Search Results (969)

Many bacteria, such as Pseudomonas aeruginosa, have encoded many toxins like RhsP2 that target non-coding RNAs (ncRNAs) in a similar mechanism to ART components; bacterial RNA loses its function of amino acid translation. A virtual screening approach was used to investigate RhsP2, which targets 16s rRNAs and then disrupts the translation of bacterial amino acids to proteins. Rifamycin is the bioreference as it forms a stable complex with the bacterial RNA in its active sites. Using different docking software can determine the best predicted conformations between RhsP2/16S and rRNA, and analyzing the docking score for both Affinity Binding and the root mean square deviation (RMSD) of particle coordinates helps choose the most appropriate drugs by using tools such as bioinformatics platforms and databases. Full article

The global impact of the COVID-19 crisis has underscored the need for novel therapeutic candidates capable of efficiently targeting essential viral proteins. Existing therapeutic strategies continue to encounter limitations such as reduced efficacy against emerging variants, safety concerns, and suboptimal pharmacodynamics, which emphasize the potential of natural-origin compounds as supportive agents with immunomodulatory, anti-inflammatory, and antioxidant benefits. The present study significantly advances prior molecular docking research through comprehensive virtual screening of structurally related analogs derived from antiviral phytochemicals. These compounds were evaluated specifically against the SARS-CoV-2 main protease (3CLpro) and papain-like protease (PLpro). Utilizing chemical similarity algorithms via the ChEMBL database, over 600 candidate molecules were retrieved and subjected to automated docking, interaction pattern analysis, and comprehensive ADMET profiling. Several analogs showed enhanced binding scores relative to their parent scaffolds, with CHEMBL1720210 (a shogaol-derived analog) demonstrating strong interaction with PLpro (−9.34 kcal/mol), and CHEMBL1495225 (a 6-gingerol derivative) showing high affinity for 3CLpro (−8.04 kcal/mol). Molecular interaction analysis revealed that CHEMBL1720210 forms hydrogen bonds with key PLpro residues including GLY163, LEU162, GLN269, TYR265, and TYR273, complemented by hydrophobic interactions with TYR268 and PRO248. CHEMBL1495225 establishes multiple hydrogen bonds with the 3CLpro residues ASP197, ARG131, TYR239, LEU272, and GLY195, along with hydrophobic contacts with LEU287. Gene expression predictions via DIGEP-Pred indicated that the top-ranked compounds could influence biological pathways linked to inflammation and oxidative stress, processes implicated in COVID-19’s pathology. Notably, CHEMBL4069090 emerged as a lead compound with favorable drug-likeness and predicted binding to PLpro. Overall, the applied in silico framework facilitated the rational prioritization of bioactive analogs with promising pharmacological profiles, supporting their advancement toward experimental validation and therapeutic exploration against SARS-CoV-2. Full article

Phosphatidylinositol 3-kinase alpha (PI3Kα) is frequently mutated in head and neck squamous cell carcinoma (HNSCC), leading to the constitutive activation of the PI3K/Akt pathway, which promotes tumor cell proliferation, survival, and metastasis. PI3Kα allosteric inhibitors demonstrate therapeutic potential as both monotherapy and combination therapy, particularly in patients with PIK3CA mutations or resistance to immunotherapy, through the precise targeting of mutant PI3Kα. Compared to ATP-competitive PI3Kα inhibitors such as Alpelisib, the allosteric inhibitor RLY-2608 exhibits enhanced selectivity for mutant PI3Kα while minimizing the inhibition of wild-type PI3Kα, thereby reducing side effects such as hyperglycemia. To date, no allosteric PI3Kα inhibitors have been approved for clinical use. To develop novel PI3Kα inhibitors with improved safety and efficacy, we employed a scaffold hopping approach to structurally modify RLY-2608 and constructed a compound library. Based on the structural information of the PI3Kα allosteric site, we conducted the systematic virtual screening of 11,550 molecules from databases to identify lead compounds. Through integrated approaches, including molecular docking studies, target validation, druggability evaluation, molecular dynamics simulations, and metabolic pathway and metabolite analyses, we successfully identified a promising novel allosteric PI3Kα inhibitor, H-18 (3-(2-chloro-5-fluorophenyl)isoindolin-1-one). H-18 has not been previously reported as a PI3Kα inhibitor, and provides an excellent foundation for subsequent lead optimization, offering a significant starting point for the development of more potent PI3Kα allosteric inhibitors. Full article

Background: Huang Qin Decoction (HQD) is a well-established Traditional Chinese Medicine (TCM) formulation recognized for its application in the treatment of colorectal cancer (CRC). However, the precise therapeutic mechanisms remain inadequately defined. Methods: This study integrates metabolomics from a mouse model and network pharmacology to screen potential targets and bio-active ingredients of HQD. The pharmacological activity of HQD for CRC was evidenced via single-cell RNA sequencing (scRNA-seq), molecular docking, and molecular dynamics simulations. Atomic force microscopy (AFM) assays and cellular experimental validation were used to confirm the relative mechanisms. Results: The metabolite profile undergoes significant alterations, with metabolic reprogramming evident during the malignant progression of CRC liver metastasis. Network pharmacology analysis identified that HQD regulates several metabolic pathways, including arginine biosynthesis, alanine, aspartate, and glutamate metabolism, nitrogen metabolism, phenylalanine metabolism, and linoleic acid metabolism, by targeting key proteins such as aspartate aminotransferase (GOT1), cytochrome P450 1A2 (CYP1A2), and carbonic anhydrase 2 (CA2). ScRNA-seq analysis indicated that HQD may enhance the functionality of cytotoxic T cells, thereby reversing the immunosuppressive microenvironment. Virtual verification revealed a strong binding affinity between the identified hub targets and active constituents of HQD, a finding subsequently corroborated by AFM assays. Cellular experiments confirmed that naringenin treatment inhibits the proliferation, migration, and invasion of CRC cells by downregulating GOT1 expression and disrupting glutamine metabolism. Conclusions: Computational prediction and in vitro validation reveal the active ingredients, potential targets, and molecular mechanisms of HQD against CRC liver metastasis, thereby providing a scientific foundation for the application of TCM in CRC treatment. Full article

Cancer remains one of the most formidable challenges to human health; hence, developing effective treatments is critical for saving lives. An important strategy involves reactivating tumor suppressor genes, particularly p53, by targeting their negative regulator MDM2, which is essential in promoting cell cycle arrest and apoptosis. Leveraging a drug repurposing approach, we screened over 24,000 clinically tested molecules to identify new MDM2 inhibitors. A key innovation of this work is the development and application of a selective cleaning algorithm that systematically filters assay data to mitigate noise and inconsistencies inherent in large-scale bioactivity datasets. This approach significantly improved the predictive accuracy of our machine learning model for pIC₅₀ values, reducing RMSE by 21.6% and achieving state-of-the-art performance (R² = 0.87)—a substantial improvement over standard data preprocessing pipelines. The optimized model was integrated with structure-based virtual screening via molecular docking to prioritize repurposing candidate compounds. We identified two clinical CB1 antagonists, MePPEP and otenabant, and the statin drug atorvastatin as promising repurposing candidates based on their high predicted potency and binding affinity toward MDM2. Interactions with the related proteins MDM4 and BCL2 suggest these compounds may enhance p53 restoration through multi-target mechanisms. Quantum mechanical (ONIOM) optimizations and molecular dynamics simulations confirmed the stability and favorable interaction profiles of the selected protein–ligand complexes, resembling that of navtemadlin, a known MDM2 inhibitor. This multiscale, accuracy-boosted workflow introduces a novel data-curation strategy that substantially enhances AI model performance and enables efficient drug repurposing against challenging cancer targets. Full article

Epilepsy remains a widespread neurological disorder, with approximately 30% of patients showing resistance to current antiepileptic therapies. To address this unmet need, a series of 2-(isoindolin-2-yl) esters derived from natural amino acids were designed and evaluated for their potential interaction with the GABA_A receptor. Sixteen derivatives were subjected to in silico assessments, including physicochemical and ADMET profiling, virtual screening–ensemble docking, and enhanced sampling molecular dynamics simulations (metadynamics calculations). Among these, compounds derived from the aromatic amino acids, phenylalanine, tyrosine, tryptophan, and histidine, exhibited superior predicted affinity, attributed to π–π stacking interactions at the benzodiazepine binding site of the GABA_A receptor. Based on computational performance, the tyrosine and tryptophan derivatives were synthesized and further assessed in vivo using the pentylenetetrazole-induced seizure model in zebrafish (Danio rerio). The tryptophan derivative produced comparable behavioral seizure reduction to the reference drug diazepam at the tested concentrations. The results implies that aromatic amino acid-derived isoindoline esters are promising anticonvulsant candidates and support the hypothesis that π–π interactions may play a critical role in modulating GABA_A receptor binding affinity. Full article

Clear cell renal cell carcinoma (ccRCC), the most common RCC subtype, displays significant intratumoral heterogeneity driven by metabolic reprogramming, which complicates our understanding of disease progression and limits treatment efficacy. This study aimed to construct a comprehensive cellular and transcriptional landscape of ccRCC, with emphasis on gene expression dynamics during malignant progression. An integrated analysis of 90 scRNA-seq samples comprising 534,227 cells revealed a progressive downregulation of sodium ion transport-related genes, particularly CHP1 (calcineurin B homologous protein isoform 1), which is predominantly expressed in epithelial cells. Reduced CHP1 expression was confirmed at both mRNA and protein levels using bulk RNA-seq, CPTAC proteomics, immunohistochemistry, and ccRCC cell lines. Survival analysis showed that high CHP1 expression correlated with improved prognosis. Functional analyses, including pseudotime trajectory, Mfuzz clustering, and cell–cell communication modeling, indicated that CHP1⁺ epithelial cells engage in immune interaction via PPIA–BSG signaling. Transcriptomic profiling and molecular docking suggested that CHP1 modulates amino acid transport through SLC38A1. ZNF460 was identified as a potential transcription factor of CHP1. Virtual screening identified arbutin and imatinib mesylate as candidate CHP1-targeting compounds. These findings establish CHP1 downregulation as a novel molecular feature of ccRCC progression and support its utility as a prognostic biomarker. Full article

Lactate dehydrogenase (LDH) catalyzes the reversible interconversion of pyruvate and lactate, coupled with the redox cycling of NADH and NAD⁺. While LDHA has been extensively studied as a therapeutic target, particularly in cancer, due to its role in the Warburg effect, LDHB remains underexplored, despite its involvement in the metabolic reprogramming of specific cancer types, including breast and lung cancers. Most known LDH inhibitors are designed against the LDHA isoform and act competitively at the active site. In contrast, LDHB exhibits distinct kinetic properties, substrate preferences, and structural features, warranting isoform-specific screening strategies. In this study, 115 natural compounds previously reported as LDHA inhibitors were systematically evaluated for LDHB inhibition using an integrated in silico and in vitro approach. Virtual screening identified 16 lead phytochemicals, among which luteolin and quercetin exhibited uncompetitive inhibition of LDHB, as demonstrated by enzyme kinetic assays. These findings were strongly supported by molecular docking analyses, which revealed that both compounds bind at an allosteric site located at the dimer interface, closely resembling the binding mode of the established LDHB uncompetitive inhibitor AXKO-0046. In contrast, comparative docking against LDHA confirmed their active-site binding and competitive inhibition, underscoring their isoform-specific behavior. Our findings highlight the necessity of assay conditions tailored to LDHB’s physiological role and demonstrate the application of a previously validated colorimetric assay for high-throughput screening. This work lays the foundation for the rational design of selective LDHB inhibitors from natural product libraries. Full article

Background/Objectives: Human carbonic anhydrases (hCAs) are metalloenzymes involved in essential physiological processes, and their selective inhibition holds therapeutic potential across a wide range of disorders. However, the high degree of structural similarity among isoforms poses a significant challenge for the design of selective inhibitors. In this work, we present a machine learning (ML)-based platform for the isoform-specific prediction and profiling of small molecules targeting hCA I, II, IX, and XII. Methods: By integrating four molecular representations with four ML algorithms, we built 64 classification models, each extensively optimized and validated. The best-performing models for each isoform were applied in a virtual screening campaign for ~2 million compounds. Results: Following a multi-step refinement process, 12 candidates were identified, purchased, and experimentally tested. Several compounds showed potent inhibitory activity in the nanomolar to submicromolar range, with selectivity profiles across the isoforms. To gain mechanistic insights, SHAP-based feature importance analysis and molecular docking supported by molecular dynamics simulations were employed, highlighting the structural determinants of the predicted activity. Conclusions: This study demonstrates the effectiveness of integrating ML, cheminformatics, and experimental validation to accelerate the discovery of selective carbonic anhydrase inhibitors and provides a generalizable framework for activity profiling across enzyme isoforms. Full article

The Chikungunya virus (CHIKV) continues to pose a significant global health challenge due to the absence of effective antiviral treatments and limited vaccine availability. This study employed a comprehensive in silico workflow, incorporating high-throughput virtual screening, binding free-energy calculations, ADMET (absorption, distribution, metabolism, excretion, and toxicity) analysis, and 200 ns molecular dynamics (MD) simulations, to identify new inhibitors targeting the E1–E2 glycoprotein complex, crucial for CHIKV entry and membrane fusion. Four promising candidates were identified from a library of 20,000 compounds, with CID 136801451 showing the most potent binding (docking score: −10.227; ΔG_bind: −51.53 kcal/mol). The top four compounds exhibited favorable ADMET profiles, meeting nearly all criteria. MD simulations confirmed stable binding and strong interactions between CID 136801451 and the E1–E2 complex, evidenced by consistently low RMSD values. These findings highlight CID 136801451 as a promising CHIKV entry inhibitor, warranting further in vitro and in vivo evaluation to advance the development of effective anti-CHIKV therapeutics. Full article

Background/Objectives: Severe malaria, mainly caused by Plasmodium falciparum, remains a significant therapeutic challenge due to increasing drug resistance and adverse effects. Flavonoids, known for their wide range of bioactivities, offer a promising route for antimalarial drug discovery. The aim of this study was to elucidate key structural features associated with antimalarial activity in flavonoids and to develop accurate, interpretable predictive models. Methods: Curated databases of flavonoid structures and their activity against P. falciparum strains and enzymes were constructed. Molecular fingerprinting and decision tree analyses were used to identify key pharmacophoric groups. Subsequently, molecular descriptors were generated and reduced to build multiple classification and regression models. Results: These models demonstrated high predictive accuracy, with test set accuracies ranging from 92.85% to 100%, and R² values from 0.64 to 0.97. Virtual screening identified novel flavonoid candidates with potential inhibitory activity. These were further evaluated using molecular docking and molecular dynamics simulations to assess binding affinity and stability with Plasmodium proteins (FabG, FabZ, and FabI). The predicted active ligands exhibited stable pharmacophore interactions with key protein residues, providing insights into binding mechanisms. Conclusions: This study provides highly predictive models for antimalarial flavonoids and enhances the understanding of structure–activity relationships, offering a strong foundation for further experimental validation. Full article

This study focuses on exploring potential inhibitors of the Marburg virus interferon inhibitory domain protein (MARV-VP35), which is responsible for immune evasion and immunosuppression during viral manifestation. A combination of in silico techniques was applied, including structure-based pharmacophore virtual screening, molecular docking, absorption, distribution, metabolism, excretion, and toxicity (ADMET) analysis, molecular dynamics (MD), and molecular stability assessment of the identified hits. The docking scores of the 14 selected ligands ranged between −6.88 kcal/mol and −5.28 kcal/mol, the latter being comparable to the control ligand. ADMET and drug likeness evaluation identified Mol_01 and Mol_09 as the most promising candidates, both demonstrating good predicted antiviral activity against viral targets. Density functional theory (DFT) calculations, along with relevant quantum chemical descriptors, correlated well with the docking score hierarchy, and molecular electrostatic potential (MEP) mapping confirmed favorable electronic distributions supporting the docking orientation. Molecular dynamics simulations further validated complex stability, with consistent root mean square deviation (RMSD), root mean square fluctuation (RMSF), and secondary structure element (SSE) profiles. These findings support Mol_01 and Mol_09 as viable candidates for experimental validation. Full article

Umami is a fundamental taste sensation, often described as a delicious and pleasant flavor perception. To enhance or complement the original flavor and meet the tastes of diverse regions, umami dipeptides have been extensively utilized in global food manufacturing. Currently, the application and purification techniques of dipeptides are relatively mature, while their umami mechanisms and molecular modification are both scarce. In this work, the 3D structure of the umami dipeptide target T1R1/T1R3 was first obtained through sequence alignment and homology modeling, then followed by the successful construction of a database containing 400 samples of dipeptides. Subsequently, the complex models of T1R1/T1R3, respectively, with DG (Asp-Gly) and EK (Glu-Lys) (i.e., T1R1_DG/T1R3, T1R1/T1R3_DG, T1R1_EK/T1R3, and T1R1/T1R3_EK) were obtained via molecular docking and virtual screening. Finally, based on comparative molecular dynamics (MD) simulation trajectories, the binding free energy was calculated to investigate receptor–ligand recognition and conformational changes, providing some implications for potential modifications of umami dipeptides. T1R1 tends to bind relatively small umami dipeptides, whereas T1R3 does the opposite, both of which favor the recognition of acidic and hydrophilic dipeptides. By comparing strategies such as hydroxyl introduction and chain length alteration, electrostatic effects may be more important than non-polar effects in molecular design. This work not only explores the recognition mechanism of umami dipeptides with the receptor T1R1/T1R3 showing certain theoretical significance, but also provides feasible suggestions for dipeptide screening and modification having certain application value. Full article

Poly(ADP-ribose) polymerases (PARP) are a family of enzymes that were proven to play an essential role in the initiation and activation of DNA repair processes in the case of DNA single-strand breaks. The inhibition of PARP enzymes might be a promising option for the treatment of several challenging types of cancers, including triple-negative breast cancer (TNBC) and non-small cell lung carcinoma (NSCLC). This study utilizes several techniques to screen the compound collection of the Leibniz Institute of Plant Biochemistry (IPB) to identify novel hPARP-1 inhibitors. First, an in silico pharmacophore-based docking study was conducted to virtually screen compounds with potential inhibitory effects. To evaluate these compounds in vitro, a cell-free enzyme assay was developed, optimized, and employed to identify hPARP-1 inhibitors, resulting in the discovery of two novel scaffolds represented by compounds 54 and 57, with the latter being the most active one from the compound library. Furthermore, fluorescence microscopy and synergism assays were performed to investigate the cellular and nuclear pathways of hPARP-1 inhibitor 57 and its potential synergistic effect with the DNA-damaging agent temozolomide. The findings suggest that the compound requires further lead optimization to enhance its ability to target the nuclear PARP enzyme effectively. Nonetheless, this new scaffold demonstrated a five-fold higher PARP inhibitory activity at the enzyme level compared to the core structure of olaparib (OLP), phthalazin-1(2H)-one. Full article

Large-scale blooms of Ulva prolifera severely impact coastal ecosystems and economic development. In addressing Ulva management, the development of high-value utilization approaches for this macroalga remains crucial. Compared to other marine algae, Ulva prolifera exhibits higher protein content with diverse amino acid profiles, and existing studies demonstrate that hydrolyzed Ulva prolifera proteins can yield biologically active peptides with functional potential. Conventional methods for producing bioactive peptides are often cost-intensive. Here, we employed in silico enzymatic hydrolysis to generate small peptides from Ulva prolifera protein. Through computer screening, molecular docking with the Keap1 protein, and molecular dynamics simulations, we identified a potential antioxidant peptide, DWS (Asp-Trp-Ser). Molecular docking and dynamics simulations revealed that DWS forms stable complexes with Keap1 by establishing hydrogen bonds and Pi bonds with conserved amino acid residues (Leu557, Gly558, Ile559, Val604, Val606, and Arg415). In vitro antioxidant assays demonstrated that DWS exhibits potent DPPH and ABTS radical scavenging activities as well as reducing power. Cellular experiments showed that DWS effectively alleviates LPS-induced oxidative stress and inflammation in RAW264.7 macrophages. Full article