Use of molecular docking computational tools in drug discovery

Assessment of sedative activity of fraxin: In vivo approach along with receptor binding affinity and molecular interaction with GABAergic system

Insomnia is a sleep disorder in which you have trouble falling and/or staying asleep. This research aims to evaluate the sedative effects of fraxin (FX) on sleeping mice induced by thiopental sodium (TS). In addition, a molecular docking study was conducted to investigate the molecular processes underlying these effects. The study used adult male Swiss albino mice and administered FX (10 and 20 mg/kg, i.p.) and diazepam (DZP) (2 mg/kg) either separately or in combination within the different groups to examine their modulatory effects. After a period of 30 min, the mice that had been treated were administered (TS: 20 mg/kg, i.p.) to induce sleep. The onset of sleep for the mice and the length of their sleep were manually recorded. Additionally, a computational analysis was conducted to predict the role of gamma-aminobutyric acid (GABA) receptors in the sleep process and evaluate their pharmacokinetics and toxicity. The outcomes indicated that FX extended the length of sleep and reduced the time it took to fall asleep. When the combined treatment of FX and DZP showed synergistic sedative action. Also, FX had a binding affinity of -7.2 kcal/mol, while DZP showed -8.4 kcal/mol. The pharmacokinetic investigation of FX demonstrated favorable drug-likeness and strong pharmacokinetic characteristics. Ultimately, FX demonstrated a strong sedative impact in the mouse model, likely via interacting with the GABAA receptor pathways.

Tolerance enhancement of Dendrobium officinale by salicylic acid family-related metabolic pathways under unfavorable temperature

BACKGROUND

Unfavorable temperatures significantly constrain the quality formation of Dendrobium officinale, severely limiting its food demand. Salicylic acid (SA) enhances the resistance of D. officinale to stress and possesses various analogs. The impact and mechanism of the SA family on improving the quality of D. officinale under adverse temperature conditions remains unclear.

RESULTS

Combined with molecular docking analysis, chlorophyll fluorescence and metabolic analysis after treatments with SA analogues or extreme temperatures are performed in this study. The results demonstrate that both heat and cold treatments impede several main parameters of chlorophyll fluorescence of D. officinale, including the ΦPSII parameter, a sensitive growth indicator. However, this inhibition is mitigated by SA or its chemically similar compounds. Comprehensive branch imaging of ΦPSII values revealed position-dependent improvement of tolerance. Molecular docking analysis using a crystal structure model of NPR4 protein reveals that the therapeutic effects of SA analogs are determined by their binding energy and the contact of certain residues. Metabolome analysis identifies 17 compounds are considered participating in the temperature-related SA signaling pathway. Moreover, several natural SA analogs such as 2-hydroxycinnamic acid, benzamide, 2-(formylamino) benzoic acid and 3-o-methylgallic acid, are further found to have high binding ability to NPR4 protein and probably enhance the tolerance of D. officinale against unfavorable temperatures through flavone and guanosine monophosphate degradation pathways.

CONCLUSIONS

These results reveal that the SA family with a high binding capability of NPR4 could improve the tolerance of D. officinale upon extreme temperature challenges. This study also highlights the collaborative role of SA-related natural compounds present in D. officinale in the mechanism of temperature resistance and offers a potential way to develop protective agents for the cultivation of D. officinale.

Exploration of the underlying mechanism of Astragaloside III in attenuating immunosuppression via network pharmacology and vitro/vivo pharmacological validation

ETHNOPHARMACOLOGICAL RELEVANCE

Astragalus mongholicus Bunge (AM, recorded in http://www.worldfloraonline.org, 2023-08-03) is a kind of medicine food homology plant with a long medicinal history in China. Astragaloside III (AS-III) has immunomodulatory effects and is one of the most active components in AM. However, its underlying mechanism of action is still not fully explained.

AIM OF THE STUDY

The research was designed to discuss the protective effects of AS-III on immunosuppression and to elucidate its prospective mechanism.

MATERIALS AND METHODS

Molecular docking methods and network pharmacology analysis were used to comprehensively investigate potential targets and relative pathways for AS-III and immunosuppression. In order to study and verify the pharmacological activity and mechanism of AS-III in alleviating immunosuppression, immunosuppression mouse model induced by cyclophosphamide (CTX) in vivo and macrophage RAW264.7 cell model induced by hypoxia/lipopolysaccharide (LPS) in vitro were used.

RESULTS

A total of 105 common targets were obtained from the AS-III-related and immunosuppression-related target networks. The results of network pharmacology and molecular docking demonstrate that AS-III may treat immunosuppression through by regulating glucose metabolism-related pathways such as regulation of lipolysis in adipocytes, carbohydrate digestion and absorption, cGMP-PKG signaling pathway, central carbon metabolism in cancer together with HIF-1 pathway. The results of molecular docking showed that AS-III has good binding relationship with LDHA, AKT1 and HIF1A. In CTX-induced immunosuppressive mouse model, AS-III had a significant protective effect on the reduction of body weight, immune organ index and hematological indices. It can also protect immune organs from damage. In addition, AS-III could significantly improve the expression of key proteins involved in energy metabolism and serum inflammatory factors. To further validate the animal results, an initial inflammatory/immune response model of macrophage RAW264.7 cells was constructed through hypoxia and LPS. AS-III improved the immune function of macrophages, reduced the release of NO, TNF-α, IL-1β, PDHK-1, LDH, lactate, HK, PK and GLUT-1, and restored the decrease of ATP caused by hypoxia. Besides, AS-III was also demonstrated that it could inhibit the increase of HIF-1α, PDHK-1 and LDH by adding inhibitors and agonists.

CONCLUSIONS

In this study, the main targets of AS-III for immunosuppressive therapy were initially analyzed. AS-III was systematically confirmed to attenuates immunosuppressive state through the HIF-1α/PDHK-1 pathway. These findings offer an experimental foundation for the use of AS-III as a potential candidate for the treatment of immunosuppression.

Extending molecular dynamics with dipolar NMR tensors as constraints to chiral phosphorus compounds

Molecular dynamics with orientational constraints (MDOC) simulations use NMR parameters as tensorial constraints in the stereochemical analysis of small molecules. 13C-31P Residual dipolar couplings-aided MDOC simulations of small phosphorus molecules determined the relative configurations of rigid molecules after including 3JH-H-couplings as additional constraints. However, flexible molecules remain a problem.

Exploring the globoid cell leukodystrophy protein network and therapeutic interventions

Globoid cell leukodystrophy is a severe rare disorder characterized by white matter degradation, resulting in a progressive loss of physical and mental abilities and has extremely limited therapeutic interventions. Therefore, this study aimed to delve into the Globoid cell leukodystrophy associated intricate network of differentially expressed genes (p < 0.05, |Fc|> 1) to identify potential druggable targets and possible therapeutic interventions using small molecules. The disease-associated neuronal protein circuit was constructed and analyzed, identifying 53 nodes (minimum edge cutoff 1), among which five (FOS, FOSB, GDNF, GFRA1, and JUN) were discerned as potential core protein nodes. Although our research enumerates the potential small molecules to target various protein nodes in the proposed disease network, we particularly underscore T-5224 to inhibit c-Jun activity as JUN was identified as one of the pivotal elements within the disease-associated neuronal protein circuit. The evaluation of T-5224 binding energy (- 11.0 kcal/mol) from docking study revealed that the compound to exhibit a notable affinity towards Jun/CRE complex. Moreover, the structural integrity of complex was affirmed through comprehensive molecular dynamics simulations, indicating a stable hydrophilic interaction between T-5224 and the Jun/CRE complex, thereby enhancing protein compactness and reducing solvent accessibility. This binding energy was further substantiated by free binding analysis, revealing a substantial thermodynamics complex state (- 448.00 ± 41.73 kJ/mol). Given that this investigation is confined to a computational framework, we additionally propose a hypothetical framework to ascertain the feasibility of inhibiting the Jun/CRE complex with T-5224 against Globoid cell leukodystrophy, employing a combination of in vitro and in vivo methodologies as a prospective avenue of this study.

Echinatin suppresses cutaneous squamous cell carcinoma by targeting GSTM3-mediated ferroptosis

BACKGROUND

Cutaneous squamous cell carcinoma (cSCC) is one of the most common skin cancers for which effective drugs are urgently needed. Echinatin, a natural compound extracted from Glycyrrhiza plants, has shown promising antitumour effects. However, the efficacy and the direct target of echinatin in cSCC remain unclear.

PURPOSE

This study conducted a systematic investigation of the antitumour effects of echinatin on cSCC and the underlying mechanisms involved.

STUDY DESIGN AND METHODS

Three cSCC cell lines, a xenograft model, and a UV-induced cSCC mouse model were used to investigate the potential protective effects of echinatin. The interactions between echinatin and glutathione S-transferase mu3 (GSTM3) and between echinatin and peroxiredoxin-2 (PRDX2) were evaluated by a proteome microarray assay, pull-down LC‒MS/MS analysis, surface plasmon resonance, and molecular docking. The potential mechanisms of GSTM3-mediated echinatin activity were analysed by using western blotting, lentivirus infection and small interfering RNA (siRNA) transfection.

RESULTS

In this study, we found that echinatin inhibited the proliferation and migration of cSCC cells but had no cytotoxic effect on primary human keratinocytes. Furthermore, echinatin significantly inhibited tumour growth in vivo. Mechanistically, our data showed that echinatin could directly bind to GSTM3 and PRDX2. Notably, echinatin inhibited GSTM3 and PRDX2 levels by promoting their proteasomal degradation, which led to the disruption of ROS production. We then revealed that echinatin increased mitochondrial ROS production by inhibiting GSTM3. Moreover, echinatin triggered ferroptosis by inhibiting GSTM3-mediated ferroptosis negative regulation (FNR) proteins. In addition, echinatin regulated GSTM3-mediated ROS/MAPK signalling.

CONCLUSION

Echinatin has good antitumour effects both in vitro and in vivo. Moreover, our findings indicate that GSTM3 and PRDX2 could function as viable targets of echinatin in cSCC. Consequently, echinatin represents a novel treatment for cSCC through the targeting of GSTM3-mediated ferroptosis.

Ginsenoside Rb1 reduced ischemic stroke-induced apoptosis through endoplasmic reticulum stress-associated IRE1/TRAF2/JNK pathway

The neuroprotective function of ginsenoside Rb1 (GRb1) in cerebral ischemia-reperfusion (I/R) was lately emphasized. However, whether GRb1 plays a regulatory role on endoplasmic reticulum (ER) stress-associated pathway in cerebral I/R damage is still unclear. The aim of this study is to explore the function of GRb1 in cerebral ischemia-induced ER stress and the underlying mechanism related to IRE1/TRAF2/JNK pathway. Longa method, cerebral infarct volume, and HE staining were used to evaluate the efficacy of GRb1 in mice with a mouse model of middle cerebral artery occlusion reperfusion (MCAO/R). We also investigated the effect and mechanism of GRb1 against ischemic stroke using in vitro oxygen-glucose deprivation reperfusion (OGD/R) model. We found that GRb1 could improve neurological scores, infarct volume, and histological injury in ischemic mice. Ischemic attack also activated neuronal apoptosis and ER stress, and this effect was attenuated by GRb1. In addition, GRb1 significantly reduced I/R-induced IRE1-TRAF2 interaction, IRE1, and JNK phosphorylation. The present study also confirmed that GRb1 significantly improved OGD/R-induced PC12 cells injury. GRb1 could decrease ER stress in OGD/R-injured PC12 cells, which was reflected by the decreased expression of GRP78 and CHOP. The ER stress inducer tunicamycin partially prevented the effects of GRb1 on cell viability, ER stress, and apoptosis after OGD/R, whereas the ER stress inhibitor 4-PBA exerted the opposite effect. Moreover, GRb1 markedly decreased IRE1-TRAF2 interaction, IRE1, and JNK phosphorylation in the presence of OGD/R insult. Furthermore, JNK inhibitor SP600125 and IRE1 inhibitor DBSA pretreatment further promoted the inhibition of GRb1 on ER stress induction and cell damage induced by OGD/R. Molecular docking further elucidated that the mechanism by which GRb1 improves cerebral ischemia maybe related to its direct binding to the kinase domain of IRE1, which in turn inhibited the phosphorylation of IRE1. Collectively, these results demonstrated that GRb1 reduced ischemic stroke-induced apoptosis through the ER stress-associated IRE1/TRAF2/JNK pathway and GRb1 has the potential as a protective drug for the treatment of cerebral ischemia.

In silico exploration of deep-sea fungal metabolites as inhibitor of Ebola and Marburg VP35 and VP40

VP30 and VP40 proteins of Ebola and Marburg viruses have been recognized as potential targets for antiviral drug development due to their essential roles in the viral lifecycle. Targeting these proteins could disrupt key stages of the viral replication process, inhibiting the viruses' ability to propagate and cause disease. The current study aims to perform molecular docking and virtual screening on deep-sea fungal metabolites targeting Marburg virus VP40 Dimer, matrix protein VP40 from Ebola virus Sudan, Ebola VP35 Interferon Inhibitory Domain, and VP35 from Marburg virus. The top ten compounds for each protein target were chosen using the glide score. All the compounds obtained indicate a positive binding interaction. Furthermore, AdmetSAR was utilized to investigate the pharmacokinetics of the inhibitors chosen. Gliotoxin was used as a ligand with Marburg virus VP40 Dimer, Austinol with matrix protein VP40 from Ebola virus Sudan, Ozazino-cyclo-(2,3-dihydroxyl-trp-tyr) with Ebola VP35 Interferon Inhibitory Domain, and Dehydroaustinol with VP35 from Marburg virus. MD modeling and MMPBSA studies were used to provide a better understanding of binding behaviors. Pre-clinical experiments can assist validate our in-silico studies and assess whether the molecule can be employed as an anti-viral drug.

Functional characterization and structural prediction of hypothetical proteins in monkeypox virus and identification of potential inhibitors

The excessive activation of the monkeypox virus (MPXV-Congo_8-156) is linked to various skin and respiratory disorders such as rashes, fluid-filled blisters, swollen lymph nodes and encephalitis (inflammation of the brain), highlighting MPXV-Congo_8-156 as a promising target for drug intervention. Despite the effectiveness of Cidofovir, in inhibiting MPXV activity, its limited ability to penetrate the skin and its strong side effects restrict its application. To address this challenge, we screened 500 compounds capable of penetrating the skin and gastrointestinal tract to identify potent MPXV inhibitors. Various characterization schemes and structural models of MPXV-Congo_8-156 were explored with bioinformatics tools like PROTPARAM, SOPMA, SWISS-MODEL and PROCHECK. Using molecular docking in PyRx, we evaluated the binding affinities of these compounds with MPXV-Congo_8-156 and identified the top five candidates ranging from - 9.2 to - 8.8 kcal/mol. ADMET analysis indicated that all five compounds were safer alternatives, showing no AMES toxicity or carcinogenicity in toxicological assessments. Molecular dynamics (MD) simulations, conducted for 100 ns each, confirmed the docking interactions of the top five compounds alongside the control (Cidofovir), validating their potential as MPXV inhibitors. The compounds with PubChem CID numbers 4061636, 4422538, 3583576, 4856107 and 4800629 demonstrated strong support in terms of root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), radius of gyration (Rg), solvent-accessible surface area (SASA) value, hydrogen bond analysis, and Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA) analysis. Thus, our investigation identified these five compounds as promising inhibitors of MPXV, offering potential therapeutic avenues. However, further in vivo studies are necessary to validate our findings.

Anti-inflammatory and antiophidic effects of extract of Hymenaea eriogyne Benth and structure-activity relationship prediction of the major markers in silico

ETHNOPHARMACOLOGICAL RELEVANCE

Hymenaea eriogyne Benth (Fabaceae) is popularly known as "Jatobá". Despite its use in folk medicine to treat inflammatory disorders, there are no descriptions that show its anti-inflammatory potential.

AIM OF THE STUDY

In this sense, this study aimed to evaluate the anti-inflammatory and antivenom action of bark and leaves extract of H. eriogyne.

MATERIALS AND METHODS

The in vivo anti-inflammatory activity was conducted by carrageenan-induced paw edema and zymosan-induced air pouch models, evaluating the edematogenic effect, leukocyte migration, protein concentration, levels of pro-inflammatory cytokines, malondialdehyde (MDA) and MPO activity. The antivenom potential was investigated in vitro on the enzymatic action (proteolytic, phospholipase and hyaluronidase) of Bothrops brazili and B. leucurus venom, as well as in vivo on the paw edema model induced by B. leucurus. Furthermore, the influence of its markers (astilbin and rutin) on myeloperoxidase (MPO) activity was investigated in silico. For molecular docking, AutodockVina, Biovia Discovery Studio, and Chimera 1.16 software were used.

RESULTS

The extracts and bark and leaves of H. eriogyne revealed a high anti-inflammatory effect, with a reduction in all inflammatory parameters evaluated. The bark extract showed superior results when compared to the leaf extract, suggesting the influence of the astilbin concentration, higher in the bark, on the anti-inflammatory action. In addition, only the H. eriogyne bark extract was able to reduce MDA, indicating an associated antioxidant effect. Regarding the in vitro antivenom action, the extracts (bark and leaves) revealed the ability to inhibit the proteolytic, phospholipase and hyaluronidase action of both bothropic venom, with a greater effect against B. leucurus venom. In vivo, extracts from the bark and leaves of H. eriogyne (50 - 200 mg/kg) showed antiedematogenic activity, reducing the release of MPO and pro-inflammatory cytokines, indicating the presence of bioactive components useful in controlling the inflammatory process induced by the venom. In the in silico assays, astilbin and rutin showed reversible interactions of 9 possible positions and orientations towards MPO, with affinities of -9.5 and -10.4 kcal/mol and interactions with Phe407, Gln91, His95 and Arg239, important active pockets of MPO. Rutin demonstrated more effective types of interactions with MPO.

CONCLUSION

This approach reveals for the first time the anti-inflammatory action of H. eriogyne bark and leaf extracts in vivo, as well as its antiophidic potential. Moreover, the distinct effect of pharmacogens as antioxidant agents and distinct effect of astilbin and rutin under MPO sheds light on the different anti-inflammatory mechanisms of bioactive compounds present in H. eriogyne extracts, with high potential for the prospection of new pharmacological agents.

Exploring essential oil-based bio-composites: molecular docking and in vitro analysis for oral bacterial biofilm inhibition

Oral bacterial biofilms are the main reason for the progression of resistance to antimicrobial agents that may lead to severe conditions, including periodontitis and gingivitis. Essential oil-based nanocomposites can be a promising treatment option. We investigated cardamom, cinnamon, and clove essential oils for their potential in the treatment of oral bacterial infections using in vitro and computational tools. A detailed analysis of the drug-likeness and physicochemical properties of all constituents was performed. Molecular docking studies revealed that the binding free energy of a Carbopol 940 and eugenol complex was -2.0 kcal/mol, of a Carbopol 940-anisaldehyde complex was -1.9 kcal/mol, and a Carbapol 940-eugenol-anisaldehyde complex was -3.4 kcal/mol. Molecular docking was performed against transcriptional regulator genes 2XCT, 1JIJ, 2Q0P, 4M81, and 3QPI. Eugenol cinnamaldehyde and cineol presented strong interaction with targets. The essential oils were analyzed against Staphylococcus aureus and Staphylococcus epidermidis isolated from the oral cavity of diabetic patients. The cinnamon and clove essential oil combination presented significant minimum inhibitory concentrations (MICs) (0.0625/0.0312 mg/mL) against S. epidermidis and S. aureus (0.0156/0.0078 mg/mL). In the anti-quorum sensing activity, the cinnamon and clove oil combination presented moderate inhibition (8 mm) against Chromobacterium voilaceum with substantial violacein inhibition (58% ± 1.2%). Likewise, a significant biofilm inhibition was recorded in the case of S. aureus (82.1% ± 0.21%) and S. epidermidis (84.2% ± 1.3%) in combination. It was concluded that a clove and cinnamon essential oil-based formulation could be employed to prepare a stable nanocomposite, and Carbapol 940 could be used as a compatible biopolymer.

Huperzine A Regulates the Physiological Homeostasis of Amyloid Precursor Protein Proteolysis and Tau Protein Conformation-A Computational and Experimental Investigation

The beneficial actions of the natural compound Huperzine A (Hup A) against age-associated learning and memory deficits promote this compound as a nootropic agent. Alzheimer's disease (AD) pathophysiology is characterized by the accumulation of amyloid beta (Aβ). Toxic Aβ oligomers account for the cognitive dysfunctions much before the pathological lesions are manifested in the brain. In the present study, we investigated the effects of Hup A on amyloid precursor protein (APP) proteolysis in SH-SY5Y neuroblastoma cells. Hup A downregulated the expression of β-site amyloid precursor protein cleaving enzyme 1 (BACE1) and presenilin 1 (PS1) levels but augmented the levels of A disintegrin and metalloproteinase 10 (ADAM10) with significant decrement in the Aβ levels. We herein report for the first time an in silico molecular docking analysis that revealed that Hup A binds to the functionally active site of BACE1. We further analyzed the effect of Hup A on glycogen synthase kinase-3 β (GSK3β) and phosphorylation status of tau. In this scenario, based on the current observations, we propose that Hup A is a potent regulator of APP processing and capable of modulating tau homeostasis under physiological conditions holding immense potential in preventing and treating AD like disorders.

Antiviral Drug Discovery

A vast and painful price has been paid in the battle against viruses in global health [...].

Unveiling Moroccan Nature's Arsenal: A Computational Molecular Docking, Density Functional Theory, and Molecular Dynamics Study of Natural Compounds against Drug-Resistant Fungal Infections

Candida albicans and Aspergillus fumigatus are recognized as significant fungal pathogens, responsible for various human infections. The rapid emergence of drug-resistant strains among these fungi requires the identification and development of innovative antifungal therapies. We undertook a comprehensive screening of 297 naturally occurring compounds to address this challenge. Using computational docking techniques, we systematically analyzed the binding affinity of each compound to key proteins from Candida albicans (PDB ID: 1EAG) and Aspergillus fumigatus (PDB ID: 3DJE). This rigorous in silico examination aimed to unveil compounds that could potentially inhibit the activity of these fungal infections. This was followed by an ADMET analysis of the top-ranked compound, providing valuable insights into the pharmacokinetic properties and potential toxicological profiles. To further validate our findings, the molecular reactivity and stability were computed using the DFT calculation and molecular dynamics simulation, providing a deeper understanding of the stability and behavior of the top-ranking compounds in a biological environment. The outcomes of our study identified a subset of natural compounds that, based on our analysis, demonstrate notable potential as antifungal candidates. With further experimental validation, these compounds could pave the way for new therapeutic strategies against drug-resistant fungal pathogens.

Network toxicological and molecular docking to investigate the mechanisms of toxicity of agricultural chemical Thiabendazole

Food safety is closely linked to human health. Thiabendazole is widely used as a fungicide and deodorant on agricultural products like vegetables and fruits to prevent fungal infections during transport and storage. This study aims to investigate the toxicity and potential mechanisms of Thiabendazole using novel network toxicology and molecular docking techniques. First, the ADMETlab2.0 and ADMETsar databases, along with literature, predicted Thiabendazole's potential to induce cancer and liver damage. Disease target libraries were constructed using GeneCards and TCMIP databases, while Thiabendazole target libraries were constructed using Swiss Target Prediction and TCMIP databases. The Venn database identified potential targets associated with Thiabendazole-induced cancer and liver injury. Protein-protein interaction (PPI) networks were derived from the STRING database, and gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathways were obtained from the DAVID database. Molecular docking assessed the binding affinity between Thiabendazole and core targets. The study revealed 29 potential targets for Thiabendazole-induced cancer and 30 potential targets for liver injury. PPI identified 5 core targets for Thiabendazole-induced cancers and 4 core targets for induced liver injury. KEGG analysis indicated that Thiabendazole might induce gastric and prostate cancer via cyclin-dependent kinase 2 (CDK2) and epidermal growth factor receptor (EGFR) targets, and liver injury through the same targets, with the p53 signaling pathway being central. GO analysis indicated that Thiabendazole-induced cancers and liver injuries were related to mitotic cell cycle G2/M transition and DNA replication. Molecular docking showed stable binding of Thiabendazole with core targets including CDK1, CDK2, EGFR, and checkpoint kinase 1 (CHEK1). These findings suggest Thiabendazole may affect the G2/M transition of the mitotic cell cycle through the p53 signaling pathway, potentially inducing cancer and liver injury. This study provides a theoretical basis for understanding the potential molecular mechanisms underlying Thiabendazole toxicity, aiding in the prevention and treatment of related diseases. Additionally, the network toxicology approach accelerates the elucidation of toxic pathways for uncharacterized agricultural chemicals.

The mechanism of Gejie Zhilao Pill in treating tuberculosis based on network pharmacology and molecular docking verification

Introduction

Gejie Zhilao Pill (GJZLP), a traditional Chinese medicine formula is known for its unique therapeutic effects in treating pulmonary tuberculosis. The aim of this study is to further investigate its underlying mechanisms by utilizing network pharmacology and molecular docking techniques.

Methods

Using TCMSP database the components, potential targets of GJZLP were identified. Animal-derived components were supplemented through the TCMID and BATMAN-TCM databases. Tuberculosis-related targets were collected from the TTD, OMIM, and GeneCards databases. The intersection target was imported into the String database to build the PPI network. The Metascape platform was employed to carry out Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Heatmaps were generated through an online platform (https://www.bioinformatics.com.cn). Molecular docking was conducted between the core targets and core compounds to explore their binding strengths and patterns at the molecular level.

Results

61 active ingredients and 118 therapeutic targets were identified. Quercetin, Luteolin, epigallocatechin gallate, and beta-sitosterol showed relatively high degrees in the network. IL6, TNF, JUN, TP53, IL1B, STAT3, AKT1, RELA, IFNG, and MAPK3 are important core targets. GO and KEGG revealed that the effects of GJZLP on tuberculosis mainly involve reactions to bacterial molecules, lipopolysaccharides, and cytokine stimulation. Key signaling pathways include TNF, IL-17, Toll-like receptor and C-type lectin receptor signaling. Molecular docking analysis demonstrated a robust binding affinity between the core compounds and the core proteins. Stigmasterol exhibited the lowest binding energy with AKT1, indicating the most stable binding interaction.

Discussion

This study has delved into the efficacious components and molecular mechanisms of GJZLP in treating tuberculosis, thereby highlighting its potential as a promising therapeutic candidate for the treatment of tuberculosis.

Lomitapide repurposing for treatment of malignancies: A promising direction

The development of novel drugs from basic science to clinical practice requires several years, much effort, and cost. Drug repurposing can promote the utilization of clinical drugs in cancer therapy. Recent studies have shown the potential effects of lomitapide on treating malignancies, which is currently used for the treatment of familial hypercholesterolemia. We systematically review possible functions and mechanisms of lomitapide as an anti-tumor compound, regarding the aspects of apoptosis, autophagy, and metabolism of tumor cells, to support repurposing lomitapide for the clinical treatment of tumors.

The role of artificial intelligence and data science in nanoparticles development: a review

Artificial intelligence has revolutionized many sectors with unparalleled predictive capabilities supported by machine learning (ML). So far, this tool has not been able to provide the same level of development in pharmaceutical nanotechnology. This review discusses the current data science methodologies related to polymeric drug-loaded nanoparticle production from an innovative multidisciplinary perspective while considering the strictest data science practices. Several methodological and data interpretation flaws were identified by analyzing the few qualified ML studies. Most issues lie in following appropriate analysis steps, such as cross-validation, balancing data, or testing alternative models. Thus, better-planned studies following the recommended data science analysis steps along with adequate numbers of experiments would change the current landscape, allowing the exploration of the full potential of ML.

Assessment of fragment docking and scoring with the endothiapepsin model system

Fragment-based screening has become indispensable in drug discovery. Yet, the weak binding affinities of these small molecules still represent a challenge for the reliable detection of fragment hits. The extent of this issue was illustrated in the literature for the aspartic protease endothiapepsin: When seven biochemical and biophysical in vitro screening methods were applied to screen a library of 361 fragments, very poor overlap was observed between the hit fragments identified by the individual approaches, resulting in high levels of false positive and/or false negative results depending on the mutually compared methods. Here, the reported in vitro findings are juxtaposed with the results from in silico docking and scoring approaches. The docking programs GOLD and Glide were considered with the scoring functions ASP, ChemScore, ChemPLP, GoldScore, DSXCSD, and GlideScore. First, the ranking power and scoring power were assessed for the named scoring functions. Second, the capability of reproducing the crystallized fragment binding modes was tested in a structure-based redocking approach. The redocking success notably depended on the ligand efficiency of the considered fragments. Third, a blinded virtual screening approach was employed to evaluate whether in silico screening can compete with in vitro methods in the enrichment of fragment databases.

Network toxicology and molecular docking for the toxicity analysis of food contaminants: A case of Aflatoxin B1

The present study aims to promote network toxicology and molecular docking strategies for the efficient evaluation of the toxicity of food contaminants. With the example of liver injury induced by the food contaminant Aflatoxin B1(AFB1), this study effectively investigated the putative toxicity of food contaminants and the potentially molecular mechanisms. The study found that AFB1 regulates multiple signalling pathways by modulating core targets such as AKT1, BCL2, TNF, CASP3, SRC and EGFR. These pathways encompass Pathways in cancer, PI3K-Akt signalling pathway, Endocrine resistance, Lipid and atherosclerosis, Apoptosis and other pathways, subsequently impacting immunotoxicity, inflammatory responses, apoptosis, cytogenetic mutations, and ultimately leading to liver injury. We provide a theoretical basis for understanding the molecular mechanisms of AFB1 hepatotoxicity and for the prevention and treatment of cancers caused by the food contaminant AFB1. Furthermore, our network toxicology and molecular docking methods also provide an effective method for the rapid evaluation of the toxicity of food contaminants, which effectively solves the cost and ethical problems associated with the use of experimental animals.

Inhibition of protein misfolding and aggregation by steroidal quinoxalin-2(1H)-one and their molecular docking studies

A series of D-ring fused 16-substituted steroidal quinoxalin-2(1H)-one attached to an electron-releasing (ER) or electron-withdrawing (EW) groups via steroidal oxoacetate intermediate were synthesized to investigate their protein aggregation inhibition potential using human lysozyme (HLZ). The influence of the type of substituent at the C-6 positions of the quinoxalin-2(1H)-one ring on the protein aggregation inhibition potential was observed, showing that the EW moiety improved the protein aggregation inhibition potency. Of all the evaluated compounds, NO2-substituted quinoxalin-2(1H)-one derivative 13 was the most active compound and had a maximum protein aggregation inhibition effect. Significant stabilization effects strongly support the binding of the most biologically active steroidal quinoxalin-2(1H)-one with docking studies. The predicted physicochemical and ADME properties lie within a drug-like space which shows no violation of Lipinski's rule of five except compounds 12 and 13. Combined, our results suggest that D-ring fused 16-substituted steroidal quinoxalin-2(1H)-one has the potential to modulate the protein aggregation inhibition effect.

Identification of potential inhibitors of cholinergic and β-secretase enzymes from phytochemicals derived from Gongronema latifolium Benth leaf: an integrated computational analysis

Neurodegenerative disorders (NDDs) are associated with increased activities of the brain acetylcholinesterase (AChE), butyrylcholinesterase (BChE) and β-secretase enzyme (BACE1). Inhibition of these enzymes affords therapeutic option for managing NDDs such as Alzheimer's disease (AD) and Parkinson's disease (PD). Although, Gongronema latifolium Benth (GL) has been widely documented in ethnopharmacological and scientific reports for the management of NDDs, there is paucity of information on its underlying mechanism and neurotherapeutic constituents. Herein, 152 previously reported Gongronema latifolium derived-phytochemicals (GLDP) were screened against hAChE, hBChE and hBACE-1 using molecular docking, molecular dynamics (MD) simulations, free energy of binding calculations and cluster analysis. The result of the computational analysis identified silymarin, alpha-amyrin and teraxeron with the highest binding energies (-12.3, -11.2, -10.5 Kcal/mol) for hAChE, hBChE and hBACE-1 respectively as compared with those of the reference inhibitors (-12.3, -9.8 and - 9.4 for donepezil, propidium and aminoquinoline compound respectively). These best docked phytochemicals were found to be orientated in the hydrophobic gorge where they interacted with the choline-binding pocket in the A-site and P-site of the cholinesterase and subsites S1, S3, S3' and flip (67-75) residues of the pocket of the BACE-1. The best docked phytochemicals complexed with the target proteins were stable in a 100 ns molecular dynamic simulation. The interactions with the catalytic residues were preserved during the simulation as observed from the MMGBSA decomposition and cluster analyses. The presence of these phytocompounds most notably silymarin, which demonstrated dual high binding tendencies to both cholinesterases, were identified as potential neurotherapeutics subject to further investigation.

Hippuric acid alleviates dextran sulfate sodium-induced colitis via suppressing inflammatory activity and modulating gut microbiota

Inflammatory bowel disease (IBD) is a chronic inflammatory disease associated with metabolic disorder and gut dysbiosis. Decreased abundance of hippuric acid (HA) was found in patients with IBD. HA, metabolized directly from benzoic acid in the intestine and indirectly from polyphenols, serves as a marker of polyphenol catabolism. While polyphenols and benzoic acid have been shown to alleviate intestinal inflammation, the role of HA in this context remains unknown. Herein, we investigated the effects and mechanism of HA on DSS-induced colitis mice. The results revealed that HA alleviated clinical activity and intestinal barrier damage, decreased pro-inflammatory cytokine production. Metagenomic sequencing suggested that HA treatment restored the gut microbiota, including an increase in beneficial gut bacteria such as Adlercreutzia, Eubacterium, Schaedlerella and Bifidobacterium_pseudolongum. Furthermore, we identified 113 candidate genes associated with IBD that are potentially under HA regulation through network pharmacological analyses. 10 hub genes including ALB, IL-6, HSP90AA1, and others were identified using PPI analysis and validated using molecular docking and mRNA expression analysis. Additionally, KEGG analysis suggested that the renin-angiotensin system (RAS), NF-κB signaling and Rap1 signaling pathways were important pathways in the response of HA to colitis. Thus, HA may provide novel biotherapy options for IBD.

Advancing Ligand Docking through Deep Learning: Challenges and Prospects in Virtual Screening

Molecular docking, also termed ligand docking (LD), is a pivotal element of structure-based virtual screening (SBVS) used to predict the binding conformations and affinities of protein-ligand complexes. Traditional LD methodologies rely on a search and scoring framework, utilizing heuristic algorithms to explore binding conformations and scoring functions to evaluate binding strengths. However, to meet the efficiency demands of SBVS, these algorithms and functions are often simplified, prioritizing speed over accuracy.The emergence of deep learning (DL) has exerted a profound impact on diverse fields, ranging from natural language processing to computer vision and drug discovery. DeepMind's AlphaFold2 has impressively exhibited its ability to accurately predict protein structures solely from amino acid sequences, highlighting the remarkable potential of DL in conformation prediction. This groundbreaking advancement circumvents the traditional search-scoring frameworks in LD, enhancing both accuracy and processing speed and thereby catalyzing a broader adoption of DL algorithms in binding pose prediction. Nevertheless, a consensus on certain aspects remains elusive.In this Account, we delineate the current status of employing DL to augment LD within the VS paradigm, highlighting our contributions to this domain. Furthermore, we discuss the challenges and future prospects, drawing insights from our scholarly investigations. Initially, we present an overview of VS and LD, followed by an introduction to DL paradigms, which deviate significantly from traditional search-scoring frameworks. Subsequently, we delve into the challenges associated with the development of DL-based LD (DLLD), encompassing evaluation metrics, application scenarios, and physical plausibility of the predicted conformations. In the evaluation of LD algorithms, it is essential to recognize the multifaceted nature of the metrics. While the accuracy of binding pose prediction, often measured by the success rate, is a pivotal aspect, the scoring/screening power and computational speed of these algorithms are equally important given the pivotal role of LD tools in VS. Regarding application scenarios, early methods focused on blind docking, where the binding site is unknown. However, recent studies suggest a shift toward identifying binding sites rather than solely predicting binding poses within these models. In contrast, LD with a known pocket in VS has been shown to be more practical. Physical plausibility poses another significant challenge. Although DLLD models often achieve higher success rates compared to traditional methods, they may generate poses with implausible local structures, such as incorrect bond angles or lengths, which are disadvantageous for postprocessing tasks like visualization. Finally, we discuss the future perspectives for DLLD, emphasizing the need to improve generalization ability, strike a balance between speed and accuracy, account for protein conformation flexibility, and enhance physical plausibility. Additionally, we delve into the comparison between generative and regression algorithms in this context, exploring their respective strengths and potential.

Calycosin action against atherosclerosis: integrating network pharmacology and in-silico investigation

Atherosclerosis, caused by lipid deposit in the arterial wall for narrowing the arteries, is an increased risk factor of developing heart failure. Presently, clinical first-line drug therapy can be found with side effects, and thus new substitute medication should be developed needfully. Calycosin is one of the most bioactive products refined from natural plant, and it exerts promising cardiovascular protective effect. However, the pharmacological mechanisms of calycosin against atherosclerosis have not been elaborated. In this study, a systematic network pharmacology combined with molecular docking analysis was used to reveal the interaction activity and biological target in calycosin against atherosclerosis. We screened all preparative targets linked to calycosin and atherosclerosis from the available public databases. These results indicated total 409 putative targets in calycosin action, 71 of which were interacted with atherosclerosis. Further biological docking analysis suggested that calycosin displayed the powerful binding affinities with target proteins, including interleukin-6 (IL6) and mitogen-activated protein kinase 3 (MAPK3) MAPK3. Then enrichment findings revealed that calycosin action to treat atherosclerosis might be related to inhibition of inflammatory reaction and oxidative stress through modulating nucleolus transcription factor for improving lipid metabolism. In conclusion, the anti-atherosclerotic targets and molecular mechanisms in calycosin action were revealed systematically through preclinical evaluation. And calycosin may be a potential natural compound for the treatment of atherosclerosis.

A Docking and Network Pharmacology Study on the Molecular Mechanisms of Curcumin in Dental Caries and Streptococcus mutans

BACKGROUND

Dental caries is a dynamic, multifactorial disease that destroys teeth and can affect anyone's quality of life because it can cause tooth loss and make chewing difficult. Dental caries involves various factors, such as Streptococcus mutans and host factors. Currently, adjuvant therapies, such as curcumin, have emerged, but how they work has not been adequately described. Therefore, this work aims to identify the molecular mechanism of curcumin in caries and Streptococcus mutans.

METHODS

We obtained differentially expressed genes from a GEO dataset, and curcumin targets were obtained from other databases. The common targets were analyzed according to gene ontology enrichment, key genes were obtained, and binding to curcumin was verified by molecular docking.

RESULTS

Our analysis showed that curcumin presents 134 therapeutic targets in caries. According to the gene ontology analysis, these targets are mainly involved in apoptosis and inflammation. There are seven key proteins involved in the action of curcumin on caries: MAPK1, BCL2, KRAS, CXCL8, TGFB1, MMP9, and IL1B, all of which spontaneously bind curcumin. In addition, curcumin affects metabolic pathways related to lipid, purine, and pyrimidine metabolism in Streptococcus mutans.

CONCLUSIONS

Curcumin affects both host carious processes and Streptococcus mutans.

Daidzein and Equol: Ex Vivo and In Silico Approaches Targeting COX-2, iNOS, and the Canonical Inflammasome Signaling Pathway

BACKGROUND

The inflammasome is a cytosolic multiprotein complex associated with multiple autoimmune diseases. Phytochemical compounds in soy (Glycine max) foods, such as isoflavones, have been reported for their anti-inflammatory properties.

AIM

the anti-inflammatory activity of DZ (daidzein) and EQ (equol) were investigated in an ex vivo model of LPS-stimulated murine peritoneal macrophages and by molecular docking correlation.

METHODS

Cells were pre-treated with DZ (25, 50, and 100 µM) or EQ (5, 10, and 25 µM), followed by LPS stimulation. The levels of PGE2, NO, TNF-α, IL-6, and IL-1β were analyzed by ELISA, whereas the expressions of COX-2, iNOS, NLRP3, ASC, caspase 1, and IL-18 were measured by Western blotting. Also, the potential for transcriptional modulation by targeting NF-κB, COX-2, iNOS, NLRP3, ASC, and caspase 1 was investigated by molecular docking.

RESULTS

The anti-inflammatory responses observed may be due to the modulation of NF-κB due to the binding of DZ or EQ, which is translated into decreased TNF-α, COX-2, iNOS, NLRP3, and ASC levels.

CONCLUSION

This study establishes that DZ and EQ inhibit LPS-induced inflammatory responses in peritoneal murine macrophages via down-regulation of NO and PGE2 generation, as well as the inhibition of the canonical inflammasome pathway, regulating NLRP3, and consequently decreasing IL-1β and IL-18 activation.

New oxomethacrylate and acetamide: synthesis, characterization, and their computational approaches: molecular docking, molecular dynamics, and ADME analyses

The compounds 2-chloro-N-(3-methoxyphenyl)acetamide (m-acetamide) and 2-(3-methoxyphenylamino)-2-oxoethyl methacrylate (3MPAEMA) were synthesized in this study for the first time in the literature. FTIR, 1H, and 13C NMR spectroscopic techniques were used to characterize it. Subsequently, computational techniques were used to assess various ADME factors, such as drug-likeness properties, bioavailability score, and adherence to Lipinski's rule. Finally, molecular docking experiments were conducted with the human topoisomerase α2 (TOP2A) protein to verify and validate the reliability and stability of the docking procedure. The results of the docking scores, which quantify binding affinity, indicated that these derivatives exhibited a stronger affinity for TOP2A.

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

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

Identification of Helicobacter pylori-carcinogenic TNF-alpha-inducing protein inhibitors via daidzein derivatives through computational approaches

Gastric cancer is considered a class 1 carcinogen that is closely linked to infection with Helicobacter pylori (H. pylori), which affects over 1 million people each year. However, the major challenge to fight against H. pylori and its associated gastric cancer due to drug resistance. This research gap had led our research team to investigate a potential drug candidate targeting the Helicobacter pylori-carcinogenic TNF-alpha-inducing protein. In this study, a total of 45 daidzein derivatives were investigated and the best 10 molecules were comprehensively investigated using in silico approaches for drug development, namely pass prediction, quantum calculations, molecular docking, molecular dynamics simulations, Lipinski rule evaluation, and prediction of pharmacokinetics. The molecular docking study was performed to evaluate the binding affinity between the target protein and the ligands. In addition, the stability of ligand-protein complexes was investigated by molecular dynamics simulations. Various parameters were analysed, including root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), radius of gyration (Rg), hydrogen bond analysis, principal component analysis (PCA) and dynamic cross-correlation matrix (DCCM). The results has confirmed that the ligand-protein complex CID: 129661094 (07) and 129664277 (08) formed stable interactions with the target protein. It was also found that CID: 129661094 (07) has greater hydrogen bond occupancy and stability, while the ligand-protein complex CID 129664277 (08) has greater conformational flexibility. Principal component analysis revealed that the ligand-protein complex CID: 129661094 (07) is more compact and stable. Hydrogen bond analysis revealed favourable interactions with the reported amino acid residues. Overall, this study suggests that daidzein derivatives in particular show promise as potential inhibitors of H. pylori.

Accurate Characterization of Binding Kinetics and Allosteric Mechanisms for the HSP90 Chaperone Inhibitors Using AI-Augmented Integrative Biophysical Studies

The binding kinetics of drugs to their targets are gradually being recognized as a crucial indicator of the efficacy of drugs in vivo, leading to the development of various computational methods for predicting the binding kinetics in recent years. However, compared with the prediction of binding affinity, the underlying structure and dynamic determinants of binding kinetics are more complicated. Efficient and accurate methods for predicting binding kinetics are still lacking. In this study, quantitative structure-kinetics relationship (QSKR) models were developed using 132 inhibitors targeting the ATP binding domain of heat shock protein 90α (HSP90α) to predict the dissociation rate constant (koff), enabling a direct assessment of the drug-target residence time. These models demonstrated good predictive performance, where hydrophobic and hydrogen bond interactions significantly influence the koff prediction. In subsequent applications, our models were used to assist in the discovery of new inhibitors for the N-terminal domain of HSP90α (N-HSP90α), demonstrating predictive capabilities on an experimental validation set with a new scaffold. In X-ray crystallography experiments, the loop-middle conformation of apo N-HSP90α was observed for the first time (previously, the loop-middle conformation had only been observed in holo-N-HSP90α structures). Interestingly, we observed different conformations of apo N-HSP90α simultaneously in an asymmetric unit, which was also observed in a holo-N-HSP90α structure, suggesting an equilibrium of conformations between different states in solution, which could be one of the determinants affecting the binding kinetics of the ligand. Different ligands can undergo conformational selection or alter the equilibrium of conformations, inducing conformational rearrangements and resulting in different effects on binding kinetics. We then used molecular dynamics simulations to describe conformational changes of apo N-HSP90α in different conformational states. In summary, the study of the binding kinetics and molecular mechanisms of N-HSP90α provides valuable information for the development of more targeted therapeutic approaches.

Chinese medicine in the treatment of non-alcoholic fatty liver disease based on network pharmacology: a review

Non-alcoholic fatty liver disease (NAFLD) is a clinicopathological syndrome characterized by abnormalities in hepatic fat deposition, the incidence of which has been increasing year by year in recent years. It has become the largest chronic liver disease globally and one of the important causes of cirrhosis and even primary liver cancer formation. The pathogenesis of NAFLD has not yet been fully clarified. Modern medicine lacks targeted clinical treatment protocols for NAFLD, and most drugs lack efficacy and have high side effects. In contrast, Traditional Chinese Medicine (TCM) has significant advantages in the treatment and prevention of NAFLD, which have been widely recognized by scholars around the world. In recent years, through the establishment of a "medicine-disease-target-pathway" network relationship, network pharmacology can explore the molecular basis of the role of medicines in disease prevention and treatment from various perspectives, predicting the pharmacological mechanism of the corresponding medicines. This approach is compatible with the holistic view and treatment based on pattern differentiation of TCM and has been widely used in TCM research. In this paper, by searching relevant databases such as PubMed, Web of Science, and Embase, we reviewed and analyzed the relevant signaling pathways and specific mechanisms of action of single Chinese medicine, Chinese medicine combinations, and Chinese patent medicine for the treatment of NAFLD in recent years. These related studies fully demonstrated the therapeutic characteristics of TCM with multi-components, multi-targets, and multi-pathways, which provided strong support for the exact efficacy of TCM exerted in the clinic. In conclusion, we believe that network pharmacology is more in line with the TCM mindset of treating diseases, but with some limitations. In the future, we should eliminate the potential risks of false positives and false negatives, clarify the interconnectivity between components, targets, and diseases, and conduct deeper clinical or experimental studies.

Gigantol, a promising natural drug for inflammation: a literature review and computational based study

Gigantol, a bibenzyl compound extracted from various medicinal plants, has shown a number of biological activities, making it an attractive candidate for potential medical applications. This systematic review aims to shed light on gigantol's promising role in inflammation treatment and its underlying mechanisms. Gigantol exhibits potential anti-inflammatory properties in pre-clinical pharmacological test systems. It effectively reduced the levels of pro-inflammatory markers and arachidonic acid metabolites through various pathways, such as NF-κB, AKT, PI3K, and JNK/cPLA2/12-LOX. The in-silico investigations demonstrated that the MMP-13 enzyme served as the most promising target for gigantol with highest binding affinity (docking score = -8.8 kcal/mol). Encouragingly, the absorption, distribution, metabolism, excretion, and toxicity (ADMET) analysis of gigantol confirmed its compatibility with the necessary physiochemical, pharmacokinetic, and toxicity properties, bolstering its potential as a drug candidate. Gigantol, with its well-documented anti-inflammatory properties, could be a promising agent for treating inflammation in the near future.

From Proteins to Ligands: Decoding Deep Learning Methods for Binding Affinity Prediction

Accurate in silico prediction of protein-ligand binding affinity is important in the early stages of drug discovery. Deep learning-based methods exist but have yet to overtake more conventional methods such as giga-docking largely due to their lack of generalizability. To improve generalizability, we need to understand what these models learn from input protein and ligand data. We systematically investigated a sequence-based deep learning framework to assess the impact of protein and ligand encodings on predicting binding affinities for commonly used kinase data sets. The role of proteins is studied using convolutional neural network-based encodings obtained from sequences and graph neural network-based encodings enriched with structural information from contact maps. Ligand-based encodings are generated from graph-neural networks. We test different ligand perturbations by randomizing node and edge properties. For proteins, we make use of 3 different protein contact generation methods (AlphaFold2, Pconsc4, and ESM-1b) and compare these with a random control. Our investigation shows that protein encodings do not substantially impact the binding predictions, with no statistically significant difference in binding affinity for KIBA in the investigated metrics (concordance index, Pearson's R Spearman's Rank, and RMSE). Significant differences are seen for ligand encodings with random ligands and random ligand node properties, suggesting a much bigger reliance on ligand data for the learning tasks. Using different ways to combine protein and ligand encodings did not show a significant change in performance.

Investigation of bioactive components responsible for the antibacterial and anti-biofilm activities of Caroxylon volkensii by LC-QTOF-MS/MS analysis and molecular docking

Caroxylon volkensii is a wild desert plant of the family Amaranthaceae. This study represents the first report of the metabolomic profiling of C. volkensii by liquid chromatography quadrupole-time-of-flight tandem mass spectrometry (LC-QTOF-MS/MS). The dereplication study of its secondary metabolites led to the characterization of 66 known compounds. These compounds include catecholamines, tyramine derivatives, phenolic acids, triterpenoids, flavonoids, and others. A new tyramine derivative, alongside other known compounds, was reported for the first time in the Amaranthaceae family. The new derivative and the first-reported compounds were putatively identified through MS/MS fragmentation data. Given the notorious taxonomical challenges within the genus Salsola, to which C. volkensii previously belonged, our study could offer a valuable insight into its chemical fingerprint and phylogenetic relationship to different Salsola species. The antibacterial potential of C. volkensii methanolic extract (CVM) against Pseudomonas aeruginosa was screened. The minimum inhibitory concentration (MIC) of CVM ranged from 32 to 256 μg mL-1. The anti-quorum sensing potential of CVM resulted in a decrease in the percentage of strong and moderate biofilm-forming isolates from 47.83% to 17.39%. It revealed a concentration-dependent inhibitory activity on violacein formation by Chromobacterium violaceum. Moreover, CVM exhibited an in vivo protective potential against the killing capacity of P. aeruginosa isolates. A molecular docking study revealed that the quorum-sensing inhibitory effect of CVM can be attributed to the binding of tyramine conjugates, ethyl-p-digallate, and isorhamnetin to the transcriptional global activator LasR.

Tribuloside: Mechanisms and Efficacy in Treating Acute Lung Injury Revealed by Network Pharmacology and Experimental Validation

Background

Acute lung injury (ALI) is a serious illness that has few treatment options available. Tribuloside, a natural flavonoid extracted from the Tribulus Terrestris plant in China, is potent in addressing many health issues such as headaches, dizziness, itching, and vitiligo.

Objective

This study intends to explore the mechanisms of action of Tribuloside in treating ALI through a combination of network pharmacology and experimental validation.

Methods

We obtained the 2D structure and SMILES number of Tribuloside from the PubChem database. We used the SwissTargetPrediction database to identify pharmacological targets. We found 1215 targets linked to ALI by examining the GeneCards database. We used the String database and Cytoscape software to create the "drug or disease-target" network as well as the protein-protein interactions (PPI). Key targets were identified by evaluating associated biological processes and pathway enrichment. A Venny Diagram showed 49 intersection points between Tribuloside and ALI. Molecular docking with AutoDockTools found that Tribuloside had a high affinity for IL6, BCL2, TNF, STAT3, IL1B, and MAPK3, the top 6 targets in the PPI network by Degree values. To test Tribuloside's therapeutic efficacy in ALI, an acute lung damage model in mice was constructed using lipopolysaccharide. Tribuloside treatment reduced inflammatory cell infiltration, decreased fibrotic area, repaired damaged alveoli, and suppressed inflammatory factors IL-6, TNF-α, and IL-1β in the lungs through many pathways and targets.

Conclusion

This study reveals that Tribuloside has the potential to treat ALI by targeting various pathways and targets, according to network pharmacology predictions and experimental confirmation.

Protective effect of Pterospermum rubiginosum bark extract on bone mineral density and bone remodelling in estrogen deficient ovariectomized Sprague-Dawley (SD) rats

Osteoporosis is a common metabolic old age disorder characterised by low bone mass content (BMC) and mineral density (BMD) with micro-architectural deterioration of the extracellular matrix, further increasing bone fragility risk. Several traditional remedies, including plant extracts and herbal formulations, are used worldwide by local healers to improve the overall bone health and metabolism as an excellent osteoregenerative agent. Pteropsermum rubiginosum is an underexplored medicinal plant used by tribal peoples of Western Ghats, India, to treat bone fractures and associated inflammation. The proposed study evaluates the elemental profiling and phytochemical characterisation of P. rubiginosum methanolic bark extract (PRME), along with detailed In vitro and In vivo biological investigation in MG-63 cells and Sprague-Dawley (SD) rats. AAS and ICP-MS analysis showed the presence of calcium, phosphorus, and magnesium and exceptional levels of strontium, chromium, and zinc in PRME. The NMR characterisation revealed the presence of vanillic acid, Ergost-4-ene-3-one and catechin. The molecular docking studies revealed the target pockets of isolated compounds and various marker proteins in the bone remodelling cycle. In vitro studies showed a significant hike in ALP and calcium content, along with upregulated mRNA expression of the ALP and COL1, which confirmed the osteoinductive activity of PRME in human osteoblast-like MG-63 cells. The in vivo evaluation in ovariectomised (OVX) rats showed remarkable recovery in ALP, collagen and osteocalcin protein after 3 months of PRME treatment. DEXA scanning reports in OVX rats supported the above in vitro and in vivo results, significantly enhancing the BMD and BMC. The results suggest that PRME can induce osteogenic activity and enhance bone formation with an excellent osteoprotective effect against bone loss in OVX animals due to estrogen deficiency.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-024-03942-7.

Identification of Corn Peptides with Alcohol Dehydrogenase Activating Activity Absorbed by Caco-2 Cell Monolayers

Alcohol dehydrogenase (ADH) plays a pivotal role in constraining alcohol metabolism. Assessing the ADH-activating activity in vitro can provide insight into the capacity to accelerate ethanol metabolism in vivo. In this study, ADH-activating peptides were prepared from corn protein meal (CGM) using enzymatic hydrolysis, and these peptides were subsequently identified following simulated gastrointestinal digestion and their absorption through the Caco-2 cell monolayer membrane. The current investigation revealed that corn protein hydrolysate hydrolyzed using alcalase exhibited the highest ADH activation capability, maintaining an ADH activation rate of 52.93 ± 2.07% following simulated gastrointestinal digestion in vitro. After absorption through the Caco-2 cell monolayer membrane, ADH-activating peptides were identified. Among them, SSNCQPF, TGCPVLQ, and QPQQPW were validated to possess strong ADH activation activity, with EC50 values of 1.35 ± 0.22 mM, 2.26 ± 0.16 mM, and 2.73 ± 0.13 mM, respectively. Molecular Docking revealed that the activation of ADH occurred via the formation of a stable complex between the peptide and the active center of ADH by hydrogen bonds and hydrophobic interactions. The results of this study also suggest that corn protein hydrolysate could be a novel functional dietary element that helps protects the liver from damage caused by alcohol and aids in alcohol metabolism.

Benchmarking Active Learning Protocols for Ligand-Binding Affinity Prediction

Active learning (AL) has become a powerful tool in computational drug discovery, enabling the identification of top binders from vast molecular libraries. To design a robust AL protocol, it is important to understand the influence of AL parameters, as well as the features of the data sets on the outcomes. We use four affinity data sets for different targets (TYK2, USP7, D2R, Mpro) to systematically evaluate the performance of machine learning models [Gaussian process (GP) model and Chemprop model], sample selection protocols, and the batch size based on metrics describing the overall predictive power of the model (R2, Spearman rank, root-mean-square error) as well as the accurate identification of top 2%/5% binders (Recall, F1 score). Both models have a comparable Recall of top binders on large data sets, but the GP model surpasses the Chemprop model when training data are sparse. A larger initial batch size, especially on diverse data sets, increased the Recall of both models as well as overall correlation metrics. However, for subsequent cycles, smaller batch sizes of 20 or 30 compounds proved to be desirable. Furthermore, adding artificial Gaussian noise to the data up to a certain threshold still allowed the model to identify clusters with top-scoring compounds. However, excessive noise (<1σ) did impact the model's predictive and exploitative capabilities.

Antiemetic activity of abietic acid possibly through the 5HT3 and muscarinic receptors interaction pathways

The present study was designed to evaluate the antiemetic activity of abietic acid (AA) using in vivo and in silico studies. To assess the effect, doses of 50 mg/kg b.w. copper sulfate (CuSO4⋅5H2O) were given orally to 2-day-old chicks. The test compound (AA) was given orally at two doses of 20 and 40 mg/kg b.w. On the other hand, aprepitant (16 mg/kg), domperidone (6 mg/kg), diphenhydramine (10 mg/kg), hyoscine (21 mg/kg), and ondansetron (5 mg/kg) were administered orally as positive controls (PCs). The vehicle was used as a control group. Combination therapies with the referral drugs were also given to three separate groups of animals to see the synergistic and antagonizing activity of the test compound. Molecular docking and visualization of ligand-receptor interaction were performed using different computational tools against various emesis-inducing receptors (D2, D3, 5HT3, H1, and M1-M5). Furthermore, the pharmacokinetics and toxicity properties of the selected ligands were predicted by using the SwissADME and Protox-II online servers. Findings indicated that AA dose-dependently enhances the latency of emetic retching and reduces the number of retching compared to the vehicle group. Among the different treatments, animals treated with AA (40 mg/kg) exhibited the highest latency (98 ± 2.44 s) and reduced the number of retching (11.66 ± 2.52 times) compared to the control groups. Additionally, the molecular docking study indicated that AA exhibits the highest binding affinity (- 10.2 kcal/mol) toward the M4 receptors and an elevated binding affinity toward the receptors 5HT3 (- 8.1 kcal/mol), M1 (- 7.7 kcal/mol), M2 (- 8.7 kcal/mol), and H1 (- 8.5 kcal/mol) than the referral ligands. Taken together, our study suggests that AA has potent antiemetic effects by interacting with the 5TH3 and muscarinic receptor interaction pathways. However, additional extensive pre-clinical and clinical studies are required to evaluate the efficacy and toxicity of AA.

In-silico identification and exploration of small molecule coumarin-1,2,3-triazole hybrids as potential EGFR inhibitors for targeting lung cancer

Globally, lung cancer is a significant public health concern due to its role as the leading cause of cancer-related mortalities. The promising target of EGFR for lung cancer treatment has been identified, providing a potential avenue for more effective therapies. The purpose of the study was to design a library of 1843 coumarin-1,2,3-triazole hybrids and screen them based on a designed pharmacophore to identify potential inhibitors targeting EGFR in lung cancer with minimum or no side effects. Pharmacophore-based screening was carried out and 60 hits were obtained. To gain a better understanding of the binding interactions between the compounds and the targeted receptor, molecular docking was conducted on the 60 screened compounds. In-silico ADME and toxicity studies were also conducted to assess the drug-likeness and safety of the identified compounds. The results indicated that coumarin-1,2,3-triazole hybrids COUM-0849, COUM-0935, COUM-0414, COUM-1335, COUM-0276, and COUM-0484 exhibit dock score of - 10.2, - 10.2, - 10.1, - 10.1, - 10, - 10 while reference molecule - 7.9 kcal/mol for EGFR (PDB ID: 4HJO) respectively. The molecular docking and molecular dynamics simulations revealed that the identified compounds formed stable interactions with the active site of EGFR, indicating their potential as inhibitors. The in-silico ADME and toxicity studies showed that the compounds had favorable drug-likeness properties and low toxicity, further supporting their potential as therapeutic agents. Finally, we performed DFT studies on the best-selected ligands to gain further insights into their electronic properties. The findings of this study provide important insights into the potential of coumarin-1,2,3-triazole hybrids as promising EGFR inhibitors for the management of lung cancer.

Computational Chemistry in Structure-Based Solute Carrier Transporter Drug Design: Recent Advances and Future Perspectives

Solute carrier transporters (SLCs) are a class of important transmembrane proteins that are involved in the transportation of diverse solute ions and small molecules into cells. There are approximately 450 SLCs within the human body, and more than a quarter of them are emerging as attractive therapeutic targets for multiple complex diseases, e.g., depression, cancer, and diabetes. However, only 44 unique transporters (∼9.8% of the SLC superfamily) with 3D structures and specific binding sites have been reported. To design innovative and effective drugs targeting diverse SLCs, there are a number of obstacles that need to be overcome. However, computational chemistry, including physics-based molecular modeling and machine learning- and deep learning-based artificial intelligence (AI), provides an alternative and complementary way to the classical drug discovery approach. Here, we present a comprehensive overview on recent advances and existing challenges of the computational techniques in structure-based drug design of SLCs from three main aspects: (i) characterizing multiple conformations of the proteins during the functional process of transportation, (ii) identifying druggability sites especially the cryptic allosteric ones on the transporters for substrates and drugs binding, and (iii) discovering diverse small molecules or synthetic protein binders targeting the binding sites. This work is expected to provide guidelines for a deep understanding of the structure and function of the SLC superfamily to facilitate rational design of novel modulators of the transporters with the aid of state-of-the-art computational chemistry technologies including artificial intelligence.

Compromise in Docking Power of Liganded Crystal Structures of Mpro SARS-CoV-2 Surpasses 90% Success Rate

Herein, we present the capacity of three different molecular docking programs (AutoDock, AutoDock Vina, and PLANTS) to identify and reproduce the binding modes of ligands present in 247 covalent and 169 noncovalent complex crystal structures of the severe acute respiratory syndrome coronavirus 2 main protease (Mpro). The compromise in docking power is evaluated with respect to their ability to generate poses similar to the crystal structure binding mode (heavy atoms' root-mean-square deviation < 2 Å) and their ability to recognize the native binding mode with an included compensation for the scoring function error. Noncovalently bound inhibitors are best modeled by AutoDock Vina (90.6% success rate in the active site), while the most relevant results for covalently bound inhibitors are produced by PLANTS (93.0%). AutoDock shows acceptable performance for both types of ligands, 81.1 and 76.4% for noncovalent and covalent complexes, respectively. All three programs manifest worse performance when reproducing surface-bound ligands. Comparison with other works illustrates the importance of crystal structure processing (12% of noncovalent and 26% of covalent ligands had to be manually corrected), proper sampling protocol settings, and inclusion of root-mean-square deviation (RMSD)/scoring function error compensations in crystal structure pose identification. Results are analyzed with respect to a clustering scheme of the noncovalently bound ligands and the chemical reaction type of the covalent ligand bound to the Cys145 residue. A comparison of screening power based on the docking scores of noncovalent ligands from the crystal structures with a "Directory of Useful Decoys, Enhanced" set of known decoys (6562 compounds) and ZINC15 in vivo subset (60,394 compounds) is provided. Ligand and protein input files are provided for future benchmarking purposes.

Natural autophagy activators: A promising strategy for combating photoaging

BACKGROUND

Photodamage to the skin stands out as one of the most widespread epidermal challenges globally. Prolonged exposure to sunlight containing ultraviolet radiation (UVR) instigates stress, thereby compromising the skin's functionality and culminating in photoaging. Recent investigations have shed light on the importance of autophagy in shielding the skin from photodamage. Despite the acknowledgment of numerous phytochemicals possessing photoprotective attributes, their potential to induce autophagy remains relatively unexplored.

PURPOSE

Diminished autophagy activity in photoaged skin underscores the potential benefits of restoring autophagy through natural compounds to enhance photoprotection. Consequently, this study aims to highlight the role of natural compounds in safeguarding against photodamage and to assess their potential to induce autophagy via an in-silico approach.

METHODS

A thorough search of the literature was done using several databases, including PUBMED, Science Direct, and Google Scholar, to gather relevant studies. Several keywords such as Phytochemical, Photoprotection, mTOR, Ultraviolet Radiation, Reactive oxygen species, Photoaging, and Autophagy were utilized to ensure thorough exploration. To assess the autophagy potential of phytochemicals through virtual screening, computational methodologies such as molecular docking were employed, utilizing tools like AutoDock Vina. Receptor preparation for docking was facilitated using MGLTools.

RESULTS

The initiation of structural and functional deterioration in the skin due to ultraviolet radiation (UVR) or sunlight-induced reactive oxygen species/reactive nitrogen species (ROS/RNS) involves the modulation of various pathways. Natural compounds like phenolics, flavonoids, flavones, and anthocyanins, among others, possess chromophores capable of absorbing light, thereby offering photoprotection by modulating these pathways. In our molecular docking study, these phytochemicals have shown binding affinity with mTOR, a negative regulator of autophagy, indicating their potential as autophagy modulators.

CONCLUSION

This integrated review underscores the photoprotective characteristics of natural compounds, while the in-silico analysis reveals their potential to modulate autophagy, which could significantly contribute to their anti-photoaging properties. The findings of this study hold promise for the advancement of cosmeceuticals and therapeutics containing natural compounds aimed at addressing photoaging and various skin-related diseases. By leveraging their dual benefits of photoprotection and autophagy modulation, these natural compounds offer a multifaceted approach to combatting skin aging and related conditions.

Ramelteon alleviates myocardial ischemia/reperfusion injury (MIRI) through Sirt3--dependent regulation of cardiomyocyte apoptosis

Reperfusion stands as a pivotal intervention for ischemic heart disease. However, the restoration of blood flow to ischemic tissue always lead to further damage, which is known as myocardial ischemia/reperfusion injury (MIRI). Ramelteon is an orally administered drug used to improve sleep quality, which is famous for its high bioadaptability and absence of notable addictive characteristics. However, the specific mechanism by which it improves MIRI is still unclear. Sirtuin-3 (Sirt3), primarily located in mitochondria, is crucial in mitigating many cardiac diseases, including MIRI. Based on the structure of Sirt3, we simulated molecular docking and identified several potential amino acid binding sites between it and ramelteon. Therefore, we propose a hypothesis that ramelteon may exert cardioprotective effects by activating the Sirt3 signaling pathway. Our results showed that the activation levels and expression level of Sirt3 were significantly decreased in MIRI tissue and H2O2 stimulated H9C2 cells, while ramelteon treatment upregulated Sirt3 activity and expression. After treat with 3-TYP, a classic Sirt3 activity inhibitor, we constructed myocardial ischemia/reperfusion surgery in vivo and induced H9C2 cells with H2O2 in vitro. The results showed that the myocardial protection and anti-apoptotic effects of ramelteon were antagonized by 3-TYP, indicating that the activation of Sirt3 is a key mechanism for ramelteon to exert myocardial protection. In summary, our results confirm a novel mechanism by which ramelteon improves MIRI by activating Sirt3 signaling pathway, providing strong evidence for the treatment of MIRI with ramelteon.

Drugging the entire human proteome: Are we there yet?

Each of the ∼20,000 proteins in the human proteome is a potential target for compounds that bind to it and modify its function. The 3D structures of most of these proteins are now available. Here, we discuss the prospects for using these structures to perform proteome-wide virtual HTS (VHTS). We compare physics-based (docking) and AI VHTS approaches, some of which are now being applied with large databases of compounds to thousands of targets. Although preliminary proteome-wide screens are now within our grasp, further methodological developments are expected to improve the accuracy of the results.

Machine learning driven web-based app platform for the discovery of monoamine oxidase B inhibitors

Monoamine oxidases (MAOs), specifically MAO-A and MAO-B, play important roles in the breakdown of monoamine neurotransmitters. Therefore, MAO inhibitors are crucial for treating various neurodegenerative disorders, including Parkinson's disease (PD), Alzheimer's disease (AD), and amyotrophic lateral sclerosis (ALS). In this study, we developed a novel cheminformatics pipeline by generating three diverse molecular feature-based machine learning-assisted quantitative structural activity relationship (ML-QSAR) models concerning MAO-B inhibition. PubChem fingerprints, substructure fingerprints, and one-dimensional (1D) and two-dimensional (2D) molecular descriptors were implemented to unravel the structural insights responsible for decoding the origin of MAO-B inhibition in 249 non-reductant molecules. Based on a random forest ML algorithm, the final PubChem fingerprint, substructure fingerprint, and 1D and 2D molecular descriptor prediction models demonstrated significant robustness, with correlation coefficients of 0.9863, 0.9796, and 0.9852, respectively. The significant features of each predictive model responsible for MAO-B inhibition were extracted using a comprehensive variance importance plot (VIP) and correlation matrix analysis. The final predictive models were further developed as a web application, MAO-B-pred ( https://mao-b-pred.streamlit.app/ ), to allow users to predict the bioactivity of molecules against MAO-B. Molecular docking and dynamics studies were conducted to gain insight into the atomic-level molecular interactions between the ligand-receptor complexes. These findings were compared with the structural features obtained from the ML-QSAR models, which supported the mechanistic understanding of the binding phenomena. The presented models have the potential to serve as tools for identifying crucial molecular characteristics for the rational design of MAO-B target inhibitors, which may be used to develop effective drugs for neurodegenerative disorders.

Harnessing deep learning for enhanced ligand docking

Ligand docking (LD), a technology for predicting protein-ligand (PL)-binding conformations and strengths, plays key roles in virtual screening (VS). However, the accuracy and speed of current LD methodologies remain suboptimal. Here, we discuss how deep learning (DL) could help to bridge this gap by examining recent advancements and projecting future trends.

Design, synthesis, In-vitro, In-silico and DFT studies of novel functionalized isoxazoles as antibacterial and antioxidant agents

A series of new isoxazolederivatives incorporating the sulfonate ester function has been synthesized from 2-benzylidenebenzofuran-3(2 H)-one, known as aurone. The synthesis of the target compounds was carried out following an efficient methodology that allows access to the desired products in a reproducible way and with good yield. The structures of the synthesized compounds were established using NMR (1H and 13C) spectroscopy and mass spectrometry. A theoretical study was performed to optimize the geometrical structures and to calculate the structural and electronic parameters of the synthesized compounds. The calculations were also carried out to understand the influence and the effect of substitutions on the chemical reactivity of the studied compounds. The synthesized isoxazoles were screened for their antioxidant and antibacterial activities. The findings demonstrate that the studied compounds exhibit good to moderate antibacterial activity against the tested bacteria (Staphylococcus aureus, Bacillus subtilis, and Escherichia coli). Moreover, a number of the tested isoxazole derivatives exhibit high effectiveness against DPPH free radicals. Besides that, molecular docking studies were carried out to predict binding affinity and identify the most likely binding interactions between the active molecules and the target microorganisms' proteins. A 100 ns molecular dynamics study was then conducted to examine the dynamic behavior and stability of the highly potent isoxazole 4e in complex with the target bacterial proteins. Finally, the ADMET analyses suggest that all the synthesized isoxazoles have good pharmacokinetic profiles and non-toxicity and non-carcinogenicity in biological systems.

Novel pyrimidines as COX-2 selective inhibitors: synthesis, DFT analysis, molecular docking and dynamic simulation studies

Pyrimidine and its derivatives are associated with varieties of biological properties. Therefore, we herein reported the synthesis of four novel pyrimidines (2, 3, and 4a, b) derivatives. The structure of these molecules is confirmed by spectroscopic methods such as IR, NMR, and Mass analysis. The electronic behavior of synthesized compounds 4a, b and in silico drug design 4 c, d was explained by Density Functional Theory estimations at the DFT/B3LYP level via 6-31 G++ (d, p) replicates the structure and geometry. All the synthesized compounds were screened for their in vitro COX-1 and COX-2 inhibitory activity compared to standards Celecoxib and Ibuprofen. Compounds 3 and 4a afforded excellent COX-1 and COX-2 inhibitory activities at IC50 = 5.50 and 5.05 μM against COX-1, 0.85 and 0.65 μM against COX-2, respectively. The standard drugs Celecoxib and Ibuprofen showed inhibitory activity at IC50 = 6.34 and 3.1 μM against COX-1, 0.56 and 1.2 μM against COX-2, respectively. Further, these compounds showed high potential docking with SARS-CoV-2 Omicron protease & COX-2 and predicted drug-likeness for the pyrimidine analogs by using Molinspiration. The protein stability, fluctuations of APO-protein, protein-ligand complexes were investigated through Molecular Dynamics simulations studies using Desmond Maestro 11.3 and potential lead molecules were identified.Communicated by Ramaswamy H. Sarma.