Combining mass spectrometry and machine learning to discover bioactive peptides

Understanding peptide hormones: from precursor proteins to bioactive molecules

Peptide hormones are fundamental regulators of biological processes involved in homeostasis regulation and are often dysregulated in endocrine diseases. Despite their biological significance and established therapeutic potential, there is still a gap in our knowledge of their processing and post-translational modifications, as well as in the technologies for their discovery and detection. In this review, we cover insights into the peptidome landscape, including the proteolytic processing and post-translational modifications of peptide hormones. Understanding the full landscape of peptide hormones and their modifications could provide insights into leveraging proteolytic mechanisms to identify novel peptides with therapeutic potential. Therefore, we also discuss the need for future research aiming at better predicting, detecting, and characterizing new peptides with biological activities.

Prohormone cleavage prediction uncovers a non-incretin anti-obesity peptide

Peptide hormones, a class of pharmacologically active molecules, have a critical role in regulating energy homeostasis. Prohormone convertase 1/3 (also known as PCSK1/3) represents a key enzymatic mechanism in peptide processing, as exemplified with the therapeutic target glucagon-like peptide 1 (GLP-1)1,2. However, the full spectrum of peptides generated by PCSK1 and their functional roles remain largely unknown. Here we use computational drug discovery to systematically map more than 2,600 previously uncharacterized human proteolytic peptide fragments cleaved by prohormone convertases, enabling the identification of novel bioactive peptides. Using this approach, we identified a 12-mer peptide, BRINP2-related peptide (BRP). When administered pharmacologically, BRP reduces food intake and exhibits anti-obesity effects in mice and pigs without inducing nausea or aversion. Mechanistically, BRP administration triggers central FOS activation and acts independently of leptin, GLP-1 receptor and melanocortin 4 receptor. Together, these data introduce a method to identify new bioactive peptides and establish pharmacologically that BRP may be useful for therapeutic modulation of body weight.

Advances in AI-based strategies and tools to facilitate natural product and drug development

Natural products and their derivatives have been important for treating diseases in humans, animals, and plants. However, discovering new structures from natural sources is still challenging. In recent years, artificial intelligence (AI) has greatly aided the discovery and development of natural products and drugs. AI facilitates to: connect genetic data to chemical structures or vice-versa, repurpose known natural products, predict metabolic pathways, and design and optimize metabolites biosynthesis. More recently, the emergence and improvement in neural networks such as deep learning and ensemble automated web based bioinformatics platforms have sped up the discovery process. Meanwhile, AI also improves the identification and structure elucidation of unknown compounds from raw data like mass spectrometry and nuclear magnetic resonance. This article reviews these AI-driven methods and tools, highlighting their practical applications and guide for efficient natural product discovery and drug development.

Intelligent design and synthesis of energy catalytic materials

Efficient energy conversion and storage are crucial for the sustainable development and growth of renewable energy sources. However, the limited varieties of traditional energy catalytic materials cannot match the fast-expansion requirement of raising various clean energy for industrial applications. Thus, accelerating the design and synthesis of high-performance catalysts is necessary for the application of energy equipment. Recently, with artificial intelligence (AI) technology being advanced by leaps and bounds, it is feasible to efficiently and precisely screen materials and optimize synthesis conditions in a huge unknown space. Here, we introduce and review AI techniques used in the development of catalytic materials in detail. We describe the workflow for designing and synthesizing new materials using machine learning (ML) and robotics. We summarize the sources of data collection, the intelligent algorithms commonly used to build ML models, and the laboratory modules for the intelligent synthesis of materials. We provide the illustrations of predicting the properties of catalytic materials with ML assistance in different material types. In addition, we present the potential strategies for finding material synthesis pathways, and advances in robotics to accelerate high-performance catalytic materials synthesis in the review. Finally, the summary, challenges, and potential directions in the development of AI-assisted catalytic materials are presented and discussed.

Advancing micro-nano supramolecular assembly mechanisms of natural organic matter by machine learning for unveiling environmental geochemical processes

The nano-self-assembly of natural organic matter (NOM) profoundly influences the occurrence and fate of NOM and pollutants in large-scale complex environments. Machine learning (ML) offers a promising and robust tool for interpreting and predicting the processes, structures and environmental effects of NOM self-assembly. This review seeks to provide a tutorial-like compilation of data source determination, algorithm selection, model construction, interpretability analyses, applications and challenges for big-data-based ML aiming at elucidating NOM self-assembly mechanisms in environments. The results from advanced nano-submicron-scale spatial chemical analytical technologies are suggested as input data which provide the combined information of molecular interactions and structural visualization. The existing ML algorithms need to handle multi-scale and multi-modal data, necessitating the development of new algorithmic frameworks. Interpretable supervised models are crucial owing to their strong capacity of quantifying the structure-property-effect relationships and bridging the gap between simply data-driven ML and complicated NOM assembly practice. Then, the necessity and challenges are discussed and emphasized on adopting ML to understand the geochemical behaviors and bioavailability of pollutants as well as the elemental cycling processes in environments resulting from the NOM self-assembly patterns. Finally, a research framework integrating ML, experiments and theoretical simulation is proposed for comprehensively and efficiently understanding the NOM self-assembly-involved environmental issues.

Vaccine development using artificial intelligence and machine learning: A review

The COVID-19 pandemic has underscored the critical importance of effective vaccines, yet their development is a challenging and demanding process. It requires identifying antigens that elicit protective immunity, selecting adjuvants that enhance immunogenicity, and designing delivery systems that ensure optimal efficacy. Artificial intelligence (AI) can facilitate this process by using machine learning methods to analyze large and diverse datasets, suggest novel vaccine candidates, and refine their design and predict their performance. This review explores how AI can be applied to various aspects of vaccine development, such as predicting immune response from protein sequences, discovering adjuvants, optimizing vaccine doses, modeling vaccine supply chains, and predicting protein structures. We also address the challenges and ethical issues that emerge from the use of AI in vaccine development, such as data privacy, algorithmic bias, and health data sensitivity. We contend that AI has immense potential to accelerate vaccine development and respond to future pandemics, but it also requires careful attention to the quality and validity of the data and methods used.

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.

Peptides Evaluated In Silico, In Vitro, and In Vivo as Therapeutic Tools for Obesity: A Systematic Review

Bioinformatics has emerged as a valuable tool for screening drugs and understanding their effects. This systematic review aimed to evaluate whether in silico studies using anti-obesity peptides targeting therapeutic pathways for obesity, when subsequently evaluated in vitro and in vivo, demonstrated effects consistent with those predicted in the computational analysis. The review was framed by the question: "What peptides or proteins have been used to treat obesity in in silico studies?" and structured according to the acronym PECo. The systematic review protocol was developed and registered in PROSPERO (CRD42022355540) in accordance with the PRISMA-P, and all stages of the review adhered to these guidelines. Studies were sourced from the following databases: PubMed, ScienceDirect, Scopus, Web of Science, Virtual Heath Library, and EMBASE. The search strategies resulted in 1015 articles, of which, based on the exclusion and inclusion criteria, 7 were included in this systematic review. The anti-obesity peptides identified originated from various sources including bovine alpha-lactalbumin from cocoa seed (Theobroma cacao L.), chia seed (Salvia hispanica L.), rice bran (Oryza sativa), sesame (Sesamum indicum L.), sea buckthorn seed flour (Hippophae rhamnoides), and adzuki beans (Vigna angularis). All articles underwent in vitro and in vivo reassessment and used molecular docking methodology in their in silico studies. Among the studies included in the review, 46.15% were classified as having an "uncertain risk of bias" in six of the thirteen criteria evaluated. The primary target investigated was pancreatic lipase (n = 5), with all peptides targeting this enzyme demonstrating inhibition, a finding supported both in vitro and in vivo. Additionally, other peptides were identified as PPARγ and PPARα agonists (n = 2). Notably, all peptides exhibited different mechanisms of action in lipid metabolism and adipogenesis. The findings of this systematic review underscore the effectiveness of computational simulation as a screening tool, providing crucial insights and guiding in vitro and in vivo investigations for the discovery of novel anti-obesity peptides.

Antiprotozoal peptide prediction using machine learning with effective feature selection techniques

Background

Protozoal pathogens pose a considerable threat, leading to notable mortality rates and the ongoing challenge of developing resistance to drugs. This situation underscores the urgent need for alternative therapeutic approaches. Antimicrobial peptides stand out as promising candidates for drug development. However, there is a lack of published research focusing on predicting antimicrobial peptides specifically targeting protozoal pathogens. In this study, we introduce a successful machine learning-based framework designed to predict potential antiprotozoal peptides effective against protozoal pathogens.

Objective

The primary objective of this study is to classify and predict antiprotozoal peptides using diverse negative datasets.

Methods

A comprehensive literature review was conducted to gather experimentally validated antiprotozoal peptides, forming the positive dataset for our study. To construct a robust machine learning classifier, multiple negative datasets were incorporated, including (i) non-antimicrobial, (ii) antiviral, (iii) antibacterial, (iv) antifungal, and (v) antimicrobial peptides excluding those targeting protozoal pathogens. Various compositional features of the peptides were extracted using the pfeature algorithm. Two feature selection methods, SVC-L1 and mRMR, were employed to identify highly relevant features crucial for distinguishing between the positive and negative datasets. Additionally, five popular classifiers i.e. Decision Tree, Random Forest, Support Vector Machine, Logistic Regression, and XGBoost were used to build efficient decision models.

Results

XGBoost was the most effective in classifying antiprotozoal peptides from each negative dataset based on the features selected by the mRMR feature selection method. The proposed machine learning framework efficiently differentiate the antiprotozoal peptides from (i) non-antimicrobial (ii) antiviral (iii) antibacterial (iv) antifungal and (v) antimicrobial with accuracy of 97.27 %, 93.64 %, 86.36 %, 90.91 %, and 89.09 % respectively on the validation dataset.

Conclusion

The models are incorporated in a user-friendly web server (www.soodlab.com/appred) to predict the antiprotozoal activity of given peptides.

Harnessing Protein Hydrolysates and Peptides for Hyperuricemia Management: Insights into Sources, Mechanisms, Techniques, and Future Directions

Hyperuricemia (HUA) is a metabolic disorder characterized by an imbalance in uric acid production and excretion, frequently leading to gout and various chronic conditions. Novel bioactive compounds offer effective alternatives for managing HUA, reducing side effects of traditional medications. Recent studies have highlighted the therapeutic potential of protein hydrolysates and peptides in managing HUA. This review focuses on preparing and applying protein hydrolysates to treat HUA and explores peptides for xanthine oxidase inhibition. Particularly, we discuss their origins, enzymatic approaches, and mechanisms of action in detail. The review provides an updated understanding of HUA pathogenesis, current pharmacological interventions, and methodologies for the preparation, purification, identification, and assessment of these compounds. Furthermore, to explore the application of protein hydrolysates and peptides in the food industry, we also address challenges and propose solutions related to the safety, bitterness, oral delivery, and the integration of artificial intelligence in peptide discovery. Bridging traditional pharmacological approaches and innovative dietary interventions, this study paves the way for future research and development in HUA management, contributing to the utilization of proteins from different food sources. In conclusion, protein hydrolysates and peptides show significant promise as safe agents and dietary interventions for preventing and treating HUA.

Peptide clustering enhances large-scale analyses and reveals proteolytic signatures in mass spectrometry data

Recent advances in mass spectrometry-based peptidomics have catalyzed the identification and quantification of thousands of endogenous peptides across diverse biological systems. However, the vast peptidomic landscape generated by proteolytic processing poses several challenges for downstream analyses and limits the comparability of clinical samples. Here, we present an algorithm that aggregates peptides into peptide clusters, reducing the dimensionality of peptidomics data, improving the definition of protease cut sites, enhancing inter-sample comparability, and enabling the implementation of large-scale data analysis methods akin to those employed in other omics fields. We showcase the algorithm by performing large-scale quantitative analysis of wound fluid peptidomes of highly defined porcine wound infections and human clinical non-healing wounds. This revealed signature phenotype-specific peptide regions and proteolytic activity at the earliest stages of bacterial colonization. We validated the method on the urinary peptidome of type 1 diabetics which revealed potential subgroups and improved classification accuracy.

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.

Challenges in computational discovery of bioactive peptides in 'omics data

Peptides have a plethora of activities in biological systems that can potentially be exploited biotechnologically. Several peptides are used clinically, as well as in industry and agriculture. The increase in available 'omics data has recently provided a large opportunity for mining novel enzymes, biosynthetic gene clusters, and molecules. While these data primarily consist of DNA sequences, other types of data provide important complementary information. Due to their size, the approaches proven successful at discovering novel proteins of canonical size cannot be naïvely applied to the discovery of peptides. Peptides can be encoded directly in the genome as short open reading frames (smORFs), or they can be derived from larger proteins by proteolysis. Both of these peptide classes pose challenges as simple methods for their prediction result in large numbers of false positives. Similarly, functional annotation of larger proteins, traditionally based on sequence similarity to infer orthology and then transferring functions between characterized proteins and uncharacterized ones, cannot be applied for short sequences. The use of these techniques is much more limited and alternative approaches based on machine learning are used instead. Here, we review the limitations of traditional methods as well as the alternative methods that have recently been developed for discovering novel bioactive peptides with a focus on prokaryotic genomes and metagenomes.

Cross-linked polysiloxane-coated stable bond O-9-(2,6-diisopropylphenylcarbamoyl)quinine and quinidine chiral stationary phases as well as application in enantioselective cryo-HPLC

In this work, brush-type chiral stationary phases (CSPs) with O-9-(2,6-diisopropylphenylcarbamoyl)-modified quinidine (DIPPCQD-brush/-SH) and O-9-(2,6-diisopropylphenylcarbamoyl)-modified quinine (DIPPCQN-brush/-SH) were prepared as benchmarks for comparison with new corresponding polymeric CSPs with more stable bonding chemistry. These polymeric CSPs were prepared by coating a thin poly(3-mercaptopropyl)-methylsiloxane film together with the chiral selector onto vinyl-modified silica. In a second step, immobilization of the quinine/quinidine derivatives as well as cross-linking of the polysiloxane film to the vinyl-silica is achieved by a double thiol-ene click reaction. The polymeric CSPs exhibited similar enantioselectivity as the corresponding brush phases, but showed lower chromatographic efficiencies. Chiral acidic substances were separated into enantiomers (e.g., N-protected amino acids, herbicides like dichlorprop) in accordance with an enantioselective anion-exchange process. Oxidation of residual thiol groups of the polymer DIPPCQN-CSP introduced sulfonic acid co-ligands on the silica surface, which resulted in greatly reduced retention times. Acting as immobilized counterions, they allowed to reduce the concentration of counterions in the mobile phase, which is favorable for liquid chromatography (LC)-electrospray ionization-mass spectrometry application. Ibuprofen showed a single peak under ambient column temperature. However, application of cryogenic cooling of the column enabled to achieve baseline separation at -20°C column temperature. It can be explained by an enthalpically dominated separation, which leads to an increase in separation factors when the temperature is reduced. While it is quite uncommon to work at subzero degree column temperature, this work illustrates the potential to exploit such temperature regime for optimization of LC enantiomer separations.

Introducing enzymatic cleavage features and transfer learning realizes accurate peptide half-life prediction across species and organs

Peptide drugs are becoming star drug agents with high efficiency and selectivity which open up new therapeutic avenues for various diseases. However, the sensitivity to hydrolase and the relatively short half-life have severely hindered their development. In this study, a new generation artificial intelligence-based system for accurate prediction of peptide half-life was proposed, which realized the half-life prediction of both natural and modified peptides and successfully bridged the evaluation possibility between two important species (human, mouse) and two organs (blood, intestine). To achieve this, enzymatic cleavage descriptors were integrated with traditional peptide descriptors to construct a better representation. Then, robust models with accurate performance were established by comparing traditional machine learning and transfer learning, systematically. Results indicated that enzymatic cleavage features could certainly enhance model performance. The deep learning model integrating transfer learning significantly improved predictive accuracy, achieving remarkable R2 values: 0.84 for natural peptides and 0.90 for modified peptides in human blood, 0.984 for natural peptides and 0.93 for modified peptides in mouse blood, and 0.94 for modified peptides in mouse intestine on the test set, respectively. These models not only successfully composed the above-mentioned system but also improved by approximately 15% in terms of correlation compared to related works. This study is expected to provide powerful solutions for peptide half-life evaluation and boost peptide drug development.

Computational Modeling of the Interactions between DPP IV and Hemorphins

Type 2 diabetes is a chronic metabolic disorder characterized by high blood glucose levels due to either insufficient insulin production or ineffective utilization of insulin by the body. The enzyme dipeptidyl peptidase IV (DPP IV) plays a crucial role in degrading incretins that stimulate insulin secretion. Therefore, the inhibition of DPP IV is an established approach for the treatment of diabetes. Hemorphins are a class of short endogenous bioactive peptides produced by the enzymatic degradation of hemoglobin chains. Numerous in vitro and in vivo physiological effects of hemorphins, including DPP IV inhibiting activity, have been documented in different systems and tissues. However, the underlying molecular binding behavior of these peptides with DPP IV remains unknown. Here, computational approaches such as protein-peptide molecular docking and extensive molecular dynamics (MD) simulations were employed to identify the binding pose and stability of peptides in the active site of DPP IV. Findings indicate that hemorphins lacking the hydrophobic residues LVV and VV at the N terminal region strongly bind to the conserved residues in the active site of DPP IV. Furthermore, interactions with these critical residues were sustained throughout the duration of multiple 500 ns MD simulations. Notably, hemorphin 7 showed higher binding affinity and sustained interactions by binding to S1 and S2 pockets of DPP IV.

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.

Milk and Its Derivatives as Sources of Components and Microorganisms with Health-Promoting Properties: Probiotics and Bioactive Peptides

Milk is a source of many valuable nutrients, including minerals, vitamins and proteins, with an important role in adult health. Milk and dairy products naturally containing or with added probiotics have healthy functional food properties. Indeed, probiotic microorganisms, which beneficially affect the host by improving the intestinal microbial balance, are recognized to affect the immune response and other important biological functions. In addition to macronutrients and micronutrients, biologically active peptides (BPAs) have been identified within the amino acid sequences of native milk proteins; hydrolytic reactions, such as those catalyzed by digestive enzymes, result in their release. BPAs directly influence numerous biological pathways evoking behavioral, gastrointestinal, hormonal, immunological, neurological, and nutritional responses. The addition of BPAs to food products or application in drug development could improve consumer health and provide therapeutic strategies for the treatment or prevention of diseases. Herein, we review the scientific literature on probiotics, BPAs in milk and dairy products, with special attention to milk from minor species (buffalo, sheep, camel, yak, donkey, etc.); safety assessment will be also taken into consideration. Finally, recent advances in foodomics to unveil the probiotic role in human health and discover novel active peptide sequences will also be provided.

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.

A class of secreted mammalian peptides with potential to expand cell-cell communication

Peptide hormones and neuropeptides are signaling molecules that control diverse aspects of mammalian homeostasis and physiology. Here we provide evidence for the endogenous presence of a sequence diverse class of blood-borne peptides that we call "capped peptides." Capped peptides are fragments of secreted proteins and defined by the presence of two post-translational modifications - N-terminal pyroglutamylation and C-terminal amidation - which function as chemical "caps" of the intervening sequence. Capped peptides share many regulatory characteristics in common with that of other signaling peptides, including dynamic physiologic regulation. One capped peptide, CAP-TAC1, is a tachykinin neuropeptide-like molecule and a nanomolar agonist of mammalian tachykinin receptors. A second capped peptide, CAP-GDF15, is a 12-mer peptide cleaved from the prepropeptide region of full-length GDF15 that, like the canonical GDF15 hormone, also reduces food intake and body weight. Capped peptides are a potentially large class of signaling molecules with potential to broadly regulate cell-cell communication in mammalian physiology.

The secreted peptide BATSP1 promotes thermogenesis in adipocytes

Although brown adipose tissue (BAT) has historically been viewed as a major site for energy dissipation through thermogenesis, its endocrine function has been increasingly recognized. However, the circulating factors in BAT that play a key role in controlling systemic energy homeostasis remain largely unexplored. Here, we performed a peptidomic analysis to profile the extracellular peptides released from human brown adipocytes upon exposure to thermogenic stimuli. Specifically, we identified a secreted peptide that modulates adipocyte thermogenesis in a cell-autonomous manner, and we named it BATSP1. BATSP1 promoted BAT thermogenesis and induced browning of white adipose tissue in vivo, leading to increased energy expenditure under cold stress. BATSP1 treatment in mice prevented high-fat diet-induced obesity and improved glucose tolerance and insulin resistance. Mechanistically, BATSP1 facilitated the nucleocytoplasmic shuttling of forkhead transcription factor 1 (FOXO1) and released its transcriptional inhibition of uncoupling protein 1 (UCP1). Overall, we provide a comprehensive analysis of the human brown adipocyte extracellular peptidome following acute forskolin (FSK) stimulation and identify BATSP1 as a novel regulator of thermogenesis that may offer a potential approach for obesity treatment.

Recent progress in identification of water disinfection byproducts and opportunities for future research

Numerous disinfection by-products (DBPs) are formed from reactions between disinfectants and organic/inorganic matter during water disinfection. More than seven hundred DBPs that have been identified in disinfected water, only a fraction of which are regulated by drinking water guidelines, including trihalomethanes, haloacetic acids, bromate, and chlorite. Toxicity assessments have demonstrated that the identified DBPs cannot fully explain the overall toxicity of disinfected water; therefore, the identification of unknown DBPs is an important prerequisite to obtain insights for understanding the adverse effects of drinking water disinfection. Herein, we review the progress in identification of unknown DBPs in the recent five years with classifications of halogenated or nonhalogenated, aliphatic or aromatic, followed by specific halogen groups. The concentration and toxicity data of newly identified DBPs are also included. According to the current advances and existing shortcomings, we envisioned future perspectives in this field.

Top-Down Proteomics of Mouse Islets With Beta Cell CPE Deletion Reveals Molecular Details in Prohormone Processing

Altered prohormone processing, such as with proinsulin and pro-islet amyloid polypeptide (proIAPP), has been reported as an important feature of prediabetes and diabetes. Proinsulin processing includes removal of several C-terminal basic amino acids and is performed principally by the exopeptidase carboxypeptidase E (CPE), and mutations in CPE or other prohormone convertase enzymes (PC1/3 and PC2) result in hyperproinsulinemia. A comprehensive characterization of the forms and quantities of improperly processed insulin and other hormone products following Cpe deletion in pancreatic islets has yet to be attempted. In the present study we applied top-down proteomics to globally evaluate the numerous proteoforms of hormone processing intermediates in a β-cell-specific Cpe knockout mouse model. Increases in dibasic residue-containing proinsulin and other novel proteoforms of improperly processed proinsulin were found, and we could classify several processed proteoforms as novel substrates of CPE. Interestingly, some other known substrates of CPE remained unaffected despite its deletion, implying that paralogous processing enzymes such as carboxypeptidase D (CPD) can compensate for CPE loss and maintain near normal levels of hormone processing. In summary, our quantitative results from top-down proteomics of islets provide unique insights into the complexity of hormone processing products and the regulatory mechanisms.

DeepPeptide predicts cleaved peptides in proteins using conditional random fields

MOTIVATION

Peptides are ubiquitous throughout life and involved in a wide range of biological processes, ranging from neural signaling in higher organisms to antimicrobial peptides in bacteria. Many peptides are generated post-translationally by cleavage of precursor proteins and can thus not be detected directly from genomics data, as the specificities of the responsible proteases are often not completely understood.

RESULTS

We present DeepPeptide, a deep learning model that predicts cleaved peptides directly from the amino acid sequence. DeepPeptide shows both improved precision and recall for peptide detection compared to previous methodology. We show that the model is capable of identifying peptides in underannotated proteomes.

AVAILABILITY AND IMPLEMENTATION

DeepPeptide is available online at ku.biolib.com/DeepPeptide.