Search Results (154)

Osteoclastogenesis—the activation and differentiation of osteoclasts—is one of the pivotal processes of bone remodeling and is regulated by RANKL/RANK signaling, the decoy function of osteoprotegerin (OPG), and a cascade of pro- and anti-inflammatory cytokines. The disruption of this balance leads to pathological bone loss in diseases such as osteoporosis and rheumatoid arthritis. FFAR4 (Free Fatty Acid Receptor 4), a G protein-coupled receptor for long-chain omega-3 fatty acids, has been confirmed as a key mediator of metabolic and anti-inflammatory effects. This review focuses on how FFAR4 acts as the selective receptor for the omega-3 fatty acid docosahexaenoic acid (DHA). It activates two divergent signaling pathways. The Gαq-dependent cascade facilitates intracellular calcium mobilization and ERK1/2 activation. Meanwhile, β-arrestin-2 recruitment inhibits NF-κB. These collective actions reshape the cytokine environment. In macrophages, DHA–FFAR4 signaling lowers the levels of TNF-α, interleukin-6 (IL-6), and IL-1β while increasing IL-10 secretion. Consequently, the activation of NFATc1 and NF-κB p65 is profoundly suppressed under TNF-α or RANKL stimulation. Additionally, DHA modulates the RANKL/OPG axis in osteoblastic cells by suppressing RANKL expression, thereby reducing osteoclast differentiation in an inflammatory mouse model. Full article

Background: The treatment and management of atrial fibrillation poses substantial complexity. A delicate balance in the trade-off between the minimising risk of stroke without increasing the risk of bleeding through anticoagulant optimisations. Natural compounds are often associated with low-toxicity effects, and their effects on atrial fibrillation have yet to be fully understood. Whilst deep learning (a subtype of machine learning that uses multiple layers of artificial neural networks) methods may be useful for drug compound interaction and discovery analysis, graphical processing units (GPUs) are expensive and often required for deep learning. Furthermore, in limited-resource settings, such as low- and middle-income countries, such technology may not be easily available. Objectives: This study aims to discover the presence of any new therapeutic candidates from a large set of natural compounds that may support the future treatment and management of atrial fibrillation anywhere using a low-cost technique. The objective is to develop a deep learning approach under a low-resource setting where suitable high-performance NVIDIA graphics processing units (GPUs) are not available and to apply to atrial fibrillation as a case study. Methods: The primary training dataset is the MINER-DTI dataset from the BIOSNAP collection. It includes 13,741 DTI pairs from DrugBank, 4510 drug compounds, and 2181 protein targets. Deep cross-modal attention modelling was developed and applied. The Database of Useful Decoys (DUD-E) was used to fine-tune the model using contrastive learning. This application and evaluation of the model were performed on the natural compound NPASS 2018 dataset as well as a dataset curated by a clinical pharmacist and a clinical scientist. Results: the new model showed good performance when compared to existing state-of-the-art approaches under low-resource settings in both the validation set (PR AUC: 0.8118 vs. 0.7154) and test set (PR AUC: 0.8134 vs. 0.7206). Tenascin-C (TNC; NPC306696) and deferoxamine (NPC262615) were identified as strong natural compound interactors of the arrhythmogenic targets ADRB1 and HCN1, respectively. A strong natural compound interactor of the bleeding-related target Factor X was also identified as sequoiaflavone (NPC194593). Conclusions: This study presented a new high-performing model under low-resource settings that identified new natural therapeutic candidates for pharmacological cardioversion and anticoagulation. Full article

Increasing recognition of the importance of the tumor microenvironment (TME) in cancer therapeutic strategies has led to more efforts to target molecules in the TME. Vasorin (VASN) is a transmembrane glycoprotein that can be cleaved and released into the extracellular matrix in a soluble form (sVASN), which is regarded as a decoy that inhibits the TGF-β signaling pathway. VASN is upregulated under hypoxic or tumorigenic conditions to regulate tumor progression. In this study, cell surface CD71 was identified as a specific binding protein of sVASN and mediated the internalization of sVASN in cancerous, endothelial and T cells. Endocytosed sVASN enhanced the nuclear translocation of p-STAT3(Tyr705), leading to the activation of a cascade of genes, ultimately contributing to tumor malignant progression. In cancer cells, sVASN promoted cell proliferation and migration by upregulating the YAP1/TAZ or mTOR-AKT pathways and it promotes stemness maintenance by regulating Notch1. In endothelial cells, sVASN facilitated angiogenesis through the VEGF signaling pathway. In T cells, sVASN inhibited the activation of T cells through AKT pathway. This study elucidated the mechanism by which sVASN acts as a tumor-promoting factor to accelerate tumor malignant progression through cell-surface CD71 and presented sVASN as a novel target for cancer therapy. Full article

Accurately predicting protein–ligand binding affinity is an important step in the drug discovery process. Deep learning (DL) methods have improved binding affinity prediction by using diverse categories of molecular data. However, many models rely heavily on interaction and sequence data, which impedes proper learning and limits performance in de novo applications. To address these limitations, we developed a novel graph neural network model, called StructureNet (structure-based graph neural network), to predict protein–ligand binding affinity. StructureNet improves existing DL methods by focusing entirely on structural descriptors to mitigate data memorization issues introduced by sequence and interaction data. StructureNet represents the protein and ligand structures as graphs, which are processed using a GNN-based ensemble deep learning model. StructureNet achieved a PCC of 0.68 and an AUC of 0.75 on the PDBBind v.2020 Refined Set, outperforming similar structure-based models. External validation on the DUDE-Z dataset showed that StructureNet can effectively distinguish between active and decoy ligands. Further testing on a small subset of well-known drugs indicates that StructureNet has high potential for rapid virtual screening applications. We also hybridized StructureNet with interaction- and sequence-based models to investigate their impact on testing accuracy and found minimal difference (0.01 PCC) between merged models and StructureNet as a standalone model. An ablation study found that geometric descriptors were the key drivers of model performance, with their removal leading to a PCC decrease of over 15.7%. Lastly, we tested StructureNet on ensembles of binding complex conformers generated using molecular dynamics (MD) simulations and found that incorporating multiple conformations of the same complex often improves model accuracy by capturing binding site flexibility. Overall, the results show that structural data alone are sufficient for binding affinity predictions and can address pattern recognition challenges introduced by sequence and interaction features. Additionally, structural representations of protein–ligand complexes can be considerably improved using geometric and topological descriptors. We made StructureNet GUI interface freely available online. Full article

Background/Objectives: In the tumor microenvironment, hypoxia regulates genes that support tumor cell invasion and angiogenesis under the control of the hypoxia-inducible transcription factors (HIFs). Pleiotrophin (PTN) is a secreted protein that activates cell migration in endothelial and cancer cells that express α_νβ₃ integrin but has inhibitory effects in cells that do not express α_νβ₃ integrin. In both cases, the protein tyrosine phosphatase receptor zeta 1 (PTPRZ1) seems to mediate the effects of PTN. In the present work, we studied the effect of hypoxia on PTN and PTPRZ1 expression and the functional consequences of this effect. Methods: Western blot, quantitative real-time PCR, and luciferase assays were used to study the impact of hypoxia at the protein, mRNA, and transcriptional levels, respectively. Decoy oligonucleotides (ODNs), siRNA technology, and plasmid overexpression were used to study the involvement of the transcription factors studied. Functional assays were used to study the effect of hypoxia on cell proliferation and migration. Results: Hypoxia increases PTN expression through the transcriptional activation of the corresponding gene in α_νβ₃ integrin-expressing cells. The transcription factors HIF-1α, HIF-2α, and AP-1 mediate the up-regulation of PTN by hypoxia. Functional assays in endothelial cells from PTN knockout mice or endothelial and cancer cells following the downregulation of PTN expression showed that PTN negatively affects chemical hypoxia-induced cell proliferation and migration. In cancer cells that do not express α_νβ₃ integrin, hypoxia or chemical hypoxia inhibits PTN expression in a HIF-1α-, HIF-2α-, and AP-1-independent manner. The expression of PTPRZ1 is up-regulated by chemical hypoxia, is HIF-1α- and HIF-2α-dependent, and seems to limit the activation of HIF-1α, at least in endothelial cells. Conclusions: Hypoxia or chemical hypoxia regulates PTN and PTPRZ1 expressions to restrict the stimulatory effects of hypoxia on endothelial and cancer cell migration. Full article

Alzheimer’s disease (AD), one of the neurodegenerative disorders, afflicts negatively across the whole world. Due to its complex etiology, no available treatments are disease-altering. This study aimed to explore isolated saponins profiles from Platycodon grandiflorum in the binding pockets of six target proteins of AD using computational and quantum chemistry simulations. Initially, saponin compounds were docked to AD enzymes, such as GSK-3β and synapsin I, II, and III. The subsequent research from MD simulations of the best three docked compounds (polygalacin D2, polygalacin D, and platycodin D) suggested that their profiles match with the binding of standard active drugs like ifenprodil and donepezil to the six enzymes. Moreover, analyzing DFT quantum calculations of top-scoring compounds fully unravels their electronic and quantum properties and potential in anti-AD. The subtle differences between polygalacin D and D2, and platycodin D, were studied at the level of theory DFT/B3LYP, showing that the electron-donating effect of the hydroxy ethyl group in platycodin D rendering this compound of moderate electrophilicity and reactivity. Polygalacin D2 diglucoside substituent in position-2 contributed to its best binding and intermolecular interactions more than polygalacin D and prosapogenin D, which acted as the negative decoy drug. Full article

Heart failure (HF) remains a major cause of mortality worldwide. While novel approaches, including gene and cell therapies, show promise, efficient delivery methods for such biologics to the heart are critically needed. One emerging strategy is lung-to-heart delivery using nanoparticle (NP)-encapsulated biologics. This study examines the efficiency of delivering a therapeutic peptide conjugated to a cell-penetrating peptide (CPP) to the heart via the lung-to-heart route through intratracheal (IT) injection in mice. The CPP, a tandem repeat of NP2 (dNP2) derived from the human novel LZAP-binding protein (NLBP), facilitates intracellular delivery of the therapeutic payload. The therapeutic peptide, SE, is a decoy peptide designed to inhibit protein phosphatase 1 (PP1)-mediated dephosphorylation of phospholamban (PLN). Our results demonstrated that IT injection of dNP2-SE facilitated efficient delivery to the heart, with peak accumulation at 3 h post-injection. The administration of dNP2-SE significantly ameliorated morphological and functional deterioration of the heart under myocardial infarction. At the molecular level, dNP2-SE effectively prevented PLN dephosphorylation in the heart. Immunoprecipitation experiments further revealed that dNP2-SE binds strongly to PP1 and disrupts its interaction with PLN. Collectively, our findings suggest that lung-to-heart delivery of a CPP-conjugated therapeutic peptide, dNP2-SE, represents a promising approach for the treatment of HF. Full article

Cytochromes P450 are a superfamily of heme-containing monooxygenases involved in a variety of oxidative metabolic reactions, primarily catalyzing the insertion of an oxygen atom into a C-H bond. CYP102 represents the first example of a bacterial P450 that can be classified as a type II (eukaryotic-like) P450 and functions as a catalytically self-sufficient enzyme. These unique features have made CYP102 an attractive system for studying P450 structure and function. However, an overall picture of the specific amino acid residues that are crucial to the functioning of CYP102 and the effect of mutations on the P450 structure and catalysis is yet to be reported. Such an approach will aid protein engineering approaches used to improve this enzyme. To address this research knowledge gap, we have investigated 105 CYP102 crystal structures in this study. We demonstrate that the CYP102 active site is highly dynamic and flexible. Amino acid residues that play critical roles in substrate binding, orientation, and anchoring were identified. Mutational studies highlighted the roles of amino acids and provided possible bioengineering improvement strategies for CYP102. Decoy molecules are a promising agent for deceiving CYP102 and permitting non-native substrates into the active site. Ru(II)-diimine photosensitizers and zinc/cobalt (III) sepulchrate (Co(III)Sep) could be used as alternative electron sources. The present study serves as a reference for understanding the structure–functional analysis of CYP102 family members precisely and of P450 enzymes in general. Significantly, this work contributes to the effort to develop an improved CYP102 enzyme, thereby advancing the field of P450 research and potentially leading to new industrial applications. Full article

Background/Objectives: Accurately predicting protein–ligand binding affinity is essential in drug discovery for identifying effective compounds. While existing sequence-based machine learning models for binding affinity prediction have shown potential, they lack accuracy and robustness in pattern recognition, which limits their generalizability across diverse and novel binding complexes. To overcome these limitations, we developed GNNSeq, a novel hybrid machine learning model that integrates a Graph Neural Network (GNN) with Random Forest (RF) and XGBoost. Methods: GNNSeq predicts ligand binding affinity by extracting molecular characteristics and sequence patterns from protein and ligand sequences. The fully optimized GNNSeq model was trained and tested on subsets of the PDBbind dataset. The novelty of GNNSeq lies in its exclusive reliance on sequence features, a hybrid GNN framework, and an optimized kernel-based context-switching design. By relying exclusively on sequence features, GNNSeq eliminates the need for pre-docked complexes or high-quality structural data, allowing for accurate binding affinity predictions even when interaction-based or structural information is unavailable. The integration of GNN, XGBoost, and RF improves GNNSeq performance by hierarchical sequence learning, handling complex feature interactions, reducing variance, and forming a robust ensemble that improves predictions and mitigates overfitting. The GNNSeq unique kernel-based context switching scheme optimizes model efficiency and runtime, dynamically adjusts feature weighting between sequence and basic structural information, and improves predictive accuracy and model generalization. Results: In benchmarking, GNNSeq performed comparably to several existing sequence-based models and achieved a Pearson correlation coefficient (PCC) of 0.784 on the PDBbind v.2020 refined set and 0.84 on the PDBbind v.2016 core set. During external validation with the DUDE-Z v.2023.06.20 dataset, GNNSeq attained an average area under the curve (AUC) of 0.74, demonstrating its ability to distinguish active ligands from decoys across diverse ligand–receptor pairs. To further evaluate its performance, we combined GNNSeq with two additional specialized models that integrate structural and protein–ligand interaction features. When tested on a curated set of well-characterized drug–target complexes, the hybrid models achieved an average PCC of 0.89, with the top-performing model reaching a PCC of 0.97. GNNSeq was designed with a strong emphasis on computational efficiency, training on 5000+ complexes in 1 h and 32 min, with real-time affinity predictions for test complexes. Conclusions: GNNSeq provides an efficient and scalable approach for binding affinity prediction, offering improved accuracy and generalizability while enabling large-scale virtual screening and cost-effective hit identification. GNNSeq is publicly available in a server-based graphical user interface (GUI) format. Full article

Introduction: Osteoprotegerin (OPG), encoded by the TNFRSF11B gene, is linked to the development of breast cancer via several pathways, including interactions with the receptor activator of nuclear factor-κB (RANK) ligands, apoptosis-inducing proteins like TRAIL, and genetic variations such as single nucleotide polymorphisms (SNPs), directly altering gene expression. This review aims to investigate the role of OPG expression in breast cancer. Methods: A comprehensive literature search was conducted using PubMed Medline, Google Scholar, and ScienceDirect. Only full-text English publications from inception to September 2024 were included. Results: Studies have demonstrated that certain SNPs in the OPG gene, specifically rs3102735 and rs2073618, are linked to a higher risk of breast cancer development. Additionally, OPG’s function as a TRAIL decoy receptor may inhibit the death of cancer cells. Furthermore, OPG in the serum and its interactions with BRCA mutations are being investigated for their potential influence on breast cancer progression. Studies have found that OPG promotes tumorigenesis by enhancing cell proliferation, angiogenesis, and aneuploidy in normal mammary epithelial cells. Moreover, OPG mediates the tumor-promoting effects of interleukin-1 beta and may serve as a biomarker for breast cancer risk, particularly in BRCA1 mutation carriers, through its role in dysregulated RANK signaling. Lastly, the use of recombinant OPG in mouse models has been found to exert anti-tumor effects. Conclusions: In this review, the role of OPG in breast cancer is examined. OPG has a multifaceted role in breast cancer tumorigenesis and exerts its effects through genetic variations (SNPs), interactions with TNF-related apoptosis-inducing ligand (TRAIL), and the modulation of the pro-tumorigenic microenvironment effects of angiogenesis, cell survival, and metastasis. Additionally, OPG’s dual role as a tumor suppressor and promoter serves as a possible therapeutic target to enhance apoptosis, limit bone metastasis, and modulate the tumor microenvironment. Whilst much is now known, further studies are necessary to fully delineate the role of OPG. Full article

Porcine reproductive and respiratory syndrome virus (PRRSV), an important pathogen affecting the pig industry, is an RNA virus with high genetic diversity. In this study, 12,299 clinical samples were collected from northern China during 2021–2023 to investigate the molecular epidemiological characteristics and genetic evolution of PRRSV. All samples were screened using qRT-PCR and further analyzed through ORF5 gene and whole-genome sequencing. The results showed that the positive rate of PRRSV in northern China was 18.42%, and positivity rates were relatively high in spring. The phylogenetic analysis of the ORF5 gene indicated that the 174 gene sequences were classified as PRRSV-2, predominantly found in Lineage 1.8 (L1.8), Lineage 1.5 (L1.5), and Lineage 8 (L8). L1.8 and L1.5 showed considerable polymorphism at decoy and neutralizing epitopes. Mutations of specific amino acids were present in L1.8 and L1.5 at T- and B-cell epitopes. Moreover, the 27 whole-genome sequences were analyzed. As indicated, 24 of them were exposed to gene recombination, and L1.8 provided the backbone for recombination events. The predominant recombination modes were L1.8 + L8.7 + L1.5/L3, with L1.5 and L3.5 generally yielding GP2~GP6 structural proteins. Recombination hotspots were primarily located within the ranges of 780~2200 (Nsp1~Nsp2), 5400~6200 (Nsp3~Nsp4), 7800~9000 (Nsp9), and 12,200~14,800 (ORF2~ORF6). This study enriches the epidemiological data of PRRSV in northern China, thereby providing theoretical references for the prevention and control of PRRSV in northern China. Full article

The rise in antibiotic-resistant Staphylococcal infections necessitates innovative approaches to identify new therapeutic agents. This study investigates the application of machine learning models to identify potential phytochemical inhibitors against BacA, a target related to Staphylococcal infections. Active compounds were retrieved from BindingDB while the decoy was generated from DUDE. The RDKit was utilized for feature engineering. Machine learning models such as k-nearest neighbors (KNN), the support vector machine (SVM), random forest (RF), and naive Bayes (NB) were trained on an initial dataset consisting of 226 active chemicals and 2550 inert compounds. Accompanied by an MCC of 0.93 and an accuracy of 96%, the RF performed better. Utilizing the RF model, a library of 9000 phytochemicals was screened, identifying 300 potentially active compounds, of which 192 exhibited drug-like properties and were further analyzed through molecular docking studies. Molecular docking results identified Ergotamine, Withanolide E, and DOPPA as top inhibitors of the BacA protein, accompanied by interaction affinities of −8.8, −8.1, and −7.9 kcal/mol, respectively. Molecular dynamics (MD) was applied for 100 ns to these top hits to evaluate their stability and dynamic behavior. RMSD, RMSF, SASA, and Rg analyses showed that all complexes remained stable throughout the simulation period. Binding energy calculations using MMGBSA analysis revealed that the BacA_Withanolide E complex exhibited the most favorable binding energy profile with significant van der Waals interactions and a substantial reduction in gas-phase energy. It also revealed that van der Waals interactions contributed significantly to the binding stability of Withanolide E, while electrostatic interactions played a secondary role. The integration of machine learning models with molecular docking and MD simulations proved effective in identifying promising phytochemical inhibitors, with Withanolide E emerging as a potent candidate. These findings provide a pathway for developing new antibacterial agents against Staphylococcal infections, pending further experimental validation and optimization. Full article

The mutational drift of SARS-CoV-2 and the appearance of multiple variants, including the latest Omicron variant and its sub-lineages, has significantly reduced (and in some cases abolished) the protective efficacy of Wuhan spike-antigen-based vaccines and therapeutic antibodies. One of the most functionally constrained and thus largely invariable regions of the spike protein is the one involved in the interaction with the ACE2 receptor mediating the cellular entry of SARS-CoV-2. Engineered ACE2, both as a full-length protein or as an engineered polypeptide fragment, has been shown to be capable of preventing the host-cell binding of all viral variants and to be endowed with potent SARS-CoV-2 neutralization activity both in vitro and in vivo. Here, we report on the biochemical and antiviral properties of rationally designed ACE2 N-terminal, three-helix fragments that retain a native-like conformation. One of these fragments, designated as PRP8_3H and produced in recombinant form, bears structure-stabilizing and binding-affinity enhancing mutations in α-helix-I and in both α-helix I and II, respectively. While the native-like, unmodified three α-helices ACE2 fragment proved to be thermally unstable and without any detectable pseudovirion neutralization capacity, PRP8_3H was found to be highly thermostable and capable of binding to the SARS-CoV-2 spike receptor-binding domain with nanomolar affinity and to neutralize both Wuhan and Omicron spike-expressing pseudovirions at (sub)micromolar concentrations. PRP8_3H thus lends itself as a highly promising ACE2 decoy prototype suitable for a variety of formulations and prophylactic applications. Full article

Even though COVID-19 is no longer the primary focus of the global scientific community, its high mutation rate (nearly 30 substitutions per year) poses a threat of a potential comeback. Effective vaccines have been developed and administered to the population, ending the pandemic. Nonetheless, reinfection by newly emerging subvariants, particularly the latest JN.1 strain, remains common. The rapid mutation of this virus demands a fast response from the scientific community in case of an emergency. While the immune escape of earlier variants was extensively investigated, one still needs a comprehensive understanding of how specific mutations, especially in the newest subvariants, influence the antigenic escape of the pathogen. Here, we tested comprehensive in silico approaches to identify methods for fast and accurate prediction of antibody neutralization by various mutants. As a benchmark, we modeled the complexes of the murine antibody 2B04, which neutralizes infection by preventing the SARS-CoV-2 spike glycoprotein’s association with angiotensin-converting enzyme (ACE2). Complexes with the wild-type, B.1.1.7 Alpha, and B.1.427/429 Epsilon SARS-CoV-2 variants were used as positive controls, while complexes with the B.1.351 Beta, P.1 Gamma, B.1.617.2 Delta, B.1.617.1 Kappa, BA.1 Omicron, and the newest JN.1 Omicron variants were used as decoys. Three essentially different algorithms were employed: forced placement based on a template, followed by two steps of extended molecular dynamics simulations; protein–protein docking utilizing PIPER (an FFT-based method extended for use with pairwise interaction potentials); and the AlphaFold 3.0 model for complex structure prediction. Homology modeling was used to assess the 3D structure of the newly emerged JN.1 Omicron subvariant, whose crystallographic structure is not yet available in the Protein Database. After a careful comparison of these three approaches, we were able to identify the pros and cons of each method. Protein–protein docking yielded two false-positive results, while manual placement reinforced by molecular dynamics produced one false positive and one false negative. In contrast, AlphaFold resulted in only one doubtful result and a higher overall accuracy-to-time ratio. The reasons for inaccuracies and potential pitfalls of various approaches are carefully explained. In addition to a comparative analysis of methods, some mechanisms of immune escape are elucidated herein. This provides a critical foundation for improving the predictive accuracy of vaccine efficacy against new viral subvariants, introducing accurate methodologies, and pinpointing potential challenges. Full article

Soluble CD163 (sCD163) is a circulating inflammatory mediator, indicative of acute and chronic, systemic and non-systemic inflammatory conditions. It is the cleavage outcome, consisting of almost the entire extracellular domain, of the CD163, a receptor expressed in monocytic lineages. Its expression is proportional to the abundance of CD163⁺ macrophages. Various mechanisms trigger the shedding of the CD163 receptor or the accumulation of CD163-expressing macrophages, inducing the sCD163 concentration in the circulation and bodily fluids. The activities of sCD163 range from hemoglobin (Hb) scavenging, macrophage marker, decoy receptor for cytokines, participation in immune defense mechanisms, and paracrine effects in various tissues, including the endothelium. It is an established marker of macrophage activation and thus participates in many diseases, including chronic inflammatory conditions, such as atherosclerosis, asthma, and rheumatoid arthritis; acute inflammatory conditions, such as sepsis, hepatitis, and malaria; insulin resistance; diabetes; and tumors. The sCD163 levels have been correlated with the severity, stage of the disease, and clinical outcome for many of these conditions. This review article summarizes the expression and role of sCD163 and its precursor protein, CD163, outlines the sCD163 generation mechanisms, the biological activities, and the known underlying molecular mechanisms, with an emphasis on its impact on the endothelium and its contribution in the pathophysiology of human diseases. Full article