Discovery Of Bioactive Peptides Research Articles

Special Issue on RNA‐Based Catalysts that Revolutionized the Discovery of Bioactive Peptides

Special Issue on <i>RNA‐Based Catalysts that Revolutionized the Discovery of Bioactive Peptides</i>

A Novel Workflow for In Silico Prediction of Bioactive Peptides: An Exploration of Solanum lycopersicum By-Products.

Resource-intensive processes currently hamper the discovery of bioactive peptides (BAPs) from food by-products. To streamline this process, in silico approaches present a promising alternative. This study presents a novel computational workflow to predict peptide release, bioactivity, and bioavailability, significantly accelerating BAP discovery. The computational flowchart has been designed to identify and optimize critical enzymes involved in protein hydrolysis but also incorporates multi-enzyme screening. This feature is crucial for identifying the most effective enzyme combinations that yield the highest abundance of BAPs across different bioactive classes (anticancer, antidiabetic, antihypertensive, anti-inflammatory, and antimicrobial). Our process can be modulated to extract diverse BAP types efficiently from the same source. Here, we show the potentiality of our method for the identification of diverse types of BAPs from by-products generated from Solanum lycopersicum, the widely cultivated tomato plant, whose industrial processing generates a huge amount of waste, especially tomato peel. In particular, we optimized tomato by-products for bioactive peptide production by selecting cultivars like Line27859 and integrating large-scale gene expression. By integrating these advanced methods, we can maximize the value of by-products, contributing to a more circular and eco-friendly production process while advancing the development of valuable bioactive compounds.

Peptide-rich extracts from leaves of &lt;i&gt;Newbouldia laevis&lt;/i&gt; (P. Beauv.) Seem. ex. Bureau (Bignoniaceae) with antimicrobial and brine shrimp lethality activities

Global concerns over antibiotic-resistant microbial pathogens have spurred a quest for innovative, stable, and target-specific molecules like bioactive peptides. This study evaluates the antimicrobial properties and toxicity of peptide-enriched extracts from Newbouldia laevis leaves. Aqueous extraction, thin layer chromatography (TLC), and Phenomenex's Stratum Giant Tube C18-E were used to obtain partly purified fraction (PPF) and crude peptide extracts (CPE). Antimicrobial inhibitory capacity was determined using p-INT dye, with gentamicin and fluconazole as standards. Cytotoxicity was assessed with Artemia salina nauplii. Microsoft Excel and GraphPad Prism 5 was used in analysing the data. Peptides were detected in CPE and PPF via TLC and modified G-250. PPF demonstrated more efficacy (IC50: 5.89μg/mL to 33.94μg/mL) against bacteria and fungi, with low toxicity (LC50: 5964.87μg/mL for PPFand 1094.20μg/mL for CPE) in the Brine shrimp lethality assay. The study presents the discovery of bioactive peptides from the leaves of Newbouldia laevis. Consequently, bioactive peptides extracted from this plant hold potential as foundational compounds for the development of novel broad-spectrum antibiotics aimed at combating microbial infections.

Virtual Screening of Peptide Libraries: The Search for Peptide-Based Therapeutics Using Computational Tools.

Over the last few decades, we have witnessed growing interest from both academic and industrial laboratories in peptides as possible therapeutics. Bioactive peptides have a high potential to treat various diseases with specificity and biological safety. Compared to small molecules, peptides represent better candidates as inhibitors (or general modulators) of key protein-protein interactions. In fact, undruggable proteins containing large and smooth surfaces can be more easily targeted with the conformational plasticity of peptides. The discovery of bioactive peptides, working against disease-relevant protein targets, generally requires the high-throughput screening of large libraries, and in silico approaches are highly exploited for their low-cost incidence and efficiency. The present review reports on the potential challenges linked to the employment of peptides as therapeutics and describes computational approaches, mainly structure-based virtual screening (SBVS), to support the identification of novel peptides for therapeutic implementations. Cutting-edge SBVS strategies are reviewed along with examples of applications focused on diverse classes of bioactive peptides (i.e., anticancer, antimicrobial/antiviral peptides, peptides blocking amyloid fiber formation).

Analytical Tools for Dynamic Combinatorial Libraries of Cyclic Peptides.

Target-directed dynamic combinatorial chemistry is a very attractive strategy for the discovery of bioactive peptides. However, its application has not yet been demonstrated, presumably due to analytical challenges that arise from the diversity of a peptide library with combinatorial side-chains. We previously reported an efficient method to generate, under biocompatible conditions, large dynamic libraries of cyclic peptides grafted with amino acid's side-chains, by thiol-to-thioester exchanges. In this work, we present analytical tools to easily characterize such libraries by HPLC and mass spectrometry, and in particular to simplify the isomers' distinction requiring sequencing by MS/MS fragmentations. After structural optimization, the cyclic scaffold exhibits a UV-tag, absorbing at 415 nm, and an ornithine residue which favors the regioselective ring-opening and simultaneous MS/MS fragmentation, in the gas-phase.

A Metalloproteinase Cocktail from the Venom of Protobothrops flavoviridis Cleaves Amyloid Beta Peptides at the α-Cleavage Site.

A disintegrin and metalloproteinase (ADAM) family proteins are a major class of membrane-anchored multidomain proteinases that are responsible for the shedding of cell surface protein ectodomains, including amyloid precursor protein (APP). Human ADAM 9, 10, and 17 proteolyze APPs and produce non-amyloid-genic p3 peptides, instead of neurotoxic amyloid-β peptides (Aβs; Aβ40 and Aβ42), which form fibrils and accumulate in the brain of patients with Alzheimer's disease (AD). The ADAM family is closely related to snake venom metalloproteinases (SVMPs), which are derived from ancestral ADAMs but act as soluble proteinases. To test the therapeutic potential of SVMPs, we purified SVMPs from Protobothrops flavoviridis venom using metal ion affinity and pooled into a cocktail. Thus, 9 out of 11 SVMPs in the P. flavoviridis genome were identified in the cocktail. SVMPs inhibited Aβ secretion when added to human cell culture medium without affecting APP proteolysis. SVMPs degraded synthetic Aβ40 and Aβ42 peptides at the same cleavage site (α-site of APP) as ADAM9, 10, and 17. SVMPs did not degrade Aβ fibrils but interfered with their formation, assessed using thioflavin-T. Thus, SVMPs have therapeutic potential for AD as an Aβ-degrading protease, and the finding adds to the discovery of bioactive peptides from venoms as novel therapeutics.

UniDL4BioPep: a universal deep learning architecture for binary classification in peptide bioactivity.

Identification of potent peptides through model prediction can reduce benchwork in wet experiments. However, the conventional process of model buildings can be complex and time consuming due to challenges such as peptide representation, feature selection, model selection and hyperparameter tuning. Recently, advanced pretrained deep learning-based language models (LMs) have been released for protein sequence embedding and applied to structure and function prediction. Based on these developments, we have developed UniDL4BioPep, a universal deep-learning model architecture for transfer learning in bioactive peptide binary classification modeling. It can directly assist users in training a high-performance deep-learning model with a fixed architecture and achieve cutting-edge performance to meet the demands in efficiently novel bioactive peptide discovery. To the best of our best knowledge, this is the first time that a pretrained biological language model is utilized for peptide embeddings and successfully predicts peptide bioactivities through large-scale evaluations of those peptide embeddings. The model was also validated through uniform manifold approximation and projection analysis. By combining the LM with a convolutional neural network, UniDL4BioPep achieved greater performances than the respective state-of-the-art models for 15 out of 20 different bioactivity dataset prediction tasks. The accuracy, Mathews correlation coefficient and area under the curve were 0.7-7, 1.23-26.7 and 0.3-25.6% higher, respectively. A user-friendly web server of UniDL4BioPep for the tested bioactivities is established and freely accessible at https://nepc2pvmzy.us-east-1.awsapprunner.com. The source codes, datasets and templates of UniDL4BioPep for other bioactivity fitting and prediction tasks are available at https://github.com/dzjxzyd/UniDL4BioPep.

Bioactive Peptide Discovery from Edible Insects for Potential Applications in Human Health and Agriculture.

In the past decade, there has been fast-growing interest among researchers to discover bioactive peptides from edible insects and to evaluate their potential applications in the management of human, livestock, and plant health. This review summarizes current knowledge of insect-derived peptides and their potential role in tackling human health issues and solving agriculture problems by protecting crops and livestock against their pathogens. Numerous bioactive peptides have been identified from edible insect species, including peptides that were enzymatically liberated from insect proteins and endogenous peptides that occur naturally in insects. The peptides exhibited diverse bioactivities, encompassing antioxidant, anti-angiotensin-converting enzyme, anti-dipeptidyl peptidase-IV, anti-glucosidase, anti-lipase, anti-lipoxygenase, anti-cyclooxygenase, anti-obesity, and hepatoprotective activities. Such findings point to their potential contribution to solving human health problems related to inflammation, free radical damage, diabetes, hypertension, and liver damage, among others. Although most of the experiments were performed in vitro, evidence for the in vivo efficacy of some peptides is emerging. Evidence of the protective effects of insect-derived endogenous antimicrobial peptides in combating farm animal and plant pathogens is available. The ability of insect-derived endogenous neuropeptides to protect plants against herbivorous insects has been demonstrated as well. Nevertheless, the potency of peptides identified from insect protein hydrolysates in modulating livestock and plant health remains a knowledge gap to be filled.

In Silico Prospecting for Novel Bioactive Peptides from Seafoods: A Case Study on Pacific Oyster (Crassostrea gigas)

Pacific oyster (Crassostrea gigas), an abundant bivalve consumed across the Pacific, is known to possess a wide range of bioactivities. While there has been some work on its bioactive hydrolysates, the discovery of bioactive peptides (BAPs) remains limited due to the resource-intensive nature of the existing discovery pipeline. To overcome this constraint, in silico-based prospecting is employed to accelerate BAP discovery. Major oyster proteins were digested virtually under a simulated gastrointestinal condition to generate virtual peptide products that were screened against existing databases for peptide bioactivities, toxicity, bitterness, stability in the intestine and in the blood, and novelty. Five peptide candidates were shortlisted showing antidiabetic, anti-inflammatory, antihypertensive, antimicrobial, and anticancer potential. By employing this approach, oyster BAPs were identified at a faster rate, with a wider applicability reach. With the growing market for peptide-based nutraceuticals, this provides an efficient workflow for candidate scouting and end-use investigation for targeted functional product preparation.

Computational biology in topical bioactive peptide discovery for cosmeceutical application: a concise review

Computational biology in topical bioactive peptide discovery for cosmeceutical application: a concise review

Streamlining Bioactive Peptide Discovery With In Silico Prospecting: An investigation on Seaweed Pacific Dulse

ObjectivesBioactive hydrolysates and peptides from seaweed sources have been shown to exert beneficial effects in both in vitro and in vivo models for metabolic health. However, the discovery approach relies heavily on the conventional hydrolysate preparation and screening model selection, both driven by resource availability and time constraints. Considering the potential for functional food and therapeutics development, efficient and cost-effective strategies are needed to scope out as much prospect that can be screened for increased chances of hits to find potent, stable, and commercializable bioactive peptides (BAPs). This work aimed to identify multifunctional BAPs from seaweed Pacific Dulse, and to determine which hydrolytic condition can favor the release of more BAPs of interest from this protein-rich macroalgae. MethodsVirtual hydrolysates were prepared from dulse proteins using three digestion platforms (ExPASy, SystemsBio, and Rapid Peptide Generator) under three production stream contexts (hydrolysate preparation, gastrointestinal digestion, and microbial fermentation). Peptide products were then subjected to in silico bioactivity prospecting for anti-inflammatory, antidiabetic, and antihypertensive potential. Stability in the intestine and blood was predicted along with potential toxicity. Non-toxic and stable peptides predicted to have multifunctionalities were shortlisted for bioactivity validation, and production streams predicted to generate most number of BAPs were noted for test hydrolysate production. ResultsWith our approach, we identified 13 novel prospect BAPs that have multiple bioactivities, high stability, and low toxicity. Hydrolysate production using bromelain and ficin favored the 5–10aa long mBAPs generation. Simulated gastrointestinal digestion and microbial fermentation also showed promise in the release of some BAPs of interest. ConclusionsWhen consumed in either hydrolyzed, whole, or fermented form, BAPs can be generated from seaweed Pacific Dulse proteins. Bioactivity testing of the novel BAPs identified, and microscale production of the promising BAP production stream are underway. Funding SourcesOregon Agricultural Experiment Station.

ANGT_HUMAN[448-462], an Anorexigenic Peptide Identified Using Plasma Peptidomics.

The discovery of bioactive peptides is an important research target that enables the elucidation of the pathophysiology of human diseases and provides seeds for drug discovery. Using a large number of native peptides previously identified using plasma peptidomics technology, we sequentially synthesized selected sequences and subjected them to functional screening using human cultured cells. A 15-amino-acid residue proangiotensinogen-derived peptide, designated ANGT_HUMAN[448–462], elicited cellular responses and bound to cultured human cells. Synthetic fluorescent-labeled and biotinylated ANGT_HUMAN[448–462] peptides were rendered to bind to cell- and tissue-derived proteins and peptide-cell protein complexes were retrieved and analyzed using liquid chromatography–tandem mass spectrometry, revealing the β-subunit of ATP synthase as its cell-surface binding protein. Because ATP synthase mediates the effects of anorexigenic peptides, the ability of ANGT_HUMAN[448–462] to modulate eating behavior in mice was investigated. Both intraperitoneal and intracerebroventricular injections of low doses of ANGT_HUMAN[448–462] suppressed spontaneous food and water intake throughout the dark phase of the diurnal cycle without affecting locomotor activity. Immunoreactive ANGT_HUMAN[448–462], distributed throughout human tissues and in human-derived cells, is mostly co-localized with angiotensin II and is occasionally present separately from angiotensin II. In this study, an anorexigenic peptide, ANGT_HUMAN[448–462], was identified by exploring cell surface target proteins of the human native peptides identified using plasma peptidomics.

Accelerating bioactive peptide discovery via mutual information-based meta-learning

Recently, machine learning methods have been developed to identify various peptide bio-activities. However, due to the lack of experimentally validated peptides, machine learning methods cannot provide a sufficiently trained model, easily resulting in poor generalizability. Furthermore, there is no generic computational framework to predict the bioactivities of different peptides. Thus, a natural question is whether we can use limited samples to build an effective predictive model for different kinds of peptides. To address this question, we propose Mutual Information Maximization Meta-Learning (MIMML), a novel meta-learning-based predictive model for bioactive peptide discovery. Using few samples from various functional peptides, MIMML can sufficiently learn the discriminative information amongst various functions and characterize functional differences. Experimental results show excellent performance of MIMML though using far fewer training samples as compared to the state-of-the-art methods. We also decipher the latent relationships among different kinds of functions to understand what meta-model learned to improve a specific task. In summary, this study is a pioneering work in the field of functional peptide mining and provides the first-of-its-kind solution for few-sample learning problems in biological sequence analysis, accelerating the new functional peptide discovery. The source codes and datasets are available on https://github.com/TearsWaiting/MIMML.

Multiple Classes of Antimicrobial Peptides in Amaranthus tricolor Revealed by Prediction, Proteomics, and Mass Spectrometric Characterization.

Traditional medicinal plants are rich reservoirs of antimicrobial agents, including antimicrobial peptides (AMPs). Advances in genomic sequencing, in silico AMP predictions, and mass spectrometry-based peptidomics facilitate increasingly high-throughput bioactive peptide discovery. Herein, Amaranthus tricolor aerial tissue was profiled via MS-based proteomics/peptidomics, identifying AMPs predicted in silico. Bottom-up proteomics identified seven novel peptides spanning three AMP classes including lipid transfer proteins, snakins, and a defensin. Characterization via top-down peptidomic analysis of Atr-SN1, Atr-DEF1, and Atr-LTP1 revealed unexpected proteolytic processing and enumerated disulfide bonds. Bioactivity screening of isolated Atr-LTP1 showed activity against the high-risk ESKAPE bacterial pathogens (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, and Enterobacter cloacae). These results highlight the potential for integrating AMP prediction algorithms with complementary -omics approaches to accelerate characterization of biologically relevant AMP peptidoforms.

Development of Bioactive Foldamers Using Ribosomally Synthesized Nonstandard Peptide Libraries

Abstract Genetic code manipulation enables the ribosomal synthesis of peptide libraries bearing diverse nonproteinogenic amino acids, which can be applied to the discovery of bioactive peptides in combination with screening methodologies, such as mRNA display. Despite a tremendous number of successes in incorporation of l-α-amino acids with non-proteinogenic sidechains and N-methyl-l-α-amino acids into nascent peptide chains, d-, β-, and γ-amino acids have suffered from low translation efficiency. This obstacle has been hindering their integration into such peptide libraries. However, the use of engineered tRNAs, which can effectively recruit EF-Tu or/and EF-P, has recently made possible significant improvement of their incorporation efficiency into nascent peptides. This article comprehensively summarizes advances in such methodology and applications to the discovery of peptide ligands against target proteins of interest.

Bioinformatics of edible yellow mealworm (Tenebrio molitor) proteome reveal the cuticular proteins as promising precursors of dipeptidyl peptidase-IV inhibitors.

Bioinformatics was applied for strategic processing of yellow mealworm (Tenebrio molitor) proteins to produce dipeptidyl peptidase (DPP)-IV inhibiting peptides. In silico analysis of 384 mealworm proteins revealed structural proteins as better precursors of DPP-IV inhibiting peptides, compared with other protein types, after pepsin and papain hydrolysis. This was associated with the higher hydropathicity and amounts of residues associated with DPP-IV inhibition in the structural (cuticular) proteins. In silico, the peptides were mostly released with pepsin than papain. Cuticular (CP) and non-cuticular proteins (NC) were extracted from yellow mealworm and hydrolyzed with pepsin and papain in vitro to validate the virtual findings. CP hydrolysate with papain inhibited DPP-IV the most compared to CP hydrolysate with pepsin, whereas NC hydrolysates were mostly inactive. CP had higher hydrophobic-hydrophilic amino acid ratios and contents of the activity-associated residues than NC. The findings demonstrate the application of bioinformatics in processing proteins for bioactive peptide production. PRACTICAL APPLICATIONS: The discovery of bioactive peptides from food proteins is typically based on the classic approach involving working with a small number of protein-protease combinations in vitro. For the first time, this study reported the application of in silico tools in comprehensively studying hundreds of proteins from yellow mealworm (an edible insect) as sources of DPP-IV inhibitors, followed by in vitro processing and validation guided by the results obtained in silico. The advantage of this approach is that it allows for analysis of several protein-protease combinations (with multiple datasets of structural, functional, and bioactivity parameters) in a short time. This work is relevant in advancing research on emerging or alternative proteins as well as structure-informed food protein processing. The bioinformatics approach can be adapted for strategic processing of proteins in the food industry prior to making major resource investments.

Dual-function peptides derived from egg white ovalbumin: Bioinformatics identification with validation using in vitro assay

Abstract A complete integrated bioinformatics approach was developed to identify angiotensin-converting enzyme (ACE) and dipeptidyl peptidase-4 (DPP-4) inhibitory peptides from egg white ovalbumin. The sequence of the protein was initially obtained from the NCBI database followed by in silico digestion using different enzymes or combination of enzymes. The results showed that 71 peptides were produced. Their bioactivities were then predicted based on the amino acid compositions and sequences as well as the peptide interactions with ACE and DPP-4. Ten highly potential peptides (CF, KM, ELPF, AM, ADHPF, LPR, PR, FR, PRM and GR) were chosen as they obtained p-value less than 0.01. The results from the in vitro validation experiment showed that all peptides were able to inhibit the ACE and DPP-4. Overall, this study underlined the in silico approach as an alternative way for bioactive peptide discovery which could tremendously reduce the cost and time of research.

FeptideDB: A web application for new bioactive peptides from food protein

BackgroundBioactive peptides derived from food are important sources for alternative medicine and possess therapeutic activity. Several biochemical methods have been achieved to isolate bioactive peptides from food, which are tedious and time consuming. In silico methods are an alternative process to reduce cost and time with respect to bioactive peptide production. In this paper, FeptideDB was used to collect bioactive peptide (BP) data from both published research articles and available bioactive peptide databases. FeptideDB was developed to assist in forecasting bioactive peptides from food by combining peptide cleavage tools and database matching. Furthermore, this application was able to predict the potential of cleaved peptides from ‘enzyme digestion module’ to identify new ACE (angiotensin converting enzyme) inhibitors using an automatic molecular docking approach. ResultsThe FeptideDB web application contains tools for generating all possible peptides cleaved from input protein by various available enzymes. This database was also used for analysis and visualization to assist in bioactive peptide discovery. One module of FeptideDB has the ability to create 3-dimensional peptide structures to further predict inhibitors for the target protein, ACE (angiotensin converting enzyme). ConclusionsFeptideDB is freely available to researchers who are interested in exploring bioactive peptides. The FeptideDB interface is easy to use, allowing users to rapidly retrieve data based on desired search criteria. FeptideDB is freely available at http://www4g.biotec.or.th/FeptideDB/. Ultimately, FeptideDB is a computational aid for assessing peptide bioactivities.

Natural ACE inhibitory peptides discovery from Spirulina (Arthrospira platensis) strain C1

Bioactive peptides from natural sources are utilized as food supplements for disease prevention and are increasingly becoming targets for drug discovery due to their specificity, efficacy and the absence of undesirable side effects, among others. Hence, the ‘SpirPep’ platform was developed to facilitate the in silico-based bioactive peptide discovery of these highly sought-after biomolecules from Spirulina(Arthrospira platensis) and to select the protease (thermolysin) used for in vitro digestion. Analysis of the predicted and experimentally-derived peptides suggested that they were mainly involved in ACE inhibition; thus, an ACEi assay was used to study the ACE inhibitory activity of five candidate peptides (SpirPep1-5), chosen from common peptides with multifunctional bioactivity and 100% bioactive peptide coverage, originating from phycobiliproteins. Results showed that SpirPep1 inhibited the activity of ACE with IC50 of 1.748 mM and was non-toxic to fibroblasts of African green monkey kidney and human dermal skin. The molecular docking and MD simulation analysis revealed SpirPep1 had significantly lower binding scores than others and showed greater specificity to ACE. The non-bonded interaction energy of SpirPep1 and ACE was −883 kJ/mol. The SpirPep1 indirectly bound to ACE via the ACE substrate binding sites residues (D121, E123, S516, and S517) found in natural ACE inhibitory peptides (angiotensin II and bradykinin potentiating peptides). In addition, two unreported substrate binding sites including R124 and S219 were found. These results indicate that ‘SpirPep’ platform could increase the success rate for natural bioactive peptide discovery.

Identification of bioactive short peptides in cow milk by high-performance liquid chromatography on C18 and porous graphitic carbon coupled to high-resolution mass spectrometry.

Short peptides are important compounds in a variety of fields, including food and nutraceutical applications, but also biomarker discovery, bioactive peptide discovery and peptide drug separation. Despite the importance of short peptides, they are currently less studied than other peptides because of the lack of dedicated methods for their characterization. The method described in this paper comprises a combination of strategies to tackle the main limitations in short peptide analysis. In particular, in this work an untargeted peptidomic approach based on ultrahigh-performance liquid chromatography coupled to high-resolution mass spectrometry was developed for the identification of short peptides in cow milk samples. After milk defatting and precipitation, the sample was purified by cotton-hydrophilic interaction liquid chromatography (HILIC) micro tip in order to avoid suppression phenomena due to contaminants present in milk, such as carbohydrates. The sample was then separated by means of two chromatographic columns, with a complementary selectivity mechanism, namely reversed-phase C18 column and porous graphitic carbon (PGC). By this approach, the method allowed the separation and characterization of di-, tri- and tetrapeptides. A total of 57 and 41 peptides were identified by using a C18 and a PGC column, respectively; in particular, 31 were exclusively identified by using the C18 column, 15 unique peptides were identified by using the PGC column, while 26 were in common between the two data sets, demonstrating that the two columns have a different selectivity mechanism. The results indicated that an integrated approach may be appropriate to improve the separation of different peptides and increase the number of identifications because of the wide range of polarity of short peptides. The method allowed the untargeted identification of short peptides in milk, a complex matrix chosen as a representative real sample for method application, and provides complementary information to that accessible by ordinary peptidomics. Graphical abstract.