Progress in molecular docking

Multimodal fused deep learning for drug property prediction: Integrating chemical language and molecular graph

Accurately predicting molecular properties is a challenging but essential task in drug discovery. Recently, many mono-modal deep learning methods have been successfully applied to molecular property prediction. However, mono-modal learning is inherently limited as it relies solely on a single modality of molecular representation, which restricts a comprehensive understanding of drug molecules. To overcome the limitations, we propose a multimodal fused deep learning (MMFDL) model to leverage information from different molecular representations. Specifically, we construct a triple-modal learning model by employing Transformer-Encoder, Bidirectional Gated Recurrent Unit (BiGRU), and graph convolutional network (GCN) to process three modalities of information from chemical language and molecular graph: SMILES-encoded vectors, ECFP fingerprints, and molecular graphs, respectively. We evaluate the proposed triple-modal model using five fusion approaches on six molecule datasets, including Delaney, Llinas2020, Lipophilicity, SAMPL, BACE, and pKa from DataWarrior. The results show that the MMFDL model achieves the highest Pearson coefficients, and stable distribution of Pearson coefficients in the random splitting test, outperforming mono-modal models in accuracy and reliability. Furthermore, we validate the generalization ability of our model in the prediction of binding constants for protein-ligand complex molecules, and assess the resilience capability against noise. Through analysis of feature distributions in chemical space and the assigned contribution of each modal model, we demonstrate that the MMFDL model shows the ability to acquire complementary information by using proper models and suitable fusion approaches. By leveraging diverse sources of bioinformatics information, multimodal deep learning models hold the potential for successful drug discovery.

Novel dual-targeting inhibitors of NSD2 and HDAC2 for the treatment of liver cancer: structure-based virtual screening, molecular dynamics simulation, and in vitro and in vivo biological activity evaluations

Liver cancer exhibits a high degree of heterogeneity and involves intricate mechanisms. Recent research has revealed the significant role of histone lysine methylation and acetylation in the epigenetic regulation of liver cancer development. In this study, five inhibitors capable of targeting both histone lysine methyltransferase nuclear receptor-binding SET domain 2 (NSD2) and histone deacetylase 2 (HDAC2) were identified using a structure-based virtual screening approach. Notably, DT-NH-1 displayed a potent inhibition of NSD2 (IC50 = 0.08 ± 0.03 μM) and HDAC2 (IC50 = 5.24 ± 0.87 nM). DT-NH-1 also demonstrated a strong anti-proliferative activity against various liver cancer cell lines, particularly HepG2 cells, and exhibited a high level of biological safety. In an experimental xenograft model involving HepG2 cells, DT-NH-1 showed a significant reduction in tumour growth. Consequently, these findings indicate that DT-NH-1 will be a promising lead compound for the treatment of liver cancer with epigenetic dual-target inhibitors.

Eugenol's electrochemical behavior, complexation interaction with copper chloride, antioxidant activity, and potential drug molecular docking application for Covid-19

Electrochemical studies were conducted to analyze the behavior of eugenol, CuCl2, and their complex using cyclic voltammetry. The oxidation mechanisms of eugenol and the redox behavior of copper ions were elucidated, showing differences in reversibility and charge transfer coefficients. Various kinetic and solvation parameters were determined. The redox behavior of CuCl2 was found to be more reversible compared to the copper-eugenol complex. The copper-eugenol complex exhibited enhanced antioxidant activity compared to eugenol and standard ascorbic acid. The eugenol was oxidized to form eugenol quinone methide through two postulated irreversible mechanisms. Molecular docking studies suggested higher potential bioactivity of the copper-eugenol complex towards the target protein of COVID-19 than the eugenol ligand.

Antifungal bioactivity of Sarcococca hookeriana var. digyna Franch. against fluconazole-resistant Candida albicans in vitro and in vivo

ETHNOPHARMACOLOGICAL RELEVANCE

Sarcococca hookeriana var. digyna Franch. has been widely utilized in folk medicine by the Miao people in the southwestern region of China for treating skin sores which may be associated with microbial infection.

AIM OF THE STUDY

To investigate the antifungal bioactivity of S. hookeriana var. digyna against fluconazole-resistant Candida albicans in vitro and in vivo, as well as its underlying mechanism and the key bioactive component.

MATERIALS AND METHODS

The antifungal bioactivity of 80% ethanol extract of S. hookeriana var. digyna (SHE80) was investigated in vitro using the broth microdilution method, time-growth curve, and time-kill assay. Its key functional component and antifungal mechanism were explored with combined approaches including UPLC-Q-TOF-MS, network pharmacology and metabolomics. The antifungal pathway was further supported via microscopic observation of fungal cell morphology and examination of its effects on fungal biofilm and cell membranes using fluorescent staining reagents. In vivo assessment of antifungal bioactivity was conducted using a mouse model infected with C. albicans on the skin.

RESULTS

S. hookeriana var. digyna suppressed fluconazole-resistant C. albicans efficiently (MIC = 16 μg/mL, MFC = 64 μg/mL). It removed fungal biofilm, increased cell membrane permeability, induced protein leakage, reduced membrane fluidity, disrupted mitochondrial membrane potential, induced the release of reactive oxygen species, promoted cell apoptosis, and inhibited the transformation of fungi from the yeast state to the hyphal state significantly. In terms of mechanism, it affected sphingolipid metabolism and signaling pathway. Moreover, the predicted bioactive component, sarcovagine D, was supported by antifungal bioactivity evaluation in vitro (MIC = 4 μg/mL, MFC = 16 μg/mL). Furthermore, S. hookeriana var. digyna promoted wound healing, reduced the number of colony-forming units, and reduced inflammation effectively in vivo.

CONCLUSIONS

The traditional use of S. hookeriana var. digyna for fungal skin infections was supported by antifungal bioactivity investigated in vitro and in vivo. Its mechanism and bioactive component were predicted and confirmed by experiments, which also provided a new antifungal agent for future research.

Acovenosigenin A β-glucoside mediates JAK2-STAT3 signaling pathway by targeting GP130 in A549 and H460 cells based on integrative analysis of transcriptome and proteome and biological verification

Acovenosigenin A β-glucoside (AAG) is a cardiac glycoside derived from Streptocaulon juventas (Lour.) Merr, which exhibited the potential in treating lung cancer in our previous research. However, the action mechanism remains unclear. In this research, JAK2-STAT3 signaling pathway was predicted to be the critical regulation pathway based on the integrative analysis of transcriptome and proteome. Western blotting and qPCR assays were performed to identify that AAG can regulate JAK2-STAT3 signaling pathway and its downstream genes, such as c-Myc, Survivin, Cyclin B1, CDK1, Bcl-2. And this action of AAG depended on the suppression of STAT3 phosphorylation and its nuclear translocation through the experiments of Immunofluorescence, transient transfection and cryptotanshinone treatment. Additionally, AAG was discovered to mediate the JAK2-STAT3 pathway in IL-6-driven A549 and H460 cells, which in turn inhibited cell proliferation, promoted mitochondria-related apoptosis, and arrested the cell cycle progression. By molecular docking analysis, CETSA and SIP experiments, the protein of GP130 was identified as the specific target of AAG in A549 and H460 cells. Further studies suggested that AAG inhibited JAK2-STAT3 pathway and its downstream genes by targeting GP130 in nude mice xenograft model in vivo. This research presented that AAG exhibits the promising potential in the treatment of NSCLC.

Advancements in production, assessment, and food applications of salty and saltiness-enhancing peptides: A review

Salt is important for food flavor, but excessive sodium intake leads to adverse health consequences. Thus, salty and saltiness-enhancing peptides are developed for sodium-reduction products. This review elucidates saltiness perception process and analyses correlation between the peptide structure and saltiness-enhancing ability. These peptides interact with taste receptors to produce saltiness perception, including ENaC, TRPV1, and TMC4. This review also outlines preparation, isolation, purification, characterization, screening, and assessment techniques of these peptides and discusses their potential applications. These peptides are from various sources and produced through enzymatic hydrolysis, microbial fermentation, or Millard reaction and then separated, purified, identified, and screened. Sensory evaluation, electronic tongue, bioelectronic tongue, and cell and animal models are the primary saltiness assessment approaches. These peptides can be used in sodium-reduction food products to produce "clean label" items, and the peptides with biological activity can also serve as functional ingredients, making them very promising for food industry.

Chirality in Insecticide Design and Efficacy

Chirality plays a crucial role in the design and efficacy of insecticides, significantly influencing their biological activity, selectivity, and environmental impact. Recent advancements in chiral insecticides have focused on enhancing their effectiveness, reducing toxicity to nontarget organisms, and improving environmental sustainability. This review provides a comprehensive overview of the current state of knowledge on chiral insecticides, including neonicotinoids, isoxazolines, and sulfiliminyls. We discuss the stereochemistry, synthetic development, mode of action, and environmental fate of these compounds. The review highlights the importance of chirality in optimizing insecticidal properties and underscores the need for continued research into novel chiral compounds and advanced synthesis technologies. By understanding the role of chirality, we can develop more effective and environmentally friendly insecticides for sustainable pest management.

Efficient synthesis of vanillylamine through bioamination of lignin-derived vanillin by recombinant E. coli containing ω-transaminase from Caulobacter sp. D5 in dimethyl sulfoxide-water

Lignin is a plentiful and readily accessible renewable resource. Vanillylamine is a crucial raw material used to synthesize pharmaceuticals and high-value furan compounds that can be acquired by aminating lignin-derived vanillin (Van). However, effectually achieving the biocatalytic synthesis of vanillylamine has remained challenging. In this study, a dimethyl sulfoxide (DMSO)-H2O (1:9, vol/vol) bioreaction medium was constructed, and a recombinant E. coli ATA1012 carrying ω-transaminase from Caulobacter sp. D5 was used as the ω-transaminase biocatalyst to acquire the effectual biocatalytic synthesis of vanillylamine. Under optimized bioreaction conditions (37 ℃ and pH 7.5) by supplementary of isopropylamine (IPA) (Van/IPA = 1:5, mol/mol), 80-100 mM Van could be effectually converted by ATA1012 whole cells in DMSO-H2O (1:9, vol/vol) within 12 h, yielding 91.2 %-95.4 % vanillylamine, with >99 % selectivity. An efficient amination process was developed using ATA1012 with superior transaminase catalytic activity and substrate tolerance to effectively convert Van to vanillylamine in a DMSO-H2O medium.

Screening of anti-inflammatory active components in Sabia schumanniana Diels by affinity ultrafiltration and UHPLC-Q-Exactive Orbitrap mass spectrometry

ETHNOPHARMACOLOGICAL RELEVANCE

Sabia schumanniana Diels is a traditional botanical used to treat lumbago and arthralgia. However, there has been limited research on the pharmacological effects of its chemical components.

AIM OF THE STUDY

This study aimed to rapidly screen for anti-inflammatory compounds in Sabia schumanniana Diels.

MATERIALS AND METHODS

An affinity ultrafiltration method based on UHPLC-Q-Exactive Orbitrap MS was established to rapidly screen and identify cyclooxygenase-2 (COX-2) receptor ligands. The reliability of this method was verified by molecular docking analysis and experiments with RAW264.7 cells.

RESULTS

Seventeen ligands were identified from Sabia schumanniana Diels using affinity ultrafiltration. Molecular docking results indicated that these ligands specifically docked with COX-2. Among them, N-nornuciferine exhibited notable anti-inflammatory activity.

CONCLUSIONS

The combination of affinity ultrafiltration and UHPLC-Q-Exactive Orbitrap MS is an effective and precise method for screening anti-inflammatory compounds. This study provides a foundation for further research on Sabia schumanniana Diels and offers guidance for its potential clinical applications.

Discovery of putative inhibitors of human Pkd1 enzyme: Molecular docking, dynamics and simulation, QSAR, and MM/GBSA

Polycystic kidney disease is the most prevalent hereditary kidney disease globally and is mainly linked to the overexpression of a gene called PKD1. To date, there is no effective treatment available for polycystic kidney disease, and the practicing treatments only provide symptomatic relief. Discovery of the compounds targeting the PKD1 gene by inhibiting its expression under the disease condition could be crucial for effective drug development. In this study, a molecular docking and molecular dynamic simulation, QSAR, and MM/GBSA-based approaches were used to determine the putative inhibitors of the Pkd1 enzyme from a library of 1379 compounds. Initially, fourteen compounds were selected based on their binding affinities with the Pkd1 enzyme using MOE and AutoDock tools. The selected drugs were further investigated to explore their properties as drug candidates and the stability of their complex formation with the Pkd1 enzyme. Based on the physicochemical and ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) properties, and toxicity profiling, two compounds including olsalazine and diosmetin were selected for the downstream analysis as they demonstrated the best drug-likeness properties and highest binding affinity with Pkd1 in the docking experiment. Molecular dynamic simulation using Gromacs further confirmed the stability of olsalazine and diosmetin complexes with Pkd1 and establishing interaction through strong bonding with specific residues of protein. High biological activity and binding free energies of two complexes calculated using 3D QSAR and Schrodinger module, respectively further validated our results. Therefore, the molecular docking and dynamics simulation-based in-silico approach used in this study revealed olsalazine and diosmetin as potential drug candidates to combat polycystic kidney disease by targeting Pkd1 enzyme.

A comprehensive review of artificial intelligence for pharmacology research

With the innovation and advancement of artificial intelligence, more and more artificial intelligence techniques are employed in drug research, biomedical frontier research, and clinical medicine practice, especially, in the field of pharmacology research. Thus, this review focuses on the applications of artificial intelligence in drug discovery, compound pharmacokinetic prediction, and clinical pharmacology. We briefly introduced the basic knowledge and development of artificial intelligence, presented a comprehensive review, and then summarized the latest studies and discussed the strengths and limitations of artificial intelligence models. Additionally, we highlighted several important studies and pointed out possible research directions.

Repurposing Duloxetine as a Potent Butyrylcholinesterase Inhibitor: Potential Cholinergic Enhancing Benefits for Elderly Individuals with Depression and Cognitive Impairment

Despite the advent of new treatment strategies, cholinesterase inhibitors (ChEIs) are still the go-to treatment for dementia disorders. ChEIs act by inhibiting the main acetylcholine-degrading enzyme, acetylcholinesterase (AChE). Nonetheless, accumulating evidence indicates that the impact of inhibition of the sister enzyme, butyrylcholinesterase (BChE), could be even broader in older adults due to the multifaceted role of BChE in several biological functional pathways. Therefore, we employed an in silico modeling-based drug repurposing strategy to identify novel potent BChE inhibitors from the FDA drug database. This was followed by in vitro screening and ex vivo enzyme kinetic validation using human plasma samples as the source of BChE. The analysis revealed that the antidepressant drug, duloxetine, inhibited BChE with high selectivity in comparison to AChE. In contrast, two other antidepressants, namely, citalopram and escitalopram exhibited a weak to moderate activity. Ex vivo enzyme inhibition kinetic analyses indicated that duloxetine acted as a competitive inhibitor of BChE with an inhibition constant (K i) of 210 nM. This K i value is comparable with 100-400 nM concentration of duloxetine following normal dosages in humans, thereby indicating that duloxetine should be able to induce a pharmacologically and biologically relevant in vivo inhibition of BChE. Additionally, we performed the enzyme inhibition kinetic assessment in parallel for ethopropazine, a known potent selective BChE inhibitor, and physostigmine, a dual inhibitor of AChE and BChE. These analyses indicated that duloxetine should be considered a potent BChE inhibitor since its K i was comparable with ethopropazine (K i = 150 nM) but was 4 times smaller than that of physostigmine (K i = 840 nM). In conclusion, this study reports the discovery of duloxetine being a highly potent selective competitive BChE inhibitor. This, in turn, indicates that duloxetine could be the choice of antidepressive treatment in older adults with both depressive and dementia symptoms since it may offer additional clinically beneficial effects via this secondary mode of cholinergic enhancing action.

Investigation of Folate-Functionalized Magnetic-Gold Nanoparticles Based Targeted Drug Delivery for Liver: In Vitro, In Vivo and Docking Studies

Magnetic nanoparticles used for targeted drug administration present a promising approach in cancer treatment owing to its notable advantages, such as targeted and enhanced encapsulation ability and improved bio protection compared with conventional drug delivery methods. Au shell-iron core nanoparticles (Fe3O4@Au) were manufactured by a chemical process, coated with dextran to encapsulate curcumin, and functionalized for precision drug delivery using folic acid to combat liver cancer. Dynamic light scattering, scanning electron microscopy, transmission electron microscopy, vibrational spectroscopy, and magnetometry were applied to assess the synthesis of the Fe3O4@Au-DEX-CU-FA compound. The mean size, zeta potential, and polydispersity of Fe3O4@Au-DEX-CU-FA were 63.3 ± 2.33 nm, -68.3 ± 1.78 mV, and 0.041 ± 0.008, respectively. Molecular docking models were created to examine the relationship between Fe3O4@Au-CU and BCL-XL, BAK, and to identify potential binding sites. The loading efficiency and release profile tests examined the medication delivery system's ability. MTT assay was subsequently utilized to determine the optimal dosage and therapeutic efficacy of Fe3O4@Au-DEX-CU-FA on cancer SNU-449 and healthy THLE-2 cell lines. Flow cytometry demonstrated that Fe3O4@Au-DEX-CU-FA effectively induced cancer cell death. Fe3O4@Au-DEX-FA showed a regulated release profile of free curcumin at 37 °C and pH values of 7.4 and 5.4. Real-time PCR revealed increased BAK expression and decreased BCL-XL expression. Nude tumor-bearing mice were used for in vivo experiments. Fe3O4@Au-DEX-CU-FA treatment dramatically reduced the swelling size compared with free CU and control treatments. It also resulted in a longer lifespan, expanded splenocyte proliferation, increased IFN-γ levels, and decreased IL-4 levels. The regular cells showed no cytotoxic effect compared with the cancer type, confirming that Fe3O4@Au-DEX-CU-FA maintained its potent anticancer actions. The data suggests that Fe3O4@Au-DEX-CU-FA possesses a promising potential as a therapeutic agent for combating tumors.

In silico study on antidiabetic and antioxidant activity of bioactive compounds in Ficus carica L

Hyperglycemia is one of the diagnostic issues in diabetes mellitus and is considered as a complex metabolic condition. It has been one of the most prevalent illnesses of the twenty-first century and still rising at an alarming rate across the globe and expected to impact 693 million individuals by 2045. Therefore, it is mandatory to develop more effective and safer treatments to manage diabetes. One of the ways to manage hyperglycemia is through inhibiting carbohydrate digestion and thereby lowering the glucose formation in the human body. The enzyme salivary amylase and pancreatic amylase is responsible for cleaving α-1,4-glucoside bond. Amylase inhibitors can lower blood glucose in diabetics by slowing digestion. Ficus carica is commonly known for its medicinal properties due to its various phytochemicals. In the present study, 10 phytochemicals present in F. carica compounds named, β-carotene, lutein, cyanidin-3-glucoside, gallic acid, luteolin, catechin, kaempferol, vanillic acid, peonidin-3-glucoside, and quercetin hydrate were taken to study their inhibition potential against pancreatic amylase and salivary amylase through molecular docking and molecular dynamics simulations. Further, density functional theory calculations are used to investigate the delocalization of electron density on the molecule as well as study ADME properties of the molecules take. A QSAR model has been developed using the binding energy obtained using molecular docking and thermodynamic parameters from DFT calculations.Communicated by Ramaswamy H. Sarma.

Computational and experimental approaches manifest the leishmanicidal potential of α-mangostin resourced from Garcinia cowa

Owing to the persistent number of parasitic deaths, Visceral leishmaniasis continues to haunt several economically weaker sections of India. The disease causes over 30,000 deaths and threatens millions annually on a global scale. The standard pentavalent antimonials, on the other hand, are associated with health adversities and disease relapse. The current study is focused on the search for the most potential natural bioactive phytocompound from the bark extract of the Northeastern Indian plant, Garcinia cowa, that shows potent anti-leishmanial properties. The High Resonance Liquid Chromatography followed by Mass Spectrometry (HR-LCMS) study followed by an in silico molecular docking using computational tools revealed that α-mangostin might potentially possess antiparasitic activity. To validate the anti-leishmanial efficacy of the compound, a cell viability assay was performed, which demonstrated the parasite-specific inhibitory activity of α-mangostin; with IC50 values ranging from 4.95 - 7.37 µM against the different forms of Leishmania donovani parasite. The flow cytometric analysis of the phytocompound treated parasites indicated an oxidative and nitrosative stress-mediated apoptotic cell death in the parasites, by the suggestive surge in nuclear fragmentation and mitochondrial dysfunction. Simultaneously, a cytokine profiling study suggested approximate two-to-three-fold upregulated levels of pro-inflammatory cytokines post-compound treatment, which is predicted to actively contribute to parasite-killing. α-mangostin was also found to reduce the chances of parasite survival by inhibiting arginase enzyme activity, which in favorable conditions facilitates its sustenance. This study thereby substantiates that α-mangostin significantly possesses anti-leishmanial potentiality that can be developed into a cure for this infectious disease.

An In Silico-Guided Approach for Assessing Herb-Drug Interaction Potential: A Case Study with Cudrania tricuspidata Leaf Extracts

Cudrania tricuspidata leaf extracts have long been utilized as traditional oriental medicines across Asian countries like Korea, China, and Japan. These extracts are renowned for their therapeutic benefits in addressing inflammation, tumors, obesity, and diabetes, maintaining their status as a pivotal folk remedy. Given the rising trend of combining medicinal herbs with conventional medications, it is imperative to explore the potential herb-drug interactions. However, there is a dearth of research on evaluating the herb-drug interactions of C. tricuspidata leaf extracts. Also, the intricate chemical composition of medicinal herbs presents methodological hurdles in establishing causal relationships between their constituents and herb-drug interactions. To overcome these challenges, a combined in silico and in vitro workflow was developed and effectively applied to evaluate the potential herb-drug interaction of C. tricuspidata leaf extracts along with the associated chemical factors. In in vitro CYP inhibition assays, C. tricuspidata leaf extracts exhibited potent inhibition of CYP1A2 and CYP2C8, with quercetin, kaempferol, and their glycosides identified as the major constituents. In silico analysis based on the prediction tools (ADMETlab 2.0 and pkCSM) identified key contributors to CYP inhibition, quercetin and kaempferol. Additionally, molecular docking analysis validated the binding of ligands (quercetin and kaempferol) to proteins (CYP1A2 and CYP2C8). These findings suggest that C. tricuspidata leaf extracts could inhibit CYP1A2 and CYP2C8, aiding in understanding the herb-drug interaction potential of C. tricuspidata leaf extracts for safe clinical application. Furthermore, this approach can be broadly applied to study herb-drug interactions of various medicinal herbs, enhancing their therapeutic benefits and reducing adverse reactions by considering chemical profiles relevant to herb-drug interaction potential in herbal preparations.

Optimization of Fermentation Conditions of Cordyceps militaris and In Silico Analysis of Antifungal Property of Cordycepin Against Plant Pathogens

Cordyceps militaris, a medicinal fungus, has gained considerable attention owing to its potential health benefits, notably the production of bioactive compounds such as cordycepin. Cordycepin possesses significant antifungal, antibacterial, and antiviral properties. The present study focused on optimizing the fermentation conditions for C. militaris to boost the production of mycelia and cordycepin, alongside investigating its antifungal properties using in silico and in vitro approaches. The optimal conditions, yielding the highest cordycepin and mycelial biomass, were a temperature of 20°C and a pH range of 4-6, with glucose and sucrose as carbon sources and yeast extract and casein hydrolysate as nitrogen sources. Under these conditions, cordycepin production peaked at low pH (600-1000 mg/L) and with carbon and maltose (400-500 mg/L). The low temperature favored cordycepin production (400 mg/L), whereas casein hydrolysate as a nitrogen source boosted cordycepin yield (600 mg/L). The docking analysis indicated that cordycepin had the highest binding affinity for the tubulin beta chain 2 (-10.4 kcal/mol) compared to the fungicide tebuconazole (-7.9 kcal/mol for both targets). The in silico results were corroborated by in vitro studies, where the mycelial extract of C. militaris inhibited approximately 75% of fungal growth at a concentration of 6000 ppm. These findings suggest that optimizing fermentation conditions significantly enhances cordycepin production, and cordycepin shows antifungal solid activity, making it a promising agent for biocontrol in agriculture.

Exploring the inhibitory potential of phytochemicals from Vernonia glaberrima leaves against snake venom toxins through computational simulation and experimental validation

Phospholipase A2 (PLA2) is an enzyme present in appreciable quantity in snake venoms which catalyze the hydrolysis of glycerophospholipids at sn-2 position and promote the release of lysophospholipids and fatty acids. 5-methylcoumarin-4-β-glucoside (5MC4BG) and lupeol were previously isolated from the leaves of V. glaberrima. The aim of this research was to evaluate effect of these compounds as potential inhibitors of snake venom toxins of Naja nigricollis using an in vitro and in silico studies. Antisnake venom studies was conducted using acidimetry while the molecular docking analysis against PLA2 enzyme from N. nigricollis was performed using Auto Dock Vina and ADME-Tox analysis was evaluated using swissADME and ProTox-II online servers. The two compounds (5MC4BG and Lupeol) were able to inhibit the hydrolytic actions of PLA2 enzyme with percentage inhibition ranging from 23.99 to 72.36 % and 21.97-24.82 % at 0.0625-1.00 mg/mL respectively while the standard ASV had 82.63 % at 1.00 mg/mL after 10 min incubation at 37 °C. Similar effects were observed after 30 min incubation, although there was significant increase in percentage inhibition of 5MC4BG and lupeol ranging from 66.51 to 83.73 % and 54.87-59.60 % at similar concentrations. Furthermore, the compounds were able to bind to the active site of PLA2 enzyme with high affinity (-7.7 to -6.3 kcal/mol); the standard ligand, Varespladib had a docking score of -6.9 kcal/mol and they exhibited favorable drug-likeness and pharmacokinetic properties and according to toxicity predictions, the two compounds are toxic. In conclusion, the leaf of V. glaberrima contains phytoconstituents with antisnake activity and thus, validates the hypothesis that, the phytoconstituents of V. glaberrima leaves has antisnake venom activity against N. nigricollis venom and thus, should be studied further for the development as antisnake venom agents.

MSH-DTI: multi-graph convolution with self-supervised embedding and heterogeneous aggregation for drug-target interaction prediction

BACKGROUND

The rise of network pharmacology has led to the widespread use of network-based computational methods in predicting drug target interaction (DTI). However, existing DTI prediction models typically rely on a limited amount of data to extract drug and target features, potentially affecting the comprehensiveness and robustness of features. In addition, although multiple networks are used for DTI prediction, the integration of heterogeneous information often involves simplistic aggregation and attention mechanisms, which may impose certain limitations.

RESULTS

MSH-DTI, a deep learning model for predicting drug-target interactions, is proposed in this paper. The model uses self-supervised learning methods to obtain drug and target structure features. A Heterogeneous Interaction-enhanced Feature Fusion Module is designed for multi-graph construction, and the graph convolutional networks are used to extract node features. With the help of an attention mechanism, the model focuses on the important parts of different features for prediction. Experimental results show that the AUROC and AUPR of MSH-DTI are 0.9620 and 0.9605 respectively, outperforming other models on the DTINet dataset.

CONCLUSION

The proposed MSH-DTI is a helpful tool to discover drug-target interactions, which is also validated through case studies in predicting new DTIs.

Elucidating Chiral Resolution of Aromatic Amino Acids Using Glycopeptide Selectors: A Combined Molecular Docking and Chromatographic Study

An asymmetric synthesis is a favorable approach for obtaining enantiomerically pure substances, but racemic resolution remains an efficient strategy. This study aims to elucidate the chiral resolution of aromatic amino acids and their elution order using glycopeptides as chiral selectors through molecular docking analysis. Chiral separation experiments were conducted using Vancomycin as a chiral additive in the mobile phase (CMPA) at various concentrations, coupled with an achiral amino column as the stationary phase. The Autodock Vina 1.1.2 software was employed to perform molecular docking simulations between each enantiomer (ligand) and Vancomycin (receptor) to evaluate binding affinities, demonstrate enantiomeric resolution feasibility, and elucidate chiral recognition mechanisms. Utilizing Vancomycin as CMPA at a concentration of 1.5 mM enabled the separation of tryptophan enantiomers with a resolution of 3.98 and tyrosine enantiomers with a resolution of 2.97. However, a poor chiral resolution was observed for phenylalanine and phenylglycine. Molecular docking analysis was employed to elucidate the lack of separation and elution order for tryptophan and tyrosine enantiomers. By calculating the binding energy, docking results were found to be in good agreement with experimental findings, providing insights into the underlying mechanisms governing chiral recognition in this system and the interaction sites. This comprehensive approach clarifies the complex relationship between chiral discrimination and molecular architecture, offering valuable information for creating and improving chiral separation protocols.

In Silico and In Vivo Studies of β-Sitosterol Nanoparticles as a Potential Therapy for Isoprenaline-Induced Cognitive Impairment in Myocardial Infarction, Targeting Myeloperoxidase

OBJECTIVE

This study aimed to compare the effects of β-sitosterol nanoparticles (BETNs) and β-sitosterol (BET) on cognitive impairment, oxidative stress, and inflammation in a myocardial infarction (MI) rat model using in silico and in vivo methods.

METHODS

β-Sitosterol (BET) and myeloperoxidase (MPO) ligand-receptor binding affinities were evaluated using Autodock Vina for docking and Gromacs for dynamics simulations. BET nanoparticles, prepared via solvent evaporation, had their size confirmed by a nanoparticle analyzer. ISO-induced cognitive impairment in rats was assessed through Morris water maze and Cook's pole climbing tests. Oxidative stress, inflammation, and cardiac injury were evaluated by measuring GSH, SOD, MDA, MPO, CkMB, LDH, lipid profiles, and ECGs. Histopathology of the CA1 hippocampus and myocardial tissue was performed using H&E staining.

RESULTS

In silico analyses revealed strong binding affinities between BET and MPO, suggesting BET's potential anti-inflammatory effect. BETN (119.6 ± 42.6 nm; PDI: 0.809) significantly improved MI-induced cognitive dysfunction in rats (p < 0.001 ***), increased hippocampal GSH (p < 0.01 **) and SOD (p < 0.01 **) levels, and decreased hippocampal MDA (p < 0.05 *) and MPO levels (p < 0.01 **). BETNs also elevated cardiac GSH (p < 0.01 **) and SOD (p < 0.01 **) levels and reduced cardiac MPO (p < 0.01 **), CkMB (p < 0.001 **) and LDH (p < 0.001 **) levels. It restored lipid profiles, normalized ECG patterns, and improved histology in the hippocampal CA1 region and myocardium.

CONCLUSIONS

Compared with BET treatment, BETNs were more effective in improving cognitive impairment, oxidative damage, and inflammation in MI rats, suggesting its potential in treating cognitive dysfunction and associated pathological changes in MI.

Virtual Screening and Validation of Affinity DNA Functional Ligands for IgG Fc Segment

The effective attachment of antibodies to the immune sensing interface is a crucial factor that determines the detection performance of immunosensors. Therefore, this study aims to investigate a novel antibody immobilization material with low molecular weight, high stability, and excellent directional immobilization effect. In this study, we employed molecular docking technology based on the ZDOCK algorithm to virtually screen DNA functional ligands (DNAFL) for the Fc segment of antibodies. Through a comprehensive analysis of the key binding sites and contact propensities at the interface between DNAFL and IgG antibody, we have gained valuable insights into the affinity relationship, as well as the principles governing amino acid and nucleotide interactions at this interface. Furthermore, molecular affinity experiments and competitive binding experiments were conducted to validate both the binding ability of DNAFL to IgG antibody and its actual binding site. Through affinity experiments using multi-base sequences, we identified bases that significantly influence antibody-DNAFL binding and successfully obtained DNAFL with an enhanced affinity towards the IgG Fc segment. These findings provide a theoretical foundation for the targeted design of higher-affinity DNAFLs while also presenting a new technical approach for immunosensor preparation with potential applications in biodetection.

Screening of Potential Inhibitors Targeting the Main Protease Structure of SARS-CoV-2 via Molecular Docking, and Approach with Molecular Dynamics, RMSD, RMSF, H-Bond, SASA and MMGBSA

Severe Acute Respiratory Syndrome caused by a coronavirus is a recent viral infection. There is no scientific evidence or clinical trials to indicate that possible therapies have demonstrated results in suspected or confirmed patients. This work aims to perform a virtual screening of 1430 ligands through molecular docking and to evaluate the possible inhibitory capacity of these drugs about the Mpro protease of Covid-19. The selected drugs were registered with the FDA and available in the virtual drug library, widely used by the population. The simulation was performed using the MolAiCalD algorithm, with a Lamarckian genetic model (GA) combined with energy estimation based on rigid and flexible conformation grids. In addition, molecular dynamics studies were also performed to verify the stability of the receptor-ligand complexes formed through analyses of RMSD, RMSF, H-Bond, SASA, and MMGBSA. Compared to the binding energy of the synthetic redocking coupling (-6.8 kcal/mol/RMSD of 1.34 Å), which was considerably higher, it was then decided to analyze the parameters of only three ligands: ergotamine (-9.9 kcal/mol/RMSD of 2.0 Å), dihydroergotamine (-9.8 kcal/mol/RMSD of 1.46 Å) and olysio (-9.5 kcal/mol/RMSD of 1.5 Å). It can be stated that ergotamine showed the best interactions with the Mpro protease of Covid-19 in the in silico study, showing itself as a promising candidate for treating Covid-19.

Haloketones: A class of unregulated priority DBPs with high contribution to drinking water cytotoxicity

Although unregulated aliphatic disinfection byproducts (DBPs) had a much higher concentration and cytotoxicity than known aromatic DBPs, a recent study indicated that seven classes of regulated and unregulated priority DBPs (one and two-carbon-atom DBPs) just accounted for 16.2% of disinfected water cytotoxicity in the U.S., meaning some of the highly toxic aliphatic DBPs may be overlooked. Haloketones (HKs) are an essential class of priority DBPs with a 1-100 µg/L concentration in drinking water but lack cytotoxicity data. This study investigated the cytotoxicity of seven HKs using Chinese hamster ovary (CHO) cells. The order for cytotoxicity of HKs from most to least toxic was: 1,3-dichloroacetone (LC50: 1.0 ± 0.20 μM) ≈ 1,3-dibromoacetone (1.5 ± 0.19 μM) ≈ bromoacetone (1.9 ± 0.49 μM) > chloroacetone (4.3 ± 0.22 μM) > 1,1,3-trichloropropanone (6.6 ± 0.46 μM) > 1,1,1-trichloroacetone (222 ± 7.7 μM) > hexachloroacetone (3269 ± 344 μM). The cytotoxicity of HKs was higher than most regulated and priority aliphatic DBPs in mono-halogenated, di-halogenated, and tri-halogenated categories. A prediction model of HK cytotoxicity was developed based on the quantitative structure-activity relationship (QSAR), optimizing structures and computing descriptors with Gaussian 09 W. The average concentrations of HKs in representative drinking water samples from South Carolina (U.S.) and Suzhou (China) were 12.4 and 0.9 μg/L, respectively, accounting for 18.8% and 1.7% of their specific total DBPs measured (i.e. not TOX). For South Carolina drinking water, their contributions to total calculated additive cytotoxicity of aliphatic DBPs and overall drinking water cytotoxicity were 86.7% and 14.0%, respectively, demonstrating that HKs are an essential class of overlooked DBPs with a high contribution to drinking water cytotoxicity. Our study can help to explain the conflict that why regulated and priority DBPs (except HKs) just accounted for 16% of chlorinated drinking water cytotoxicity even enough they had much higher concentration and cytotoxicity than known aromatic DBPs.

Artificial intelligence and cheminformatics tools: a contribution to the drug development and chemical science

In the ever-evolving field of drug discovery, the integration of Artificial Intelligence (AI) and Machine Learning (ML) with cheminformatics has proven to be a powerful combination. Cheminformatics, which combines the principles of computer science and chemistry, is used to extract chemical information and search compound databases, while the application of AI and ML allows for the identification of potential hit compounds, optimization of synthesis routes, and prediction of drug efficacy and toxicity. This collaborative approach has led to the discovery, preclinical evaluations and approval of over 70 drugs in recent years. To aid researchers in the pursuit of new drugs, this article presents a comprehensive list of databases, datasets, predictive and generative models, scoring functions and web platforms that have been launched between 2021 and 2022. These resources provide a wealth of information and tools for computer-assisted drug development, and are a valuable asset for those working in the field of cheminformatics. Overall, the integration of AI, ML and cheminformatics has greatly advanced the drug discovery process and continues to hold great potential for the future. As new resources and technologies become available, we can expect to see even more groundbreaking discoveries and advancements in these fields.Communicated by Ramaswamy H. Sarma.

Exploring SDHI fungicide resistance in Botrytis cinerea through genetic transformation system and AlphaFold model-based molecular docking

BACKGROUND

Gray mold caused by Botrytis cinerea is one of the most serious diseases affecting strawberry. Succinate dehydrogenase inhibitor (SDHI) fungicides have been used for more than a decade to control the disease. Monitoring resistance and improving in-depth understanding of resistance mechanisms are essential for the control of B. cinerea.

RESULTS

In this study, resistance monitoring of a SDHI fungicide boscalid was conducted on B. cinerea isolated from strawberries in Korea during 2020 and 2021, with resistance rates of 76.92% and 72.25%, respectively. In resistant strains, mutations P225F/H and H272R were found in SdhB, with P225F representing the dominant mutation type. Simultaneous mutations G85A, I93V, M158V, and V168I in SdhC were detected in 54.84% of sensitive strains. Sensitivity profiles of different Sdh genotypes of B. cinerea strains to six SDHIs were determined in vitro and in vivo. In addition, the mutation(s) were genetically validated through in situ SdhB (SdhC) expression. Docking assays between SDHIs and AlphaFold model-based Sdh complexes revealed generally consistent patterns with their in vitro phenotypes.

CONCLUSION

Resistance of B. cinerea to SDHI fungicide on strawberry was systematically investigated in this study. Deciphering of SDHI resistance through the genetic transformation system and AlphaFold model-based molecular docking will provide valuable insights into other target site-based fungicide resistance in fungal pathogens. © 2024 The Author(s). Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

A Robust Induced Fit Docking Approach with the Combination of the Hybrid All-Atom/United-Atom/Coarse-Grained Model and Simulated Annealing

Molecular docking remains an indispensable tool in computational biology and structure-based drug discovery. However, the correct prediction of binding poses remains a major challenge for molecular docking, especially for target proteins where a substrate binding induces significant reorganization of the active site. Here, we introduce an Induced Fit Docking (IFD) approach named AA/UA/CG-SA-IFD, which combines a hybrid All-Atom/United-Atom/Coarse-Grained model with Simulated Annealing. In this approach, the core region is represented by the All-Atom(AA) model, while the protein environment beyond the core region and the solvent are treated with either the United-Atom (UA) or the Coarse-Grained (CG) model. By combining the Elastic Network Model (ENM) for the CG region, the hybrid model ensures a reasonable description of ligand binding and the environmental effects of the protein, facilitating highly efficient and reliable sampling of ligand binding through Simulated Annealing (SA) at a high temperature. Upon validation with two testing sets, the AA/UA/CG-SA-IFD approach demonstrates remarkable accuracy and efficiency in induced fit docking, even for challenging cases where the docked poses significantly deviate from crystal structures.

Chemoinformatics Study of Benzodiazepine-1, 2, 3-triazole Derivatives Targeting Butyrylcholinesterase

This study aims to assess the potential bioactivity of newly designed benzodiazepine-1,2,3-triazole derivatives using in-silico methodologies, with a primary focus on elucidating their inhibitory interactions with the butyrylcholinesterase (BuChE) enzyme, which is implicated in Alzheimer's disease. We employed multiple linear regression (MLR) methods to conduct a quantitative structure-activity relationship (QSAR) analysis on a collection of 31 benzodiazepine-1,2,3-triazole derivatives, with the goal of investigating, assessing, and predicting their activities, as well as designing novel compounds. This approach yielded highly accurate results, with coefficients of determination (R²) of 0.77 and 0.81 for the training and test datasets, respectively. Additionally, the optimized compounds were subjected to an Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) analysis, demonstrating their potential as non-hepatotoxic agents with enhanced absorption and blood-brain barrier permeability. To further validate these findings, the most favorable docking conformations were analyzed using molecular dynamics (MD) simulations with GROMACS software, predicting the stability of the formed complexes. These simulations underscored the critical role of hydrogen bonds in stabilizing the compounds at the BuChE receptor binding site. The results hold great promise for the development of innovative benzodiazepine-1,2,3-triazole derivatives as effective BuChE inhibitors, potentially leading to therapeutic interventions for Alzheimer's disease.

FDI-6 inhibits VEGF-B expression in metastatic breast cancer: a combined in vitro and in silico study

Angiogenesis is the process by which new blood vessels are formed to meet the oxygen and nutrient needs of tissues. This process is vitally important in many physiological and pathological conditions such as tumor growth, metastasis, and chronic inflammation. Although the relationship of FDI-6 compound with FOXM1 protein is well known in the literature, its relationship with angiogenesis is not adequately elucidated. This study investigates the relationship of FDI-6 with angiogenesis and vascular endothelial growth factor B (VEGF-B) protein expression alterations. Furthermore, the study aims to elucidate the in silico interaction of FDI-6 with the VEGFR1 protein, a key player in initiating the angiogenic process, which is activated through its binding with VEGF-B. Our results demonstrate a significant effect of FDI-6 on cell viability. Specifically, we determined that the IC50 value of FDI-6 in HUVEC cells after 24 h of treatment is 24.2 μM, and in MDA-MB-231 cells after 24 h of application, it is 10.8 μM. These findings suggest that the cytotoxic effect of FDI-6 varies depending on the cell type. In wound healing experiments, FDI-6 significantly suppressed wound closure in MDA-MB-231 cells but did not show a similar effect in HUVEC cells. This finding suggests FDI-6 may have potential cell-type-specific effects. Molecular docking studies reveal that FDI-6 exhibits a stronger interaction with the VEGFR1 protein compared to its inhibitor, a novel interaction not previously reported in the literature. Molecular dynamic simulation results demonstrate a stable interaction between FDI-6 and VEGFR1. This interaction suggests that FDI-6 might modulate mechanisms associated with angiogenesis. Our Western blot analysis results show regulatory effects of FDI-6 on the expression of the VEGF-B protein. We encourage exploration of FDI-6 as a potential therapeutic agent in pathological processes related to angiogenesis. In conclusion, this study provides a detailed examination of the relationship between FDI-6 and both the molecular interactions and protein expressions of VEGF-B. Our findings support FDI-6 as a potential therapeutic agent in pathological processes associated with angiogenesis.

Identification of extracellular polymeric substances layer barrier in chloroquine phosphate-disturbed anammox consortia and mechanism dissection on cytotoxic behavior by computational chemistry

The over-dosing use of chloroquine phosphate (CQ) poses severe threats to human beings and ecosystem due to the high persistence and biotoxicity. The discharge of CQ into wastewater would affect the biomass activity and process stability during the biological processes, e.g., anammox. However, the response mechanism of anammox consortia to CQ remain unknown. In this study, the accurate role of extracellular polymeric substances barrier in attenuating the negative effects of CQ, and the mechanism on cytotoxic behavior were dissected by molecular spectroscopy and computational chemistry. Low concentrations (≤6.0 mg/L) of CQ hardly affected the nitrogen removal performance due to the adaptive evolution of EPS barrier and anammox bacteria. Compact protein of EPS barrier can bind more CQ (0.24 mg) by hydrogen bond and van der Waals force, among which O-H and amide II region respond CQ binding preferentially. Importantly, EPS contributes to the microbiota reshape with selectively enriching Candidatus_Kuenenia for self-protection. Furthermore, the macroscopical cytotoxic behavior was dissected at a molecular level by CQ fate/distribution and computational chemistry, suggesting that the toxicity was ascribed to attack of CQ on functional proteins of anammox bacteria with atom N17 (f-=0.1209) and C2 (f+=0.1034) as the most active electrophilic and nucleophilic sites. This work would shed the light on the fate and risk of non-antibiotics in anammox process.

DiffBindFR: an SE(3) equivariant network for flexible protein-ligand docking

Molecular docking, a key technique in structure-based drug design, plays pivotal roles in protein-ligand interaction modeling, hit identification and optimization, in which accurate prediction of protein-ligand binding mode is essential. Conventional docking approaches perform well in redocking tasks with known protein binding pocket conformation in the complex state. However, in real-world docking scenario without knowing the protein binding conformation for a new ligand, accurately modeling the binding complex structure remains challenging as flexible docking is computationally expensive and inaccurate. Typical deep learning-based docking methods do not explicitly consider protein side chain conformations and fail to ensure the physical plausibility and detailed atomic interactions. In this study, we present DiffBindFR, a full-atom diffusion-based flexible docking model that operates over the product space of ligand overall movements and flexibility and pocket side chain torsion changes. We show that DiffBindFR has higher accuracy in producing native-like binding structures with physically plausible and detailed interactions than available docking methods. Furthermore, in the Apo and AlphaFold2 modeled structures, DiffBindFR demonstrates superior advantages in accurate ligand binding pose and protein binding conformation prediction, making it suitable for Apo and AlphaFold2 structure-based drug design. DiffBindFR provides a powerful flexible docking tool for modeling accurate protein-ligand binding structures.

Unveiling potent Schiff base derivatives with selective xanthine oxidase inhibition: In silico and in vitro approach

This research describes the synthesis by an environmentally-friendly method, microwave irradiation, development and analysis of three novel and one previously identified Schiff base derivative as a potential inhibitor of bovine xanthine oxidase (BXO), a key enzyme implicated in the progression of gout. Meticulous experimentation revealed that these compounds (10, 9, 4, and 7) have noteworthy inhibitory effects on BXO, with IC50 values ranging from 149.56 µM to 263.60 µM, indicating their good efficacy compared to that of the standard control. The validation of these results was further enhanced through comprehensive in silico studies, which revealed the pivotal interactions between the inhibitors and the catalytic sites of BXO, with a particular emphasis on the imine group (-C = N-) functionalities. Intriguingly, the compounds exhibiting the highest inhibition rates also showcase advantageous ADMET profiles, alongside encouraging initial assessments via PASS, hinting at their broad-spectrum potential. The implications of these findings are profound, suggesting that these Schiff base derivatives not only offer a new vantage point for the inhibition of BXO but also hold considerable promise as innovative therapeutic agents in the management and treatment of gout, marking a significant leap forward in the quest for more effective gout interventions.

Effect of Poria cocos Terpenes: Verifying Modes of Action Using Molecular Docking, Drug-Induced Transcriptomes, and Diffusion Network Analyses

We characterized the therapeutic biological modes of action of several terpenes in Poria cocos F.A Wolf (PC) and proposed a broad therapeutic mode of action for PC. Molecular docking and drug-induced transcriptome analysis were performed to confirm the pharmacological mechanism of PC terpene, and a new analysis method, namely diffusion network analysis, was proposed to verify the mechanism of action against Alzheimer's disease. We confirmed that the compound that exists only in PC has a unique mechanism through statistical-based docking analysis. Also, docking and transcriptomic analysis results could reflect results in clinical practice when used complementarily. The detailed pharmacological mechanism of PC was confirmed by constructing and analyzing the Alzheimer's disease diffusion network, and the antioxidant activity based on microglial cells was verified. In this study, we used two bioinformatics approaches to reveal PC's broad mode of action while also using diffusion networks to identify its detailed pharmacological mechanisms of action. The results of this study provide evidence that future pharmacological mechanism analysis should simultaneously consider complementary docking and transcriptomics and suggest diffusion network analysis, a new method to derive pharmacological mechanisms based on natural complex compounds.

Comprehensive review of the repositioning of non-oncologic drugs for cancer immunotherapy

Drug repositioning or repurposing has gained worldwide attention as a plausible way to search for novel molecules for the treatment of particular diseases or disorders. Drug repurposing essentially refers to uncovering approved or failed compounds for use in various diseases. Cancer is a deadly disease and leading cause of mortality. The search for approved non-oncologic drugs for cancer treatment involved in silico modeling, databases, and literature searches. In this review, we provide a concise account of the existing non-oncologic drug molecules and their therapeutic potential in chemotherapy. The mechanisms and modes of action of the repurposed drugs using computational techniques are also highlighted. Furthermore, we discuss potential targets, critical pathways, and highlight in detail the different challenges pertaining to drug repositioning for cancer immunotherapy.

Time-dependent nonmonotonic concentration-response and synergism of alkyl glycosides with different alkyl side chain to Vibrio qinghaiensis sp. -Q67

Alkyl glycosides (AGs), commonly used nonionic surfactants, may have toxic effects on the environmental organisms. However, the complex concentration-response patterns of AGs with varying alkyl side chains and their mixtures have not been thoroughly studied. Therefore, the luminescence inhibition toxicities of six AGs with different alkyl side chains, namely, ethyl (AG02), butyl (AG04), hexyl (AG06), octyl (AG08), decyl (AG10), and dodecyl (AG12) glucosides, were determined in Vibrio qinghaiensis sp. -Q67 (Q67) at 0.25, 3, 6, 9, and 12 h. The six AGs exhibited time- and side-chain-dependent nonmonotonic concentration- responses toward Q67. AG02, with a short side chain, presented a concentration-response curve (CRC) with two peaks after 6 h and stimulated the luminescence of Q67 at both 6 and 9 h. AG04, AG06, and AG08 showed S-shaped CRCs at five exposure time points, and their toxicities increased with the side-chain length. AG10 and AG12, with long side chains, exhibited hormesis at 9 and 12 h. Molecular docking was performed to explore the mechanism governing the possible influence of AGs on the luminescence response. The effects of AGs on Q67 could be attributed to multiple luminescence-regulatory proteins, including LuxA, LuxC, LuxD, LuxG, LuxI, and LuxR. Notably, LuxR was identified as the primary binding protein among the six AGs. Given that they may co-exist, binary mixtures of AG10 and AG12 were designed to explore their concentration-response patterns and interactions. The results revealed that all AG10-AG12 binary mixture rays showed time-dependent hormesis on Q67, similar to that shown by their individual components. The interactions of these binary mixtures were mainly characterized by low-concentration additive action and high-concentration synergism at different times.

Computational approach for identifying immunogenic epitopes and optimizing peptide vaccine through in-silico cloning against Mycoplasma genitalium

Mycoplasma genitalium is a pathogenic microorganism linked to a variety of severe health conditions including ovarian cancer, prostate cancer, HIV transmission, and sexually transmitted diseases. A more effective approach to address the challenges posed by this pathogen, given its high antibiotic resistance rates, could be the development of a peptide vaccine. In this study, we used experimentally validated 13 membrane proteins and their immunogenicity to identify suitable vaccine candidates. Thus, based on immunogenic properties and high conservation among other Mycoplasma genitalium sub-strains, the P110 surface protein is considered for further investigation. Later on, we identified T-cell epitopes and B-cell epitopes from the P110 protein to construct a multiepitope-based vaccine. As a result, the 'NIAPISFSFTPFTAA' T-cell epitope and 'KVKYESSGSNNISFDS' B-cell epitope have shown 99.53% and 87.50% population coverage along with 100% conservancy among the subspecies, and both epitopes were found to be non-allergenic. Furthermore, focusing on molecular docking analysis showed the lowest binding energy for MHC-I (-137.5 kcal/mol) and MHC-II (-183.3 kcal/mol), leading to a satisfactory binding strength between the T-cell epitopes and the MHC molecules. However, the constructed multiepitope vaccine (MEV) consisting of 54 amino acids demonstrates favorable characteristics for a vaccine candidate, including a theoretical pI of 4.25 with a scaled solubility of 0.812 and high antigenicity probabilities. Additionally, structural analyses reveal that the MEV displays substantial alpha helices and extended strands, vital for its immunogenicity. Molecular docking with the human Toll-like receptors TLR1/2 heterodimer shows strong binding affinity, reinforcing its potential to elicit an immune response. Our immune simulation analysis demonstrates immune memory development and robust immunity, while codon adaptation suggests optimal expression in E. coli using the pET-28a(+) vector. These findings collectively highlight the MEV's potential as a valuable vaccine candidate against M. genitalium.

Pachymic acid activates TP53INP2/TRAF6/caspase-8 pathway to promote apoptosis in renal cell carcinoma cells

While pachymic acid (PA), a key component of Poria cocos (Schw.), has demonstrated anti-tumor effects in lung, breast, and pancreatic cancers, its impact on renal cell carcinoma (RCC) is unclear. This study evaluated the effect of PA on proliferation, migration, and apoptosis in human renal cancer A498 and ACHN cells as well as in cancer xenograft mice using wound scratch test, Western blotting, and co-immunoprecipitation assays. In a dose- and time-dependent manner, PA exhibited significant inhibition of RCC cell proliferation, migration, and invasion, accompanied by the induction of apoptosis. Additionally, PA upregulated the expression of tumor protein p53-inducible nuclear protein 2 (TP53INP2) and tumor necrosis factor receptor-associated factor 6 (TRAF6), which were downregulated in renal papillary and chromophobe carcinoma, resulting in inhibited tumor growth in mice. PA treatment elevated cleaved-caspase 3 and 8, and PARP levels, and facilitated TP53INP2 and TRAF6 binding to caspase 8, promoting its ubiquitination. Molecular docking revealed interactions between PA and TP53INP2, TRAF6. In summary, PA inhibits RCC development by upregulating TP53INP2 and promoting TRAF6-induced caspase 8 ubiquitination, activating apoptotic pathways.

Synthesis, molecular docking analysis, drug-likeness evaluation, and inhibition potency of new pyrazole-3,4-dicarboxamides incorporating sulfonamide moiety as carbonic anhydrase inhibitors

A series of novel pyrazole-dicarboxamides were synthesized from pyrazole-3,4-dicarboxylic acid chloride and various primary and secondary sulfonamides. The structures of the new compounds were confirmed by FT-IR, 1H-NMR, 13C-NMR, and HRMS. Then the inhibition effects of newly synthesized molecules on human erythrocyte hCA I and hCA II isoenzymes were investigated. Ki values of the compounds were in the range of 0.024-0.496 µM for hCA I and 0.006-5.441 µM for hCA II. Compounds 7a and 7i showed nanomolar level of inhibition of hCA II, and these compounds exhibited high selectivity for this isoenzyme. Molecular docking studies were performed between the most active compounds 7a, 7b, 7i, and the reference inhibitor AAZ and the hCAI and hCAII to investigate the binding mechanisms between the compounds and the isozymes. These compounds showed better interactions than the AAZ. ADMET and drug-likeness analyses for the compounds have shown that the compounds can be used pharmacologically in living organisms.

Cross-linked enzyme aggregates of polyethylene terephthalate hydrolyse (PETase) from Ideonella sakaiensis for the improvement of plastic degradation

Polyethylene terephthalate (PET) is one of the most produced plastics globally and its accumulation in the environment causes harm to the ecosystem. Polyethylene terephthalate hydrolyse (PETase) is an enzyme that can degrade PET into its monomers. However, free PETase lacks operational stabilities and is not reusable. In this study, development of cross-linked enzyme aggregate (CLEA) of PETase using amylopectin (Amy) as cross-linker was introduced to solve the limitations of free PETase. PETase-Amy-CLEA exhibited activity recovery of 81.9 % at its best immobilization condition. Furthermore, PETase-Amy-CLEA exhibited 1.37-, 2.75-, 2.28- and 1.36-fold higher half-lives than free PETase at 50 °C, 45 °C, 40 °C and 35 °C respectively. Moreover, PETase-Amy-CLEA showed broader pH stability from pH 5 to 10 and could be reused up to 5 cycles. PETase-Amy-CLEA retained >70 % of initial activity after 40 days of storage at 4 °C. In addition, lower Km of PETase-Amy-CLEA indicated better substrate affinity than free enzyme. PETase-Amy-CLEA corroded PET better and products yielded was 66.7 % higher than free PETase after 32 h of treatment. Hence, the enhanced operational stabilities, storage stability, reusability and plastic degradation ability are believed to make PETase-Amy-CLEA a promising biocatalyst in plastic degradation.

Characterization of Plant-Derived Natural Inhibitors of Dipeptidyl Peptidase-4 as Potential Antidiabetic Agents: A Computational Study

Diabetes, characterized by elevated blood sugar levels, poses significant health and economic risks, correlating with complications like cardiovascular disease, kidney failure, and blindness. Dipeptidyl peptidase-4 (DPP-4), also referred to as T-cell activation antigen CD26 (EC 3.4.14.5.), plays a crucial role in glucose metabolism and immune function. Inhibiting DPP-4 was anticipated as a potential new therapy for diabetes. Therefore, identification of plant-based natural inhibitors of DPP-4 would help in eradicating diabetes worldwide. Here, for the identification of the potential natural inhibitors of DPP-4, we developed a phytochemicals library consisting of over 6000 phytochemicals detected in 81 medicinal plants that exhibited anti-diabetic potency. The library has been docked against the target proteins, where isorhamnetin, Benzyl 5-Amino-5-deoxy-2,3-O-isopropyl-alpha-D-mannofuranoside (DTXSID90724586), and 5-Oxo-7-[4-(trifluoromethyl) phenyl]-4H,6H,7H-[1,2]thiazolo[4,5-b]pyridine 3-carboxylic acid (CHEMBL3446108) showed binding affinities of -8.5, -8.3, and -8.3 kcal/mol, respectively. These compounds exhibiting strong interactions with DPP-4 active sites (Glu205, Glu206, Tyr547, Trp629, Ser630, Tyr662, His740) were identified. ADME/T and bioactivity predictions affirmed their pharmacological safety. Density functional theory calculations assessed stability and reactivity, while molecular dynamics simulations demonstrated persistent stability. Analyzing parameters like RMSD, RG, RMSF, SASA, H-bonds, MM-PBSA, and FEL confirmed stable protein-ligand compound formation. Principal component analysis provided structural variation insights. Our findings suggest that those compounds might be possible candidates for developing novel inhibitors targeting DPP-4 for treating diabetes.

Discovery of potential quality markers of Fritillariae thunbergii bulbus in pneumonia by combining UPLC-QTOF-MS, network pharmacology, and molecular docking

Fritillariae thunbergii bulbus (FTB) is a popular Chinese herbal medicine with various applications in respiratory diseases. The quality evaluation of FTB has been insufficient to date, as the active ingredients and mechanisms of action of FTB remain unclear. This study proposes a novel strategy for exploring the quality markers (Q-markers) of FTB based on UPLC-QTOF-MS analysis, network pharmacology, molecular docking, and molecular dynamics (MD) simulation. A total of 26 compounds in FTB were identified by UPLC-QTOF-MS. Ten of these compounds were screened as Q-markers based on network pharmacology for their anti-pneumonia effects, including imperialine, peimisine, peiminine, ebeiedinone, zhebeirine, puqiedine, 9-hydroxy-10,12-octadecadienoic acid, (9Z,12Z,15Z)-13-hydroxy-9,12,15-octadecatrienoic acid, 9,12,15-octadecatrienoic acid, and (2E,4Z,7Z,10Z,13Z,16Z,19Z)-2,4,7,10,13,16,19-docosaheptaenoic acid methyl ester (DAME). These Q-markers were predicted to act on multiple targets and pathways associated with pneumonia. Molecular docking results revealed that most of the Q-markers showed high affinity with at least one of the main targets of pneumonia, and the top ten complexes were confirmed with MD simulation. Network pharmacology indicated that FTB may act on the TNF signaling pathway, HIF-1 signaling pathway, JAK-STAT signaling pathway, etc. The results demonstrated that imperialine (P8), peimisine (P9), peiminine (P11), ebeiedine (P15), zhebeirine (P16), and puqiedine (P18) may be potential Q-markers of FTB, and AKT1, IL-6, VEGFA, TP53, EGFR, STAT3, PPARG, MMP9, and CASP3 may be promising therapeutic targets for pneumonia treatment that are worthy of further research.

GEFormerDTA: drug target affinity prediction based on transformer graph for early fusion

Predicting the interaction affinity between drugs and target proteins is crucial for rapid and accurate drug discovery and repositioning. Therefore, more accurate prediction of DTA has become a key area of research in the field of drug discovery and drug repositioning. However, traditional experimental methods have disadvantages such as long operation cycles, high manpower requirements, and high economic costs, making it difficult to predict specific interactions between drugs and target proteins quickly and accurately. Some methods mainly use the SMILES sequence of drugs and the primary structure of proteins as inputs, ignoring the graph information such as bond encoding, degree centrality encoding, spatial encoding of drug molecule graphs, and the structural information of proteins such as secondary structure and accessible surface area. Moreover, previous methods were based on protein sequences to learn feature representations, neglecting the completeness of information. To address the completeness of drug and protein structure information, we propose a Transformer graph-based early fusion research approach for drug-target affinity prediction (GEFormerDTA). Our method reduces prediction errors caused by insufficient feature learning. Experimental results on Davis and KIBA datasets showed a better prediction of drugtarget affinity than existing affinity prediction methods.

Assessing the performance of MM/PBSA and MM/GBSA methods. 10. Prediction reliability of binding affinities and binding poses for RNA-ligand complexes

Ribonucleic acid (RNA)-ligand interactions play a pivotal role in a wide spectrum of biological processes, ranging from protein biosynthesis to cellular reproduction. This recognition has prompted the broader acceptance of RNA as a viable candidate for drug targets. Delving into the atomic-scale understanding of RNA-ligand interactions holds paramount importance in unraveling intricate molecular mechanisms and further contributing to RNA-based drug discovery. Computational approaches, particularly molecular docking, offer an efficient way of predicting the interactions between RNA and small molecules. However, the accuracy and reliability of these predictions heavily depend on the performance of scoring functions (SFs). In contrast to the majority of SFs used in RNA-ligand docking, the end-point binding free energy calculation methods, such as molecular mechanics/generalized Born surface area (MM/GBSA) and molecular mechanics/Poisson Boltzmann surface area (MM/PBSA), stand as theoretically more rigorous approaches. Yet, the evaluation of their effectiveness in predicting both binding affinities and binding poses within RNA-ligand systems remains unexplored. This study first reported the performance of MM/PBSA and MM/GBSA with diverse solvation models, interior dielectric constants (εin) and force fields in the context of binding affinity prediction for 29 RNA-ligand complexes. MM/GBSA is based on short (5 ns) molecular dynamics (MD) simulations in an explicit solvent with the YIL force field; the GBGBn2 model with higher interior dielectric constant (εin = 12, 16 or 20) yields the best correlation (Rp = -0.513), which outperforms the best correlation (Rp = -0.317, rDock) offered by various docking programs. Then, the efficacy of MM/GBSA in identifying the near-native binding poses from the decoys was assessed based on 56 RNA-ligand complexes. However, it is evident that MM/GBSA has limitations in accurately predicting binding poses for RNA-ligand systems, particularly compared with notably proficient docking programs like rDock and PLANTS. The best top-1 success rate achieved by MM/GBSA rescoring is 39.3%, which falls below the best results given by docking programs (50%, PLNATS). This study represents the first evaluation of MM/PBSA and MM/GBSA for RNA-ligand systems and is expected to provide valuable insights into their successful application to RNA targets.

A new paradigm for applying deep learning to protein-ligand interaction prediction

Protein-ligand interaction prediction presents a significant challenge in drug design. Numerous machine learning and deep learning (DL) models have been developed to accurately identify docking poses of ligands and active compounds against specific targets. However, current models often suffer from inadequate accuracy or lack practical physical significance in their scoring systems. In this research paper, we introduce IGModel, a novel approach that utilizes the geometric information of protein-ligand complexes as input for predicting the root mean square deviation of docking poses and the binding strength (pKd, the negative value of the logarithm of binding affinity) within the same prediction framework. This ensures that the output scores carry intuitive meaning. We extensively evaluate the performance of IGModel on various docking power test sets, including the CASF-2016 benchmark, PDBbind-CrossDocked-Core and DISCO set, consistently achieving state-of-the-art accuracies. Furthermore, we assess IGModel's generalizability and robustness by evaluating it on unbiased test sets and sets containing target structures generated by AlphaFold2. The exceptional performance of IGModel on these sets demonstrates its efficacy. Additionally, we visualize the latent space of protein-ligand interactions encoded by IGModel and conduct interpretability analysis, providing valuable insights. This study presents a novel framework for DL-based prediction of protein-ligand interactions, contributing to the advancement of this field. The IGModel is available at GitHub repository https://github.com/zchwang/IGModel.

Potential bioactive compound and hub gene identification of endometrial carcinoma using systems biology

Endometrial carcinoma is a frequent cancer of the female genital tract. Endometrial carcinoma accounts for 97% of all uterine malignancies and 3 % of sarcomas that develop from the endometrium's glands. Endometrial cancer is frequently found in its early stages since most women quickly report postmenopausal vaginal hemorrhage. The need for more advanced medications to improve survival in such situations is still unfulfilled. As a result, there is growing interest in employing an herbal treatment to treat endometriosis, which seems to be an effective strategy. We have discovered a few unintended targets (ligands) in our investigation that are active components of common therapeutic herbs. The differentially expressed genes (DEG - target protein) for endometrial cancer were found using the NCBI and CIViC databases. In our investigation, the protein used for docking and simulation was PDB ID: 3THW. Using the Cytoscape server, the gene-encoding protein network has been identified. It was discovered that the Protein 3THW's binding energy to the bioactive substance (Asarone) was -7.15 Kcal/mol. It was discovered that the crucial interacting amino acid residues were ILE648, PHE650, ILE651, VAL802, TYR815, VAL817. The properties of the pharmaceutical target are further investigated by employing a molecular simulation study for 100 ns with NAMD software. Low RMSD and SASA (Solvent accessible surface area), high RMSF, High hydrogen bonds, between Asarone and MSH2 demonstrated their potency as endometrial cancer inhibitor compounds. Based on these analyses we infer that the bioactive substances originating from medicinal plants may be an effective treatment for endometrial cancer.Communicated by Ramaswamy H. Sarma.

Anticancer, antioxidant, and antimicrobial studies and molecular docking of a new hexanuclear Zn(II) complex, together with its X-ray crystal analysis

A new hexanuclear Zn(II) complex with the ligand 2,2'-(piperazine-1,4-diyl)bis(ethan-1-amine), [L3Zn6(OH)6][ClO4]6·3MeOH·7H2O, was synthesized. The crystal structure of this complex showed that each Zn atom is in a distorted tetrahedral coordination environment, surrounded by two nitrogen atoms from each ligand and two hydroxide groups, each of which bridges to another Zn atom. The anticancer activities of the ligand and its metal complex against the breast cancer cell line (MCF-7) indicated that the zinc complex had a greater anticancer activity. The free ligand and its metal complex were evaluated for antioxidant activity using the DPPH scavenging method. In addition, the antibacterial activities of both compounds were screened against Gram-positive (Staphylococcus aureus) and Gram-negative (Escherichia coli) bacteria. The interaction of these compounds with DNA and AChE was also investigated using molecular docking.

Antimicrobial potential of phytocompounds of Acorus calamus: in silico approach

Medicinal plants are used from prehistoric time to cure various life-threatening bacterial diseases. Acorus calamus is an important medicinal plant widely used to cure gastrointestinal, respiratory, kidney and liver disorders. The objective of the current research was to investigate the interaction of major phytoconstituents of Acorus calamus with bacterial (6VJE) and fungal (1EA1) protein targets. Protein-ligand interactions were estimated using the AutoDock software, drug likeness was predicted by using the molinspiration server and toxicity was predicted with the swissADME and protox II servers. MD simulation of phytocompounds with the best profiles was done on the GROMACS software for 100 ns. Molecular docking results showed among all the selected major phytoconstituents, that β-cadinene showed best binding interaction in complex with bacterial (6VJE) and fungal (1EA1) protein targets with binding energy -7.66 ± 0.1 and -7.73 ± 0.15 kcal mol-1, respectively. Drug likeness and toxicity predictions showed that β-cadinene follows all rules of drug likeness and toxicity. MD simulation study revealed that β-cadinene fit in binding pocket of bacterial and fungal targets and found to be stable throughout the duration of the simulation. Based on the observations from this in-silico study it is being proposed that β-cadinene, a major phytocompound of Acorus calamus, can be considered for the treatment of bacterial and fungal infections since the study shows that it might be one of the compounds that contributes majorly to the plant's biological activity. This study needs in vitro and in vivo validation.Communicated by Ramaswamy H. Sarma.

pH-dependent and whole-cell catalytic decolorization of dyes using recombinant dye-decolorizing peroxidase from Rhodococcus jostii

Dyes in wastewater have adverse effects on the environment and human health. Dye-decolorizing peroxidase (DyP) is a promising biocatalyst to dyes degradation, but the decolorization rates varied greatly which influencing factors and mechanisms remain to be fully disclosed. To explore an effective decolorizing approach, we have studied a DyP from Rhodococcus jostii (RhDyPB) which was overexpressed in Escherichia coli to decolorize four kinds of dyes, Reactive blue 19, Eosin Y, Indigo carmine, and Malachite green. We found the decolorization rates of the dyes by purified RhDyPB were all pH-dependent and the highest one was 94.4% of Malachite green at pH 6.0. ESI-MS analysis of intermediates in the decolorization process of Reactive blue 19 proved the degradation was due to peroxidase catalysis. Molecular docking predicated the interaction of RhDyPB with dyes, and a radical transfer reaction. In addition, we performed decolorization of dyes with whole E. coli cell with and without expressing RhDyPB. It was found that decolorization of dyes by E. coli cell was due to both cell absorption and degradation, and RhDyPB expression improved the degradation rates towards Reactive blue 19, Indigo carmine and Malachite green. The effective decolorization of Malachite green and the successful application of whole DyP-overexpressed cells in dye decolorization is conducive to the bioremediation of dye-containing wastewaters by DyPs.

Dissecting the Interaction Fingerprints and Binding Affinity of BYL719 Analogs Targeting PI3Kα

Phosphatidylinositol-3-kinase Alpha (PI3Kα) is a lipid kinase which regulates signaling pathways involved in cell proliferation. Dysregulation of these pathways promotes several human cancers, pushing for the development of anticancer drugs to target PI3Kα. One such medicinal chemistry campaign at Novartis led to the discovery of BYL719 (Piqray, Alpelicib), a PI3Kα inhibitor approved by the FDA in 2019 for treatment of HR+/HER2-advanced breast cancer with a PIK3CA mutation. Structure-based drug design played a key role in compound design and optimization throughout the discovery process. However, further characterization of potency drivers via structural dynamics and energetic analyses can be advantageous for ensuing PI3Kα programs. Here, our goal is to employ various in-silico techniques, including molecular simulations and machine learning, to characterize 14 ligands from the BYL719 analogs and predict their binding affinities. The structural insights from molecular simulations suggest that although the ligand-hinge interaction is the primary driver of ligand stability at the pocket, the R group positioning at C2 or C6 of pyridine/pyrimidine also plays a major role. Binding affinities predicted via thermodynamic integration (TI) are in good agreement with previously reported IC50s. Yet, computationally demanding techniques such as TI might not always be the most efficient approach for affinity prediction, as in our case study, fast high-throughput techniques were capable of classifying compounds as active or inactive, and one docking approach showed accuracy comparable to TI.

Advancing 3D printed microfluidics with computational methods for sweat analysis

The intricate tapestry of biomarkers, including proteins, lipids, carbohydrates, vesicles, and nucleic acids within sweat, exhibits a profound correlation with the ones in the bloodstream. The facile extraction of samples from sweat glands has recently positioned sweat sampling at the forefront of non-invasive health monitoring and diagnostics. While extant platforms for sweat analysis exist, the imperative for portability, cost-effectiveness, ease of manufacture, and expeditious turnaround underscores the necessity for parameters that transcend conventional considerations. In this regard, 3D printed microfluidic devices emerge as promising systems, offering a harmonious fusion of attributes such as multifunctional integration, flexibility, biocompatibility, a controlled closed environment, and a minimal requisite analyte volume-features that leverage their prominence in the realm of sweat analysis. However, formidable challenges, including high throughput demands, chemical interactions intrinsic to the printing materials, size constraints, and durability concerns, beset the landscape of 3D printed microfluidic devices. Within this paradigm, we expound upon the foundational aspects of 3D printed microfluidic devices and proffer a distinctive perspective by delving into the computational study of printing materials utilizing density functional theory (DFT) and molecular dynamics (MD) methodologies. This multifaceted approach serves manifold purposes: (i) understanding the complexity of microfluidic systems, (ii) facilitating comprehensive analyses, (iii) saving both cost and time, (iv) improving design optimization, and (v) augmenting resolution. In a nutshell, the allure of 3D printing lies in its capacity for affordable and expeditious production, offering seamless integration of diverse components into microfluidic devices-a testament to their inherent utility in the domain of sweat analysis. The synergistic fusion of computational assessment methodologies with materials science not only optimizes analysis and production processes, but also expedites their widespread accessibility, ensuring continuous biomarker monitoring from sweat for end-users.