Molecular Diversity最新文献

{"title":"Decoding the gut microbiota metabolite-matrix metalloproteinase-3 axis in breast cancer: a multi-omics and network pharmacology study.","authors":"Tangyu Yuan, Jiayin Xing, Pengtao Liu","doi":"10.1007/s11030-025-11351-y","DOIUrl":"https://doi.org/10.1007/s11030-025-11351-y","url":null,"abstract":"<p><p>Breast cancer is a malignant tumor originating from the breast epithelium, and emerging evidence suggests that the gut microbiota influences its development, progression, and treatment, although its role remains underexplored. In this study, we employed an integrative multi-omics framework that combined network pharmacology, machine learning, SHapley Additive exPlanations (SHAP), and single-cell RNA sequencing to systematically investigate key interactions between microbial metabolites and their targets. Core regulators were further validated using Mendelian randomization (MR), while molecular docking was applied to evaluate the binding affinity of candidate metabolites. Matrix metalloproteinase-3 (MMP3) emerged as a central molecule involved in multiple cancer-related signaling pathways, including PI3K-AKT, MAPK, and HIF-1, with promising druggable potential. Eight non-toxic gut microbial metabolites-such as indole-3-propionic acid, glycocholic acid, and 4-hydroxyphenylpyruvate-demonstrated strong binding affinity to MMP3 and favorable pharmacokinetic properties, highlighting a previously unappreciated microbiota-MMP3 axis as a promising avenue for therapeutic intervention in breast cancer. These findings provide a basis for subsequent in vitro and in vivo validation and underscore the translational potential of the identified microbial metabolites, thereby supporting the development of microbiome-derived therapeutic strategies for breast cancer.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145058224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ACP-EPC: an interpretable deep learning framework for anticancer peptide prediction utilizing pre-trained protein language model and multi-view feature extracting strategy.","authors":"Jingwei Lv, Kexin Li, Yike Wang, Junlin Xu, Yajie Meng, Feifei Cui, Leyi Wei, Qingchen Zhang, Zilong Zhang","doi":"10.1007/s11030-025-11352-x","DOIUrl":"https://doi.org/10.1007/s11030-025-11352-x","url":null,"abstract":"<p><p>Cancer remains a major global health challenge, as conventional chemotherapy often causes extensive damage to healthy cells and leads to severe side effects. Anticancer peptides (ACPs) have emerged as a promising therapeutic alternative, capable of selectively targeting and eliminating cancer cells while improving patient quality of life and treatment outcomes. Nevertheless, identifying ACPs through traditional biological experiments is both labor-intensive and time-consuming. To address this limitation, we developed ACP-EPC, a deep learning framework which predicts ACPs directly from protein sequences. ACP-EPC integrates contextual representations from Evolutionary Scale Modeling 2 (ESM-2) with handcrafted physicochemical descriptors and employs a Cross-Attention mechanism for multimodal feature fusion. The model was rigorously evaluated using tenfold cross-validation and two test sets, ACP135 and ACP99, achieving accuracy of 0.935 and 0.984, respectively. These results substantially outperform existing models, underscoring the advantages of combining diverse feature representations. To promote accessibility, we have also deployed ACP-EPC as a publicly available web server at http://www.bioai-lab.com/ACP-EPC .</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145058231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantum chemical optimization and residue-specific stabilization of CDK20 inhibitors in hepatocellular carcinoma.","authors":"Ahmed I Foudah, Mohammed H Alqarni, Tariq M Aljarba, Talha Jawaid, Osama A Alkhamees, Saud M Alsanad, Khalid I AlHussaini, Aftab Alam","doi":"10.1007/s11030-025-11339-8","DOIUrl":"https://doi.org/10.1007/s11030-025-11339-8","url":null,"abstract":"<p><p>Cyclin-dependent kinase 20 (CDK20), also known as cell cycle-related kinase (CCRK), plays a pivotal role in hepatocellular carcinoma (HCC) progression by regulating β-catenin signaling and promoting uncontrolled proliferation. Despite its emerging significance, selective small-molecule inhibitors of CDK20 remain unexplored. In this study, a known CDK20 inhibitor, ISM042-2-048, was employed as a reference to retrieve structurally similar compounds from the PubChem database using an 85% similarity threshold. Out of 6,235 candidates, the top three compounds (153295720, 145037521, and 163292314) were shortlisted through MTiOpenScreen-based virtual screening. Geometry optimizations using density functional theory (B3LYP/cc-pVDZ) refined each ligand's electronic properties before re-docking against the AlphaFold-derived CDK20 structure. 153295720 exhibited the highest binding affinity (- 11.8 kcal/mol), engaging critical active-site residues such as Met⁸⁴, Lys³³, Ala¹³¹, and Asp¹⁴⁵ through polar and hydrophobic interactions. Molecular dynamics simulations (500 ns) confirmed the complex's structural stability, with 153295720 showing the lowest RMSD and RMSF fluctuations and highly persistent hydrogen bonding. MM/GBSA analysis further supported its superiority, revealing the most favorable binding energy (- 69.09 ± 8.29 kcal/mol), dominated by van der Waals and electrostatic interactions. Free energy landscape analysis revealed a single dominant basin, and superimposition of MD-derived minima with the docked pose yielded an RMSD of 1.464 Å, supporting pose fidelity. Comparatively, the reference compound displayed greater conformational drift and reduced energetic convergence. This integrative computational approach establishes 153295720 as a structurally and dynamically superior inhibitor, capable of stabilizing key catalytic residues of CDK20. These findings provide a rational basis for the biochemical targeting of CDK20 in HCC and highlight residues essential for selective inhibition, paving the way for experimental validation and lead optimization.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145028792","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Oral bioavailability property prediction based on task similarity transfer learning.","authors":"Chen Zeng, Chengcheng Xu, Yingxu Liu, Yunya Jiang, Lidan Zheng, Yang Liu, Yanmin Zhang, Yadong Chen, Haichun Liu, Rui Gu","doi":"10.1007/s11030-025-11345-w","DOIUrl":"https://doi.org/10.1007/s11030-025-11345-w","url":null,"abstract":"<p><p>Drug absorption significantly influences pharmacokinetics. Accurately predicting human oral bioavailability (HOB) is essential for optimizing drug candidates and improving clinical success rates. The traditional method based on experiment is a common way to obtain HOB, but the experimental method is time-consuming and costly. Recently, using AI models to predict ADMET properties has become a new and effective method. However, this method has some data dependence problems. To address this issue, we combine physicochemical properties with graph-based deep learning methods to improve HOB prediction, providing an efficient and interpretable alternative to traditional experimental and computational approaches for ADMET property studies in data-scarce scenarios. We propose a similarity-guided transfer learning framework, Task Similarity-guided Transfer Learning based on Molecular Graphs (TS-GTL), which includes a deep learning model, PGnT (pKa Graph-based Knowledge-driven Transformer). PGnT incorporates common molecular descriptors as external knowledge to guide molecular graph representation, leveraging GNNs and Transformer encoders to enhance feature extraction. Additionally, we introduce MoTSE to quantify the similarity between physicochemical properties and HOB. Notably, training with data pretrained model on logD properties showed the best performance in transfer learning. TS-GTL also outperformed machine learning algorithms and deep learning predictive tools, underscoring the critical role of task similarity in transfer learning.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145028720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design and synthesis of novel indolinone Aurora B kinase inhibitors based on fragment-based drug discovery (FBDD).","authors":"Baoxing Xie, Miaomiao Shi, Dan Tang, Shan Yang, Yan Zeng, Lifei Nie, Chao Niu","doi":"10.1007/s11030-025-11353-w","DOIUrl":"https://doi.org/10.1007/s11030-025-11353-w","url":null,"abstract":"<p><p>Aurora kinases are a group of serine/threonine kinases essential for cell mitosis, comprising Aurora A, B, and C. However, the Aurora B is overexpressed in multiple tumors and the aurone has been proved to exhibit potent inhibitory activity against Aurora B kinase by our group. The indolinone was considered as an aurone scaffold hopping analog, and the indolinone-based Aurora B inhibitor library (3577 molecules) was constructed by FBDD strategy. After pharmacophore model and molecular docking, the candidate molecules were identified, then synthesized via Suzuki-Miyaura and Knoevenagel reactions. The compounds 3-17a, 3-17d and 3-17 k especially inhibited Aurora B in the nanomolar range (IC₅₀ = 1.100, 1.518 and 0.8911 nM, respectively), showing no significant inhibition of Aurora A. Notably, the most potent 3-17 k demonstrated the strongest antiproliferative activity against HGC27 (IC₅₀ = 2.05 μM) and HT-29 (IC₅₀ = 2.07 μM) cell lines, as well as Aurora B over-expression cells, including OVCAR8 (IC₅₀ = 3.02 μM), T24 (IC₅₀ = 10.21 μM), NCIH1299 (IC₅₀ = 7.32 μM) and SW480 (IC₅₀ = 4.45 μM), while maintaining a lower cytotoxicity in normal human cells (GES-1 and NCM460). Additionally, molecular dynamics simulation were conducted to explore the binding interactions between 3-17 k and Aurora B (PDB: 5EYK), revealing favorable binding free energy (-33.34 kcal·mol-1). Based on available data, compound 3-17 k warrants comprehensive investigation to evaluate its potential as an anticancer drug candidate.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145028743","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Asymmetric Mannich reaction enabled synthesis of alkaloids.","authors":"Summaya Fatima, Asim Mansha, Samreen Gul Khan, Syed Makhdoom Hussain, Bushra Parveen, Ameer Fawad Zahoor, Aqsa Mushtaq, Rabia Ashraf, Aijaz Rasool Chaudhry, Ahmad Irfan","doi":"10.1007/s11030-025-11341-0","DOIUrl":"https://doi.org/10.1007/s11030-025-11341-0","url":null,"abstract":"<p><p>The catalytic asymmetric Mannich reaction is a multicomponent reaction which affords β-amino carbonyl compounds by utilizing an aldehyde, a primary or secondary amine/ammonia, and a ketone. β-amino carbonyl scaffolds are crucial intermediates for the synthesis of naturally occurring bioactive compounds and their derivatives. The synthesized natural compounds exhibit a broad spectrum of biological activities including anti-fungal, anti-cancer, anti-bacterial, anti-HIV, anti-oxidant, and anti-inflammatory activities. Considering the significance of asymmetric Mannich reaction to access diverse biologically active natural products, its applications to afford the synthesis of naturally occurring alkaloids have been summarized here. This review article showcases the key role of asymmetric Mannich reaction in the synthesis of pharmaceutically potent naturally occurring alkaloids, i.e., indole alkaloids, monoterpenoid-indole alkaloids, diterpenoid alkaloids, iso-quinoline alkaloids, polyketide alkaloids, and pyrrolizidine alkaloids, etc., reported since 2015.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145022554","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction: A novel ligand-based convolutional neural network for identification of P-glycoprotein ligands in drug discovery.","authors":"Mary Margarat Valentine A Neela, Subbarao Peram","doi":"10.1007/s11030-025-11331-2","DOIUrl":"10.1007/s11030-025-11331-2","url":null,"abstract":"","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145022523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dendrobium officinale Kimura et Migo regulates the proliferation and migration of colon adenocarcinoma via LGALS4.","authors":"Jiawei Miao, Bingrui Luo, Xiuzhen He, Guoli Li, Lu Dou, Yongbo Yan, Xiaoyan Tang","doi":"10.1007/s11030-025-11335-y","DOIUrl":"https://doi.org/10.1007/s11030-025-11335-y","url":null,"abstract":"<p><p>Dendrobium officinale Kimura et Migo (DO) has demonstrated potential anti-colon adenocarcinoma (COAD) effects; however, its underlying mechanisms of action require further elucidation. In this study, DO (work concentrations of 0, 0.1, and 0.01 μg/mL) was administered to HCT116 and Caco-2 cells to evaluated cell viability and migration. Herb databases were utilized to identify potential DO targets. Differential gene screening and weighted gene co-expression network analysis were conducted on the GSE35782 dataset, followed by enrichment analysis and immune infiltration analysis. Key genes were validated using The Cancer Genome Atlas (TCGA), molecular docking, and real-time polymerase chain reaction. The study found that DO significantly inhibited the viability and migration of HCT116 or Caco-2 cells (P < 0.05). A total of 26 key genes were identified, among which galectin-4 (LGALS4) was significantly downregulated in TCGA-COAD (P < 0.05) and was correlated with prognosis (P < 0.05). Molecular docking revealed that the binding energy between LGALS4 and lactose, dendronobiloside A, and dendronobiloside B was-7.22,-9.05, and-8.05 kcal/mol, respectively, forming 5, 11, and 8 hydrogen bonds. Overall, DO effectively suppresses the viability and migration of COAD HCT116 or Caco-2 cells, with LGALS4 potentially serving as a key target.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144999445","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"In silico identification of peptidomimetic inhibitors targeting PXR and RXR interaction to overcome the inactivation of vitamin D in asthma.","authors":"Boutaina Elgharbaoui, E L Mehdi Bouricha, Kaoutar El Guenouni, Meryam Magri, Lahcen Belyamani, Azeddine Ibrahimi, Rachid ELjaoudi, Naima Elhafidi","doi":"10.1007/s11030-025-11336-x","DOIUrl":"https://doi.org/10.1007/s11030-025-11336-x","url":null,"abstract":"<p><p>Asthma is a chronic inflammatory disorder of the airways. Standard treatments, such as inhaled corticosteroids like fluticasone, beclomethasone, and budesonide, are effective in managing asthma symptoms by reducing inflammation through immune suppression. However, prolonged corticosteroid therapy can impair vitamin D metabolism, exacerbating vitamin D deficiency, which is essential for immune regulation and anti-inflammatory responses via the vitamin D receptor (VDR). Activation of the pregnane X receptor (PXR) by corticosteroids induces cytochrome P450 enzyme CYP24A1, accelerating vitamin D catabolism and reducing its anti-inflammatory efficacy. This effect is mediated through the interaction between PXR and its nuclear partner, the retinoid X receptor (RXR), which together regulate gene transcription. Disrupting this PXR-RXR dimerization could offer a selective means to prevent vitamin D degradation without interfering with other physiological functions of PXR or RXR.In this study, we aimed to inhibit the PXR and retinoid X receptor (RXR) interaction by designing peptidomimetic molecules based on the key RXR residues interacting with PXR. To achieve this, we used a multifaceted approach, incorporating pharmacophore and similarity-based peptidomimetics screening, molecular docking, ADMET analysis, and molecular dynamics (MD) simulations. The molecular docking results indicated that 38 compounds had a docking score higher than - 7. Among them, six showed favorable ADMET properties. These molecules were then subjected to MD simulations, where two molecules, notably MMs02510246 and MMs03733211, showed strong interaction with PXR during the 300 ns of MD simulation. Two others partially changed the starting binding site, while two others completely retained their initial binding site and bound to another site. Our study identified two potential molecules that could inhibit the PXR-RXR interaction. These two molecules could potentially inhibit the PXR-RXR interaction, which may help reduce corticosteroid-induced vitamin D inactivation, thereby improving asthma management outcomes without compromising vitamin D's anti-inflammatory benefits. Further experimental analyses are needed to validate our results.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144999472","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Computational insights and activity evaluation of novel SHP-2 inhibitors for targeting type 2 diabetes mellitus.","authors":"Rong Liu, Liang Zou, Maoqi Wang, Yueyue He, Mao Shu","doi":"10.1007/s11030-025-11344-x","DOIUrl":"https://doi.org/10.1007/s11030-025-11344-x","url":null,"abstract":"<p><p>Protein-tyrosine phosphatase-2 (SHP-2) has become a new target in the study of type 2 diabetes mellitus (T2DM). Currently, there are no marketed drugs targeting SHP-2 to study T2DM caused by insulin resistance. Therefore, this study screened out SHP-2 inhibitors with potential inhibitory activity from 2 million compounds, combined with ADME/T, Lipinski &Veber rules, molecular docking and molecular dynamics simulation. It is understood that the mechanism of action to inhibit the activity of SHP-2 protein by compounds is mainly protein amino acid residues PHE-113, GLU-250, LEU-254, GLN-257, PRO-491, and GLN-495 bind to ligands to produce stable conformation. Finally, a series of in vitro preliminary evaluation experiments were conducted to verify the primary activity of the lead compounds. It provides a meaningful reference for the future study of SHP-2 inhibitors with better efficacy, safety, drug-like, bioavailability and drug resistance.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144991220","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}