Subsequent Docking Research Articles

Developing a multi-epitope vaccine against MR766 strain of Zika virus: A bioinformatics approach

The Zika Virus has emerged as a significant global health concern, particularly due to its association with severe neurological complications such as microcephaly and Guillain-Barré syndrome. Given the urgent need for effective intervention strategies, this study aims to develop a targeted vaccine leveraging bioinformatics methodologies to identify B- and T-cell epitopes within the Zika virus Strain MR766 structural proteins. Through comprehensive epitope prediction algorithms, novel epitopes were identified and selected based on criteria including toxicity, immunogenicity, and antigenicity. These epitopes were then integrated into a multiple epitope vaccine constructs, incorporating various linker sequences (EAAAK, AAY, GPGPG) to optimize immunogenicity. Subsequent molecular docking simulations facilitated the design of a vaccine structure capable of effectively interacting with its target receptors. The vaccine construct was cloned into a pET-28a (+) vector for expression in Escherichia coli using SnapGene software. Evaluation of the expressed vaccine demonstrated a robust immune response in preclinical studies. Computational tools were employed to analyze the vaccine's efficacy against diverse Zika virus strains. The resulting Multi-Epitope Vaccine (MEV) emerges as a promising candidate for combating Zika virus infections, offering potential benefits in global public health efforts against this pathogen.

Cavity-Based Discovery of New Fatty Acid Photodecarboxylases.

Light-dependent fatty acid photodecarboxylases (FAPs) hold significant potential for biotechnology, due to their capability to produce alka(e)nes directly from the corresponding (un)saturated natural fatty acids requiring light as the only reagent. This study expands the family of FAPs through cavity-based enzyme discovery methods. Thirty enzyme candidates with potential photodecarboxylation activity were identified by matching the cavities of four related template structures against the Protein Data Bank's flavoproteins, a library of proteins identified via the Foldseek Search Server, and homology models of sequences resulting from BLAST. Subsequent docking experiments narrowed this library to ten promising enzymes, which were expressed and assessed in vitro, identifying four photodecarboxylases. Out of these enzymes, the GMC oxidoreductase from Coccomyxa sp. Obi (CoFAP) was characterized in detail, which revealed high activity in the decarboxylation reactions of palmitic acid and octanoic acid and a broad pH tolerance (pH 6.5-9.5).

Paeoniflorin-mediated downregulation of VEGFA: unveiling the therapeutic mechanism of buyang huanwu decoction in diabetic retinopathy.

Diabetic retinopathy (DR) is a leading cause of blindness globally. Buyang Huanwu decoction (BHD) is a traditional Chinese medicine for treating DR, but its therapeutic mechanisms are not fully understood. This study aimed to elucidate and validate the underlying mechanisms of BHD in DR treatment through network pharmacology and in vitro experiments. We identified active compounds in BHD and their associated targets using the TCMSP and SwissTargetPrediction. DR-related targets were sourced from GeneCards, NCBI, and OMIM databases. The protein-protein interaction (PPI) network and enrichment analyses were employed to predict common targets and pathways. Subsequent molecular docking and in vitro experiments, including cell viability assays, RT-qPCR, flow cytometry, and Western blot, were conducted to validate the anti-DR mechanism of BHD. Network pharmacology identified paeoniflorin as a key active compound in BHD for treating DR, with VEGFA emerging as a central target. Molecular docking suggested a strong binding affinity between paeoniflorin and VEGFA. In vitro experiments confirmed that paeoniflorin attenuated high glucose-induced increases in cell viability, migration, apoptosis, and inflammatory cytokine expression in retinal pigment epithelial cells. The therapeutic effect of paeoniflorin was primarily mediated through the downregulation of VEGFA expression. Our study demonstrates that paeoniflorin, a key active compound in BHD, effectively mitigates DR by downregulating VEGFA expression and reducing high glucose-induced cellular alterations, thereby highlighting its potential as a therapeutic agent for DR.

Quinoline- and Pyrimidine-based Allosteric Modulators of the Sarco/Endoplasmic Reticulum Calcium ATPase.

Small-molecule allosteric activators of the enzyme sarco/endoplasmic reticulum calcium ATPase (SERCA) hold promise as novel experimental tools to manipulate intracellular calcium concentrations and as therapeutic agents to treat medical conditions associated with elevated cytosolic calcium levels. Here, we synthesized and characterized 20 analogs of the known allosteric SERCA activator CDN1163 and tested their ability to stimulate SERCA activity. The structures of the compounds varied in the alkyl group of the parent scaffold's ether moiety as well as in the composition of the nitrogenous aromatic ring system. The most active compounds exhibited potencies in the sub-micromolar range while increasing enzyme activity by more than 25 %. The observed structure-activity relationships indicated that bulky alkyl groups in the ether moiety along with a quinoline ring methyl substituent were beneficial for activity. Replacement of the quinoline by a pyrimidine ring system reduced activity. To conceive a potential mechanism of action, we generated a molecular model of the transition state of SERCA when undergoing the rate-limiting step of its catalytic cycle. Subsequent blind docking with CDN1163 identified a high-affinity binding site close to the enzyme's ATP binding pocket, suggesting that the activators may accelerate SERCA's catalytic cycle by aiding in ATP binding and positioning.

Phytochemicals Neogitogenin and Samogenin Hold Potentials for Hepatocyte Growth Factor Receptor-Targeted Cancer Treatment.

Protein kinases are key targets for cancer therapies, with the c-Met receptor tyrosine kinase (MET) and its ligand, hepatocyte growth factor, playing a role in various cancers, including non-small cell lung cancer, gastric cancer, and hepatocellular carcinoma. Although small-molecule inhibitors have been designed to target MET, the development of drug resistance remains a significant challenge to advancing therapeutic strategies. In this study, we employed virtual screening of plant-based compounds sourced from the IMPPAT 2.0 databank to identify potent inhibitors of MET. Preliminary filtering based on the physicochemical parameters following Lipinski's rule of five and pan-assay interference compounds criteria were applied to prioritize hits. Subsequent molecular docking, pharmacokinetic evaluation, prediction of activity spectra for biologically active substances, and specificity assessments facilitated the identification of two promising phytochemicals, neogitogenin and samogenin. Both phytochemicals exhibited considerable drug-like properties with notable binding affinity and selectivity toward MET. Molecular dynamics simulation studies showed the conformational stability of MET with neogitogenin and samogenin. Taken together, these findings suggest that neogitogenin and samogenin hold potential as lead molecules for the development of MET-targeted therapeutics. We call for further evaluations of these phytochemicals in preclinical and experimental studies for anticancer drug discovery and development.

Graph Neural Networks and Molecular Docking as Two Complementary Approaches for Virtual Screening: A Case Study on Cruzain

AbstractMolecular docking is one of the most widely used techniques for virtual screening (VS) of potential drug candidates. Despite its popularity, docking accuracy is often limited due to the trade‐off between speed and precision required for screening large compound libraries. In the present work, we leverage graph convolutional networks (GCNs), a state‐of‐the‐art deep neural network architecture, to enhance docking capacity for prioritizing active compounds from a library of ∼200,000 compounds screened against Cruzain. We propose strategies to integrate both techniques into a single VS pipeline. By applying the GCN as a pre‐docking filter, the compound library was enriched with active molecules, resulting in higher hit rates in subsequent docking screenings. Additionally, to further enhance the docking performance, the GCN‐learned atomic embeddings were directly incorporated into the docking process through pharmacophoric restraints. Unlike common approaches that use deep learning (DL) scoring functions to rank pre‐generated docking poses, the approaches we propose here have the advantage that only compounds that passed the DL filters need to be screened by the more computationally demanding docking method. This work might serve as a proof of concept for combining deep learning and classical docking in drug discovery.

In silico analysis shows slc1a4 as a potential target of hsa-mir-133a for regulating glutamine metabolism in gastric cancer

Cutting-edge research has spotlighted glutamine metabolism as a promising therapeutic target in managing gastric cancer. This investigation highlights the upregulated glutamine transporters by leveraging clinical data from the TCGA Database and the expression analysis of the transcriptome profile of stomach adenocarcinoma (STAD) patients. Notably, it identifies SLC1A4 as a potential glutamine transporter in STAD. The screening of human miRNAs conducted using the TargetScan database, and the subsequent docking analysis present multiple miRNAs with the potential of being explored as therapeutic agents. By integrating transcriptome profiling, miRNA screening, and molecular docking, this study reveals, for the first time, the potential of hsa-mir-133a-1 in targeting slc1a4, along with its known target mTOR, in stomach cancer. The myriad interactions that can be regulated by this silencing mechanism are anticipated to ultimately reduce glutamine uptake in STAD. This study provides compelling evidence of glutamine transport via SLC1A4 in stomach cancer and delves into how it might impact mTOR and some of its pivotal downstream molecules. Considering these findings, novel therapeutic strategies can be devised to further enhance existing methods for combating gastric cancer.

Deep eutectic solvent-based ultrasonic-assisted extraction of polyphenol from Chenopodium quinoa Willd.: Optimization and lipid-lowering activity

Hyperlipidemia poses a serious threat to human health, but its medication remains some issues including significant adverse reactions. Polyphenols exhibit great potential in lowering blood lipids and the lipid-lowering effects of quinoa polyphenols are still waiting to be explored. In this study, a deep eutectic solvent-based ultrasonic-assisted extraction method of quinoa polyphenols was developed. Then, the constituents of quinoa polyphenols after purification CQP were analyzed. Besides, their lipid-lowering activities and mechanism were explored. Results suggested that CQP comprised at least 12 kinds of polyphenols. CQP could inhibit lipase, adsorb cholesterol, inhibit oxidative stress and lipid peroxidation. Subsequent network pharmacology, cellular experiments and molecular docking revealed that CQP might influence the expression levels or bind to AKT1 and FOXO1, thereby affecting their content or activity, ultimately regulating their functions and leading to changes of the cellular lipid levels. This study lays foundation for developing novel lipid-lowering drugs and functional foods.

Clarifying therapeutic mechanisms of Guanxinshutong capsule for cardiac fibrosis treatment by network pharmacology, molecular docking, molecular dynamics simulation, and in vivo experiments

PurposeGuanxinshutong capsule (GXST) is a Chinese patent medicine used for the treatment of cardiac fibrosis (CF). Nevertheless, the precise mechanism of action of GXST remains unclear. In this study, we applied network pharmacology, molecular docking, molecular dynamics simulation, and in vivo experiments to assess the effects and mechanisms of GXST in the treatment of CF. MethodsThe TCMSP database provided the GXST targets and active substances. The GeneCards, OMIM, and TTD databases were searched for targets relevant to cardiac fibrosis. To filter core targets, a PPI network was set up. Metascape was utilized to conduct enrichment analysis for the KEGG and GO pathways. To evaluate the interaction between putative targets and active drugs, molecular docking was utilized. MDS was performed for the optimal complex of the core ligand protein identified through molecular docking. Induction of cardiac fibrosis in SD rats using isoproterenol, and the protective effects of GXST on the heart were evaluated using echocardiography, H&E staining, and Masson staining. ResultsThrough network pharmacology analysis, we have identified 83 active components and 73 key target proteins related to cardiac fibrosis from GXST. Subsequent molecular docking and molecular dynamics simulation results suggest that quercetin, luteolin, and kaempferol can inhibit cardiac fibrosis by regulating the expression of TNF-α, IL6, and IL1B proteins. These compounds and proteins may be key factors in the treatment of CF with GXST. Further, in vivo experiments demonstrate that GXST can alleviate myocardial injury and improve cardiac function in CF rats. ConclusionThis study successfully predicted the effective components, potential targets, and related pathways involved in treating CF by GXST. GXST can improve cardiac function and alleviate pathological damage in the hearts of SD rats. This provides a new strategy for future investigations into the mechanisms of GXST in the treatment of CF.

Design, Synthesis, Biological Evaluation, and In Silico Study of N‐(Pyridin‐2/3‐yl)alkanamide Derivatives as Quorum Sensing Inhibitors Against Pseudomonas aeruginosa

AbstractThe effectiveness of treating bacterial infections is seriously jeopardized by the emergence of bacterial resistance to chemical therapy. The development of biofilm in bacteria is one of the main causes of resistance to antimicrobial drugs. By developing new antibiofilm medications, quorum sensing (QS) inhibitor was developed as an alternate therapy. QS inhibition, which focuses on the QS signaling pathway, obstructs cell–cell communication. The aim of this work is to develop newer QS inhibitors against Pseudomonas aeruginosa. N‐(Pyridin‐2/3‐yl)alkanamide derivatives were designed as QS inhibitors, and these designed molecules were synthesized. QS inhibitor activity was evaluated for the synthesized compounds (3a–l, 4a–h). The highest QS inhibitor activities for compounds 3k, 4a, and 4c were 11.66 ± 0.11, 14.66 ± 0.15, and 10.66 ± 0.05 mm, respectively. Subsequent molecular docking analyses exhibited moderate to good binding affinity values between −7.1 and −9.3 kcal/mol. Using the in silico method, the physicochemical characteristics of these prepared compounds were examined. Additionally, molecular dynamic simulation was employed to gain a deeper comprehension of stability of the protein and ligand complexes of compounds 3k, 4a, and 4c. The overall findings of the study indicated that N‐(pyridin‐2/3‐yl)alkanamide derivatives might be significant for the development of newer QS inhibitors.

Effects and Mechanisms of Andrographolide for COVID-19: A Network Pharmacology-Based and Experimentally Validated Study

Objective: This study aimed to elucidate the therapeutic effects and underlying mechanisms of andrographolide against COVID-19 through a network pharmacology approach and experimental validation. Methods: Network pharmacology approaches were employed to elucidate potential targets of andrographolide, investigating their overlap with proteins implicated in SARS-CoV-2 infection. The identified targets underwent further analysis through protein-protein interaction (PPI) mapping, gene ontology (GO) categorization, and pathway enrichment within the framework of the Kyoto Encyclopedia of Genes and Genomes (KEGG). Subsequent molecular docking studies provided insights into the binding dynamics between andrographolide and the relevant protein targets. To validate these findings, a series of experimental procedures were conducted, including cytotoxicity assays, quantitative PCR analyses, and the deployment of a SARS-CoV-2 pseudovirus system, using BEAS-2B lung epithelial cells as a model. Results: Bioinformatics analysis revealed that andrographolide potentially modulates proinflammatory cytokines, JAK-STAT signaling, and antiviral immunity pathways in COVID-19. Molecular docking showed favorable binding interactions between andrographolide and its putative targets. In vitro studies confirmed that andrographolide suppressed the mRNA expression of IL-6, TNF-α, IL-1β, and STAT3 and inhibited pseudovirus entry and infection at pharmacologically relevant concentrations. Conclusion: The findings suggest that andrographolide could be a promising candidate for the treatment of COVID-19 due to its capacity to both inhibit SARS-CoV-2 and modulate harmful inflammation. Our findings highlight the potential therapeutic value of andrographolide in treating COVID-19, warranting further exploration in clinical trials.

Exploring the potential mechanism of Radix Bupleuri in the treatment of sepsis: a study based on network pharmacology and molecular docking

AimTo explore, using network pharmacology and RNA-seq technologies, potential active targets and mechanisms underpinning Radix Bupleuri’s effectiveness during sepsis treatment.MethodsFollowing the Sepsis-3.0 criteria, the research cohort, comprising 23 sepsis patients and 10 healthy participants, was obtained from public databases. Peripheral blood samples were collected and subjected to RNA-seq analysis. Active ingredients and potential targets of Radix Bupleuri were identified using the Bioinformatics Analysis Tool for Molecular mechANism of Traditional Chinese Medicine 2.0 (BATMAN-TCM 2.0) database and TCMSP database. Subsequently, protein-protein interaction (PPI) network construction, Gene Ontology (GO) analysis, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were conducted to explore cross-targets between disease and drugs. Survival analysis of key targets was performed using the GSE65682 dataset, and single-cell RNA-seq was employed for cellular localization analysis of key genes. Finally, molecular docking and Molecular dynamics simulation of the core target was conducted.ResultsDifferential expression analysis revealed 4253 genes associated with sepsis. Seventy-six active components and 1030 potential targets of Radix Bupleuri were identified. PPI, GO, and pathway enrichment analyses indicated involvement in the regulation of transmembrane transport, monatomic ion transport, and MAPK signaling. Survival curve analysis identified PIK3CD, ARRB2, SUCLG1, and SPI1 as key targets associated with lower mortality in the high expression group, while higher mortality was observed in the high PNP and FURIN expression groups. Single-cell RNA sequencing unveiled the cellular localization of PIK3CD, PNP, SPI1, and FURIN within macrophages, while ARRB2 and SUCLG1 exhibited localization in both macrophages and T-cells. Subsequent molecular docking and Molecular dynamics simulation indicated a potential binding interaction for Carvone-PIK3CD, Encecalin-ARRB2, Lauric Acid-SUCLG1, Pulegone-FURIN, Nootkatone-SPI1, and Saikogenin F-PNP.ConclusionRadix Bupleuri could modulate immune function by affecting PIK3CD, ARRB2, SUCLG1, FURIN, SPI1, and PNP, thereby potentially improving the prognosis of sepsis.

Pioneering bioinformatics with agent-based modelling: an innovative protocol to accurately forecast skin or respiratory allergic reactions to chemical sensitizers.

The assessment of theallergenic potential of chemicals, crucial for ensuring public health safety, faces challenges in accuracy and raises ethical concerns due to reliance on animal testing. This paper presents a novel bioinformatic protocol designed to address the critical challenge of predicting immune responses to chemical sensitizers without the use of animal testing. The core innovation lies in the integration of advanced bioinformatics tools, including the Universal Immune System Simulator (UISS), which models detailed immune system dynamics. By leveraging data from structural predictions and docking simulations, our approach provides a more accurate and ethical method for chemical safety evaluations, especially in distinguishing between skin and respiratory sensitizers. Our approach integrates a comprehensive eight-step process, beginning with the meticulous collection of chemical and protein data from databases like PubChem and the Protein Data Bank. Following data acquisition, structural predictions are performed using cutting-edge tools such as AlphaFold to model proteins whose structures have not been previously elucidated. This structural information is then utilized in subsequent docking simulations, leveraging both ligand-protein and protein-protein interactions to predict how chemical compounds may trigger immune responses. The core novelty of our method lies in the application of UISS-an advanced agent-based modelling system that simulates detailed immune system dynamics. By inputting the results from earlier stages, including docking scores and potential epitope identifications, UISS meticulously forecasts the type and severity of immune responses, distinguishing between Th1-mediated skin and Th2-mediated respiratory allergic reactions. This ability to predict distinct immune pathways is a crucial advance over current methods, which often cannot differentiate between the sensitization mechanisms. To validate the accuracy and robustness of our approach, we applied the protocol to well-known sensitizers: 2,4-dinitrochlorobenzene for skin allergies and trimellitic anhydride for respiratory allergies. The results clearly demonstrate the protocol's ability to differentiate between these distinct immune responses, underscoring its potential for replacing traditional animal-based testing methods. The results not only support the potential of our method to replace animal testing in chemical safety assessments but also highlight its role in enhancing the understanding of chemical-induced immune reactions. Through this innovative integration of computational biology and immunological modelling, our protocol offers a transformative approach to toxicological evaluations, increasing the reliability of safety assessments.

Empowering AlphaFold2 for protein conformation selective drug discovery with AlphaFold2-RAVE.

Small-molecule drug design hinges on obtaining co-crystallized ligand-protein structures. Despite AlphaFold2's strides in protein native structure prediction, its focus on apo structures overlooks ligands and associated holo structures. Moreover, designing selective drugs often benefits from the targeting of diverse metastable conformations. Therefore, direct application of AlphaFold2 models in virtual screening and drug discovery remains tentative. Here, we demonstrate an AlphaFold2-based framework combined with all-atom enhanced sampling molecular dynamics and Induced Fit docking, named AF2RAVE-Glide, to conduct computational model-based small-molecule binding of metastable protein kinase conformations, initiated from protein sequences. We demonstrate the AF2RAVE-Glide workflow on three different mammalian protein kinases and their type I and II inhibitors, with special emphasis on binding of known type II kinase inhibitors which target the metastable classical DFG-out state. These states are not easy to sample from AlphaFold2. Here, we demonstrate how with AF2RAVE these metastable conformations can be sampled for different kinases with high enough accuracy to enable subsequent docking of known type II kinase inhibitors with more than 50% success rates across docking calculations. We believe the protocol should be deployable for other kinases and more proteins generally.

Uncovering the antidiabetic potential of heart-friendly and diuretic bioactive compounds through computer-based drug design

Avicenna, a pioneer of modern medicine, recommended diuretic therapy to treat diabetes. Like Avicenna's approach, current medicine frequently prescribes oral antidiabetic pills with diuretic and hypoglycemic effects by blocking the absorption of sodium and glucose. To this end, the paper sought natural compounds with potential antidiabetic, cardioprotective, and diuretic properties through computer-based drug design (CADD) techniques, targeting the inhibition of SGLT2 proteins. We identified several bioactive compounds from various sources exhibiting potential multifunctionality through high-throughput virtual screening (HTVS) of vast compound libraries. Subsequent molecular docking and dynamics simulations were employed to assess these compounds' binding efficacy and stability with their respective targets, alongside ADMET prediction, to evaluate their pharmacokinetic and safety profiles. The top hits, phenylalanyltryptophan, tyrosyl-tryptophan, tyrosyl-tyrosine, celecoxib, and DIBOA trihexose, had superior docking scores ranging from −11,4 to −9,8 kcal/mol. The molecular dynamics simulations displayed steady interactions between target proteins and biocompounds throughout 100 ns without significant conformational shifts. These findings lay the groundwork for lead optimization and preclinical testing. This meticulous process ensures the safety and efficacy of potential treatments, marking a meaningful step toward developing innovative treatments for managing diabetes and its associated health complications.

Investigation of potential ascorbate peroxidase inhibitors for anti-leishmaniasis therapy.

L eishmaniasis is a prevalent disease that impacts 98 countries and territories, mainly in Africa, Asia, and South America. It can cause substantial illness and death, particularly in its visceral manifestation that can be specifically targeted in the development of medications to combat leishmaniasis. This study has found natural compounds with possible inhibitory activity against APX using a reliable and accurate QSAR model. Despite the severe side effects of current treatments and the absence of an effective vaccination, these compounds show promise as a potential treatment for the disease. Nine hit compounds were found, and subsequent molecular docking was performed. Estradiol cypionate showed the lowest binding energy (- 10.5kcal/mol), thus showing the strongest binding, and also had the strongest binding affinity, with a ΔGTotal of - 26.31 ± 3.01kcal/mol, second only to the control molecule. Additionally, three hits viz. cloxacillin-sodium (- 16.57 ± 2.89kcal/mol), cinchonidine (- 16.04 ± 3.27kcal/mol), and quinine hydrochloride dihydrate (13.38 ± 1.06kcal/mol) also showed significant binding affinity. Multiple interactions between drugs and active site residues demonstrated a substantial binding affinity with the target protein. The identified compounds exhibited drug-like effects and were orally bioavailable based on their ADME-toxicology features. Overall, estradiol cypionate, cloxacillin sodium, cinchonidine, and quinine hydrochloride dihydrate all exhibited inhibitory effects on the APX enzyme of Leishmania donovani. These results suggest that further investigation is needed to explore the potential of developing novel anti-leishmaniasis drugs using these compounds.

An efficient and simple approach for synthesizing indazole compounds using palladium-catalyzed Suzuki-Miyaura cross-coupling.

A series of indazole derivatives (6a-6i and 7a-7i) has been synthesized using Suzuki Miyaura cross-coupling with a palladium catalyst from readily available starting materials. An efficient and reliable methodology was employed for the synthesis, and the compounds were thoroughly characterized using 1H NMR, 13C NMR, FT-IR, and HRMS analysis to confirm their structural integrity and purity. Density function theory (DFT) computation identified four compounds (6g, 6h, 7g, and 7h) with significant energy band gaps. Additionally, the molecular electrostatic potential study highlighted the distinct electrical characteristics of these indazole molecules. Subsequent molecular docking investigations were carried out using the AUTODOCK method with two separate protein data bank (PDB) structures (6FEW, 4WA9) involved in renal cancer pathways. The results showed that eight substances PDB: 6FEW (6g, 6h, 7g, and 7h) and PDB: 4WA9 (6a, 6c, and 7c, 7g) had the highest binding energies, indicating their potential as therapeutic agents for treating kidney cancer.

E-pharmacophore and deep learning based high throughput virtual screening for identification of CDPK1 inhibitors of Cryptosporidium parvum

Cryptosporidiosis, a prevalent gastrointestinal illness worldwide, is caused by the protozoan parasite Cryptosporidium parvum. Calcium-dependent protein kinase 1 (CpCDPK1), crucial for the parasite's life cycle, serves as a promising drug target due to its role in regulating invasion and egress from host cells. While potent Pyrazolopyrimidine analogs have been identified as candidate hit molecules, they exhibit limitations in inhibiting Cryptosporidium growth in cell culture, prompting exploration of alternative scaffolds. Leveraging the most potent compound, RM-1–95, co-crystallized with CpCDPK1, an E-pharmacophore model was generated and validated alongside a deep learning model trained on known CpCDPK1 compounds. These models facilitated screening Enamine's 2 million HTS compound library for novel CpCDPK1 inhibitors. Subsequent hierarchical docking prioritized hits, with final selections subjected to Quantum polarized docking for accurate ranking. Results from docking studies and MD simulations highlighted similarities in interactions between the cocrystallized ligand RM-1–95 and identified hit molecules, indicating comparable inhibitory potential against CpCDPK1. Furthermore, assessing metabolic stability through Cytochrome 450 site of metabolism prediction offered crucial insights for drug design, optimization, and regulatory approval processes.

Gliotoxin triggers cell death through multifaceted targeting of cancer-inducing genes in breast cancer therapy

Fungal secondary metabolites have a long history of contributing to pharmaceuticals, notably in the development of antibiotics and immunosuppressants. Harnessing their potent bioactivities, these compounds are now being explored for cancer therapy, by targeting and disrupting the genes that induce cancer progression. The current study explores the anticancer potential of gliotoxin, a fungal secondary metabolite, which encompasses a multi-faceted approach integrating computational predictions, molecular dynamics simulations, and comprehensive experimental validations. In-silico studies have identified potential gliotoxin targets, including MAPK1, NFKB1, HIF1A, TDP1, TRIM24, and CTSD which are involved in critical pathways in cancer such as the NF-κB signaling pathway, MAPK/ERK signaling pathway, hypoxia signaling pathway, Wnt/β-catenin pathway, and other essential cellular processes. The gene expression analysis results indicated all the identified targets are overexpressed in various breast cancer subtypes. Subsequent molecular docking and dynamics simulations have revealed stable binding of gliotoxin with TDP1 and HIF1A. Cell viability assays exhibited a dose-dependent decreasing pattern with its remarkable IC50 values of 0.32, 0.14, and 0.53 μM for MDA-MB-231, MDA-MB-468, and MCF-7 cells, respectively. Likewise, in 3D tumor spheroids, gliotoxin exhibited a notable decrease in viability indicating its effectiveness against solid tumors. Furthermore, gene expression studies using Real-time PCR revealed a reduction of expression of cancer-inducing genes, MAPK1, HIF1A, TDP1, and TRIM24 upon gliotoxin treatment. These findings collectively underscore the promising anticancer potential of gliotoxin through multi-targeting cancer-promoting genes, positioning it as a promising therapeutic option for breast cancer.

Investigation of the Anti-Inflammatory Properties of Bioactive Compounds from Olea europaea: In Silico Evaluation of Cyclooxygenase Enzyme Inhibition and Pharmacokinetic Profiling.

In a landmark study, oleocanthal (OLC), a major phenolic in extra virgin olive oil (EVOO), was found to possess anti-inflammatory activity similar to ibuprofen, involving inhibition of cyclooxygenase (COX) enzymes. EVOO is a rich source of bioactive compounds including fatty acids and phenolics; however, the biological activities of only a small subset of compounds associated with Olea europaea have been explored. Here, the OliveNetTM library (consisting of over 600 compounds) was utilized to investigate olive-derived compounds as potential modulators of the arachidonic acid pathway. Our first aim was to perform enzymatic assays to evaluate the inhibitory activity of a selection of phenolic compounds and fatty acids against COX isoforms (COX-1 and COX-2) and 15-lipoxygenase (15-LOX). Olive compounds were found to inhibit COX isoforms, with minimal activity against 15-LOX. Subsequent molecular docking indicated that the olive compounds possess strong binding affinities for the active site of COX isoforms, and molecular dynamics (MD) simulations confirmed the stability of binding. Moreover, olive compounds were predicted to have favorable pharmacokinetic properties, including a readiness to cross biological membranes as highlighted by steered MD simulations and umbrella sampling. Importantly, olive compounds including OLC were identified as non-inhibitors of the human ether-à-go-go-related gene (hERG) channel based on patch clamp assays. Overall, this study extends our understanding of the bioactivity of Olea-europaea-derived compounds, many of which are now known to be, at least in part, accountable for the beneficial health effects of the Mediterranean diet.