Search Results (1,540)

Early diagnosis of breast cancer (BC) remains challenging. The limited sensitivity and specificity of existing serum tumor markers for reliable clinical application highlight the need to develop a more accurate and efficient screening workflow. This study analyzed serum samples from 255 breast cancer patients and 300 healthy controls using matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry, identifying 58 differentially expressed peptides (37 upregulated, 21 downregulated). Combined with machine learning, peptide identification, and external validation, a complete standardized workflow was established. Nine machine learning (ML) algorithms were employed and compared, including SVM, LightGBM, XGBoost, etc. The models were interpreted using SHAP and LIME to identify key features. Peptides of interest were sequenced via mass spectrometry. Their expression and potential prognostic value were further validated in breast cancer transcriptomic datasets. Nine machine learning algorithms showed favorable discriminatory ability in the study cohort. The LightGBM model achieved an AUC of 0.97 internally and maintained an AUC of 0.88, an accuracy of 0.8543, and a precision of 0.9799 externally. However, after correcting for the markedly elevated prevalence (80.3%) in the external cohort, the positive predictive value (PPV) decreased substantially under real-world screening scenarios, warranting prospective validation in true screening populations. Model interpretation and subsequent sequencing identified six core biomarker peptides: Apolipoprotein A-IV (APOA4), Serum Deprivation Response Protein (SDPR), Alpha-1-Antitrypsin (SERPINA1), Ezrin (EZR), Serglycin (SRGN), and Fibrinogen Alpha Chain (FGA). Transcriptomic corroboration suggested that these molecules were significantly dysregulated in breast cancer tissues and showed univariate prognostic associations with patient survival. These findings demonstrated the potential of a proteomics-driven integrated machine learning pipeline as a proof-of-concept auxiliary risk-stratification tool for enhancing early breast cancer diagnosis, warranting further prospective validation in real-world screening cohorts before clinical translation. Full article

Many people are affected by difficult-to-understand clinical phenomena associated with acute SARS-CoV-2 infection and by post-acute sequelae of COVID-19 (PASC, or long COVID, LC). The mechanisms responsible for the clinical phenomena have not been well established. The host cell receptor for SARS-CoV-2 is human angiotensin-converting enzyme 2 (ACE2), which binds the SARS-CoV-2 spike protein receptor-binding domain (RBD) to initiate infection. We hypothesized that some people may produce anti-RBD antibodies that sufficiently resemble ACE2 structure to have ACE2-like catalytic activity after infection, and such antibodies are hypothesized to contribute to disease pathogenesis. Our previous studies showed that ACE2-like catalytic activity was associated with immunoglobulin in some acute and convalescent COVID-19 patients. ACE2-like catalytic activity correlated with blood pressure changes following a moderate exercise challenge in people convalescing from COVID-19. To further establish that ACE2-like catalytic activity could be attributed to antibodies, we screened human monoclonal antibodies (mAbs) against SARS-CoV-2 spike protein from three different research centers and others purchased from a commercial source for ACE2-like catalytic activity. We identified four human monoclonal antibodies with ACE2-like catalytic activity. The ACE2-like catalytic activity of these mAbs was not inhibited by MLN-4760, a compound that inhibits native human ACE2 catalytic activity, nor by EDTA, unlike native ACE2, a zinc metalloprotease, but was inhibited by an overlapping pool of spike peptides. Enzyme kinetic studies showed that the mAbs had substantially lower Vmax and K_m values than native ACE2, consistent with the characteristics of other catalytic antibodies. The data therefore suggested that the antibodies cleave ACE2 substrate via a mechanism different from native ACE2. The identification of specific mAbs with ACE2-like catalytic activity supports the hypothesis that antibodies induced by SARS-CoV-2 infection could help mediate the pathogenesis of COVID-19 and LC, and, more generally, the hypothesis that catalytic antibodies induced by infectious agents can contribute to disease pathogenesis. Full article

Tumor angiogenesis plays a critical role in cancer growth and metastatic progression and therefore represents an important therapeutic target. Bee venom-derived peptides constitute a diverse source of bioactive molecules whose interactions with angiogenesis-associated receptors remain incompletely characterized. In this study, an integrated in silico workflow was applied to investigate the potential interactions of selected bee venom peptides, including melittin and tertiapin, with major angiogenic-associated receptors, including platelet-derived growth factor receptor alpha (PDGFR-α), vascular endothelial growth factor receptors, fibroblast growth factor receptors, and integrin αvβ3. Molecular docking was used to identify and prioritize potential peptide–receptor complexes, while molecular dynamics simulations were performed to assess their conformational stability and interaction patterns under the simulated conditions. Reactome pathway enrichment analysis was subsequently used to contextualize the selected molecular targets within angiogenesis-related biological processes. Melittin–PDGFR-α and tertiapin–integrin αvβ3 emerged as the highest-ranked complexes and exhibited relatively stable interaction patterns with persistent intermolecular contacts throughout the simulations. Pathway enrichment analysis further associated the investigated targets with PDGFR signaling, cell migration, and other angiogenesis-related processes. Collectively, these findings suggest that melittin–PDGFR-α and tertiapin–integrin αvβ3 represent promising peptide–receptor pairs for further investigation while providing a computational rational for exploring bee venom peptides as potential modulators of tumor angiogenesis. However, these findings should be considered hypothesis-generating and primarily serve to prioritize peptide-receptor pairs for future experimental validation, including binding assays, cellular assays, and relevant in vivo studies. Peptide selectivity, toxicity, stability, and targeted delivery should also be evaluated before any therapeutic application can be considered. Full article

This study used locally sourced black, red, and white quinoa from Zhangjiakou as experimental materials to optimize the preparation process of quinoa antioxidant peptides and identify their sequence characteristics. Protein was extracted using the alkali-soluble acid precipitation method. The optimal hydrolytic enzyme was selected by comparing the hydrolysis effects of eight proteases. Enzymatic hydrolysis conditions were optimized using single-factor experiments and response surface methodology. After purification by Sephadex G-15 gel filtration chromatography, peptide sequences were identified by liquid chromatography–tandem mass spectrometry (LC-MS/MS) combined with De novo sequencing technology. The results showed that black quinoa and alcalase were identified as the optimal raw material and hydrolytic enzyme, respectively. The optimal enzymatic hydrolysis conditions were an enzyme dosage of 4000 U·g⁻¹, hydrolysis time of 3.5 h, temperature of 42 °C, and pH 7.6, under which the average DPPH radical scavenging rate reached 86.61%. The purified G-1 fraction exhibited the highest antioxidant activity. A total of 1549 peptide sequences with confidence ≥90% were identified, among which peptides containing two or more hydrophobic amino acids accounted for 60.62%. This compositional feature is consistent with structural characteristics commonly associated with antioxidant peptides. This study provides a reference for the processing of quinoa-based functional ingredients. Full article

The skin of Rana amurensis serves as an important defense barrier against microbial challenges, yet the molecular basis of its cutaneous immune defense and antimicrobial peptide (AMP) repertoire remains incompletely understood. Here, we established a skin transcriptomic profile of R. amurensis following Aeromonas hydrophila infection and integrated transcriptomic and bioinformatic approaches to characterize transcriptional responses and identify candidate AMPs. A total of 1669 differentially expressed genes (DEGs) were identified between the uninfected and infected groups, including 1091 up-regulated and 578 down-regulated genes. Infection with A. hydrophila induced marked transcriptional changes in innate immune-related genes and pathways, including differential expression of genes associated with the Toll-like receptor (TLR) signaling pathway. Transcriptome-based screening identified 151 candidate AMP sequences. Based on sequence characteristics, evolutionary conservation, and physicochemical properties (length ≤50 amino acids and net charge ranging from +2 to +9), six candidates were selected and three randomly selected candidates were chemically synthesized and exhibited antimicrobial activity against bacterial pathogens, inducing less than 10% hemolysis and maintaining L929 cell viability above 79% at 64 μg/mL. These findings indicate that A. hydrophila infection induces coordinated transcriptional responses involving innate immune-related pathways and diverse candidate AMPs in R. amurensis. This study supports the utility of transcriptome-guided discovery for identifying candidate AMPs in amphibians and provides insights into the molecular basis of skin defense in R. amurensis. Full article

Persistently poor glioblastoma (GBM) survival necessitates better elucidation of tumor drug responses. After observing that low curcumin and all-trans retinoic acid (ATRA) doses stimulated cell proliferation and counteracted each other’s high-dose antiproliferative effects in U87 GBM cells, drug influences on cell growth, migration, and death and the antiproliferative interaction proteome were further studied. Cell proliferation and migration were assessed by xCELLigence Real-Time Cell Analysis (RTCA). Cell death was defined using flow cytometry. Drug interactions were determined with CompuSyn software (version 1.0). Liquid Chromatography–Tandem Mass Spectrometry (LC-MS/MS), High-Performance Liquid Chromatography (HPLC), and SequestHT software (version 1.4) were utilized for peptide generation and identification. ATRA at high doses inhibited cell growth and migration more efficiently. Curcumin was more proliferative and antagonistic against anti-growth effects at low doses. Migration inhibition and apoptosis occurred synergistically at the highest drug doses. ATRA influenced the proteome more remarkably, reducing Transforming Growth Factor Beta Induced (TGFBI), Phosphoglycerate Dehydrogenase (PHGDH), tenascin, and Sequestosome 1 (SQSTM1). These effects were alleviated by curcumin, except for SQSTM1. Uveal Autoantigen With Coiled-Coil Domains And Ankyrin Repeats (UACA) and Sad1 And UNC84 Domain Containing 2 (SUN2) were increased by ATRA and curcumin, and to a lesser extent by the combination. Hexokinase 2 (HXK2) was increased by curcumin and the combination. Heme Oxygenase 1 (HMOX1) was depleted by the combination, but not by the single agents. SQSTM1 and HMOX1 reductions may mediate anticancer synergism, while the remaining changes may indicate ongoing hormetic pathways not reflected in cell counts. Full article

Angiotensin-converting enzyme (ACE) inhibitory peptides are promising bioactive components for developing blood pressure-regulating functional foods. Idesia polycarpa meal (IPM), an underutilized protein-rich byproduct from oil processing, is currently restricted to low-value applications such as animal feed, and systematic research on its ACE inhibitory peptides remains largely absent. This study hypothesized that controlled enzymatic hydrolysis of IPM protein could release novel ACE inhibitory peptide candidates with high activity. Fourier-transform infrared spectroscopy and differential scanning calorimetry (DSC) confirmed that ultrasonic treatment induced moderate conformational loosening of IPM protein, with thermal denaturation temperature decreasing from 162.20 °C to 158.79 °C, while the core secondary structure remained intact, thereby improving enzymatic hydrolysis efficiency. After sequential purification via ultrafiltration and gel filtration chromatography, the CP3-H3 fraction (molecular weight < 3 kDa) exhibited the highest ACE inhibitory rate of 95.39% at 1.0 mg/mL. Amino acid analysis showed that the active fraction contained 48.92% hydrophobic amino acids and 11.55% aromatic amino acids, which matched the structural requirements for potent ACE inhibition. Eight novel ACE inhibitory peptides were identified via LC-MS/MS combined with multi-round in silico screening, all of which were not recorded in the Database of Food-derived Bioactive Peptides (DFBP). Molecular docking analysis revealed that all eight peptides could stably bind to the active pocket of ACE, among which WDW showed the strongest binding affinity to PTGS2 with a binding free energy of −10.7 kcal/mol. Network pharmacology analysis demonstrated that these peptides exerted antihypertensive effects through synergistic regulation of 142 core blood pressure homeostasis-related targets and multiple key pathways. The core peptide WNWD was chemically synthesized and verified to have an ACE inhibitory IC₅₀ value of 3.529 mM. This study demonstrates that IPM is a promising food-derived source of ACE inhibitory peptides, and provides a theoretical basis for the high-value utilization of Idesia polycarpa processing byproducts. Full article

Asthma is a heterogeneous disease that often begins in childhood and frequently occurs alongside allergic conditions. In asthma research, it is important to focus on proteins that are the primary regulators of cellular physiology. The differences in the proteome between children with asthma and those with an atopic background remain poorly understood. The present study included 130 serum samples from four groups of pediatric patients under the age of five: (1) with asthma and IgE-dependent allergies; (2) with non-atopic asthma; (3) non-asthmatics with IgE-dependent allergy; and (4) a control group without asthma and IgE-dependent allergies. The serum samples were used for protein–peptide profiling and proteomic identification using nanoLC-MALDI-TOF/TOF MS/MS. The obtained data were analyzed using univariate statistics and the STRING tool v12.0 to identify protein–protein potential interactions. A total of seven proteins were identified as discriminative between the study groups: A2M, AACT, IgG3, C3, ITIH2, IgG3 and IGK. All of them were upregulated in patients with IgE-dependent allergy compared to other study groups. STRING analysis identified functional associations among four proteins (AACT, A2M, C3, and ITIH2) with discriminatory potential for distinguishing between non-atopic asthma and non-asthmatic patients with IgE-dependent allergy. The results suggest that the identified putative protein markers overlap in cellular pathways, including those associated with the pathophysiology of asthma and allergic disorders. These findings provide further insight into the overall proteomic profile of pediatric patients with asthma and IgE-dependent allergy, highlighting its heterogeneity across the analyzed groups. Full article

Accurate computational prediction of antiviral peptides (AVPs) can accelerate peptide screening and reduce experimental costs. However, existing deep learning-based methods still suffer from severe class imbalance, over-reliance on handcrafted features and limited interpretability. Here, we propose PMAVP, a multi-task learning framework that integrates the ProtT5 pre-trained protein language model with a Mamba-inspired module for AVP identification and functional activity prediction. We use ProtT5 to extract deep semantic representations from peptide sequences and a Mamba module to capture long-range dependencies at a lower computational complexity. We introduce Focal Loss to mitigate class imbalance and leverage transfer learning to enhance performance on functional activity prediction. Experimental results demonstrate that our model achieves superior performance in terms of prediction accuracy, stability, and computational efficiency. Furthermore, DeepSHAP-based interpretability analysis reveals that the first 40 amino acid residues contribute substantially to AVP prediction. Full article

Background: Orientothele washanensis is a venomous spider with considerable ecological and scientific importance. Its venom, characterized by complex composition and ease of collection, serves as a valuable resource for the discovery of natural peptide drugs. Conventional wet-lab screening methods are limited by rigorous experimental conditions, high resource consumption, and long research cycles, which hinder the efficient identification of functional peptides from the venom gland transcriptome of this spider. Methods: To address these technical bottlenecks, this study developed a novel deep learning model named PepPI-DRN for peptide-protein interaction prediction. The model integrated a residual equivariant graph neural network, a residual 1-dimensional convolutional neural network, and a dual-modal attention mechanism by leveraging both sequence and structural features of peptides and proteins. Results: Results on an independent test set indicated that PepPI-DRN achieved the competitive or superior performance compared with state-of-the-art methods on multiple key evaluation metrics. Candidate peptides with high interaction probabilities against targets were obtained from the venom gland transcriptome of Orientothele washanensis. Furthermore, lead peptides with high binding strength and good structural stability were identified from candidate peptides by molecular docking, and molecular dynamics simulation. Conclusions: These results showed that the pipeline with PepPI-DRN, molecular docking and molecular dynamics simulation enabled efficient and reliable identification of lead peptides from the venom gland transcriptome of Orientothele washanensis, providing a robust and effective strategy for the discovery and development of natural peptide drugs from the spider venom. Full article

Over the past decade, advances in sequencing technologies and computational pipelines enabled the development of personalized cancer vaccines (PCVs). Current PCV strategies primarily target cancer neoantigens generated by non-synonymous DNA mutations, which can result in altered amino acid sequences capable of eliciting tumor-specific immune responses. More recently, a distinct class of tumor-specific antigens (TSA), termed noncanonical or cryptic antigens, has emerged as an additional source of immunogenic targets. Unlike canonical neoantigens, noncanonical antigens typically cannot be identified by tumor/normal whole-exome sequencing, as they do not arise from classical DNA mutations. Instead, they are often associated with less well recognized and/or aberrant processes in the pathways from DNA to human leukocyte antigen (HLA)-presented peptides. Examples include transposable elements, circular RNA, translation of alternative open reading frames and/or long non-coding RNA, among others. Emerging evidence suggests that noncanonical antigens represent a substantial portion of the tumor-specific immunopeptidome and, similar to canonical neoantigens, are absent during thymic selection and can evade central tolerance and elicit T cell responses. Technological advances have increasingly facilitated the identification of noncanonical antigens. Long-read RNA sequencing reveals noncanonical transcripts by improving transcriptome assembly, while ribosome profiling provides genome-wide maps of actively translated regions, facilitating the discovery of peptides from aberrant translation events. Specialized molecular approaches enable enrichment and sequencing of circular RNAs, and immunopeptidomics using mass spectrometry allows for direct characterization of HLA-presented peptides. Together, these technological advances have led to an increasing interest in prioritizing and targeting noncanonical antigens in the next generation of PCVs. This review provides an overview of the diverse origins of TSAs beyond classical neoantigens and discusses emerging approaches that may enable the integration of these antigens in future clinical trials. Full article

Bitter peptides are a practical barrier in food-grade protein hydrolysates, fermented products, and peptide-based supplements because they can compromise flavor before nutritional or functional value is realized. Sensory panels and mass-spectrometry-based identification remain reliable, but their throughput is limited for early screening of large peptide pools. Existing predictors usually emphasize either interpretable hand-crafted descriptors or deep sequence representations, whereas these two information sources may be complementary for food-oriented bitter peptide screening. Here, we propose iBitter-HF, a hybrid feature embedding method that integrates seven classes of hand-crafted descriptors with Unified Representation (UniRep) features. Light Gradient Boosting Machine (LGBM)-based feature-importance ranking was used to organize the candidate embeddings, and eXtreme Gradient Boosting (XGB) was used for classification of the selected feature subset. On the public BTP640 benchmark, the finalized 135-feature model achieved 96.9% accuracy on the independent test set. Literature-based comparison indicated competitive performance relative to eight reported bitter peptide predictors, and dimensionality reduction visualization suggested clearer local organization of bitter and non-bitter peptides after feature optimization. These results support iBitter-HF as a computational aid for sequence-level bitter peptide screening and debittering-oriented design of protein hydrolysates. Full article

Although the Pacific oyster (Crassostrea gigas) is a premium marine protein source, its neuroprotective peptidome remains largely uncharacterized. This study established an integrated in silico and in vitro pipeline to discover acetylcholinesterase (AChE)-targeting peptides with cellular AChE-regulating and neuroprotective peptides from simulated gastrointestinal digests of oyster. Peptidomic profiling identified 18,292 sequences, which were filtered down to seven candidates predicted to have favorable blood–brain barrier (BBB) permeability and to be non-toxic and non-allergenic (VPYPR, VPVHF, HHTF, PVHF, GPKPW, HWF, and KYW) via multi-step virtual screening. In cellular assays, simulated H₂O₂ injury (500 μM) reduced PC12 cell viability to 47.53 ± 4.53%. Compared with the model group, pretreatment with the three most potent candidates—HHTF, VPYPR, and VPVHF (200 μM)—significantly rescued injured cells, restoring cell viability to 88.31 ± 7.83%, 85.12 ± 3.35%, and 82.00 ± 3.47%, respectively (p < 0.05). These peptides effectively fortified cellular antioxidant defenses by increasing glutathione (GSH) levels to 24.24, 30.11, and 26.83 nmol/mg protein (from 20.22 nmol/mg protein in the model group) and superoxide dismutase (SOD) activity to 151.41, 153.97, and 151.96 U/mg protein (from 119.33 U/mg protein), while suppressing malondialdehyde (MDA) accumulation to 0.088, 0.064, and 0.086 nmol/mg protein (from 0.193 nmol/mg protein). Crucially, the peptides alleviated cholinergic dysfunction by normalizing the H₂O₂-induced elevation of intracellular AChE activity (11.39 nmol/min/mg protein) down to 7.02, 6.22, and 7.14 nmol/min/mg protein, respectively. Specifically, VPYPR (200 μM) restored AChE activity to a level (6.22 nmol/min/mg protein) that was not significantly different from that in the normal control group (p > 0.05). Molecular dynamics (MD) simulations (100 ns) and molecular mechanics Poisson–Boltzmann surface area (MM-PBSA) calculations identified VPYPR as the leading candidate with a remarkably low binding free energy of −49.74 ± 3.58 kcal/mol. This study demonstrates that oyster gastrointestinal digests are valuable reservoirs of multi-target neuroprotective ingredients and provides an efficient strategy for marine bioactive peptide discovery. Full article

Identification of protease cleavage sites is essential for understanding biological regulation and disease mechanisms, yet many predictive approaches rely on annotated substrates and curated databases, limiting performance for poorly characterized proteases. We present PhageScout, a framework for database-independent generation of protease-specific features to predict cleavage sites using de novo experimental substrate phage display screening. We screened a randomized 5-mer phage display library against two neutrophil serine proteases (cathepsin G, elastase). Cleaved peptides generated position weight matrices (PWMs) and peptide enrichment scores to evaluate cleavage-site likelihood across substrate sequences. Sequence-derived scores were integrated with structural features, including accessibility and flexibility, using XGBoost classification models. Performance was benchmarked against annotated cleavage sites from the MEROPS peptidase database as reference data. Phage-derived PWM scores alone captured protease preferences and discriminated cleavage sites from background sites. Without model fitting, PWM scores achieved an area under the curve (AUC) of 0.756 (95%CI: 0.714–0.797) (cathepsin G) and 0.787 (95%CI: 0.753–0.821) (elastase). Combining broad and specific phage-derived scores improved cathepsin G prediction (AUC = 0.783), whereas this improvement was not observed for elastase. Compared to only phage-derived features, XGBoost models integrating phage sequence and structural features provided modest gains for elastase (AUC = 0.775 to 0.806), with phage-derived features ranking among the strongest predictors, but not cathepsin G (AUC = 0.702 to 0.710). Our findings demonstrate that PhageScout can use experimentally derived cleavage signatures to generate protease-specific predictive features and prioritize protease cleavage sites, providing a framework that warrants further validation across diverse proteases and biological contexts. Full article

Fourteen novel antioxidant peptides were screened from the enzymatic hydrolysates of Asian swamp eel bone (ASEB) through an in silico analysis. Their ABTS and ORAC radical scavenging capacities were 1.70–4.29-fold and 2.79–5.90-fold higher than those of Trolox, respectively. Additionally, two novel peptide sequences (NVGW and WALN) were identified. Quantum chemical calculations combined with active–-site methylation experiments demonstrated that hydrogen atoms on tryptophan’s indole nitrogen, tyrosine’s phenolic hydroxyl, and arginine’s guanidinium group play crucial roles in enhancing ABTS and ORAC activities. Molecular docking further showed stable binding of ASEB peptides to myeloperoxidase (MPO) through hydrogen bonds and electrostatic interactions. Further studies indicated that ASEB alleviated oxidative stress in zebrafish by effectively reducing ROS accumulation, restoring redox homeostasis, and modulating the expression of Keap1–Nrf2 pathway-related antioxidant genes. These results enhance our understanding of the antioxidant properties of ASEB-derived peptides and support the high-value utilization of animal byproducts. Full article