates. In this review, we discuss an overview of AMR, the potential, and limitations of CADD in AMR drug discovery, and case studies of the successful application of this technique in the rapid identification of various drug candidates. This review will aid in achieving a better understanding of available CADD techniques in the discovery of novel drug candidates against resistant pathogens and other infectious agents.

Exploring NAD+ metabolism and NNAT: Insights from structure, function, and computational modeling

Nicotinamide Adenine Dinucleotide (NAD+), a coenzyme, is ubiquitously distributed and serves crucial functions in diverse biological processes, encompassing redox reactions, energy metabolism, and cellular signalling. This review article explores the intricate realm of NAD + metabolism, with a particular emphasis on the complex relationship between its structure, function, and the pivotal enzyme, Nicotinate Nucleotide Adenylyltransferase (NNAT), also known as nicotinate mononucleotide adenylyltransferase (NaMNAT), in the process of its biosynthesis. Our findings indicate that NAD + biosynthesis in humans and bacteria occurs via the same de novo synthesis route and the pyridine ring salvage pathway. Maintaining NAD homeostasis in bacteria is imperative, as most bacterial species cannot get NAD+ from their surroundings. However, due to lower sequence identity and structurally distant relationship of bacteria, including E. faecium and K. pneumonia, to its human counterpart, inhibiting NNAT, an indispensable enzyme implicated in NAD + biosynthesis, is a viable alternative in curtailing infections orchestrated by E. faecium and K. pneumonia. By merging empirical and computational discoveries and connecting the intricate NAD + metabolism network with NNAT's crucial role, it becomes clear that the synergistic effect of these insights may lead to a more profound understanding of the coenzyme's function and its potential applications in the fields of therapeutics and biotechnology.

Exploring the role of microbial proteins in controlling environmental pollutants based on molecular simulation

Molecular simulation has been widely used to study microbial proteins' structural composition and dynamic properties, such as volatility, flexibility, and stability at the microscopic scale. Herein, this review describes the key elements of molecular docking and molecular dynamics (MD) simulations in molecular simulation; reviews the techniques combined with molecular simulation, such as crystallography, spectroscopy, molecular biology, and machine learning, to validate simulation results and bridge information gaps in the structure, microenvironmental changes, expression mechanisms, and intensity quantification; illustrates the application of molecular simulation, in characterizing the molecular mechanisms of interaction of microbial proteins with four different types of contaminants, namely heavy metals (HMs), pesticides, dyes and emerging contaminants (ECs). Finally, the review outlines the important role of molecular simulations in the study of microbial proteins for controlling environmental contamination and provides ideas for the application of molecular simulation in screening microbial proteins and incorporating targeted mutagenesis to obtain more effective contaminant control proteins.

Death by a thousand cuts through kinase inhibitor combinations that maximize selectivity and enable rational multitargeting

Kinase inhibitors are successful therapeutics in the treatment of cancers and autoimmune diseases and are useful tools in biomedical research. However, the high sequence and structural conservation of the catalytic kinase domain complicate the development of selective kinase inhibitors. Inhibition of off-target kinases makes it difficult to study the mechanism of inhibitors in biological systems. Current efforts focus on the development of inhibitors with improved selectivity. Here, we present an alternative solution to this problem by combining inhibitors with divergent off-target effects. We develop a multicompound-multitarget scoring (MMS) method that combines inhibitors to maximize target inhibition and to minimize off-target inhibition. Additionally, this framework enables optimization of inhibitor combinations for multiple on-targets. Using MMS with published kinase inhibitor datasets we determine potent inhibitor combinations for target kinases with better selectivity than the most selective single inhibitor and validate the predicted effect and selectivity of inhibitor combinations using in vitro and in cellulo techniques. MMS greatly enhances selectivity in rational multitargeting applications. The MMS framework is generalizable to other non-kinase biological targets where compound selectivity is a challenge and diverse compound libraries are available.

QMrebind: incorporating quantum mechanical force field reparameterization at the ligand binding site for improved drug-target kinetics through milestoning simulations

Understanding the interaction of ligands with biomolecules is an integral component of drug discovery and development. Challenges for computing thermodynamic and kinetic quantities for pharmaceutically relevant receptor-ligand complexes include the size and flexibility of the ligands, large-scale conformational rearrangements of the receptor, accurate force field parameters, simulation efficiency, and sufficient sampling associated with rare events. Our recently developed multiscale milestoning simulation approach, SEEKR2 (Simulation Enabled Estimation of Kinetic Rates v.2), has demonstrated success in predicting unbinding (koff) kinetics by employing molecular dynamics (MD) simulations in regions closer to the binding site. The MD region is further subdivided into smaller Voronoi tessellations to improve the simulation efficiency and parallelization. To date, all MD simulations are run using general molecular mechanics (MM) force fields. The accuracy of calculations can be further improved by incorporating quantum mechanical (QM) methods into generating system-specific force fields through reparameterizing ligand partial charges in the bound state. The force field reparameterization process modifies the potential energy landscape of the bimolecular complex, enabling a more accurate representation of the intermolecular interactions and polarization effects at the bound state. We present QMrebind (Quantum Mechanical force field reparameterization at the receptor-ligand binding site), an ORCA-based software that facilitates reparameterizing the potential energy function within the phase space representing the bound state in a receptor-ligand complex. With SEEKR2 koff estimates and experimentally determined kinetic rates, we compare and interpret the receptor-ligand unbinding kinetics obtained using the newly reparameterized force fields for model host-guest systems and HSP90-inhibitor complexes. This method provides an opportunity to achieve higher accuracy in predicting receptor-ligand koff rate constants.

Synthesis of Pyrrolo[3,4-b]pyridin-5-ones via Ugi-Zhu Reaction and In Vitro-In Silico Studies against Breast Carcinoma

An Ugi-Zhu three-component reaction (UZ-3CR) coupled in a one-pot manner to a cascade process (N-acylation/aza Diels-Alder cycloaddition/decarboxylation/dehydration) was performed to synthesize a series of pyrrolo[3,4-b]pyridin-5-ones in 20% to 92% overall yields using ytterbium triflate as a catalyst, toluene as a solvent, and microwaves as a heat source. The synthesized molecules were evaluated in vitro against breast cancer cell lines MDA-MB-231 and MCF-7, finding that compound 1f, at a concentration of 6.25 μM, exhibited a potential cytotoxic effect. Then, to understand the interactions between synthesized compounds and the main proteins related to the cancer cell lines, docking studies were performed on the serine/threonine kinase 1 (AKT1) and Orexetine type 2 receptor (Ox2R), finding moderate to strong binding energies, which matched accurately with the in vitro results. Additionally, molecular dynamics were performed between proteins related to the studied cell lines and the three best ligands.

Identification of defactinib derivatives targeting focal adhesion kinase using ensemble docking, molecular dynamics simulations and binding free energy calculations

Focal adhesion kinase (FAK) belongs to the nonreceptor tyrosine kinases, which selectively phosphorylate tyrosine residues on substrate proteins. FAK is associated with bladder, esophageal, gastric, neck, breast, ovarian and lung cancers. Thus, FAK has been considered as a potential target for tumor treatment. Currently, there are six adenosine triphosphate (ATP)-competitive FAK inhibitors tested in clinical trials but no approved inhibitors targeting FAK. Defactinib (VS-6063) is a second-generation FAK inhibitor with an IC50 of 0.6 nM. The binding model of VS-6063 with FAK may provide a reference model for developing new antitumor FAK-targeting drugs. In this study, the VS-6063/FAK binding model was constructed using ensemble docking and molecular dynamics simulations. Furthermore, the molecular mechanics/generalized Born (GB) surface area (MM/GBSA) method was employed to estimate the binding free energy between VS-6063 and FAK. The key residues involved in VS-6063/FAK binding were also determined using per-residue energy decomposition analysis. Based on the binding model, VS-6063 could be separated into seven regions to enhance its binding affinity with FAK. Meanwhile, 60 novel defactinib-based compounds were designed and verified using ensemble docking. Overall, the present study improves our understanding of the binding mechanism of human FAK with VS-6063 and provides new insights into future drug designs targeting FAK.Communicated by Ramaswamy H. Sarma.

Exhaustive in silico design and screening of novel antipsychotic compounds with improved pharmacodynamics and blood-brain barrier permeation properties

Antipsychotic drugs or neuroleptics are widely used in the treatment of psychosis as a manifestation of schizophrenia and bipolar disorder. However, their effectiveness largely depends on the blood-brain barrier (BBB) permeation (pharmacokinetics) and drug-receptor pharmacodynamics. Therefore, in this study, we developed and implemented the in silico pipeline to design novel compounds (n = 260) as leads using the standard drug scaffolds with improved PK/PD properties from the standard scaffolds. As a result, the best candidates (n = 3) were evaluated in molecular docking to interact with serotonin and dopamine receptors. Finally, haloperidol (HAL) derivative (1-(4-fluorophenyl)-4-(4-hydroxy-4-{4-[(2-phenyl-1,3-thiazol-4-yl)methyl]phenyl}piperidin-1-yl)butan-1-one) was identified as a "magic shotgun" lead compound with better affinity to the 5-HT2A, 5-HT1D, D2, D3, and 5-HT1B receptors than the control molecule. Additionally, this hit substance was predicted to possess similar BBB permeation properties and much lower toxicological profiles in comparison to HAL. Overall, the proposed rational drug design platform for novel antipsychotic drugs based on the BBB permeation and receptor binding might be an invaluable asset for a medicinal chemist or translational pharmacologist.Communicated by Ramaswamy H. Sarma.

Structure based Drug Designing Approaches in SARS-CoV-2 Spike Inhibitor Design

The COVID-19 outbreak and the pandemic situation have hastened the research community to design a novel drug and vaccine against its causative organism, the SARS-CoV-2. The spike glycoprotein present on the surface of this pathogenic organism plays an immense role in viral entry and antigenicity. Hence, it is considered an important drug target in COVID-19 drug design. Several three-dimensional crystal structures of this SARS-CoV-2 spike protein have been identified and deposited in the Protein DataBank during the pandemic period. This accelerated the research in computer- aided drug designing, especially in the field of structure-based drug designing. This review summarizes various structure-based drug design approaches applied to this SARS-CoV-2 spike protein and its findings. Specifically, it is focused on different structure-based approaches such as molecular docking, high-throughput virtual screening, molecular dynamics simulation, drug repurposing, and target-based pharmacophore modelling and screening. These structural approaches have been applied to different ligands and datasets such as FDA-approved drugs, small molecular chemical compounds, chemical libraries, chemical databases, structural analogs, and natural compounds, which resulted in the prediction of spike inhibitors, spike-ACE-2 interface inhibitors, and allosteric inhibitors.

Exploration of Fuzheng Yugan Mixture on COVID-19 based on network pharmacology and molecular docking

After the World Health Organization declared coronavirus disease 2019 (COVID-19), as a global pandemic, global health workers have been facing an unprecedented and severe challenge. Currently, a mixturetion to inhibit the exacerbation of pulmonary inflammation caused by COVID-19, Fuzheng Yugan Mixture (FZYGM), has been approved for medical institution mixturetion notification. However, the mechanism of FZYGM remains poorly defined. This study aimed to elucidate the molecular and related physiological pathways of FZYGM as a potential therapeutic agent for COVID-19. Active molecules of FZYGM were obtained from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), while potential target genes of COVID-19 were identified by DrugBank and GeneCards. Compound-target networks and protein-protein interactions (PPI) were established by Cytoscape_v3.8.2 and String databases, respectively. The gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed. Finally, a more in-depth study was performed using molecular docking. Our study identified 7 active compounds and 3 corresponding core targets. The main potentially acting signaling pathways include the interleukin (IL)-17 signaling pathway, tumor necrosis factor (TNF) signaling pathway, Toll-like receptor signaling pathway, Th17 cell differentiation, and coronavirus disease-COVID-19. This study shows that FZYGM can exhibit anti-COVID-19 effects through multiple targets and pathways. Therefore, FZYGM can be considered a drug candidate for the treatment of COVID-19, and it provides good theoretical support for subsequent experiments and clinical applications of COVID-19.

Integrated plasma pharmacochemistry and network pharmacology to explore the mechanism of Gerberae Piloselloidis Herba in treatment of allergic asthma

ETHNOPHARMACOLOGICAL RELEVANCE

Gerberae Piloselloidis Herba (GPH), a commonly used traditional medicine in China, is derived from Gerbera piloselloides (Linn.) Cass. It is featured by its special bioactivities as antitussive, expectorant, anti-asthma, anti-bacterial, anti-tumor, uterine analgesia, and immunity-enhancing. With a long history of medication in ethnic minority areas in China, it is often used as an effective treatment for cough and sore throat as well as allergic asthma. Although our previous investigation also has discovered GPH performed effective treatment on allergic asthma, its underlying mechanism remains unclear.

AIM OF THE STUDY

This research aims to reveal the pharmacological mechanism of GPH in the treatment for allergic asthma through combination of plasma pharmacology and network pharmacology.

MATERIALS AND METHODS

Firstly, the components of GPH in blood samples were identified using UHPLC- Q-Orbitrap HRMS. An interaction network of "compound-target-disease" was constructed based on the compounds confirmed in blood and on their corresponding targets of allergic asthma acquired from disease gene databases, predicting the possible biological targets and potential signal pathways of GPH with the network pharmacology analysis. Then, a molecular docking between the blood ingredients and the core targets was carried out using the Autodock Vina software. Subsequently, after establishing a mouse model with allergic asthma induced by ovalbumin (OVA), the effect of GPH on allergic asthma was evaluated by analyzing a series of indicators including behavior, lung pathological changes, inflammatory factors in serum and bronchoalveolar lavage fluid (BALF). Finally, the key pathway and targets predicted by network pharmacology and molecular docking were further verified using Western blot analysis.

RESULTS

Eleven chemical constituents (such as arbutin, neochlorogenic acid, chlorogenic acid, etc.) were identified through the analysis of plasma samples, on which basis a total of 142 genes intersecting GPH and allergic asthma were collected by network pharmacology. After performing enrichment analysis of these genes in gene ontology (GO) and kyoto encyclopedia of genes and genomes (KEGG), it was found that arbutin-related targets mainly focused on phosphatidylinositol 3 kinase (PI3K)/protein kinase B (Akt) signal pathway, while luteolin and marmesin -related targets tended to locate at Interleukin-17 (IL-17) signal pathway. Meanwhile, the findings of molecular docking suggested that such components as arbutin, luteolin and marmesin entering into blood had good binding with the core targets related to PI3K/Akt and IL-17 pathways. In addition, GPH improved the OVA-induced asthma symptoms, the alveolar septa thickening and the infiltration of inflammatory cell around bronchi and bronchioles as well as reduced the levels of IgE, IL-8 and TNF-α in serum or BALF. Furthermore, GPH could inhibit the phosphorylation level of Akt and the expression of PI3K, an efficacy supported by the findings by way of Western blot which suggests that GPH in the treatment of allergic asthma was linked to PI3K/Akt signal pathway.

CONCLUSION

In this study, a comprehensive strategy to combine the UPLC-Q-Orbitrap HRMS with network pharmacology was employed to clarify the mechanism of GPH against allergic asthma, a finding where GPH may inhibit PI3K/Akt signal pathway to protect mice from OVA-induced allergic asthma. This study provides a deeper understanding of the pharmacological mechanism of GPH in treatment of asthma, offering a scientific reference for further research and clinical application of GPH in terms of allergic asthma.

Exploring the mechanism of action of dapansutrile in the treatment of gouty arthritis based on molecular docking and molecular dynamics

Purpose: Dapansutrile is an orally active β-sulfonyl nitrile compound that selectively inhibits the NLRP3 inflammasome. Clinical studies have shown that dapansutrile is active in vivo and limits the severity of endotoxin-induced inflammation and joint arthritis. However, there is currently a lack of more in-depth research on the effect of dapansutrile on protein targets such as NLRP3 in gouty arthritis. Therefore, we used molecular docking and molecular dynamics to explore the mechanism of dapansutrile on NLRP3 and other related protein targets.

Methods: We use bioinformatics to screen active pharmaceutical ingredients and potential disease targets. The disease-core gene target-drug network was established and molecular docking was used for verification. Molecular dynamics simulations were utilized to verify and analyze the binding stability of small molecule drugs to target proteins. The supercomputer platform was used to measure and analyze the binding free energy, the number of hydrogen bonds, the stability of the protein target at the residue level, the radius of gyration and the solvent accessible surface area.

Results: The protein interaction network screened out the core protein targets (such as: NLRP3, TNF, IL1B) of gouty arthritis. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed that gouty arthritis mainly played a vital role by the signaling pathways of inflammation and immune response. Molecular docking showed that dapansutrile play a role in treating gouty arthritis by acting on the related protein targets such as NLRP3, IL1B, IL6, etc. Molecular dynamics was used to prove and analyze the binding stability of active ingredients and protein targets, the simulation results found that dapansutrile forms a very stable complex with IL1B.

Conclusion: We used bioinformatics analysis and computer simulation system to comprehensively explore the mechanism of dapansutrile acting on NLRP3 and other protein targets in gouty arthritis. This study found that dapansutrile may not only directly inhibit NLRP3 to reduce the inflammatory response and pyroptosis, but also hinder the chemotaxis and activation of inflammatory cells by regulating IL1B, IL6, IL17A, IL18, MMP3, CXCL8, and TNF. Therefore, dapansutrile treats gouty arthritis by attenuating inflammatory response, inflammatory cell chemotaxis and extracellular matrix degradation by acting on multiple targets.

Exploring the mechanism of action of Xuanfei Baidu granule (XFBD) in the treatment of COVID-19 based on molecular docking and molecular dynamics

Purpose

The Corona Virus Disease 2019 (COVID-19) pandemic has become a challenge of world. The latest research has proved that Xuanfei Baidu granule (XFBD) significantly improved patient’s clinical symptoms, the compound drug improves immunity by increasing the number of white blood cells and lymphocytes, and exerts anti-inflammatory effects. However, the analysis of the effective monomer components of XFBD and its mechanism of action in the treatment of COVID-19 is currently lacking. Therefore, this study used computer simulation to study the effective monomer components of XFBD and its therapeutic mechanism.

Methods

We screened out the key active ingredients in XFBD through TCMSP database. Besides GeneCards database was used to search disease gene targets and screen intersection gene targets. The intersection gene targets were analyzed by GO and KEGG. The disease-core gene target-drug network was analyzed and molecular docking was used for verification. Molecular dynamics simulation verification was carried out to combine the active ingredient and the target with a stable combination. The supercomputer platform was used to measure and analyze the number of hydrogen bonds, the binding free energy, the stability of protein target at the residue level, the solvent accessible surface area, and the radius of gyration.

Results

XFBD had 1308 gene targets, COVID-19 had 4600 gene targets, the intersection gene targets were 548. GO and KEGG analysis showed that XFBD played a vital role by the signaling pathways of immune response and inflammation. Molecular docking showed that I-SPD, Pachypodol and Vestitol in XFBD played a role in treating COVID-19 by acting on NLRP3, CSF2, and relieve the clinical symptoms of SARS-CoV-2 infection. Molecular dynamics was used to prove the binding stability of active ingredients and protein targets, CSF2/I-SPD combination has the strongest binding energy.

Conclusion

For the first time, it was found that the important active chemical components in XFBD, such as I-SPD, Pachypodol and Vestitol, reduce inflammatory response and apoptosis by inhibiting the activation of NLRP3, and reduce the production of inflammatory factors and chemotaxis of inflammatory cells by inhibiting the activation of CSF2. Therefore, XFBD can effectively alleviate the clinical symptoms of COVID-19 through NLRP3 and CSF2.

Aucubin as a natural potential anti-acute hepatitis candidate: Inhibitory potency and hepatoprotective mechanism

BACKGROUND

Hepatic inflammation can substantially impact the development of acute hepatitis. It is a pressing need to identify and exploit novel therapeutic targets as well as effective drug therapies against acute hepatitis. Aucubin (AU) is one of the main active components extracted from the leaves of Eucommia ulmoides and possesses significant anti-inflammatory and antioxidant activities. However, the protective effect and mechanism of AU on acute hepatitis have not been reported yet.

PURPOSE

This study aims to investigate the protective effect of AU on LPS-induced acute hepatitis and the mechanism of action.

METHODS

The limma package was used to analyze differentially expressed genes (DEGs) between LPS-induced acute hepatitis and normal groups based on Gene Expression Omnibus (GEO) microarray data. Network pharmacology predicted targets for AU therapy against acute hepatitis, and Gene Ontology (GO) enrichment analysis of the biological processes involved in these targets. The key pathways were analyzed by protein-protein interaction, KEGG (Kyoto Encyclopedia of Genes and Genomes), and GSEA (Gene Set Enrichment Analysis) enrichment. The important interaction targets between AU and key pathways were evaluated by molecular simulation. The in silico predicted mechanism was verified based on in vitro and in vivo experiments.

RESULTS

A total of 116 intersection targets between AU prediction targets and differentially expressed genes were identified. They were functionally involved in the imbalance of "inflammation-anti-inflammation" and "oxidation-antioxidation" systems in the process of LPS-induced cases. In vitro experiments revealed that AU reduced inflammation in LPS-induced HepG2 cells by reducing the inflammatory cytokines TNF-α, IL-6, as well as iNOS enzyme activity levels. In addition, LPS-induced oxidative stress can be alleviated by AU via adjusting the levels of superoxide dismutase (SOD), glutathione peroxidase (GSH-Px), Malone dialdehyde (MDA) and reactive oxygen species (ROS). Protein-protein interaction and GSEA results showed that AU might exert anti-inflammatory effects mainly through the STAT3/NF-κB signal pathway. Molecular dynamics simulation as well as in vivo tests further demonstrated AU restrained nuclear transfer of NF-κB (P65), probably through reducing phosphorylation of STAT3. In addition, AU appears to reduce oxidative stress by upregulating NRF2/HO-1.

CONCLUSION

We explored potential targets and signal pathways of AU in inhibiting acute hepatitis. AU exerted anti-inflammatory and antioxidant activities and may be a useful candidate drug for the treatment of acute hepatitis.

In Silico Studies of Piperidine Derivatives as Protein Kinase B Inhibitors through 3D-QSAR, Molecular Docking and Molecular Dynamics Simulation

Background:

Protein kinase B (Akt) is a serine/threonine-protein kinase that drives the diverse physiological process. Akt is a promising therapeutic target, which involves cancer cell growth, survival, proliferation and metabolism.

Objective:

The study aims to design highly active Akt inhibitors, and to elucidate the structural requirements for their biological activity, we analyzed the key binding features and summarized the structural determinants for their bioactivities.

Methods:

A series of piperidine derivatives have been investigated employing three-dimensional quantitative structure-activity relationship (3D-QSAR), molecular docking and molecular dynamics simulation.

Results:

The statistics of the comparative molecular field analysis (CoMFA) model (Q2=0.631, R2=0.951) and the comparative molecular similarity index analysis (CoMSIA) model (Q2=0.663, R2=0.966) indicated that our 3D-QSAR model was accurate and reliable. Besides, the stability of receptor-ligand interactions under physiological conditions was then evaluated by molecular dynamics simulation, in agreement with the molecular docking results.

Conclusion:

Our study provided valuable insights for the discovery of potent Akt inhibitors.

Potential Anti-aging Components From Moringa oleifera Leaves Explored by Affinity Ultrafiltration With Multiple Drug Targets

Moringa oleifera (M. oleifera), widely used in tropical and subtropical regions, has been reported to possess good anti-aging benefits on skincare. However, the potential bioactive components responsible for its anti-aging effects, including anti-collagenase, anti-elastase, and anti-hyaluronidase activities, have not been clarified so far. In this study, M. oleifera leaf extracts were first conducted for anti-elastase and anti-collagenase activities in vitro by spectrophotometric and fluorometric assays, and the results revealed that they possessed good activities against skin aging-related enzymes. Then, multi-target bio-affinity ultrafiltration coupled to high-performance liquid chromatography-mass spectrometry (AUF-HPLC-MS) was applied to quickly screen anti-elastase, anti-collagenase, and anti-hyaluronidase ligands in M. oleifera leaf extracts. Meanwhile, 10, 8, and 14 phytochemicals were screened out as the potential anti-elastase, anti-collagenase, and anti-hyaluronidase ligands, respectively. Further confirmation of these potential bioactive components with anti-aging target enzymes was also implemented by molecule docking analysis. In conclusion, these results suggest that the M. oleifera leaves might be a very promising natural source of anti-aging agent for skincare, which can be further explored in the cosmetics and cosmeceutical industries combating aging and skin wrinkling.

Advances in computer-aided drug design for type 2 diabetes

INTRODUCTION

The number of diabetic patients is increasing, posing a heavy social and economic burden worldwide. Traditional drug development technology is time-consuming and costly, and the emergence of computer-aided drug design (CADD) has changed this situation. This study reviews the applications of CADD in diabetic drug designing.

AREAS COVERED

In this article, the authors focus on the advance in CADD in diabetic drug design by elaborating the discovery, including peroxisome proliferator-activated receptor (PPAR), G protein-coupled receptor 40 (GPR40), dipeptidyl peptidase-IV (DDP-IV), protein tyrosine phosphatase 1B (PTP1B), sodium-dependent glucose transporter 2 (SGLT-2), and glucokinase (GK). Some drug discovery of these targets is related to CADD strategies.

EXPERT OPINION

There is no doubt that CADD has contributed to the discovery of novel anti-diabetic agents. However, there are still many limitations and challenges, such as lack of co-crystal complex, dynamic simulations, water, and metal ion treatment. In the near future, artificial intelligence (AI) may be a promising strategy to accelerate drug discovery and reduce costs by identifying candidates. Moreover, AlphaFold, a deep learning model that predicts the 3D structure of proteins, represents a considerable advancement in the structural prediction of proteins, especially in the absence of homologous templates for protein structures.

Vision on Synthetic and Medicinal Facets of 1,2,4-Triazolo[3,4-b][1,3,4]thiadiazine Scaffold

The present review article strives to compile the latest synthetic approaches for the synthesis of triazolothiadiazine and its derivatives, along with their diverse pharmacological activities, viz. anticancer, antimicrobial, analgesic and anti-inflammatory, antioxidant, antiviral, enzyme inhibitors (carbonic anhydrase inhibitors, cholinesterase inhibitors, alkaline phosphatase inhibitors, anti-lipase activity, and aromatase inhibitors) and antitubercular agents. The review focuses particularly on the structure–activity relationship of biologically important 1,2,4-triazolo[3,4-b][1,3,4]thiadiazines, which have profound importance in drug design, discovery and development. In silico pharmacokinetic and molecular modeling studies have also been summarized. It is hoped that this review article will be of help to researchers engaged in the development of new biologically active entities for the rational design and development of new target-oriented 1,2,4-triazolo[3,4-b][1,3,4]thiadiazine-based drugs for the treatment of multifunctional diseases.

Isovitexin protects against acute liver injury by targeting PTEN, PI3K and BiP via modification of m6A

Isovitexin (IVT) has been shown to have a potential therapeutic effect on acute liver injury (ALI), but its underlying mechanisms especially the targets remain unclear, which was investigated in the present study. Briefly, the targets of IVT were predicted by bioinformatics and then were verified by multiple examinations using molecular docking, cellular thermal shift assay (CETSA), and Lipopolysaccharide/D-Galactosamine (LPS/D-GalN)-induced ALI animal model. The bioinformatic analysis predicted that the target genes of IVT against ALI were enriched into the PI3K/Akt and ERS-related pathways, in which, molecular docking and CETSA examination verified that the binding sites of IVT likely were PTEN, PI3K and BiP. Furthermore, the possible targets were also verified by animal experiments. The results revealed that IVT significantly ameliorated the hepatic injury, as evidenced by the attenuation of histopathological changes and the reduction in serum aminotransferase and total bilirubin activities. In addition, IVT treatment led to the reduction of PTEN, BiP and ERS-related targets expressions, as well as the elevation of PI3K, Akt and mTOR expressions. Notably, IVT significantly decreased total hepatic m6A level and m6A enrichment of PTEN and BiP, suggesting IVT regulated PTEN and BiP by modulating m6A modification. To sum up, the results indicate that IVT significantly ameliorates ALI, which is attributed to its ability to regulate the PI3K/Akt pathway and ERS by targeting PTEN, PI3K and BiP via modification of m6A. Our finding demonstrates that IVT may be a promising natural medicine for the treatment of ALI.

Peptide Affinity Chromatography Applied to Therapeutic Antibodies Purification

The interest in therapeutic monoclonal antibodies (mAbs) has significantly grown in the pharmaceutical industry, exceeding 100 FDA mAbs approved. Although the upstream processing of their industrial production has been significantly improved in the last years, the downstream processing still depends on immobilized protein A affinity chromatography. The high cost, low capacity and short half-life of immobilized protein A chromatography matrices, encouraged the design of alternative short-peptide ligands for mAb purification. Most of these peptides have been obtained by screening combinatorial peptide libraries. These low-cost ligands can be easily produced by solid-phase peptide synthesis and can be immobilized on chromatographic supports, thus obtaining matrices with high capacity and selectivity. Furthermore, matrices with immobilized peptide ligands have longer half-life than those with protein A due to the higher stability of the peptides. In this review the design and synthesis of peptide ligands, their immobilization on chromatographic supports and the evaluation of the affinity supports for their application in mAb purification is described.

Progress in the Development of Eukaryotic Elongation Factor 2 Kinase (eEF2K) Natural Product and Synthetic Small Molecule Inhibitors for Cancer Chemotherapy

Eukaryotic elongation factor 2 kinase (eEF2K or Ca2+/calmodulin-dependent protein kinase, CAMKIII) is a new member of an atypical α-kinase family different from conventional protein kinases that is now considered as a potential target for the treatment of cancer. This protein regulates the phosphorylation of eukaryotic elongation factor 2 (eEF2) to restrain activity and inhibit the elongation stage of protein synthesis. Mounting evidence shows that eEF2K regulates the cell cycle, autophagy, apoptosis, angiogenesis, invasion, and metastasis in several types of cancers. The expression of eEF2K promotes survival of cancer cells, and the level of this protein is increased in many cancer cells to adapt them to the microenvironment conditions including hypoxia, nutrient depletion, and acidosis. The physiological function of eEF2K and its role in the development and progression of cancer are here reviewed in detail. In addition, a summary of progress for in vitro eEF2K inhibitors from anti-cancer drug discovery research in recent years, along with their structure-activity relationships (SARs) and synthetic routes or natural sources, is also described. Special attention is given to those inhibitors that have been already validated in vivo, with the overall aim to provide reference context for the further development of new first-in-class anti-cancer drugs that target eEF2K.

Recent progress on cheminformatics approaches to epigenetic drug discovery

The ability of epigenetic markers to affect genome function has enabled transformative changes in drug discovery, especially in cancer and other emerging therapeutic areas. Concordant with the introduction of the term 'epi-informatics', the size of the epigenetically relevant chemical space has grown substantially and so did the number of applications of cheminformatic methods to epigenetics. Recent progress in epi-informatics has improved our understanding of the structure-epigenetic activity relationships and boosted the development of models predicting novel epigenetic agents. Herein, we review the advances in computational approaches to drug discovery of small molecules with epigenetic modulation profiles, summarize the current chemogenomics data available for epigenetic targets, and provide a perspective on the greater utility of biomedical knowledge mining as a means to advance the epigenetic drug discovery.