Peptidomics

Peptide Bond Formation Between Unprotected Amino Acids: Convergent Synthesis of Oligopeptides

In the history of peptide chemical synthesis, amino acid-protecting groups have become basic, essential, and common moieties. As the use of protecting groups for amino acids effectively suppresses unwanted side reactions and enables the desired reaction to proceed selectively, their use continues regardless of the complexities of the protection processes and the waste generated by the deprotection residues. We developed peptide bond formation between unprotected amino acids to form silacyclic dipeptides. This is the first report of the proceeding cross-condensation between an unprotected amino acid and another unprotected amino acid. The selectivity, reaction yields, and purity of the products were satisfactory. In addition, we demonstrated further elongation of these compounds and achieved convergent synthesis with peptide-peptide elongation without the use of coupling reagents. Thus, these methods showed the potential to unlock a new, more efficient synthetic path toward polypeptides.

A Multiplexed Quantitative Proteomics Approach to the Human Plasma Protein Signature

Despite the plasma proteome being able to provide a unique insight into the health and disease status of individuals, holding singular promise as a source of protein biomarkers that could be pivotal in the context of personalized medicine, only around 100 proteins covering a few human conditions have been approved as biomarkers by the US Food and Drug Administration (FDA) so far. Mass spectrometry (MS) currently has enormous potential for high-throughput analysis in clinical research; however, plasma proteomics remains challenging mainly due to the wide dynamic range of plasma protein abundances and the time-consuming procedures required. We applied a new MS-based multiplexed proteomics workflow to quantitate proteins, encompassing 67 FDA-approved biomarkers, in >1300 human plasma samples from a clinical cohort. Our results indicate that this workflow is suitable for large-scale clinical studies, showing good accuracy and reproducibility (coefficient of variation (CV) < 20 for 90% of the proteins). Furthermore, we identified plasma signature proteins (stable in time on an individual basis), stable proteins (exhibiting low biological variability and high temporal stability), and highly variable proteins (with low temporal stability) that can be used for personalized health monitoring and medicine.

Unveiling Endogenous Serum Peptides as Potential Biomarkers for Hepatocellular Carcinoma in Patients with Liver Cirrhosis

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related deaths worldwide, mainly associated with liver cirrhosis. Current diagnostic methods for HCC have limited sensitivity and specificity, highlighting the need for improved early detection and intervention. In this study, we used a comprehensive approach involving endogenous peptidome along with bioinformatics analysis to identify and evaluate potential biomarkers for HCC. Serum samples from 40 subjects, comprising 20 HCC cases and 20 patients with liver cirrhosis (CIRR), were analyzed. Among 2568 endogenous peptides, 67 showed significant differential expression between the HCC vs CIRR. Further analysis revealed three endogenous peptides (VMHEALHNHYTQKSLSLSPG, NRFTQKSLSLSPG, and SARQSTLDKEL) that showed far better performance compared to AFP in terms of area under the receiver operating characteristic curve (AUC), showcasing their potential as biomarkers for HCC. Additionally, endogenous peptide IAVEWESNGQPENNYKT that belongs to the precursor protein Immunoglobulin heavy constant gamma 4 was detected in 100% of the HCC group and completely absent in the CIRR group, suggesting a promising diagnostic biomarker. Gene ontology and pathway analysis revealed the potential involvement of these dysregulated peptides in HCC. These findings provide valuable insights into the molecular basis of HCC and may contribute to the development of improved diagnostic methods and therapeutic targets for HCC.

Comparison of Peptidomes Extracted from Healthy Tissue and Tumor Tissue of the Parotid Glands and Saliva Samples

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.

Peptide-specific chemical language model successfully predicts membrane diffusion of cyclic peptides

Biological language modeling has significantly advanced the prediction of membrane penetration for small molecule drugs and natural peptides. However, accurately predicting membrane diffusion for peptides with pharmacologically relevant modifications remains a substantial challenge. Here, we introduce PeptideCLM, a peptide-focused chemical language model capable of encoding peptides with chemical modifications, unnatural or non-canonical amino acids, and cyclizations. We assess this model by predicting membrane diffusion of cyclic peptides, demonstrating greater predictive power than existing chemical language models. Our model is versatile, able to be extended beyond membrane diffusion predictions to other target values. Its advantages include the ability to model macromolecules using chemical string notation, a largely unexplored domain, and a simple, flexible architecture that allows for adaptation to any peptide or other macromolecule dataset.

MotifQuest: An Automated Pipeline for Motif Database Creation to Improve Peptidomics Database Searching Programs

Endogenous peptides are an abundant and versatile class of biomolecules with vital roles pertinent to the functionality of the nervous, endocrine, and immune systems and others. Mass spectrometry stands as a premier technique for identifying endogenous peptides, yet the field still faces challenges due to the lack of optimized computational resources for reliable raw mass spectra analysis and interpretation. Current database searching programs can exhibit discrepancies due to the unique properties of endogenous peptides, which typically require specialized search considerations. Herein, we present a high throughput, novel scoring algorithm for the extraction and ranking of conserved amino acid sequence motifs within any endogenous peptide database. Motifs are conserved patterns across organisms, representing sequence moieties crucial for biological functions, including maintenance of homeostasis. MotifQuest, our novel motif database generation algorithm, is designed to work in partnership with EndoGenius, a program optimized for database searching of endogenous peptides and that is powered by a motif database to capitalize on biological context to produce identifications. MotifQuest aims to quickly develop motif databases without any prior knowledge, a laborious task not possible with traditional sequence alignment resources. In this work we illustrate the utility of MotifQuest to expand EndoGenius' identification utility to other endogenous peptides by showcasing its ability to identify antimicrobial peptides. Additionally, we discuss the potential utility of MotifQuest to parse out motifs from a FASTA database file that can be further validated as new peptide drug candidates.

Electromembrane extraction of peptides based on charge, hydrophobicity, and size - A large-scale fundamental study of the extraction window

This study investigated the capability of electromembrane extraction (EME) as a general technique for peptides, by extracting complex pools of peptides comprising in total of 5953 different substances, varying in size from seven to 16 amino acids. Electromembrane extraction was conducted from a sample adjusted to pH 3.0 and utilized a liquid membrane consisting of 2-nitrophenyl octyl ether and carvacrol (1:1 w/w), containing 2% (w/w) di(2-ethylhexyl) phosphate. The acceptor phase was 50 mM phosphoric acid (pH 1.8), the extraction time was 45 min, and 10 V was used. High extraction efficiency, defined as a higher peptide signal in the acceptor than the sample after extraction, was achieved for 3706 different peptides. Extraction efficiencies were predominantly influenced by the hydrophobicity of the peptides and their net charge in the sample. Hydrophobic peptides were extracted with a net charge of +1, while hydrophilic peptides were extracted when the net charge was +2 or higher. A computational model based on machine learning was developed to predict the extractability of peptides based on peptide descriptors, including the grand average of hydropathy index and net charge at pH 3.0 (sample pH). This research shows that EME has general applicability for peptides and represents the first steps toward in silico prediction of extraction efficiency.

Evaluation of quality consistency between dispensing granules and traditional decoction of Bombyx batryticatus based on peptidomics and in silico simulations

The application of traditional Chinese medicine dispensing granules is becoming increasingly prevalent. However, the consistency of dispensing granules with traditional decoction remains controversial. In this study, the consistency of peptide composition and pharmacodynamics between dispensing granules and traditional decoction of Bombyx batryticatus (BB) were assessed. A peptidomics method based on LC-tandem mass spectrometry technology was used to evaluate peptide composition similarity between BB traditional decoction and dispensing granules. The results revealed notable differences in peptide sequences between the two dosage forms, with only 8.55% of peptides shared between them. To evaluate the potential pharmacodynamic effects of the two dosage forms on epilepsy, virtual screening was used to identify potential active peptides, including blood-brain barrier permeability, toxicity prediction, and molecular docking. BB traditional decoction demonstrated a higher number and greater abundance of potential active peptides than BB dispensing granules, suggesting that BB traditional decoction may have a more favorable effect in treating epilepsy compared with BB dispensing granules. Moreover, molecular docking and molecular dynamics simulation studies confirmed the mechanism of action of active peptides to γ-aminobutyric acid transporter 1 (GAT-1). This study provides a scientific basis for the evaluation of quality consistency between BB traditional decoction and dispensing granules.

Plant-Derived Peptides: (Neglected) Natural Products for Drug Discovery

Peptides have emerged as key regulators in various physiological processes, including growth, development, stress, and defense responses within plants as well as ecological interactions of plants with microbes and animals. Understanding and harnessing plant peptides can lead to the development of innovative strategies for crop improvement, increasing agricultural productivity, and enhancing resilience to environmental challenges such as drought, pests, and diseases. Moreover, some plant peptides have shown promise in human health applications, with potential therapeutic benefits as ingredients in herbal medicines as well as novel drug leads. The exploration of plant peptides is essential for unraveling the mysteries of plant biology and advancing peptide drug discovery. This short personal commentary provides a very brief overview about the field of plant-derived peptides and a personal word of motivation to increase the number of scientists in pharmacognosy working with these fascinating biomolecules.

Discovery and development of macrocyclic peptide modulators of the cannabinoid 2 receptor

The cannabinoid type 2 receptor (CB2R), a G protein-coupled receptor, is an important regulator of immune cell function and a promising target to treat chronic inflammation and fibrosis. While CB2R is typically targeted by small molecules, including endo-, phyto-, and synthetic cannabinoids, peptides-owing to their size-may offer a different interaction space to facilitate differential interactions with the receptor. Here, we explore plant-derived cyclic cystine-knot peptides as ligands of the CB2R. Cyclotides are known for their exceptional biochemical stability. Recently, they gained attention as G protein-coupled receptor modulators and as templates for designing peptide ligands with improved pharmacokinetic properties over linear peptides. Cyclotide-based ligands for CB2R were profiled based on a peptide-enriched extract library comprising nine plants. Employing pharmacology-guided fractionation and peptidomics, we identified the cyclotide vodo-C1 from sweet violet (Viola odorata) as a full agonist of CB2R with an affinity (Ki) of 1 μM and a potency (EC50) of 8 μM. Leveraging deep learning networks, we verified the structural topology of vodo-C1 and modeled its molecular volume in comparison to the CB2R ligand binding pocket. In a fragment-based approach, we designed and characterized vodo-C1-based bicyclic peptides (vBCL1-4), aiming to reduce size and improve potency. Opposite to vodo-C1, the vBCL peptides lacked the ability to activate the receptor but acted as negative allosteric modulators or neutral antagonists of CB2R. This study introduces a macrocyclic peptide phytocannabinoid, which served as a template for the development of synthetic CB2R peptide modulators. These findings offer opportunities for future peptide-based probe and drug development at cannabinoid receptors.

Neuropeptidomics of the American Lobster Homarus americanus

The American lobster, Homarus americanus, is not only of considerable economic importance but has also emerged as a premier model organism in neuroscience research. Neuropeptides, an important class of cell-to-cell signaling molecules, play crucial roles in a wide array of physiological and psychological processes. Leveraging the recently sequenced high-quality draft genome of the American lobster, our study sought to profile the neuropeptidome of this model organism. Employing advanced mass spectrometry techniques, we identified 24 neuropeptide precursors and 101 unique mature neuropeptides in Homarus americanus. Intriguingly, 67 of these neuropeptides were discovered for the first time. Our findings provide a comprehensive overview of the peptidomic attributes of the lobster's nervous system and highlight the tissue-specific distribution of these neuropeptides. Collectively, this research not only enriches our understanding of the neuronal complexities of the American lobster but also lays a foundation for future investigations into the functional roles that these peptides play in crustacean species. The mass spectrometry data have been deposited in the PRIDE repository with the identifier PXD047230.

Harnessing the power of proteomics in precision diabetes medicine

Precision diabetes medicine (PDM) aims to reduce errors in prevention programmes, diagnosis thresholds, prognosis prediction and treatment strategies. However, its advancement and implementation are difficult due to the heterogeneity of complex molecular processes and environmental exposures that influence an individual's disease trajectory. To address this challenge, it is imperative to develop robust screening methods for all areas of PDM. Innovative proteomic technologies, alongside genomics, have proven effective in precision cancer medicine and are showing promise in diabetes research for potential translation. This narrative review highlights how proteomics is well-positioned to help improve PDM. Specifically, a critical assessment of widely adopted affinity-based proteomic technologies in large-scale clinical studies and evidence of the benefits and feasibility of using MS-based plasma proteomics is presented. We also present a case for the use of proteomics to identify predictive protein panels for type 2 diabetes subtyping and the development of clinical prediction models for prevention, diagnosis, prognosis and treatment strategies. Lastly, we discuss the importance of plasma and tissue proteomics and its integration with genomics (proteogenomics) for identifying unique type 2 diabetes intra- and inter-subtype aetiology. We conclude with a call for action formed on advancing proteomics technologies, benchmarking their performance and standardisation across sites, with an emphasis on data sharing and the inclusion of diverse ancestries in large cohort studies. These efforts should foster collaboration with key stakeholders and align with ongoing academic programmes such as the Precision Medicine in Diabetes Initiative consortium.

Immunomodulatory Peptides for Tumor Treatment

Peptides exhibit various biological activities, including biorecognition, cell targeting, and tumor penetration, and can stimulate immune cells to elicit immune responses for tumor immunotherapy. Peptide self-assemblies and peptide-functionalized nanocarriers can reduce the effect of various biological barriers and the degradation by peptidases, enhancing the efficiency of peptide delivery and improving antitumor immune responses. To date, the design and development of peptides with various functionalities have been extensively reviewed for enhanced chemotherapy; however, peptide-mediated tumor immunotherapy using peptides acting on different immune cells, to the knowledge, has not yet been summarized. Thus, this work provides a review of this emerging subject of research, focusing on immunomodulatory anticancer peptides. This review introduces the role of peptides in the immunomodulation of innate and adaptive immune cells, followed by a link between peptides in the innate and adaptive immune systems. The peptides are discussed in detail, following a classification according to their effects on different innate and adaptive immune cells, as well as immune checkpoints. Subsequently, two delivery strategies for peptides as drugs are presented: peptide self-assemblies and peptide-functionalized nanocarriers. The concluding remarks regarding the challenges and potential solutions of peptides for tumor immunotherapy are presented.

Analgesic Peptides: From Natural Diversity to Rational Design

Pain affects one-third of the global population and is a significant public health issue. The use of opioid drugs, which are the strongest painkillers, is associated with several side effects, such as tolerance, addiction, overdose, and even death. An increasing demand for novel, safer analgesic agents is a driving force for exploring natural sources of bioactive peptides with antinociceptive activity. Since the G protein-coupled receptors (GPCRs) play a crucial role in pain modulation, the discovery of new peptide ligands for GPCRs is a significant challenge for novel drug development. The aim of this review is to present peptides of human and animal origin with antinociceptive potential and to show the possibilities of their modification, as well as the design of novel structures. The study presents the current knowledge on structure-activity relationship in the design of peptide-based biomimetic compounds, the modification strategies directed at increasing the antinociceptive activity, and improvement of metabolic stability and pharmacodynamic profile. The procedures employed in prolonged drug delivery of emerging compounds are also discussed. The work summarizes the conditions leading to the development of potential morphine replacements.

An Integrated Comprehensive Peptidomics and In Silico Analysis of Bioactive Peptide-Rich Milk Fermented by Three Autochthonous Cocci Strains

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.

Origins, Technological Advancement, and Applications of Peptidomics

Peptidomics is the comprehensive characterization of peptides from biological sources instead of heading for a few single peptides in former peptide research. Mass spectrometry allows to detect a multitude of peptides in complex mixtures and thus enables new strategies leading to peptidomics. The term was established in the year 2001, and up to now, this new field has grown to over 3000 publications. Analytical techniques originally developed for fast and comprehensive analysis of peptides in proteomics were specifically adjusted for peptidomics. Although it is thus closely linked to proteomics, there are fundamental differences with conventional bottom-up proteomics. Fundamental technological advancements of peptidomics since have occurred in mass spectrometry and data processing, including quantification, and more slightly in separation technology. Different strategies and diverse sources of peptidomes are mentioned by numerous applications, such as discovery of neuropeptides and other bioactive peptides, including the use of biochemical assays. Furthermore, food and plant peptidomics are introduced similarly. Additionally, applications with a clinical focus are included, comprising biomarker discovery as well as immunopeptidomics. This overview extensively reviews recent methods, strategies, and applications including links to all other chapters of this book.

Current Challenges and Future Directions in Peptidomics

The field of peptidomics has been under development since its start more than 20 years ago. In this chapter we provide a personal outlook for future directions in this field. The applications of peptidomics technologies are spreading more and more from classical research of peptide hormones and neuropeptides towards commercial applications in plant and food-science. Many clinical applications have been developed to analyze the complexity of biofluids, which are being addressed with new instrumentation, automization, and data processing. Additionally, the newly developed field of immunopeptidomics is showing promise for cancer therapies. In conclusion, peptidomics will continue delivering important information in classical fields like neuropeptides and peptide hormones, benefiting from improvements in state-of-the-art technologies. Moreover, new directions of research such as immunopeptidomics will further complement classical omics technologies and may become routine clinical procedures. Taken together, discoveries of new substances, networks, and applications of peptides can be expected in different disciplines.

Hallmarks of the metabolic secretome

The identification of novel secreted factors is advancing at an unprecedented pace. However, there is a critical need to consolidate and integrate this knowledge to provide a framework of their diverse mechanisms, functional significance, and inter-relationships. Complicating this effort are challenges related to nonstandardized methods, discrepancies in sample handling, and inconsistencies in the annotation of unknown molecules. This Review aims to synthesize the rapidly expanding field of the metabolic secretome, encompassing the five major types of secreted factors: proteins, peptides, metabolites, lipids, and extracellular vesicles. By systematically defining the functions and detection of the components within the metabolic secretome, this Review provides a primer into the advances of the field, and how integration of the techniques discussed can provide a deeper understanding of the mechanisms underlying metabolic homeostasis and its disorders.

Bioassays for Identifying and Characterizing Plant Regulatory Peptides

Plant peptides are a new frontier in plant biology, owing to their key regulatory roles in plant growth, development, and stress responses. Synthetic peptides are promising biological agents that can be used to improve crop growth and protection in an environmentally sustainable manner. Plant regulatory peptides identified in pioneering research, including systemin, PSK, HypSys, RALPH, AtPep1, CLV3, TDIF, CLE, and RGF/GLV/CLEL, hold promise for crop improvement as potent regulators of plant growth and defense. Mass spectrometry and bioinformatics are greatly facilitating the discovery and identification of new plant peptides. The biological functions of most novel plant peptides remain to be elucidated. Bioassays are an essential part in studying the biological activity of identified and putative plant peptides. Root growth assays and cultivated plant cell cultures are widely used to evaluate the regulatory potential of plant peptides during growth, differentiation, and stress reactions. These bioassays can be used as universal approaches for screening peptides from different plant species. Development of high-throughput bioassays can facilitate the screening of large numbers of identified and putative plant peptides, which have recently been discovered but remain uncharacterized for biological activity.

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.

Parahydrogen hyperpolarized NMR detection of underivatized short oligopeptides

Parahydrogen hyperpolarization has evolved into a versatile tool in NMR, allowing substantial sensitivity enhancements in analysis of biological samples. Herein we show how its application scope can be extended from small metabolites to underivatized oligopeptides in solution. Based on a homologous series of alanine oligomers, we report on an experimental and DFT study on the structure of the oligopeptide and hyperpolarization catalyst complexes formed in the process. We demonstrate that alanine oligomers coordinate to the iridium carbene-based catalyst in three different ways, each giving rise to distinctive hydride signals. Moreover, the exact structures of the transient oligopeptide-catalyst complexes are oligomer-specific. This work gives a first insight into how the organometallic iridium-N-heterocyclic carbene-based parahydrogen hyperpolarization catalyst interacts with biopolymers that have multiple catalyst binding sites. A preliminary application example is demonstrated for oligopeptide detection in urine, a complex biological mixture.

Positional proteomics: is the technology ready to study clinical cohorts?

INTRODUCTION

Positional proteomics provides proteome-wide information on protein termini and their modifications, uniquely enabling unambiguous identification of site-specific, limited proteolysis. Such proteolytic cleavage irreversibly modifies protein sequences resulting in new proteoforms with distinct protease-generated neo-N and C-termini and altered localization and activity. Misregulated proteolysis is implicated in a wide variety of human diseases. Protein termini, therefore, constitute a huge, largely unexplored source of specific analytes that provides a deep view into the functional proteome and a treasure trove for biomarkers.

AREAS COVERED

We briefly review principal approaches to define protein termini and discuss recent advances in method development. We further highlight the potential of positional proteomics to identify and trace specific proteoforms, with a focus on proteolytic processes altered in disease. Lastly, we discuss current challenges and potential for applying positional proteomics in biomarker and pre-clinical research.

EXPERT OPINION

Recent developments in positional proteomics have provided significant advances in sensitivity and throughput. In-depth analysis of proteolytic processes in clinical cohorts thus appears feasible in the near future. We argue that this will provide insights into the functional state of the proteome and offer new opportunities to utilize proteolytic processes altered or targeted in disease as specific diagnostic, prognostic and companion biomarkers.

Cyclic Peptides with Antifungal Properties Derived from Bacteria, Fungi, Plants, and Synthetic Sources

Fungal infections remain a significant concern for human health. The emergence of microbial resistance, the improper use of antimicrobial drugs, and the need for fewer toxic antifungal treatments in immunocompromised patients have sparked substantial interest in antifungal research. Cyclic peptides, classified as antifungal peptides, have been in development as potential antifungal agents since 1948. In recent years, there has been growing attention from the scientific community to explore cyclic peptides as a promising strategy for combating antifungal infections caused by pathogenic fungi. The identification of antifungal cyclic peptides from various sources has been possible due to the widespread interest in peptide research in recent decades. It is increasingly important to evaluate narrow- to broad-spectrum antifungal activity and the mode of action of synthetic and natural cyclic peptides for both synthesized and extracted peptides. This short review aims to highlight some of the antifungal cyclic peptides isolated from bacteria, fungi, and plants. This brief review is not intended to present an exhaustive catalog of all known antifungal cyclic peptides but rather seeks to showcase selected cyclic peptides with antifungal properties that have been isolated from bacteria, fungi, plants, and synthetic sources. The addition of commercially available cyclic antifungal peptides serves to corroborate the notion that cyclic peptides can serve as a valuable source for the development of antifungal drugs. Additionally, this review discusses the potential future of utilizing combinations of antifungal peptides from different sources. The review underscores the need for the further exploration of the novel antifungal therapeutic applications of these abundant and diverse cyclic peptides.