Combining mass spectrometry and machine learning to discover bioactive peptides

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.

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.

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.