Journal of Biomolecular Structure & Dynamics最新文献

Secondary active transporters play a crucial role in cellular physiology by facilitating the movement of molecules across cell membranes. Identifying the functional classes of these transporters, particularly amino acid and peptide transporters, is essential for understanding their involvement in various physiological processes and disease pathways, including cancer. This study aims to develop a robust computational framework that integrates pre-trained protein language models and deep learning techniques to classify amino acid and peptide transporters within the secondary active transporter (SAT) family and predict their functional association with solute carrier (SLC) proteins. The study leverages a comprehensive dataset of 448 secondary active transporters, including 36 solute carrier proteins, obtained from UniProt and the Transporter Classification Database (TCDB). Three state-of-the-art protein language models, ProtTrans, ESM-1b, and ESM-2, are evaluated within a deep learning neural network architecture that employs a multi-window scanning technique to capture local and global sequence patterns. The ProtTrans-based feature set demonstrates exceptional performance, achieving a classification accuracy of 98.21% with 87.32% sensitivity and 99.76% specificity for distinguishing amino acid and peptide transporters from other SATs. Furthermore, the model maintains strong predictive ability for SLC proteins, with an overall accuracy of 88.89% and a Matthews Correlation Coefficient (MCC) of 0.7750. This study showcases the power of integrating pre-trained protein language models and deep learning techniques for the functional classification of secondary active transporters and the prediction of associated solute carrier proteins. The findings have significant implications for drug development, disease research, and the broader understanding of cellular transport mechanisms.

The immunophenoscore (IPS) is an important indicator for evaluating immunotherapy response. This work was designed to establish a prognostic model based on IPS-related genes in cervical cancer. Weighted correlation network analysis (WGCNA) was utilized to identify key modules related to IPS in cervical cancer data from The Cancer Genome Atlas (TCGA). The results show that the yellow module (158 genes) had a high correlation with both IPS_CTLA4_blocker and IPS_CTLA4_and PC1/PDL1/PDL2 blocker. Univariate cox regression analysis and LASSO regression analysis were performed based on 158 genes, and 9 characteristic genes were finally identified to construct the model. According to the differentially expressed genes, cervical cancer samples were divided into high-risk and low-risk groups and cluster 1.2.3. Higher risk scores associated with poorer prognosis. cluster2 and cluster3 were identified as cervical cancer subtypes with significant survival differences. cluster2 had higher immune cell infiltration levels and better prognosis, with greater sensitivity to Cyclopamine, Imatinib, MG-13, Paclitaxel, PHA-665752, Rapamycin, Sorafenib, Sunitinib, and VX-680. In contrast, cluster3 had higher TTN and PIK3CA mutations and greater sensitivity to AZ628, Dasatinib, Doxorubicin, HG-6-64-1, JQ12, Midostaurin, PF-562271, TAE684, and WH-4-023. In conclusion, we developed a feasible risk score model based on IPS-related genes for cervical cancer prognosis and identified potential drugs for different cervical cancer subtypes.

The discovery of novel, selective inhibitors targeting CDK2 and PIM1 kinases, which regulate cell survival, proliferation, and treatment resistance, is crucial for advancing cancer therapy. This study reports the design, synthesis, and biological evaluation of three novel pyrazolo[3,4-b]pyridine derivatives (6a-c), confirmed via spectral analyses. These compounds were assessed for anti-cancer activity against breast, colon, liver, and cervical cancers using the MTT assay. Among the tested compounds, 6b exhibited superior efficacy, with higher selectivity indices for HCT-116 (15.05) and HepG2 (9.88) compared to the reference drug staurosporine. Mechanistic studies revealed that 6b induced apoptosis (63.04-fold increase) and arrested the cell cycle at the G0-G1 phase, highlighting its anti-proliferative effects. In an in-vivo solid Ehrlich carcinoma (SEC) mouse model, compound 6b significantly reduced tumor weight and volume, exceeding the efficacy of doxorubicin. Additionally, 6b potently inhibited CDK2 and PIM1 kinases (IC₅₀: 0.27 and 0.67 µM, respectively) and reduced tumor-promoting TNF-alpha expression, as confirmed by histopathological and immunohistochemical studies. Computational analyses, including molecular docking, molecular dynamics simulations, and DFT calculations, provided insights into the binding stability and interaction mechanisms of 6b with CDK2 and PIM1, while in-silico pharmacokinetic and toxicity evaluations confirmed its favorable drug-like profile and safety. This study highlights compound 6b as a promising dual CDK2/PIM1 inhibitor with potent anti-cancer activity and selectivity, paving the way for its further optimization and development as a lead molecule in cancer therapy.

. Despite the significant advancements in clinical and laboratory research, breast cancer remains a formidable challenge due to its high incidence, recurrence and mortality rate. The emerging paradigm emphasizes the pivotal role of cancer stem cells in compelling cancer initiation and recurrence attributed to the resistance against conventional radio and chemotherapy, thereby leading to poor prognosis and disease relapse post-treatment. Aldehyde dehydrogenase (ALDH1A1) is the putative stemness biomarker in breast cancer stem cells. It has been attributed to drug resistance in chemotherapy, especially against the drugs derived from aldehydic intermediate in action mechanism, cell differentiation and oxidative stress response. Since time immemorial, natural products have been employed in traditional medicine systems for their therapeutic and chemopreventive properties. Curcumin, an active polyphenol present in turmeric, plays a significant role in impeding the growth of BCSCs. However, the clinical efficacy of curcumin is restrained due to its poor bioavailability, limited absorption, rapid metabolism, and systemic elimination. To address this challenge, efforts have been directed towards synthesizing curcumin conjugates with diallyl sulfide to enhance its bioavailability. Computational tools such as molecular docking, molecular dynamics simulations and end-state MMGBSA binding free-energy calculations were employed to predict the optimal binding of curcumin conjugates with ALDH1A1 and provide valuable insights into their potential binding affinity and therapeutic efficacy. The enhanced bioavailability of curcumin may be attributed to the enhanced therapeutic activity against the BCSCs. Furthermore, synthesizing curcumin conjugates holds promise in cancer Chemoprevention. .

Atorvastatin, a widely prescribed cholesterol-lowering drug, has recently shown potential anticancer effects. However, its influence on gene expression and its biological functions in cancer, in particular breast cancer, still unclear. We aim to identify the dysregulated genes associated with atorvastatin treatment and the main players in their biological network. A total of 103 differentially expressed genes (DEGs) in the unified signature were identified, and the functional enrichment analysis suggested their relation to multiple cancer-related pathways. JUN was identified as the hub gene in the protein-protein interaction (PPI) network and was shown to be responsive to atorvastatin in breast cancer. Atorvastatin exhibited notable predicted cytotoxicity against breast cancer lines, with the activity positively correlated with JUN expression. JUN was significantly downregulated in breast cancer expression inversely correlated with cancer progression, whereas higher JUN expression was linked with better survival outcomes. Atorvastatin may directly interact with JUN protein forming a more compact and stable conformation. These findings demystify the potential therapeutic mechanism of atorvastatin in breast cancer, possibly by fine tuning of JUN expression. As such, JUN might serve as a valuable prognostic biomarker in breast cancer.

Breast cancer (BC) is one of the most fatal diseases that have a profound impact on women. If the cancer is identified earlier, the proper treatment will be provided to the patients to decrease the possibility of death. Mammography is a widely used imaging modality to detect BC earlier, providing valuable information to radiologists to offer better treatment plans and outcomes. This article proposes an efficient BC detection system from mammographic images using a hyperparameter-tuned gated recurrent unit (HTGRU) with attention included in a pre-trained model. The system includes the following steps: preprocessing, segmentation, feature extraction, and classification. The proposed system performs preprocessing using Gaussian filtering and contrast-limited adaptive histogram equalization (CLAHE) for noise removal and contrast enhancement. The data augmentation is performed on the preprocessed dataset to balance the data samples of the benign and malignant classes that prevents the network form biased results. After that, a deviation theory-based fuzzy c-means (DTFCM) algorithm is utilized to segment the tumor regions from the preprocessed image. Then, the most discriminant features are extracted from the segmented tumor regions using a normalization-based attention module incorporated in the capsule network (NAMCN). Finally, HTGRU is used for classification, classifying the data into benign, malignant, and normal. The system is evaluated by the Mammographic Image Analysis Society (MIAS) and curated breast imaging subset of a digital database for screening mammography (CBIS-DDSM) datasets, and the outcomes demonstrate the proposed method's superiority over existing methods by achieving higher detection accuracy and lower false positive rates.

The doxorubicin-induced cardiotoxicity continues to be a life-threatening adverse effect in the clinic. Doxorubicin-induced acute cardiotoxicity is reversible, whereas chronic cardiotoxicity is irreversible, leading to dilated cardiomyopathy and heart failure. The aim of this study was to identify the molecular mechanisms associated with doxorubicin metabolites in doxorubicin-induced chronic cardiotoxicity. For this purpose, literature searches and in silico toxicogenomic analyses were conducted using various tools, including the Comparative Toxicogenomic Database, GeneMANIA, Metascape, MIENTURNET, ChEA3, and AutoDock. Additionally, molecular dynamics simulations were performed for 500 ns using Schrödinger software to assess the stability and dynamics of the representative docked complexes. We observed that doxorubicin biotransformed into five metabolites in the human heart and identified 11 common genes related to doxorubicin, its metabolites, dilated cardiomyopathy, and heart failure. Our findings revealed that doxorubicin and its metabolites primarily exhibited binding affinity to the beta-1 adrenergic receptor and fatty acid synthase. Furthermore, we identified several key transcription factors, especially the Homeobox protein Nkx-2.6, and hsa-miR-183-3p associated with this cardiotoxicity. Finally, we observed that, in addition to doxorubicinol, 7-deoxidoxorubicinone, another metabolite of doxorubicin, may also contribute to this cardiotoxicity. These findings contribute to our understanding of the processes underlying doxorubicin-induced chronic cardiotoxicity.

Non-small-cell lung cancer (NSCLC) is one of the most deadly tumors characterized by poor survival rates. Advances in therapeutics and precise identification of biomarkers can potentially reduce the mortality rate. Thus, this study aimed to identify a set of common and stable gene biomarkers through integrated bioinformatics approaches that might be effective for NSCLC early diagnosis, prognosis, and therapies. Four gene expression profiles (GSE19804, GSE19188, GSE10072, and GSE32863) downloaded from the Gene Expression Omnibus database to identify common differential expressed genes (DEGs). A total of 213 overlapping DEGs (oDEGs) between NSCLC and healthy samples were identified by using statistical LIMMA method. Then 6 common top-ranked key genes (KGs) (CENPF, CAV1, ASPM, CCNB2, PRC1, and KIAA0101) were selected by using four network-measurer methods in the protein- protein interaction network. The GO functional and KEGG pathway enrichment analysis were performed to reveal some significant functions and pathways associated with NSCLC progression. Transcriptional and post-transcriptional factors of KGs were identified through the regulatory interaction network. The prognostic power and expression level of KGs were validated by using the independent data through the Kaplan-Meier and Box plots, respectively. Finally, 4 KGs-guided repositioning candidate drugs (ZSTK474, GSK2126458, Masitinib, and Trametinib) were proposed. The stability of three top-ranked drug-target interactions (CAV1 vs. ZSTK474, CAV1 vs. GSK2126458, and ASPM vs. Trametinib) were investigated by computing their binding free energies for 140 ns MD-simulation based on MM-PBSA approach. Therefore, the findings of this computational study may be useful for early prognosis, diagnosis and therapies of NSCLC.

Metabolic reprogramming is one of the hallmarks of breast cancer (BC), involving elevated synthesis and uptake of lipids, for catering to increased energy demand of cancer cells and to suppress the host immune system. Besides promoting proliferation and survival of BC cells, lipid metabolism reprogramming (LMR) is associated with stemness and chemoresistance. Recently, lignans have been reported for their therapeutic potential against different cancers, including BC. Here, we explored the potential of lignans to target LMR pathways in BC through computational approach. Initially, 88 lignans having potential anticancer activities, underwent druglikeness and pharmacokinetics analysis, displaying promising pharmacokinetic properties, except for 13 molecules with violations. Molecular docking assessed the interaction of 88 lignans (NPACT) with therapeutic targets of LMR including 3-Hydroxy-3-methyl-glutaryl-coenzyme A reductase (HMGCR), Sterol regulatory element-binding proteins 1 and 2 (SREBP1 and 2), Low-density lipoprotein receptor (LDLR), Acetyl-CoA Acetyltransferase 1 (ACAT1), ATP-binding cassette transporter (ABCA1), Liver X receptor α (LXRα), Apolipoprotein A1 (APOA1), Fatty Acid Synthase (FASN), Peroxisome proliferator-activated receptor gamma (PPARG), Stearoyl-CoA desaturase (SCD1), Acetyl-CoA carboxylase 1 and 2 (ACC1/ACACA, and ACC2/ACACB). In silico screening revealed sesamin (SE) as the best-identified hit that showed stable and consistent binding with all the selected targets of LMR. The stability of these complexes was validated by a 100 ns simulation run, and their binding free energy calculation was determined by MM-PBSA method. Interestingly, SE modulated the mRNA expression of genes involved in LMR in BC cell lines, MCF-7 and MDA-MB-231, thereby suggesting its potential as an inhibitor of LMR.

The SPINK2 protein, encoded by the SPINK2 gene, plays an essential role in the normal development of spermatozoa, and its deficiency is associated with spermatogenesis disorders ranging from aspermia to azoospermia. This study aimed to identify the most deleterious variants of the SPINK2 gene and to evaluate their effects on protein structure and function through an in silico approach. A total of 8,028 variants were identified, including 72 missense variants. Using 11 bioinformatics tools, six variants (P50L, T58I, C66Y, E62A, P42S, and P45L) were predicted to have deleterious effects. Protein-protein interaction analysis using the STRING database revealed strong functional associations between SPINK2, SPINK1, and ACR, and medium-confidence associations with SPINK4, SPINK13, PMPCA, KLK4, SPINK9, SPINK6, SPACA1, and NUDT8. Local structural analysis showed that variants such as T58I and C66Y gained additional hydrophobic interactions, whereas P50L and P42S lost key interactions, potentially impairing protein stability and function. Molecular dynamics simulations using GROMACS revealed that P50L enhances protein stability, reduces amino acid flexibility, and increases the overall dimensions of the protein. T58I had a mild effect on stability, whereas E62A and C66Y decreased stability and flexibility while increasing protein size. P42S and P45L induced slight stability alterations, reduced flexibility, and enlarged the protein. Overall, these structural and dynamic changes suggest functional impairment of SPINK2. To our knowledge, this is the first study to identify six deleterious SPINK2 variants with potential roles in the disruption of spermatogenesis, providing a foundation for future functional and clinical investigations.