Search Results (65)

There is a growing demand for natural and potent α-glucosidase inhibitors due to the rising prevalence of diabetes. In this study, newly identified α-glucosidase inhibitory peptides were identified from the tryptic hydrolysate of hemp seed proteins based on peptidomics and in silico analysis. A total of 424 peptides, primarily derived from four cupin-type-1 domain-containing proteins, were identified, and 13 ultimately were selected for validation based on their higher PeptideRanker scores, solubility, non-toxicity, and favorable ADMET properties. Molecular docking revealed that these 13 peptides primarily interacted with α-glucosidase via hydrogen bonding and hydrophobic interactions. Among them, three novel peptides—NPVSLPGR (−8.7 kcal/mol), LSAERGFLY (−8.5 kcal/mol), and PDDVLANAF (−8.4 kcal/mol)—demonstrated potent α-glucosidase inhibitory activity due to their lower binding energies than acarbose (−8.1 kcal/mol), the first approved α-glucosidase inhibitor for type 2 diabetes treatment. The molecular mechanism analysis revealed that the peptides NPVSLPGR and LSAERGFLY inhibited α-glucosidase by simultaneously blocking substrate entry through occupying the entrance of the active site gorge and preventing catalysis by binding to active sites. In contrast, the peptide PDDVLANAF primarily exerted inhibitory effects by occupying the entrance of the active site gorge. Molecular dynamics simulation validated the stability of the complexes and provided additional insights into the molecular mechanism determined through docking. These findings contribute essential knowledge for the advancement of natural α-glucosidase inhibitors and offer a promising approach to effectively manage diabetes. Full article

Background: Soy allergy is an important nutritional and health issue that needs to be addressed. Lactic acid bacteria (LAB) fermentation is an effective approach to reduce soy protein allergy. Polysaccharides are commonly used in LAB-fermented products to enhance their textural properties. This study proposes a new strategy for developing hypoallergenic soy protein products. Methods: We prepared a soy protein isolate (SPI) through fermentation with LAB (FSPI) and with five types of polysaccharides supplementation, namely polydextrose (PDX), inulin (IN), long-chain inulin (LCIN), soluble soy polysaccharides (SSPS), and β-glucan (BG). The texture and microstructure of different samples were analyzed. Antigenicity and IgE-binding capacity were determined using ELISA. Finally, peptide sequencing was used to identify the degradation degree and frequency of allergenic epitopes. Results: Samples with added PDX (F-PDX) and IN (F-IN) exhibited lower hardness; smaller, irregular pores; and a honeycomb structure, whereas samples with SSPS (F-SSPS) and BG (F-BG) had higher hardness; large, regular pores; and strong sheet structures. The antigenicity and IgE-binding capacity of F-PDX and F-IN were lower both before and after 120 min of in vitro dynamic gastrointestinal digestion. The peptidomics results indicated that F-PDX and F-IN primarily facilitated the degradation of the glycinin G1 and G2 subunits, β-conglycinin α, and the β subunit. Moreover, it increased the frequency of destruction of allergenic epitopes, and further promoted the degradation of epitopes in the external α-helix structures of glycinin and β-conglycinin compared to FSPI. Conclusions: The addition of polysaccharides had a significant impact on the structure and allergenicity of the soy protein gel, especially PDX and IN. Full article

Plant metabolomics, lipidomics, ionomics, fluxomics, and peptidomics are essential approaches for exploring how plants respond to epigenetic, pathological, and environmental stimuli through comprehensive chemical profiling. Over the past decades, significant progress has been made in protocols and methodologies to address the challenges in sample collection and extraction. Despite these advancements, sample preparation remains intricate, with ongoing debates about the most effective strategies. This review emphasizes the importance of clear research questions and well-designed experiments to minimize complexity, save time, and enhance reproducibility. It provides an overview of the key steps in these fields, including harvesting, drying, extraction, and data pre-acquisition for major analytical platforms. By discussing best practices and common challenges, this review aims to streamline methods and promote more consistent and reliable research outcomes. Full article

Background: Autoantibodies are commonly used as biomarkers in autoimmune diseases, but there are limitations. For example, autoantibody biomarkers have poor sensitivity or specificity in systemic lupus erythematosus and do not exist in the spondyloarthropathies, impairing diagnosis and treatment. While autoantibodies suitable for strong biomarkers may not exist in these conditions, another possibility is that technology has limited their discovery. The purpose of this study was to use a novel high-density peptide array that enables the evaluation of IgG binding to every possible linear antigen in the entire human peptidome, as well as a novel machine learning approach that incorporates ELISA validation predictability in order to discover autoantibodies that could be developed into sensitive and specific markers of lupus or spondyloarthropathy. Methods: We used a peptide array containing the human peptidome, several viral peptidomes, and key post-translational modifications (6 million peptides) to quantify IgG binding in lupus, spondyloarthropathy, rheumatoid arthritis, Sjögren’s disease, and control sera. Using ELISA data for 70 peptides, we performed a random forest analysis that evaluated multiple array features to predict which peptides might be good biomarkers, as confirmed by ELISA. We validated the peptide prediction methodology in rheumatoid arthritis and COVID-19, conditions for which the antibody repertoire is well-understood, and then evaluated IgG binding by ELISA to peptides that we predicted would be highly bound specifically in lupus or spondyloarthropathy. Results: Our methodology performed well in validation studies, but peptides predicted to be highly and specifically bound in lupus or spondyloarthropathy could not be confirmed by ELISA. Conclusions: In a comprehensive evaluation of the entire human peptidome, highly sensitive and specific IgG autoantibodies were not identified in lupus or spondyloarthropathy. Thus, the pathogenesis of lupus and spondyloarthropathy may not depend upon unique autoantigens, and other types of molecules should be sought as optimal biomarkers in these conditions. Full article

Salivary gland tumors are highly variable in clinical presentation and histology. The World Health Organization (WHO) classifies 22 types of malignant and 11 types of benign tumors of the salivary glands. Diagnosis of salivary gland tumors is based on imaging (ultrasound, magnetic resonance imaging) and fine-needle aspiration biopsy, but the final diagnosis is based on histopathological examination of the removed tumor tissue. In this pilot study, we are testing a new approach to identifying peptide biomarkers in saliva that can be used to diagnose salivary gland tumors. The research material for the peptidomic studies was extracts from washings of neoplastic tissues and healthy tissues (control samples). At the same time, saliva samples from patients and healthy individuals were analyzed. The comparison of the peptidome composition of tissue extracts and saliva samples may allow the identification of potential peptide markers of salivary gland tumors in patients’ saliva. The peptidome compositions extracted from 18 tumor and 18 healthy tissue samples, patients’ saliva samples (11 samples), and healthy saliva samples (8 samples) were analyzed by LC-MS tandem mass spectrometry. A group of 109 peptides was identified that were present only in the tumor tissue extracts and in the patients’ saliva samples. Some of the identified peptides were derived from proteins previously suggested as potential biomarkers of salivary gland tumors (ANXA1, BPIFA2, FGB, GAPDH, HSPB1, IGHG1, VIM) or tumors of other tissues or organs (SERPINA1, APOA2, CSTB, GSTP1, S100A8, S100A9, TPI1). Unfortunately, none of the identified peptides were present in all samples analyzed. This may be due to the high heterogeneity of this type of cancer. The surprising result was that extracts from tumor tissue did not contain peptides derived from salivary gland-specific proteins (STATH, SMR3B, HTN1, HTN3). These results could suggest that the developing tumor suppresses the production of proteins that are essential components of saliva. Full article

Millions of people worldwide currently suffer from chronic kidney disease (CKD), requiring kidney replacement therapy at the end stage. Endeavors to better understand CKD pathophysiology from an omics perspective have revealed major molecular players in several sample sources. Focusing on non-invasive sources, gut microbial communities appear to be disturbed in CKD, while numerous human urinary peptides are also dysregulated. Nevertheless, studies often focus on isolated omics techniques, thus potentially missing the complementary pathophysiological information that multidisciplinary approaches could provide. To this end, human urinary peptidome was analyzed and integrated with clinical and fecal microbiome (16S sequencing) data collected from 110 Non-CKD or CKD individuals (Early, Moderate, or Advanced CKD stage) that were not undergoing dialysis. Participants were visualized in a three-dimensional space using different combinations of clinical and molecular data. The most impactful clinical variables to discriminate patient groups in the reduced dataspace were, among others, serum urea, haemoglobin, total blood protein, urinary albumin, urinary erythrocytes, blood pressure, cholesterol measures, body mass index, Bristol stool score, and smoking; relevant variables were also microbial taxa, including Roseburia, Butyricicoccus, Flavonifractor, Burkholderiales, Holdemania, Synergistaceae, Enterorhabdus, and Senegalimassilia; urinary peptidome fragments were predominantly derived from proteins of collagen origin; among the non-collagen parental proteins were FXYD2, MGP, FGA, APOA1, and CD99. The urinary peptidome appeared to capture substantial variation in the CKD context. Integrating clinical and molecular data contributed to an improved cohort separation compared to clinical data alone, indicating, once again, the added value of this combined information in clinical practice. Full article

Pancreatic cancer is a lethal disease, harboring a five-year overall survival rate of only 13%. Current treatment approaches thus require modulation, with attention shifting towards liberating the stalled efficacy of immunotherapies. Select chemotherapy drugs which possess inherent immune-modifying behaviors could revitalize immune activity against pancreatic tumors and potentiate immunotherapeutic success. In this study, we characterized the influence of gemcitabine, a chemotherapy drug approved for the treatment of pancreatic cancer, on tumor antigen presentation by human leukocyte antigen class I (HLA-I). Gemcitabine increased pancreatic cancer cells’ HLA-I mRNA transcripts, total protein, surface expression, and surface stability. Temperature-dependent assay results indicated that the increased HLA-I stability may be due to reduced binding of low affinity peptides. Mass spectrometry analysis confirmed changes in the HLA-I-presented peptide pool post-treatment, and computational predictions suggested improved affinity and immunogenicity of peptides displayed solely by gemcitabine-treated cells. Most of the gemcitabine-exclusive peptides were derived from unique source proteins, with a notable overrepresentation of translation-related proteins. Gemcitabine also increased expression of select immunoproteasome subunits, providing a plausible mechanism for its modulation of the HLA-I-bound peptidome. Our work supports continued investigation of immunotherapies, including peptide-based vaccines, to be used with gemcitabine as new combination treatment modalities for pancreatic cancer. Full article

Bioactive peptides (BPs) are molecules of paramount importance with great potential for the development of functional foods, nutraceuticals or therapeutics for the prevention or treatment of various diseases. A functional BP-rich dairy product was produced by lyophilisation of bovine milk fermented by the autochthonous strains Lactococcus lactis subsp. lactis ZGBP5-51, Enterococcus faecium ZGBP5-52 and Enterococcus faecalis ZGBP5-53 isolated from the same artisanal fresh cheese. The efficiency of the proteolytic system of the implemented strains in the production of BPs was confirmed by a combined high-throughput mass spectrometry (MS)-based peptidome profiling and an in silico approach. First, peptides released by microbial fermentation were identified via a non-targeted peptide analysis (NTA) comprising reversed-phase nano-liquid chromatography (RP nano-LC) coupled with matrix-assisted laser desorption/ionisation-time-of-flight/time-of-flight (MALDI-TOF/TOF) MS, and then quantified by targeted peptide analysis (TA) involving RP ultrahigh-performance LC (RP-UHPLC) coupled with triple-quadrupole MS (QQQ-MS). A combined database and literature search revealed that 10 of the 25 peptides identified in this work have bioactive properties described in the literature. Finally, by combining the output of MS-based peptidome profiling with in silico bioactivity prediction tools, three peptides (⁷⁵QFLPYPYYAKPA⁸⁶, ⁴⁰VAPFPEVFGK⁴⁹, ¹¹⁷ARHPHPHLSF¹²⁶), whose bioactive properties have not been previously reported in the literature, were identified as potential BP candidates. Full article

Bioactive peptides, specific protein fragments with positive health effects, are gaining traction in drug development for advantages like enhanced penetration, low toxicity, and rapid clearance. This comprehensive review navigates the intricate landscape of peptide science, covering discovery to functional characterization. Beginning with a peptidomic exploration of natural sources, the review emphasizes the search for novel peptides. Extraction approaches, including enzymatic hydrolysis, microbial fermentation, and specialized methods for disulfide-linked peptides, are extensively covered. Mass spectrometric analysis techniques for data acquisition and identification, such as liquid chromatography, capillary electrophoresis, untargeted peptide analysis, and bioinformatics, are thoroughly outlined. The exploration of peptide bioactivity incorporates various methodologies, from in vitro assays to in silico techniques, including advanced approaches like phage display and cell-based assays. The review also discusses the structure–activity relationship in the context of antimicrobial peptides (AMPs), ACE-inhibitory peptides (ACEs), and antioxidative peptides (AOPs). Concluding with key findings and future research directions, this interdisciplinary review serves as a comprehensive reference, offering a holistic understanding of peptides and their potential therapeutic applications. Full article

Thyroid cancer is a common malignancy of the endocrine system. Nodules are routinely evaluated for malignancy risk by fine needle aspiration biopsy (FNAB), and in cases such as follicular lesions, differential diagnosis between benign and malignant nodules is highly uncertain. Therefore, the discovery of new biomarkers for this disease could be helpful in improving diagnostic accuracy. Thyroid nodule biopsies were subjected to a precipitation step with both the insoluble and supernatant fractions subjected to proteome and peptidome profiling. Proteomic analysis identified annexin A1 as a potential biomarker of thyroid cancer malignancy, with its levels increased in malignant samples. Also upregulated were the acetylated peptides of annexin A1, revealed by the peptidome analysis of the supernatant fraction. In addition, supernatant peptidomic analysis revealed a number of acetylated histone peptides that were significantly elevated in the malignant group, suggesting higher gene transcription activity in malignant tissue. Two of these peptides were found to be robust malignancy predictors, with an area under the receiver operating a characteristic curve (ROC AUC) above 0.95. Thus, this combination of proteomics and peptidomics analyses improved the detection of malignant lesions and also provided new evidence linking thyroid cancer development to heightened transcription activity. This study demonstrates the importance of peptidomic profiling in complementing traditional proteomics approaches. Full article

Because of the health benefits and economic opportunities, extracting bioactive peptides from plant proteins, often food processing by-products, garners significant interest. However, the high enzyme costs and the emergence of bitter peptides have posed significant challenges in production. This study achieved the immobilization of Alcalase and Flavorzyme using cost-effective SiO₂ microparticles. Mussel-inspired chemistry and biocompatible polymers were employed, with genipin replacing glutaraldehyde for safer crosslinking. This approach yielded an enzyme loading capacity of approximately 25 mg/g support, with specific activity levels reaching around 180 U/mg for immobilized Alcalase (IA) and 35 U/mg for immobilized Flavorzyme (IF). These immobilized proteases exhibited improved activity and stability across a broader pH and temperature range. During the hydrolysis of soy proteins, the use of immobilized proteases avoided the thermal inactivation step, resulting in fewer peptide aggregates. Moreover, this study applied peptidomics and bioinformatics to profile peptides in each hydrolysate and identify bioactive ones. Cascade hydrolysis with IA and IF reduced the presence of bitter peptides by approximately 20%. Additionally, 50% of the identified peptides were predicted to have bioactive properties after in silico digestion simulation. This work offers a cost-effective way of generating bioactive peptides from soy proteins with reducing potential bitterness. Full article

(1) Background: Kidney and cardiovascular diseases are responsible for a large fraction of population morbidity and mortality. Early, targeted, personalized intervention represents the ideal approach to cope with this challenge. Proteomic/peptidomic changes are largely responsible for the onset and progression of these diseases and should hold information about the optimal means of treatment and prevention. (2) Methods: We investigated the prediction of renal or cardiovascular events using previously defined urinary peptidomic classifiers CKD273, HF2, and CAD160 in a cohort of 5585 subjects, in a retrospective study. (3) Results: We have demonstrated a highly significant prediction of events, with an HR of 2.59, 1.71, and 4.12 for HF, CAD, and CKD, respectively. We applied in silico treatment, implementing on each patient’s urinary profile changes to the classifiers corresponding to exactly defined peptide abundance changes, following commonly used interventions (MRA, SGLT2i, DPP4i, ARB, GLP1RA, olive oil, and exercise), as defined in previous studies. Applying the proteomic classifiers after the in silico treatment indicated the individual benefits of specific interventions on a personalized level. (4) Conclusions: The in silico evaluation may provide information on the future impact of specific drugs and interventions on endpoints, opening the door to a precision-based medicine approach. An investigation into the extent of the benefit of this approach in a prospective clinical trial is warranted. Full article

Huangjiu is rich in low-molecular-weight peptides and has an umami taste. In order for its umami peptides to be discovered, huangjiu was subjected to ultrafiltration, ethanol precipitation, and macroporous resin purification processes. The target fractions were gathered according to sensory evaluation. Subsequently, we used peptidomics to identify the sum of 4158 peptides in most umami fractions. Finally, six novel umami peptides (DTYNPR, TYNPR, SYNPR, RFRQGD, NFHHGD, and FHHGD) and five umami-enhancing peptides (TYNPR, SYNPR, NFHHGD, FHHGD, and TVDGPSH) were filtered via virtual screening, molecular docking, and sensory verification. Moreover, the structure–activity relationship was discussed using computational approaches. Docking analysis showed that all umami peptides tend to bind with T1R1 through hydrogen bonds and hydrophobic forces, which involve key residues HIS71, ASP147, ARG151, TYR220, SER276, and ALA302. The active site calculation revealed that the positions of the key umami residues D and R in the terminal may cause taste differences in identified peptides. Full article

Given the pathophysiological continuum of chronic kidney disease (CKD), different molecular determinants affecting progression may be associated with distinct disease phases; thus, identification of these players are crucial for guiding therapeutic decisions, ideally in a non-invasive, repeatable setting. Analyzing the urinary peptidome has been proven an efficient method for biomarker determination in CKD, among other diseases. In this work, after applying several selection criteria, urine samples from 317 early (stage 2) and advanced (stage 3b–5) CKD patients were analyzed using capillary electrophoresis coupled to mass spectrometry (CE-MS). The entire two groups were initially compared to highlight the respective pathophysiology between initial and late disease phases. Subsequently, slow and fast progressors were compared within each group in an attempt to distinguish phase-specific disease progression molecules. The early vs. late-stage CKD comparison revealed 929 significantly different peptides, most of which were downregulated and 268 with collagen origins. When comparing slow vs. fast progressors in early stage CKD, 42 peptides were significantly altered, 30 of which were collagen peptide fragments. This association suggests the development of structural changes may be reversible at an early stage. The study confirms previous findings, based on its multivariable-matched progression groups derived from a large initial cohort. However, only four peptide fragments differed between slow vs. fast progressors in late-stage CKD, indicating different pathogenic processes occur in fast and slow progressors in different stages of CKD. The defined peptides associated with CKD progression at early stage might potentially constitute a non-invasive approach to improve patient management by guiding (personalized) intervention. Full article

Cancer remains the second most common cause of death after cardiovascular diseases, accounting for nearly 10 million deaths in 2020. Although the incidence of cancer increases considerably with age, the cancer burden can also be reduced and have a high chance of cure through early detection, appropriate treatment, and care of patients. The development of high-throughput analytical approaches, like matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS), contributes to identifying a pool of proteins/peptides as putative biomarkers for the early detection, diagnosis, and tumor progression. The purpose of the current review is to present an updated outline of recent proteome/peptidome research to establish putative cancer biomarkers using MALDI-TOF MS and highlight the applicability of statistical analysis in the oncology field. The pros and cons of MALDI-TOF MS application on cancer diagnostics and monitoring will be discussed, as well as compared with tandem mass spectrometry (MS/MS)-based proteomics (e.g., liquid chromatography–tandem mass spectrometry). In addition, pre-analytical (e.g., sample quality control) and analytical (e.g., sample pre-treatment, instrumental analytical conditions) properties that influence the robustness of MALDI-TOF MS data will be also discussed. Full article