Current peptidomics: applications, purification, identification, quantification, and functional analysis

Peptidomics profiling and biological activities of grape pomace protein hydrolysates

Grape pomace protein isolate was hydrolysed by Alcalase, Flavourzyme and Protease either individually or in combination to produce hydrolysates with antihypertensive and antimicrobial properties. The degree of hydrolysis (DH) ranged between 22 and 52 % for Protease and Flavourzyme, respectively. Among all treatments, hydrolysates prepared using Flavourzyme exhibited the highest angiotensin-converting enzyme inhibitory (ACEi) activity, with an IC50 value of 91 μg/mL. The peptidomics analysis revealed that the peptides identified in Flavourzyme hydrolysate presented molecular features compatible with its bioactivity, like a high density of ACEi sequences per peptide. The hydrolysates were also able to inhibit the growth of Escherichia coli in a range between 9 and 54 % for Alcalase and Alcalase + Flavourzyme, respectively. Peptides in the most active hydrolysate evidenced a high occurrence of proline residues, which is a structural feature of some antimicrobial peptides.

Non-human peptides revealed in blood reflect the composition of intestinal microbiota

BACKGROUND

The previously underestimated effects of commensal gut microbiota on the human body are increasingly being investigated using omics. The discovery of active molecules of interaction between the microbiota and the host may be an important step towards elucidating the mechanisms of symbiosis.

RESULTS

Here, we show that in the bloodstream of healthy people, there are over 900 peptides that are fragments of proteins from microorganisms which naturally inhabit human biotopes, including the intestinal microbiota. Absolute quantitation by multiple reaction monitoring has confirmed the presence of bacterial peptides in the blood plasma and serum in the range of approximately 0.1 nM to 1 μM. The abundance of microbiota peptides reaches its maximum about 5 h after a meal. Most of the peptides correlate with the bacterial composition of the small intestine and are likely obtained by hydrolysis of membrane proteins with trypsin, chymotrypsin and pepsin - the main proteases of the gastrointestinal tract. The peptides have physicochemical properties that likely allow them to selectively pass the intestinal mucosal barrier and resist fibrinolysis.

CONCLUSIONS

The proposed approach to the identification of microbiota peptides in the blood, after additional validation, may be useful for determining the microbiota composition of hard-to-reach intestinal areas and monitoring the permeability of the intestinal mucosal barrier.

Preliminary investigation on the impact of salty and sugary former foods on pig liver and plasma profiles using OMICS approaches

Replacing cereals with food leftovers could reduce feed-food competition and keep nutrients and energy in the food chain. Former food products (FFPs) are industrial food leftovers no more intended for human but still suitable as alternative and sustainable feedstuffs for monogastric. In this study, omics approaches were applied to evaluate the impact of dietary FFPs on pig liver proteome and plasma peptidome. Thirty-six Swiss Large White male castrated pigs were randomly assigned to three dietary treatments [control (CTR), 30% CTR replaced with salty FFP (SA), 30% CTR replaced with sugary FFP (SU)] from the start of the growing phase (22.4 ± 1.7 kg) until slaughtering (110 ± 3 kg). The low number of differentially regulated proteins in each comparison matrix (SA/SU vs. CTR) and the lack of metabolic interaction indicated a marginal impact on hepatic lipid metabolism. The plasma peptidomics investigation showed low variability between the peptidome of the three dietary groups and identified three possible bioactive peptides in the SA group associated with anti-hypertension and vascular homeostasis regulation. To conclude, the limited modulation of liver proteome and plasma peptidome by the SA and SU diets strenghtened the idea of reusing FFPs as feed ingredients to make pig production more sustainable.

Peptidomic analysis reveals novel peptide PDLC promotes cell proliferation in hepatocellular carcinoma via Ras/Raf/MEK/ERK pathway

Hepatocellular carcinoma (HCC) still presents poor prognosis with low overall survival rates and limited therapeutic options available. Recently, attention has been drawn to peptidomic analysis, an emerging field of proteomics for the exploration of new potential peptide drugs for the treatment of various diseases. However, research on the potential function of HCC peptides is lacking. Here, we analyzed the peptide spectrum in HCC tissues using peptidomic techniques and explored the potentially beneficial peptides involved in HCC. Changes in peptide profiles in HCC were examined using liquid chromatography-mass spectrometry (LC-MS/MS). Analyze the physicochemical properties and function of differently expressed peptides using bioinformatics. The effect of candidate functional peptides on HCC cell growth and migration was evaluated using the CCK-8, colony formation, and transwell assays. Transcriptome sequencing analysis and western blot were employed to delve into the mode of action of potential peptide on HCC. Peptidomic analysis of HCC tissue yielded a total of 8683 peptides, of which 452 exhibited up-regulation and 362 showed down-regulation. The peptides that were differentially expressed, according to bioinformatic analysis, were closely linked to carbon metabolism and the mitochondrial inner membrane. The peptide functional validation identified a novel peptide, PDLC (peptide derived from liver cancer), which was found to dramatically boost HCC cell proliferation through the Ras/Raf/MEK/ERK signaling cascade. Our research defined the peptide's properties and pattern of expression in HCC and identified a novel peptide, PDLC, with a function in encouraging HCC progression, offering an entirely new potential therapeutic target the disease.

In-depth discovery and taste presentation mechanism studies on umami peptides derived from fermented sea bass based on peptidomics and machine learning

Umami peptides originating from fermented sea bass impart a distinctive flavor to food. Nevertheless, large-scale and rapid screening for umami peptides using conventional techniques is challenging because of problems such as prolonged duration and complicated operation. Therefore, we aimed to screen fermented sea bass using peptidomics and machine learning approaches. The taste presentation mechanism of umami peptides was assessed by molecular docking of T1R1/T1R3. Seventy umami peptides identified in fermented sea bass predominantly originated from 28 precursor proteins, including troponin, myosin, motor protein, and creatine kinase. Six umami peptides with the lowest energies formed stable complexes by binding to T1R3. SER170, SER147, GLN389, and HIS145 are critical binding sites for T1R1/T1R3. Four dominant interacting surface forces were identified: aromatic interactions, hydrogen bonding, hydrophilic bonds, and solvent-accessible surfaces. Our study unveils a method to screen umami peptides efficiently, providing a basis for further exploration of their flavor in fermented sea bass.

Advanced functional materials as reliable tools for capturing food-derived peptides to optimize the peptidomics pre-treatment enrichment workflow

Peptidomics strategies with high throughput, sensitivity, and reproducibility are key tools for comprehensively analyzing peptide composition and potential functional activities in foods. Nevertheless, complex signal interference, limited ionization efficiency, and low abundance have impeded food-derived peptides' progress in food detection and analysis. As a result, novel functional materials have been born at the right moment that could eliminate interference and perform efficient enrichment. Of note, few studies have focused on developing peptide enrichment materials for food sample analysis. This work summarizes the development of endogenous peptide, phosphopeptide, and glycopeptide enrichment utilizing materials that have been employed extensively recently: organic framework materials, carbon-based nanomaterials, bio-based materials, magnetic materials, and molecularly imprinted polymers. It focuses on the limitations, potential solutions, and future prospects for application in food peptidomics of various advanced functional materials. The size-exclusion effect of adjustable aperture and the modification of magnetic material enhanced the sensitivity and selectivity of endogenous peptide enrichment and aided in streamlining the enrichment process and cutting down on enrichment time. Not only that, the immobilization of metal ions such as Ti4+ and Nb5+ enhanced the capture of phosphopeptides, and the introduction of hydrophilic groups such as arginine, L-cysteine, and glutathione into bio-based materials effectively optimized the hydrophilic enrichment of glycopeptides. Although a portion of the carefully constructed functional materials currently only exhibit promising applications in the field of peptide enrichment for analytical chemistry, there is reason to believe that they will further advance the field of food peptidomics through improved pre-treatment steps.

Peptide PDRPS6 attenuates myocardial ischemia injury by improving mitochondrial function

Mitochondrial dynamics play a crucial role in myocardial ischemia-reperfusion (I/R) injury, where an imbalance between fusion and fission processes occurs. However, effective measures to regulate mitochondrial dynamics in this context are currently lacking. Peptide derived from the 40 S ribosomal protein S6 (PDRPS6), a peptide identified via peptidomics, is associated with hypoxic stress. This study aimed to investigate the function and mechanism of action of PDRPS6 in I/R injury. In vivo, PDRPS6 ameliorated myocardial tissue injury and cardiomyocyte apoptosis and decreased cardiac function induced by I/R injury in rats. PDRPS6 supplementation significantly reduced apoptosis in vitro. Mechanistically, PDRPS6 improved mitochondrial function by decreasing reactive oxygen species (ROS) levels, maintaining mitochondrial membrane potential (MMP), and inhibiting mitochondrial fission. Pull-down assay analyses revealed that phosphoglycerate mutase 5 (PGAM5) may be the target of PDRPS6, which can lead to the dephosphorylation of dynamin-related protein1 (Drp1) at ser616 site. Overexpression of PGAM5 partially eliminated the effect of PDRPS6 on improving mitochondrial function. These findings suggest that PDRPS6 supplementation is a novel method for treating myocardial injuries caused by I/R.

Revealing the diversity of endogenous peptides and parent proteins in human colostrum and mature milk through peptidomics analysis

Endogenous peptides and their parent proteins are important nutritional components with diverse biological functions. The objective of this study was to analyze and compare endogenous peptides and parent proteins found in human colostrum (HC) and human mature milk (HM) using a 4D label-free technique. In total, 5162 and 940 endogenous peptides derived from 258 parent proteins were identified in human milk by database (DB) search and de novo, respectively. Among these peptides, 2446 differentially expressed endogenous peptides with various bioactivities were identified. The Gene Ontology analysis unveiled the cellular components, biological processes, and molecular functions associated with these parent proteins. Metabolic pathway analysis suggested that neutrophil extracellular trap formation had the greatest significance with 24 parent proteins. These findings will offer a fresh perspective on the development of infant formula powder, highlighting the potential for incorporating these changes to enhance its nutritional composition and benefits.

A non-targeting magnetic metal-organic framework probe for highly specific peptide-mediated precise disease monitoring

Alzheimer's disease (AD) is a universal neurodegenerative disease in older adults with incurable and progressive properties, urging for precise monitoring to perform timely treatment to delay its progression. Herein, we introduced a non-targeting magnetic metal-organic framework probe coupled with high-throughput mass spectrometry, creating a rapid screening strategy for highly specific peptides associated with AD. Notably, an elution-free extraction process was proposed, significantly reducing sample preprocessing time while simultaneously ensuring the efficient detection of captured peptides. Using this elution-free extraction process, high-quality peptide profiles were rapidly extracted from the hundreds of samples from both diseased and healthy individuals. By integrating machine learning algorithms, LC-MS/MS, and Uniprot database searching, we identified three specific serum endogenous peptides (m/z = 4215.41, 2884.77 and 2704.61) closely associated with AD. Remarkably, with the use of any single specific peptide, the AUC (Area Under the Curve) values can reach approximately 0.9 during monitoring AD. Moreover, integrating three specific biomarkers provides a robust basis for machine learning algorithms to build monitoring models, with AUC value up to 1.000. This work represents a substantial advancement in the development of peptide-specific precise monitoring approaches for complex diseases, serving as a catalyst for increased dedication to the molecular detection field.

PeposX-Exhaust: A lightweight and efficient tool for identification of short peptides

Short peptides have become the focus of recent research due to their variable bioactivities, good digestibility and wide existences in food-derived protein hydrolysates. However, due to the high complexity of the samples, identifying short peptides still remains a challenge. In this work, a tool, named PeposX-Exhaust, was developed for short peptide identification. Through validation with known peptides, PeposX-Exhaust identified all the submitted spectra and the accuracy rate reached 75.36%, and the adjusted accuracy rate further reached 98.55% when with top 5 candidates considered. Compared with other tools, the accuracy rate by PeposX-Exhaust was at least 70% higher than two database-search tools and 15% higher than the other two de novo-sequencing tools, respectively. For further application, the numbers of short peptides identified from soybean, walnut, collagen and bonito protein hydrolysates reached 1145, 628, 746 and 681, respectively. This fully demonstrated the superiority of the tool in short peptide identification.

Enzymatic debittering of cheese flavoring and bitterness characterization of peptide mixture using sensory and peptidomics approach

Peptides in cheese flavoring produced through proteolysis plus fermentation generated bitterness. Bitterness of individual peptide can be quantified using quantitative structure-activity relationship, where molecular mass (M), hydrophobicity, residues, C-terminal hydrophobic amino acids (C-HAAs), and N-terminal basic ones (N-BAAs) are crucial. However, their accumulative influence on the overall bitterness of peptide mixture remains unknown. This study delved into extensive proteolysis to debitter and to correlate the multi-influencing factors of peptides and the collective bitterness. As hydrolysis increased from 7.5 % to 28.0 %, bitterness reduced from 5.0 to 0.3-2.7 scores, contingent on proteases used, in which FU was optimal. The overall bitterness cannot be predicted through the summation of individual peptide bitterness, which depended on M (0.5-3 kDa) and 5-23 residues, followed by N-BAAs and C-HAAs. Analysis of enzymatic cleavage sites and substrate characteristics revealed, to more effectively debitter bovine milk protein hydrolysates, proteases specifically cleaving Pro, Leu, Phe, and Val were desired.

Comprehensive workflow encompassing discovery, verification, and quantification of indicator peptide in snail mucin using LC-quadrupole Orbitrap high-resolution tandem mass spectrometry

Peptidomics analysis was conducted using high-resolution tandem mass spectrometry (MS2) to determine the peptide profile of snail-derived mucin extract (SM). The study was also aimed to identify an indicator peptide and validate a quantification method for this peptide. The peptide profiling and identification were conducted using discovery-based peptidomics analysis employing data-dependent acquisition, whereas the selected peptides were verified and quantified using parallel reaction monitoring acquisition. Among the 16 identified peptides, the selected octapeptide (TEAPLNPK) was quantified via precursor ion ionization (m/z 435.2400), followed by quantification of the corresponding quantifier ion fragment (m/z 639.3824) using MS2. The quantification method was optimized and validated in terms of specificity, linearity, accuracy, precision, and limit of detection/quantification. The validated method accurately quantified the TEAPLNPK content in the SM as 7.5 ± 0.2 μg/g. Our study not only identifies an indicator peptide from SM but also introduces a novel validation method, involving precursor ion ionization and quantification of specific fragments. Our findings may serve as a comprehensive workflow for the monitoring, selection, and quantification of indicator peptides from diverse food resources.

Exploring peptides from toad venom for source identification by LC-MS/MS using MRM method

Toad venom is a traditional Chinese medicine (TCM) with various sources and wide-ranging preparations. Previous quality assessment studies primarily concentrated on small molecular compounds like toad dienolactones and indole alkaloids, studies on macromolecular peptides and proteins as quality assessment standards remained at the qualitative stage, lacking the development of practical and convenient quantitative methods. In this study, to explore the peptides from toad venom as a new method for identifying and evaluating its source, a complete scan of the water extract of peptides from toad venom was conducted using HPLC-Quadrupole Time-of-Flight Mass Spectrometer (Q-TOF) 5600, leading to the identification of peptides based on mass spectrometry data. Subsequently, HPLC- Quadrupole-Linear Ion Trap Mass Spectrometer (Q-Trap) 5500 employing Multiple Reaction Monitoring (MRM) mode was utilized to quantitatively analyze peptides in various sources of toad venom, followed by Partial Least Squares Discriminant Analysis (PLS-DA) to further analyze the data and evaluate the effectiveness. This study highlights the importance of exploring macromolecular substance in natural products research and provides a foundation for further studies on toad venom.

Exploring the Potential of Bioactive Peptides: From Natural Sources to Therapeutics

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.

Integrating a Multi-label Deep Learning Approach with Protein Information to Compare Bioactive Peptides in Brain and Plasma

Peptide therapeutics is gaining momentum. Advances in the field of peptidomics have enabled researchers to harvest vital information from various organisms and tissue types concerning peptide existence, expression and function. The development of mass spectrometry techniques for high-throughput peptide quantitation has paved the way for the identification and discovery of numerous known and novel peptides. Though much has been achieved, scientists are still facing difficulties when it comes to reducing the search space of the large mass spectrometry-generated peptidomics datasets and focusing on the subset of functionally relevant peptides. Moreover, there is currently no straightforward way to analytically compare the distributions of bioactive peptides in distinct biological samples, which may reveal much useful information when seeking to characterize tissue- or fluid-specific peptidomes. In this chapter, we demonstrate how to identify, rank, and compare predicted bioactive peptides and bioactivity distributions from extensive peptidomics datasets. To aid this task, we utilize MultiPep, a multi-label deep learning approach designed for classifying peptide bioactivities, to identify bioactive peptides. The predicted bioactivities are synergistically combined with protein information from the UniProt database, which assist in navigating through the jungle of putative therapeutic peptides and relevant peptide leads.

Nanomaterials for detection of biomolecules and delivering therapeutic agents in theragnosis: A review

Nanomaterials are emerging facts used to deliver therapeutic agents in living systems. Nanotechnology is used as a compliment by implementing different kinds of nanotechnological applications such as nano-porous structures, functionalized nanomaterials, quantum dots, carbon nanomaterials, and polymeric nanostructures. The applications are in the initial stage, which led to achieving several diagnoses and therapy in clinical practice. This review conveys the importance of nanomaterials in post-genomic employment, which includes the design of immunosensors, immune assays, and drug delivery. In this view, genomics is a molecular tool containing large databases that are useful in choosing an apt molecular inhibitor such as drug, ligand and antibody target in the drug delivery process. This study identifies the expression of genes and proteins in analysis and classification of diseases. Experimentally, the study analyses the design of a disease model. In particular, drug delivery is a boon area to treat cancer. The identified drugs enter different phase trails (Trails I, II, and III). The genomic information conveys more essential entities to the phase I trials and helps to move further for other trails such as trails-II and III. In such cases, the biomarkers play a crucial role by monitoring the unique pathological process. Genetic engineering with recombinant DNA techniques can be employed to develop genetically engineered disease models. Delivering drugs in a specific area is one of the challenging issues achieved using nanoparticles. Therefore, genomics is considered as a vast molecular tool to identify drugs in personalized medicine for cancer therapy.

Applications of Mass Spectrometry in the Characterization, Screening, Diagnosis, and Prognosis of COVID-19

Mass spectrometry (MS) is a powerful analytical technique that plays a central role in modern protein analysis and the study of proteostasis. In the field of advanced molecular technologies, MS-based proteomics has become a cornerstone that is making a significant impact in the post-genomic era and as precision medicine moves from the research laboratory to clinical practice. The global dissemination of COVID-19 has spurred collective efforts to develop effective diagnostics, vaccines, and therapeutic interventions. This chapter highlights how MS seamlessly integrates with established methods such as RT-PCR and ELISA to improve viral identification and disease progression assessment. In particular, matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF-MS) takes the center stage, unraveling intricate details of SARS-CoV-2 proteins, revealing modifications such as glycosylation, and providing insights critical to formulating therapies and assessing prognosis. However, high-throughput analysis of MALDI data presents challenges in manual interpretation, which has driven the development of programmatic pipelines and specialized packages such as MALDIquant. As we move forward, it becomes clear that integrating proteomic data with various omic findings is an effective strategy to gain a comprehensive understanding of the intricate biology of COVID-19 and ultimately develop targeted therapeutic paradigms.

Exploring novel ANGICon-EIPs through ameliorated peptidomics techniques: Can deep learning strategies as a core breakthrough in peptide structure and function prediction?

Dairy-derived angiotensin-I-converting enzyme inhibitory peptides (ANGICon-EIPs) have been regarded as a relatively safe supplementary diet-therapy strategy for individuals with hypertension, and short-chain peptides may have more relevant antihypertensive benefits due to their direct intestinal absorption. Our previous explorations have confirmed that endogenous goat milk short-chain peptides are also an essential source of ANGICon-EIPs. Nonetheless, there are limited explorations on endogenous ANGICon-EIPs owing to the limitations of the extraction and enrichment of endogenous peptides, currently. This review outlined ameliorated pre-treatment strategies, data acquisition methods, and tools for the prediction of peptide structure and function, aiming to provide creative ideas for discovering novel ANGICon-EIPs. Currently, deep learning-based peptide structure and function prediction algorithms have achieved significant advancements. The convolutional neural network (CNN) and peptide sequence-based multi-label deep learning approach for determining the multi-functionalities of bioactive peptides (MLBP) can predict multiple peptide functions with absolute true value and accuracy of 0.699 and 0.708, respectively. Utilizing peptide sequence input, torsion angles, and inter-residue distance to train neural networks, APPTEST predicted the average backbone root mean square deviation (RMSD) value of peptide (5-40 aa) structures as low as 1.96 Å. Overall, with the exploration of more neural network architectures, deep learning could be considered a critical research tool to reduce the cost and improve the efficiency of identifying novel endogenous ANGICon-EIPs.

Utilizing proteomics to identify and optimize microalgae strains for high-quality dietary protein: a review

Algae-derived protein has immense potential to provide high-quality protein foods for the expanding human population. To meet its potential, a broad range of scientific tools are required to identify optimal algal strains from the hundreds of thousands available and identify ideal growing conditions for strains that produce high-quality protein with functional benefits. A research pipeline that includes proteomics can provide a deeper interpretation of microalgal composition and biochemistry in the pursuit of these goals. To date, proteomic investigations have largely focused on pathways that involve lipid production in selected microalgae species. Herein, we report the current state of microalgal proteome measurement and discuss promising approaches for the development of protein-containing food products derived from algae.

Proteomics and Peptidomics As a Tool to Compare the Proteins and Endogenous Peptides in Human, Cow, and Donkey Milk

Cow's milk is the most widely used ingredient in infant formulas. However, its specific protein composition can cause allergic reactions. Finding alternatives to replace cow's milk and fill the nutritional gap with human milk is essential for the health of infants. Proteomic and peptidomic techniques have supported the elucidation of milk's nutritional ingredients. Recently, omics approaches have attracted increasing interest in the investigation of milk because of their high throughput, precision, sensitivity, and reproducibility. This review offers a significant overview of recent developments in proteomics and peptidomics used to study the differences in human, cow, and donkey milk. All three types of milks were identified to have critical biological functions in human health, particularly in infants. Donkey milk proteins were closer in composition to human milk, were less likely to cause allergic reactions, and may be developed as novel raw materials for formula milk powders.

Characterization and biological function analysis of endogenous peptides derived from donkey colostrum proteins

Donkey colostrum, due to its abundance of active ingredients, including lysozyme, proteins, and peptides, is essential for the growth and immune defence of newborns. However, research on endogenous peptides in donkey colostrum is inadequate. This study analysed the profiles of endogenous peptides, their potential bioactivity, and the enzymes that generated these peptides using two different strategies. A total of 6202 endogenous peptides were characterised through a database search, while an additional 2997 peptides were identified de novo. Among the 1142 proteins identified, trypsin and plasmin demonstrated the highest bioactivities. Furthermore, a bioinformatics-based screening identified antioxidant peptides, angiotensin I-converting enzyme inhibitory peptides, and dipeptidyl peptidase IV inhibitory peptides as the three most active peptides. Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were conducted. These findings enhance our knowledge of endogenous peptides in donkey colostrum and provide crucial information regarding these peptides as nutritional factors for the future development of functional foods derived from donkey sources.

Zwitterionic mesoporous engineering aids peptide-dependent pattern profiling for identification of different liver diseases

Liver disease remains a global health challenge, with its incidence steadily increasing worldwide. Herein, zwitterionic mesoporous engineering was developed for the identification of different liver diseases including liver cirrhosis and liver cancer. Based on this engineering, a total of 2633 m/z signals were observed to be enriched. Notably, three key peptides were identified and showed high accuracy and precision for distinguishing the healthy and disease states, propelling the field of nanomedicine toward genuine personalized medicine.

The use of urine peptidomics to define dietary gluten peptides from patients with celiac disease and the clinical relevance

INTRODUCTION

Determination of urinary gluten immunogenic peptides (GIP) has emerged as one of the most attractive test to monitor the adherence to the gluten-free diet (GFD) of patients with celiac disease (CD), being a simple, noninvasive and direct method to detect gluten contamination of the GFD.

AREAS COVERED

We conducted a scoping review in Medline (PubMed) of articles published up to April 2023 that analyzed any aspect of the clinical relevance of the use of urinary GIP measurement in patients with CD. A total of 17 articles reporting the clinical use of urinary peptidomics for the follow-up of CD patients were finally included.

EXPERT OPINION

Available data suggest that a negative urinary GIP result is a reliable noninvasive predictor of intestinal mucosa healing in CD patients treated with the GFD, especially if testing three urine samples on different days including the weekend. Due to conflicting results about the sensitivity and the specificity of the urinary GIP determination, additional in-depth information is needed, particularly related to (1) the relationship between the amount of ingested gluten and the quantity of urinary GIP excreted in treated CD patients, (2) the GIP kinetics and best timing for sample collection.

Peptide profiles and allergy-reactivity of extensive hydrolysates of milk protein

Milk protein concentrate (MPC) is one of the major allergens in food. This study aimed to analyze the peptide profiles and potential allergenicity of the extensive hydrolysates of MPC (EMPHs) using the peptidomics approach. Results demonstrated that when the hydrolysis time was 4 h, the degree of hydrolysis of the four EMPHs (AX, Alcalase-Protamex), (AD, Alcalase-Protease A 2SD), (AE, Alcalase-Flavourzyme) and (AH, Alcalase-ProteAXH) were 12.45 %, 18.48 %, 18.87 % and 16.77 %, respectively. The results of size exclusion chromatography showed no significant difference, when the hydrolysis time exceeded 3 h. A total of 16 allergic peptides were identified in the EMPHs by LC-MS/MS. The peptide profiles and the coverage of master protein of the four EMPHs were different. The results of the enzyme-linked immunoassay and KU812 cell model showed that the allergenicity of the EMPHs samples was significantly reduced. This study provided strong support for the application of EMPHs in hypoallergenic formula foods.

Clinical Peptidomics: Advances in Instrumentation, Analyses, and Applications

Extensive effort has been devoted to the discovery, development, and validation of biomarkers for early disease diagnosis and prognosis as well as rapid evaluation of the response to therapeutic interventions. Genomic and transcriptomic profiling are well-established means to identify disease-associated biomarkers. However, analysis of disease-associated peptidomes can also identify novel peptide biomarkers or signatures that provide sensitive and specific diagnostic and prognostic information for specific malignant, chronic, and infectious diseases. Growing evidence also suggests that peptidomic changes in liquid biopsies may more effectively detect changes in disease pathophysiology than other molecular methods. Knowledge gained from peptide-based diagnostic, therapeutic, and imaging approaches has led to promising new theranostic applications that can increase their bioavailability in target tissues at reduced doses to decrease side effects and improve treatment responses. However, despite major advances, multiple factors can still affect the utility of peptidomic data. This review summarizes several remaining challenges that affect peptide biomarker discovery and their use as diagnostics, with a focus on technological advances that can improve the detection, identification, and monitoring of peptide biomarkers for personalized medicine.

Peptidomic Analysis Reveals that Novel Peptide LDP2 Protects Against Hepatic Ischemia/Reperfusion Injury

BACKGROUND AND AIMS

Hepatic ischemia/reperfusion (I/R) injury has become an inevitable issue during liver transplantation, with no effective treatments available. However, peptide drugs provide promising regimens for the treatment of this injury and peptidomics has gradually attracted increasing attention. This study was designed to analyze the spectrum of peptides in injured livers and explore the potential beneficial peptides involved in I/R injury.

METHODS

C57BL/6 mice were used to establish a liver I/R injury animal model. Changes in peptide profiles in I/R-injured livers were analyzed by mass spectrometry, and the functions of the identified peptides were predicted by bioinformatics. AML12 cells were used to simulate hepatic I/R injury in vitro. After treatment with candidate liver-derived peptides (LDPs) 1-10, the cells were collected at various reperfusion times for further study.

RESULTS

Our preliminary study demonstrated that 6 h of reperfusion caused the most liver I/R injury. Peptidomic results suggested that 10 down-regulated peptides were most likely to alleviate I/R injury by supporting mitochondrial function. Most importantly, a novel peptide, LDP2, was identified that alleviated I/R injury of AML12 cells. It increased cell viability and reduced the expression of inflammation- and apoptosis-related proteins. In addition, LDP2 inhibited the expression of proteins related to autophagy.

CONCLUSIONS

Investigation of changes in the profiles of peptides in I/R-injured livers led to identification of a novel peptide, LDP2 with potential function in liver protection by inhibiting inflammation, apoptosis, and autophagy.

Neurobiochemical, Peptidomic, and Bioinformatic Approaches to Characterize Tauopathy Peptidome Biomarker Candidates in Experimental Mouse Model of Traumatic Brain Injury

Traumatic brain injury (TBI) is a multidimensional damage, and currently, no FDA-approved medicine is available. Multiple pathways in the cell are triggered through a head injury (e.g., calpain and caspase activation), which truncate tau and generate variable fragment sizes (MW 400-45,000 K). In this study, we used an open-head TBI mouse model generated by controlled cortical impact (CCI) and collected ipsilateral (IC) and contralateral (CC) mice htau brain cortices at one (D1) three (D3), and seven (D7) days post-injury. We implemented immunological (antibody-based detection) and peptidomic approaches (nano-reversed-phase liquid chromatography/tandem mass spectrometry) to investigate proteolytic tau peptidome (low molecular weight (LMW) < 10 K)) and pathological phosphorylation sites (high-molecular-weight (HMW); > 10 K) derived from CCI-TBI animal models. Our immunoblotting analysis verified tau hyperphosphorylation, HMW, and HMW breakdown products (HMW-BDP) formation of tau (e.g., pSer₂₀₂, pThr₁₈₁, pThr₂₃₁, pSer₃₉₆, and pSer₄₀₄), following CCI-TBI. Peptidomic data revealed unique sequences of injury-dependent proteolytic peptides generated from human tau protein. Among the N-terminal tau peptides, EIPEGTTAEEAGIGDTPSLEDEAAGHVTQA (a.a. 96-125) and AQPHTEIPEGTTAEEAGIGDTPSLEDEAAGHVTQARM (a.a. 91-127). Examples of tau C-terminal peptides identified include NVSSTGSIDMVDSPQLATLADEVSASLAKQGL (a.a. 410-441) and QLATLADEVSASLAKQGL (a.a. 424-441). Our peptidomic bioinformatic tools showed the association of proteases, such as CAPN1, CAPN2, and CTSL; CASP1, MMP7, and MMP9; and ELANE, GZMA, and MEP1A, in CCI-TBI tau peptidome. In clinical trials for novel TBI treatments, it might be useful to monitor a subset of tau peptidome as targets for biomarker utility and use them for a "theranostic" approach.

Peptidome and Transcriptome Analysis of Plant Peptides Involved in Bipolaris maydis Infection of Maize

Southern corn leaf blight (SCLB) caused by Bipolaris maydis threatens maize growth and yield worldwide. In this study, TMT-labeled comparative peptidomic analysis was established between infected and uninfected maize leaf samples using liquid-chromatography-coupled tandem mass spectrometry. The results were further compared and integrated with transcriptome data under the same experimental conditions. Plant peptidomic analysis identified 455 and 502 differentially expressed peptides (DEPs) in infected maize leaves on day 1 and day 5, respectively. A total of 262 common DEPs were identified in both cases. Bioinformatic analysis indicated that the precursor proteins of DEPs are associated with many pathways generated by SCLB-induced pathological changes. The expression profiles of plant peptides and genes in maize plants were considerably altered after B. maydis infection. These findings provide new insights into the molecular mechanisms of SCLB pathogenesis and offer a basis for the development of maize genotypes with SCLB resistance.

Recent advances in mass spectrometry analysis of neuropeptides

Due to their involvement in numerous biochemical pathways, neuropeptides have been the focus of many recent research studies. Unfortunately, classic analytical methods, such as western blots and enzyme-linked immunosorbent assays, are extremely limited in terms of global investigations, leading researchers to search for more advanced techniques capable of probing the entire neuropeptidome of an organism. With recent technological advances, mass spectrometry (MS) has provided methodology to gain global knowledge of a neuropeptidome on a spatial, temporal, and quantitative level. This review will cover key considerations for the analysis of neuropeptides by MS, including sample preparation strategies, instrumental advances for identification, structural characterization, and imaging; insightful functional studies; and newly developed absolute and relative quantitation strategies. While many discoveries have been made with MS, the methodology is still in its infancy. Many of the current challenges and areas that need development will also be highlighted in this review.

An Efficient Peptidomics Screening for Exogenous Substrates and Inhibitory Peptides of the Dipeptidase ACE from Milk Hydrolysate

The dipeptidase angiotensin-I-converting enzyme (ACE) pre-incubation, liquid chromatography- mass spectrometry (LC-MS), and stable-isotope labeling were integrated for an efficient screening of ACE's exogenous substrates from milk hydrolysate. Using this approach, 31 substrates were readily identified from 478 identified peptides and their activities were confirmed using synthetic peptides. Their reactivity is highly correlated with the decreased isotope ratio observed in LC-MS. Among these substrates, the most frequently observed residue at the P1' position was Leu/Ser. It also revealed that ACE would not cleave the peptide when P1' is Pro, P2' is Asp/Glu, or P1 position is Ile. Interestingly, the sequential two-stage hydrolysis was also found. Moreover, their protective effects against ACE-mediated hydrolysis of angiotensin I (Ang-I) were also examined. The result indicated that AYFYPELFR and HLPLPLLQSW can significantly retard the hydrolysis of Ang-I and act as substrate-type inhibitors.

Microbial succession and its effect on the formation of umami peptides during sufu fermentation

Sufu, a traditional Chinese fermented food, is famous for its unique flavor, especially umami. However, the formation mechanism of its umami peptides is still unclear. Here, we investigated the dynamic change of both umami peptides and microbial communities during sufu production. Based on peptidomic analysis, 9081 key differential peptides were identified, which mainly involved in amino acid transport and metabolism, peptidase activity and hydrolase activity. Twenty-six high-quality umami peptides with ascending trend were recognized by machine learning methods and Fuzzy c-means clustering. Then, through correlation analysis, five bacterial species (Enterococcus italicus, Leuconostoc citreum, L. mesenteroides, L. pseudomesenteroides, Tetragenococcus halophilus) and two fungi species (Cladosporium colombiae, Hannaella oryzae) were identified to be the core functional microorganisms for umami peptides formation. Functional annotation of five lactic acid bacteria indicated their important functions to be carbohydrate metabolism, amino acid metabolism and nucleotide metabolism, which proved their umami peptides production ability. Overall, our results enhanced the understanding of microbial communities and the formation mechanism of umami peptides in sufu, providing novel insights for quality control and flavor improvement of tofu products.

Peptidomics analysis of plasma in patients with ankylosing spondylitis

Background

This study aimed to explore the differential expression of peptides associated with ankylosing spondylitis (AS) patients, enabling identification of potential functional peptides to provide the basis for the novel intervention targets for AS.

Material and Methods

3 AS patients and 3 healthy volunteers were enrolled in this study. The expression profiles for peptides present in the plasma of AS patients and the healthy individual were analyzed by liquid chromatography-tandem mass spectrometry (LC-MS/MS). The physicochemical properties and biological functions of identified peptides were further analyzed by bioinformatics. The results of peptide identification were verified by cell viability analysis, using CCK8 and Edu staining assay, and the differential peptides relevant to the disease were screened.

Results

52 differential peptides were successfully identified using mass spectrometry. 44 peptides were up-regulated, while eight were down-regulated. FGA-peptide (sequences: DSGEGDFLAEGGGVRGPR), C4A-peptide (sequences: NGFKSHAL), and TUBB-peptide (sequences: ISEQFTAMFR) were screened out that could significantly promote the proliferation of fibroblasts in AS patients. Bioinformatics analysis showed these differentially expressed peptides might be associated with "MHC class I protein binding" and "pathogenic Escherichia coli infection" pathways, which might further affect the progression of AS.

Conclusion

This pilot study shows 3 differentially expressed peptides may have the potential function for the occurrence and development of AS, may provide novel insights into the underlying molecular mechanisms of AS based on peptide omics.

Antioxidant peptides: Overview of production, properties, and applications in food systems

In recent years, several studies have reported the beneficial effects of antioxidant peptides in delaying oxidation reactions. Thus, a growing number of food proteins have been investigated as suitable sources for obtaining these antioxidant peptides. In this study, some of the most critical developments in the discovery of peptidic antioxidants are discussed. Initially, the primary methods to release, purify, and identify these antioxidant peptides from various food-derived sources are reviewed. Then, computer-based screening methods of the available peptides are summarized, and methods to interpret their structure-activity relationship are illustrated. Finally, approaches to the large-scale production of these bioactive peptides are described. In addition, the applications of these antioxidants in food systems are discussed, and gaps, future challenges, and opportunities in this field are highlighted. In conclusion, various food items can be considered promising sources to obtain these novel antioxidant peptides, which present various opportunities for food applications in addition to health promotion. The lack of in-depth data on the link between the structure and activity of these antioxidants, which is critical for the prediction of possible bioactive amino acid sequences and their potency in food systems and in vivo conditions (rather than in vitro systems), requires further attention. Consequently, future collaborative research activities between the industry and academia are required to realize the commercialization objectives of these novel antioxidant peptides.

Tat-hspb1 Suppresses Clear Cell Renal Cell Carcinoma (ccRCC) Growth via Lysosomal Membrane Permeabilization

Clear cell renal cell carcinoma (ccRCC) is the most prevalent kidney cancer, of which the incidence is increasing worldwide with a high mortality rate. Bioactive peptides are considered a significant class of natural medicines. We applied mass spectrometry-based peptidomic analysis to explore the peptide profile of human renal clear cell carcinoma and adjacent normal tissues. A total of 18,031 peptides were identified, of which 105 unique peptides were differentially expressed (44 were up-regulated and 61 were down-regulated in ccRCC tissues). Through bioinformatic analysis, we finally selected one peptide derived from the HSPB1 protein (amino acids 12-35 of the N-terminal region of HSPB1). Next, we fused this peptide to the HIV-Tat, generated a novel peptide named Tat-hspb1, and found that Tat-hspb1 inhibited ccRCC cells' viability while being less cytotoxic to normal epithelial cells. Furthermore, Tat-hspb1 induced apoptosis and inhibited the proliferation and migration of ccRCC cells. Furthermore, we demonstrated that Tat-hspb1 was predominantly localized in lysosomes after entering the ccRCC cell and induced lysosomal membrane permeabilization (LMP) and the release of cathepsin D from lysosomes. Taken together, Tat-hspb1 has the potential to serve as a new anticancer drug candidate.

The use of mass spectrometry in a proteome-centered multiomics study of human pituitary adenomas

A pituitary adenoma (PA) is a common intracranial neoplasm, and is a complex, chronic, and whole-body disease with multicausing factors, multiprocesses, and multiconsequences. It is very difficult to clarify molecular mechanism and treat PAs from the single-factor strategy model. The rapid development of multiomics and systems biology changed the paradigms from a traditional single-factor strategy to a multiparameter systematic strategy for effective management of PAs. A series of molecular alterations at the genome, transcriptome, proteome, peptidome, metabolome, and radiome levels are involved in pituitary tumorigenesis, and mutually associate into a complex molecular network system. Also, the center of multiomics is moving from structural genomics to phenomics, including proteomics and metabolomics in the medical sciences. Mass spectrometry (MS) has been extensively used in phenomics studies of human PAs to clarify molecular mechanisms, and to discover biomarkers and therapeutic targets/drugs. MS-based proteomics and proteoform studies play central roles in the multiomics strategy of PAs. This article reviews the status of multiomics, multiomics-based molecular pathway networks, molecular pathway network-based pattern biomarkers and therapeutic targets/drugs, and future perspectives for personalized, predeictive, and preventive (3P) medicine in PAs.

Planar Junctionless Field-Effect Transistor for Detecting Biomolecular Interactions

Label-free field-effect transistor-based immunosensors are promising candidates for proteomics and peptidomics-based diagnostics and therapeutics due to their high multiplexing capability, fast response time, and ability to increase the sensor sensitivity due to the short length of peptides. In this work, planar junctionless field-effect transistor sensors (FETs) were fabricated and characterized for pH sensing. The device with SiO₂ gate oxide has shown voltage sensitivity of 41.8 ± 1.4, 39.9 ± 1.4, 39.0 ± 1.1, and 37.6 ± 1.0 mV/pH for constant drain currents of 5, 10, 20, and 50 nA, respectively, with a drain to source voltage of 0.05 V. The drift analysis shows a stability over time of −18 nA/h (pH 7.75), −3.5 nA/h (pH 6.84), −0.5 nA/h (pH 4.91), 0.5 nA/h (pH 3.43), corresponding to a pH drift of −0.45, −0.09, −0.01, and 0.01 per h. Theoretical modeling and simulation resulted in a mean value of the surface states of 3.8 × 10¹⁵/cm² with a standard deviation of 3.6 × 10¹⁵/cm². We have experimentally verified the number of surface sites due to APTES, peptide, and protein immobilization, which is in line with the theoretical calculations for FETs to be used for detecting peptide-protein interactions for future applications.

Application of peptide biomarkers in life analysis based on liquid chromatography-mass spectrometry technology

Biomedicine is developing rapidly in the 21st century. Among them, the qualitative and quantitative analysis of peptide biomarkers is of considerable importance for the diagnosis and therapy of diseases and the quality evaluation of drugs and food. The identification and quantitative analysis of peptides have been going on for decades. Traditionally, immunoassays or biological assays are generally used to quantify peptides in biological matrices. However, the selectivity and sensitivity of these methods cannot meet the requirements of the application. The separation and analysis technique of liquid chromatography-mass spectrometry (LC-MS) supplies a reliable alternative. In contrast to immunoassays, LC-MS methods are capable of providing the analytical prowess necessary to satisfy the demands of peptide biomarker research in the life sciences arena. This review article provides a historical account of the in-roads made by LC-MS technology for the detection of peptide biomarkers in the past 10 years, with the focus on the qualification/quantification developments and their applications.

Proteomic-driven identification of short open reading frame-encoded peptides

Accumulating evidence has shown that a large number of short open reading frames (sORFs) also have the ability to encode proteins. The discovery of sORFs opens up a new research area, leading to the identification and functional study of sORF encoded peptides (SEPs) at the omics level. Besides bioinformatics prediction and ribosomal profiling, mass spectrometry (MS) has become a significant tool as it directly detects the sequence of SEPs. Though MS-based proteomics methods have proved to be effective for qualitative and quantitative analysis of SEPs, the detection of SEPs is still a great challenge due to their low abundance and short sequence. To illustrate the progress in method development, we described and discussed the main steps of large-scale proteomics identification of SEPs, including SEP extraction and enrichment, MS detection, data processing and quality control, quantification, and function prediction and validation methods. This article is protected by copyright. All rights reserved.

Functional dairy products as a source of bioactive peptides and probiotics: current trends and future prospectives

Milk is an incredibly healthy food world-wide. However, the 'lactase deficient' individuals cannot digest milk's carbohydrate lactose. A large part of the world population is depriving of highly beneficial milk proteins like casein, lactoalbumin, lactoglobulin, etc. due to lactose intolerance. Production of functional foods and bioactive peptides from milk with natural antioxidants and the addition of probiotics could be the best alternative to extend the use of milk functionalities. Among different probiotics, the lactic acid bacteria (LAB) like Lactobacillus delbrueckii sub sp. bulgaricus, Streptococcus thermophilus and some species of Bifidobacteria and their metabolites (paraprobiotics and postbiotics) have been given more preference to add in milk-derived functional foods. These species are generally considered as heat-tolerant, highly proteolytic, and peptidolytic towards milk proteins and they liberate smaller molecules of bioactive peptides during fermentation and other processes that stimulate the enzyme lactase to help people in digestion of milk carbohydrate lactose. Moreover, the incorporation of natural antioxidants in yoghurt and other dairy products prevents the rancidity of milk fat. The level of bioactive peptides produced in milk-derived functional foods can be determined by capillary zone electrophoresis, mass spectrometry, fractionation, and other modern assessment techniques. Commercial production of functional probiotic products with bioactive peptides could significantly contribute to reduce milk spoilage, enhance health benefits as well as the growth of the agro-processing industry.

Mass Spectrometry as a Crucial Analytical Basis for Omics Sciences

This review is devoted to the consideration of mass spectrometric platforms as applied to omics sciences. The most significant attention is paid to omics related to life sciences (genomics, proteomics, meta-bolomics, lipidomics, glycomics, plantomics, etc.). Mass spectrometric approaches to solving the problems of petroleomics, polymeromics, foodomics, humeomics, and exosomics, related to inorganic sciences, are also discussed. The review comparatively presents the advantages of various principles of separation and mass spectral techniques, complementary derivatization, used to obtain large arrays of various structural and quantitative information in the mentioned omics sciences.

Cell affinity screening combined with nanoLC-MS/MS based peptidomics for identifying cancer cell binding peptides from Bufo Bufo gargarizans

Animal venoms contain many peptides with high specificity and selectivity against their protein targets, a characteristic which makes venoms an invaluable source of potential drugs. High-sensitivity mass spectrometry (MS)- based peptidomic platform has evolved as a predominant method for natural peptide drug discovery due to its strength for direct and rapid identification of peptides and peptide-associated post-translational modifications (PTMs). In this study, we used cell-affinity assays combined with nanoLC-MS/MS based peptidomics to identify cancer cell binding peptides (CBPs) from Bufo Bufo gargarizans. We identified 76 potential cell binding peptides and 237 non-affinity peptides in venom extracts from Asiatic toads, and some were verified with MS-parallel reaction monitoring (PRM) mode. These peptides were further analyzed and internalized within human cells and some demonstrated anti-tumor properties in vitro. These specific peptides might be used as templates for peptide-based drug design or optimization.

Trends in In Silico Approaches to the Prediction of Biologically Active Peptides in Meat and Meat Products as an Important Factor for Preventing Food-Related Chronic Diseases

The increasing awareness of modern consumers regarding the nutritional and health value of food has changed their preferences, as well their requirements, for food products, including meat and meat products. Expanding the knowledge on the impact of food on human health is currently one of the most important research areas for scientists worldwide, and it is also of interest to consumers who want to consciously compose their daily diets. New research methods, such as in silico techniques, offer solutions to these new challenges. These research methods are preferred over food evaluation, e.g., from meat, because of their advantages, such as low costs, shorter analysis times, and general availability (e.g., online databases), and are often used to design in vitro and, subsequently, in vivo tests. This review focuses on the possible use of in silico computerized methods to assess the potential of food as a source of these health-relevant biomolecules by using examples from the literature on meat and meat products. This review also provides information and important suggestions for analyzing peptides in terms of assessing their best sources, and screening those resistant to digestive factors and that show biological activity. The information provided in this review could contribute to the development of new sources of foods as biomolecules important for preventing or treating food-related chronic diseases, such as obesity, hypertension, and diabetes.

Three Distinct Proteases Are Responsible for Overall Cell Surface Proteolysis in Streptococcus thermophilus

The lactic acid bacterium Streptococcus thermophilus was believed to display only two distinct proteases at the cell surface, namely, the cell envelope protease PrtS and the housekeeping protease HtrA. Using peptidomics, we demonstrate here the existence of an additional active cell surface protease, which shares significant homology with the SepM protease of Streptococcus mutans. Although all three proteases-PrtS, HtrA, and SepM-are involved in the turnover of surface proteins, they demonstrate distinct substrate specificities. In particular, SepM cleaves proteins involved in cell wall metabolism and cell elongation, and its inactivation has consequences for cell morphology. When all three proteases are inactivated, the residual cell-surface proteolysis of S. thermophilus is approximately 5% of that of the wild-type strain. IMPORTANCE Streptococcus thermophilus is a lactic acid bacterium used widely as a starter in the dairy industry. Due to its "generally recognized as safe" status and its weak cell surface proteolytic activity, it is also considered a potential bacterial vector for heterologous protein production. Our identification of a new cell surface protease made it possible to construct a mutant strain with a 95% reduction in surface proteolysis, which could be useful in numerous biotechnological applications.

Peptidomics and Capillary Electrophoresis

Peptides play a crucial role in many vitally important functions of living organisms. The goal of peptidomics is the identification of the "peptidome," the whole peptide content of a cell, organ, tissue, body fluid, or organism. In peptidomic or proteomic studies, capillary electrophoresis (CE) is an alternative technique for liquid chromatography. It is a highly efficient and fast separation method requiring extremely low amounts of sample. In peptidomic approaches, CE is commonly combined with mass spectrometric (MS) detection. Most often, CE is coupled with electrospray ionization MS and less frequently with matrix-assisted laser desorption/ionization MS. CE-MS has been employed in numerous studies dealing with determination of peptide biomarkers in different body fluids for various diseases, or in food peptidomic research for the analysis and identification of peptides with special biological activities. In addition to the above topics, sample preparation techniques commonly applied in peptidomics before CE separation and possibilities for peptide identification and quantification by CE-MS or CE-MS/MS methods are discussed in this chapter.

Peptidomics and proteogenomics: background, challenges and future needs

INTRODUCTION

With available genomic data and related information, it is becoming possible to better highlight mutations or genomic alterations associated with a particular disease or disorder. The advent of high-throughput sequencing technologies has greatly advanced diagnostics, prognostics, and drug development.

AREAS COVERED

Peptidomics and proteogenomics are the two post-genomic technologies that enable the simultaneous study of peptides and proteins/transcripts/genes. Both technologies add a remarkably large amount of data to the pool of information on various peptides associated with gene mutations or genome remodeling. Literature search was performed in the PubMed database and is up to date.

EXPERT OPINION

This article lists various techniques used for peptidomic and proteogenomic analyses. It also explains various bioinformatics workflows developed to understand differentially expressed peptides/proteins and their role in disease pathogenesis. Their role in deciphering disease pathways, cancer research, and biomarker discovery using biofluids is highlighted. Finally, the challenges and future requirements to overcome the current limitations for their effective clinical use are also discussed.