An in silico platform for predicting, screening and designing of antihypertensive peptides

AI4ACEIP: A Computing Tool to Identify Food Peptides with High Inhibitory Activity for ACE by Merged Molecular Representation and Rich Intrinsic Sequence Information Based on an Ensemble Learning Strategy

Hypertension is a common chronic disorder and a major risk factor for cardiovascular diseases. Angiotensin-converting enzyme (ACE) converts angiotensin I to angiotensin II, causing vasoconstriction and raising blood pressure. Pharmacotherapy is the mainstay of traditional hypertension treatment, leading to various negative side effects. Some food-derived peptides can suppress ACE, named ACEIP with fewer undesirable effects. Therefore, it is crucial to seek strong dietary ACEIP to aid in hypertension treatment. In this article, we propose a new model called AI4ACEIP to identify ACEIP. AI4ACEIP uses a novel two-layer stacked ensemble architecture to predict ACEIP relying on integrated view features derived from sequence, large language models, and molecular-based information. The analysis of feature combinations reveals that four selected integrated feature pairs exhibit enhancing performance for identifying ACEIP. For finding meta models with strong abilities to learn information from integrated feature pairs, PowerShap, a feature selection method, is used to select 40 optimal feature and meta model combinations. Compared with seven state-of-the-art methods on the source and clear benchmark data sets, AI4ACEIP significantly outperformed by 8.47 to 20.65% and 5.49 to 14.42% for Matthew's correlation coefficient. In brief, AI4ACEIP is a reliable model for ACEIP prediction and is freely available at https://github.com/abcair/AI4ACEIP.

Predicting variable-length ACE inhibitory peptides based on graph convolutional network

Traditional molecular descriptors have contributed to the prediction of angiotensin I-converting enzyme (ACE) inhibitory peptides, but they often fall short in capturing the complex structure of the molecule. To address these limitations, this study introduces molecular graphs as an advanced method for peptide characterization. Peptides containing 2-10 amino acids were represented using molecular graphs, and a graph convolutional network (GCN) model was constructed to predict variable-length peptides. This model was compared with machine learning (ML) models based on molecular descriptors, including Random Forest (RF), Support Vector Machine (SVM), and k-Nearest Neighbor (kNN), under the same benchmark. Notably, the GCN model outperformed the other models with an accuracy of 0.78, effectively identifying ACE inhibitory potential. Furthermore, the GCN model also demonstrated superior performance, exceeding existing methods with an accuracy rate of over 98 % on an independent test set. To validate our predictions, we synthesized peptides VAPE and AQQKEP with high predicted probabilities, and their IC50 values of 2.25 ± 0.11 and 3.75 ± 0.17 μM, respectively, indicating potent ACE inhibitory activity. The developed GCN model presents a powerful tool for the rapid screening and identification of ACE inhibitory peptides, offering promising opportunities for developing antihypertensive components in functional foods.

ToxinPred 3.0: An improved method for predicting the toxicity of peptides

Toxicity emerges as a prominent challenge in the design of therapeutic peptides, causing the failure of numerous peptides during clinical trials. In 2013, our group developed ToxinPred, a computational method that has been extensively adopted by the scientific community for predicting peptide toxicity. In this paper, we propose a refined variant of ToxinPred that showcases improved reliability and accuracy in predicting peptide toxicity. Initially, we utilized a similarity/alignment-based approach employing BLAST to predict toxic peptides, which yielded satisfactory accuracy; however, the method suffered from inadequate coverage. Subsequently, we employed a motif-based approach using MERCI software to uncover specific patterns or motifs that are exclusively observed in toxic peptides. The search for these motifs in peptides allowed us to predict toxic peptides with a high level of specificity with poor sensitivity. To overcome the coverage limitations, we developed alignment-free methods using machine/deep learning techniques to balance sensitivity and specificity of prediction. Deep learning model (ANN - LSTM with fixed sequence length) developed using one-hot encoding achieved a maximum AUROC of 0.93 with MCC of 0.71 on an independent dataset. Machine learning model (extra tree) developed using compositional features of peptides achieved a maximum AUROC of 0.95 with MCC of 0.78. We also developed large language models and achieved maximum AUC of 0.93 using ESM2-t33. Finally, we developed hybrid or ensemble methods combining two or more methods to enhance performance. Our specific hybrid method, which combines a motif-based approach with a machine learning-based model, achieved a maximum AUROC of 0.98 with MCC 0.81 on an independent dataset. In this study, all models were trained and tested on 80 % of data using five-fold cross-validation and evaluated on the remaining 20 % of data called independent dataset. The evaluation of all methods on an independent dataset revealed that the method proposed in this study exhibited better performance than existing methods. To cater to the needs of the scientific community, we have developed a standalone software, pip package and web-based server ToxinPred3 (https://github.com/raghavagps/toxinpred3 and https://webs.iiitd.edu.in/raghava/toxinpred3/).

Preparation, identification, and molecular docking of novel angiotensin-converting enzyme inhibitory peptides derived from rice-based distillers' spent cakes

BACKGROUND

Rice-based distillers' spent cake (RDSC), a by-product of the Chinese liquor (Baijiu) industry, is a potential source of angiotensin-converting enzyme (ACE) inhibitory peptide. Since ACE plays a crucial role in controlling hypertension, inhibition of ACE has been widely emphasized. The ACE inhibitory active peptide derived from by-products of food has been recognized as a safer and cheaper inhibitor.

RESULTS

Aimed to discover ACE-inhibiting active peptides in RDSC. Hydrolysis of RDSC by alcalase for 4 h followed by ultrafiltration yielded low-molecular-weight (< 3 kDa) fractions. Subsequently, a comprehensive method using a combination of liquid chromatography quadrupole time-of-flight mass spectrometry (LC-Q-TOF-MS) and LC-Q-Exactive-MS to identify the novel short peptides (3-5 amino acids residues; n = 7) and medium-sized peptides (more than 6 amino acids residues; n = 6). In vitro activity assay showed that the peptides KPFFPGL, GFPRPLL, GPPGVF, and VGK exhibited the highest activity with inhibitory concentration of 50% (IC50) of 11.63, 12.34, 19.55, and 33.54 μmol L-1. Molecular docking reveal that the active and inactive sites (Glu123, Asp121, Arg522, and Lys118) play important roles in enhancing the ACE inhibitory activity of peptides.

CONCLUSION

Here we report a comprehensive method that effectively extracted and identified the bioactive peptides from RDSC. Four highly active novel peptides may be the most promising candidates for functional foods against hypertension, provide significant information for enhancing value of rice-based distilled by-products. © 2024 Society of Chemical Industry.

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.

HemoDL: Hemolytic peptides prediction by double ensemble engines from Rich sequence-derived and transformer-enhanced information

Hemolytic peptides can trigger hemolysis by rupturing red blood cells' membranes and triggering cell disruption. Due to the labor-intensive and time-consuming in-lab identification process, accurate, high-throughput hemolytic peptide prediction is crucial for the growth of peptide sequence data in proteomics and peptidomics. In this study, we offer the HemoDL ensemble learning model, which learns the distinct distribution of sequence characteristics for predicting the hemolytic activity of peptides using a double LightGBM framework. To determine the most informative encoding features, we compare 17 widely used features across four benchmark datasets. Our investigation reveals that CTD, BPF, Charge, AAC, GDPC, ATC, QSO, and transformer-based features exhibit more positive contributions to detecting the hemolytic activity of peptides. Comparison with eight state-of-the-art methods demonstrates that HemoDL outperforms other models, attaining higher Matthews Correlation Coefficient values on four test datasets, ranging from 6.30% to 16.04%, 6.63%-11.26%, 4.76%-9.92%, and 7.41%-15.03%, respectively. Additionally, we provide the HemoDL with a user-friendly graphical interface available at https://github.com/abcair/HemoDL. In summary, the HemoDL model, leveraging CTD, BPF, Charge, AAC, GDPC, ATC, QSO and transformer-based encoding features within a double LightGBM learning framework, achieves high accuracy in predicting the hemolytic activity of peptides.

Exploring peptidomes of by-products generated during chhurpi production using Lactobacillus delbrueckii WS4 for identification of novel bioactive peptides

The considerable value of whey is evident from its significant potential applications and contributions to the functional food and nutraceutical market. The by-products were individually obtained during functional chhurpi and novel soy chhurpi cheese production using defined lactic acid bacterial strains of Sikkim Himalaya's traditional chhurpi. Hydrolysis of substrate proteins by starter proteinases resulted in a comparable peptide content in whey and soy whey which was associated with antioxidant and ACE inhibition potential. Peptidome analysis of Lactobacillus delbrueckii WS4 whey and soy whey revealed the presence of several bioactive peptides including the multifunctional peptides PVVVPPFLQPE and YQEPVLGPVRGPFPIIV. In silico analyses predicted the antihypertensive potential of whey and soy whey peptides with strong binding affinity for ACE active sites. QSAR models predicted the highest ACE inhibition potential (IC50) for the β-casein-derived decapeptide PVRGPFPIIV (0.95 μM) and the Kunitz trypsin inhibitor protein-derived nonapeptide KNKPLVVQF (16.64 μM). Chhurpi whey and soy whey can be explored as a valuable source of diverse and novel bioactive peptides for applications in designer functional foods development.

The first bioactive (angiotensin-converting enzyme-inhibitory) peptide isolated from pearl matrix protein

In this research, we unveil the medical potential of pearls by identifying a novel bioactive peptide within them for the first time. The peptide, termed KKCHFWPFPW, emerges as a pioneering angiotensin I-converting enzyme (ACE) inhibitor, originating from the pearl matrix of Pinctada fucata. Employing quadrupole time-of-flight mass spectrometry, this peptide was meticulously selected and pinpointed. With a molecular weight of 1417.5 Da and a theoretical isoelectric point of 9.31, its inhibitory potency was demonstrated through a half-maximal inhibitory concentration (IC50) of 4.17 μM, established via high-performance liquid chromatography. The inhibition of ACE by this peptide was found to be competitive, as revealed by Lineweaver-Burk plot analysis, where an increase in peptide concentration correlated with an enhanced rate of ACE inhibition. To delve into the interaction between KKCHFWPFPW and ACE, molecular docking simulations were conducted using the Maestro 2022-1 Glide software, shedding light on the inhibitory mechanism. This investigation suggests that peptides derived from the P. martensii pearl matrix hold promise as a novel source for antihypertensive agents.

Bioinformatics and bioactive peptides from foods: Do they work together?

We live in the Big Data Era which affects many aspects of science, including research on bioactive peptides derived from foods, which during the last few decades have been a focus of interest for scientists. These two issues, i.e., the development of computer technologies and progress in the discovery of novel peptides with health-beneficial properties, are closely interrelated. This Chapter presents the example applications of bioinformatics for studying biopeptides, focusing on main aspects of peptide analysis as the starting point, including: (i) the role of peptide databases; (ii) aspects of bioactivity prediction; (iii) simulation of peptide release from proteins. Bioinformatics can also be used for predicting other features of peptides, including ADMET, QSAR, structure, and taste. To answer the question asked "bioinformatics and bioactive peptides from foods: do they work together?", currently it is almost impossible to find examples of peptide research with no bioinformatics involved. However, theoretical predictions are not equivalent to experimental work and always require critical scrutiny. The aspects of compatibility of in silico and in vitro results are also summarized herein.

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).

Xanthan gum and pectin as beverage stabilizers reduce the digestive enzyme hydrolysis of antioxidant and antihypertensive peptides obtained from a brewery byproduct

An acidic beverage was formulated with xanthan gum (XG), pectin (P) and brewer spent grain (BSG) peptides with antioxidant and antihypertensive properties. The impact of hydrocolloids levels on peptide bioaccessibility was studied. Peptides were obtained from BSG using Purazyme and Flavourzyme enzymes. BSG peptides were fractionated by ultrafiltration (UF) and four fractions were obtained: F1 (>10 kDa), F2 (10-5 kDa), F3 (1-5 kDa), and F4 (<1 kDa). F3 showed the highest protein purity, ferulic acid content, proportion of amphipathic peptides, and bioactive properties (ABTS+ radical scavenging and ACE-I inhibitory activity). The identified peptides from F3 by tandem mass spectrometry were 138. In silico analysis showed that 26 identified peptides had ABTS+ inhibitory activity, while 59 ones presented good antihypertensive properties. The effect of XG and P levels on bioaccessibility of F3 peptides in the formulated beverages was studied by a central composite experimental design. It was observed that F3 peptides interacted with hydrocolloids by electrostatic forces at pH of formulated beverages. The addition of hydrocolloids to formulation modulated the release of the antioxidant peptides and protected the degradation of ACE-I inhibitory peptides from F3 during simulated gastrointestinal digestion. Finally, the level of hydrocolloids that produced intermediate viscosities in the formulated beverages improved the bioaccessibility of the F3 peptides.

Prospection of Peptide Inhibitors of Thrombin from Diverse Origins Using a Machine Learning Pipeline

Thrombin is a key enzyme involved in the development and progression of many cardiovascular diseases. Direct thrombin inhibitors (DTIs), with their minimum off-target effects and immediacy of action, have greatly improved the treatment of these diseases. However, the risk of bleeding, pharmacokinetic issues, and thrombotic complications remain major concerns. In an effort to increase the effectiveness of the DTI discovery pipeline, we developed a two-stage machine learning pipeline to identify and rank peptide sequences based on their effective thrombin inhibitory potential. The positive dataset for our model consisted of thrombin inhibitor peptides and their binding affinities (KI) curated from published literature, and the negative dataset consisted of peptides with no known thrombin inhibitory or related activity. The first stage of the model identified thrombin inhibitory sequences with Matthew's Correlation Coefficient (MCC) of 83.6%. The second stage of the model, which covers an eight-order of magnitude range in KI values, predicted the binding affinity of new sequences with a log room mean square error (RMSE) of 1.114. These models also revealed physicochemical and structural characteristics that are hidden but unique to thrombin inhibitor peptides. Using the model, we classified more than 10 million peptides from diverse sources and identified unique short peptide sequences (<15 aa) of interest, based on their predicted KI. Based on the binding energies of the interaction of the peptide with thrombin, we identified a promising set of putative DTI candidates. The prediction pipeline is available on a web server.

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

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

ADP-Fuse: A novel two-layer machine learning predictor to identify antidiabetic peptides and diabetes types using multiview information

Diabetes mellitus has become a major public health concern associated with high mortality and reduced life expectancy and can cause blindness, heart attacks, kidney failure, lower limb amputations, and strokes. A new generation of antidiabetic peptides (ADPs) that act on β-cells or T-cells to regulate insulin production is being developed to alleviate the effects of diabetes. However, the lack of effective peptide-mining tools has hampered the discovery of these promising drugs. Hence, novel computational tools need to be developed urgently. In this study, we present ADP-Fuse, a novel two-layer prediction framework capable of accurately identifying ADPs or non-ADPs and categorizing them into type 1 and type 2 ADPs. First, we comprehensively evaluated 22 peptide sequence-derived features coupled with eight notable machine learning algorithms. Subsequently, the most suitable feature descriptors and classifiers for both layers were identified. The output of these single-feature models, embedded with multiview information, was trained with an appropriate classifier to provide the final prediction. Comprehensive cross-validation and independent tests substantiate that ADP-Fuse surpasses single-feature models and the feature fusion approach for the prediction of ADPs and their types. In addition, the SHapley Additive exPlanation method was used to elucidate the contributions of individual features to the prediction of ADPs and their types. Finally, a user-friendly web server for ADP-Fuse was developed and made publicly accessible (https://balalab-skku.org/ADP-Fuse), enabling the swift screening and identification of novel ADPs and their types. This framework is expected to contribute significantly to antidiabetic peptide identification.

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

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

Discovery of ACE Inhibitory Peptides Derived from Green Coffee Using In Silico and In Vitro Methods

Inhibition of angiotensin-I converting enzyme (ACE) is an important means of treating hypertension since it plays an important regulatory function in the renin-angiotensin system. The aim of this study was to investigate the ACE inhibitory effect of bioactive peptides from green coffee beans using in silico and in vitro methods. Alcalase and thermolysin were employed to hydrolyze protein extract from coffee beans. Bioactive peptides were identified by LC-MS/MS analysis coupled with database searching. The potential bioactivities of peptides were predicted by in silico screening, among which five novel peptides may have ACE inhibitory activity. In vitro assay was carried out to determine the ACE inhibitory degree. Two peptides (IIPNEVY, ITPPVMLPP) were obtained with IC50 values of 57.54 and 40.37 μM, respectively. Furthermore, it was found that two inhibitors bound to the receptor protein on similar sites near the S1 active pocket of ACE to form stable enzyme-peptide complexes through molecular docking, and the Lineweaver-Burk plot showed that IIPNEVY was a noncompetitive inhibitor, and ITPPVMLPP was suggested to be a mixed-type inhibitor. Our study demonstrated that two peptides isolated from coffee have potential applications as antihypertensive agents.

Computer simulation approach to the identification of visfatin-derived angiogenic peptides

Angiogenesis plays an essential role in various normal physiological processes, such as embryogenesis, tissue repair, and skin regeneration. Visfatin is a 52 kDa adipokine secreted by various tissues including adipocytes. It stimulates the expression of vascular endothelial growth factor (VEGF) and promotes angiogenesis. However, there are several issues in developing full-length visfatin as a therapeutic drug due to its high molecular weight. Therefore, the purpose of this study was to develop peptides, based on the active site of visfatin, with similar or superior angiogenic activity using computer simulation techniques.Initially, the active site domain (residues 181∼390) of visfatin was first truncated into small peptides using the overlapping technique. Subsequently, the 114 truncated small peptides were then subjected to molecular docking analysis using two docking programs (HADDOCK and GalaxyPepDock) to generate small peptides with the highest affinity for visfatin. Furthermore, molecular dynamics simulations (MD) were conducted to investigate the stability of the protein-ligand complexes by computing root mean square deviation (RSMD) and root mean square fluctuation(RMSF) plots for the visfatin-peptide complexes. Finally, peptides with the highest affinity were examined for angiogenic activities, such as cell migration, invasion, and tubule formation in human umbilical vein endothelial cells (HUVECs). Through the docking analysis of the 114 truncated peptides, we screened nine peptides with a high affinity for visfatin. Of these, we discovered two peptides (peptide-1: LEYKLHDFGY and peptide-2: EYKLHDFGYRGV) with the highest affinity for visfatin. In an in vitrostudy, these two peptides showed superior angiogenic activity compared to visfatin itself and stimulated mRNA expressions of visfatin and VEGF-A. These results show that the peptides generated by the protein-peptide docking simulation have a more efficient angiogenic activity than the original visfatin.

A Novel LSTM-Based Machine Learning Model for Predicting the Activity of Food Protein-Derived Antihypertensive Peptides

Food protein-derived antihypertensive peptides are a representative type of bioactive peptides. Several models based on partial least squares regression have been constructed to delineate the relationship between the structure and activity of the peptides. Machine-learning-based models have been applied in broad areas, which also indicates their potential to be incorporated into the field of bioactive peptides. In this study, a long short-term memory (LSTM) algorithm-based deep learning model was constructed, which could predict the IC₅₀ value of the peptide in inhibiting ACE activity. In addition to the test dataset, the model was also validated using randomly synthesized peptides. The LSTM-based model constructed in this study provides an efficient and simplified method for screening antihypertensive peptides from food proteins.

Peptides of a Feather: How Computation Is Taking Peptide Therapeutics under Its Wing

Leveraging computation in the development of peptide therapeutics has garnered increasing recognition as a valuable tool to generate novel therapeutics for disease-related targets. To this end, computation has transformed the field of peptide design through identifying novel therapeutics that exhibit enhanced pharmacokinetic properties and reduced toxicity. The process of in-silico peptide design involves the application of molecular docking, molecular dynamics simulations, and machine learning algorithms. Three primary approaches for peptide therapeutic design including structural-based, protein mimicry, and short motif design have been predominantly adopted. Despite the ongoing progress made in this field, there are still significant challenges pertaining to peptide design including: enhancing the accuracy of computational methods; improving the success rate of preclinical and clinical trials; and developing better strategies to predict pharmacokinetics and toxicity. In this review, we discuss past and present research pertaining to the design and development of in-silico peptide therapeutics in addition to highlighting the potential of computation and artificial intelligence in the future of disease therapeutics.

In-depth peptidomic profile and molecular simulation studies on ACE-inhibitory peptides derived from probiotic fermented milk of different farm animals

Investigations into ACE inhibitory properties of probiotic fermented bovine, camel, goat, and sheep milk were performed and studied for two weeks of refrigerated storage. Results from the degree of proteolysis suggested higher susceptibility of goat milk proteins, followed by sheep and camel milk proteins, to the probiotic-mediated proteolysis. ACE-inhibitory properties displayed continuous decline in ACE-IC₅₀ values for two weeks of refrigerated storage. Overall, goat milk fermented with Pediococcus pentosaceus caused maximum ACE inhibition (IC₅₀: 262.7 µg/mL protein equivalent), followed by camel milk (IC₅₀: 290.9 µg/mL protein equivalent). Studies related to peptide identification and in silico analysis using HPEPDOCK score revealed presence of 11, 13, 9 and 9 peptides in fermented bovine, goat, sheep, and camel milk, respectively, with potent antihypertensive potential. The results obtained suggest that the goat and camel milk proteins demonstrated higher potential for generating antihypertensive peptides via fermentation when compared to bovine and sheep milk.

Lacto-Fermented and Unfermented Soybean Differently Modulate Serum Lipids, Blood Pressure and Gut Microbiota during Hypertension

Soy consumption may reduce hypertension but the impact of food processing on the antihypertensive effect is unclear. Hence, we ascertained the effects of lacto-fermented (FSB) and unfermented soybean (USB) consumption on serum atherogenic lipids, hypertension and gut microbiota of spontaneous hypertensive rats (SHR). FSB displayed a strong in vitro angiotensin converting enzyme (ACE) inhibitory ability of 70 ± 5% while USB inhibited 5 ± 3% of the enzyme activity. Consumption of USB reduced serum ACE activity by 19.8 ± 12.85 U while FSB reduced the enzyme activity by 47.6 ± 11.35 U, respectively. FSB significantly improved cholesterol levels and reduced systolic and diastolic blood pressures by 14 ± 3 mmHg and 10 ± 3 mmHg, respectively, while USB only had a marginal impact on blood pressure. Analysis of FSB showed the abundance of ACE inhibitory peptides EGEQPRPFPFP and AIPVNKP (which were absent in USB) and 30 phenolic compounds (only 12 were abundant in USB). Feeding SHR with FSB promoted the growth of Akkermansia, Bacteroides, Intestinimonas, Phocaeicola, Lactobacillus and Prevotella (short chain fatty acid producers) while USB promoted only Prevotellamassilia, Prevotella and Intestimonas levels signifying the prebiotic ability of FSB. Our results show that, relative to USB, FSB are richer in bioactive compounds that reduce hypertension by inhibiting ACE, improving cholesterol levels and mitigating gut dysbiosis.

Pfeature: A Tool for Computing Wide Range of Protein Features and Building Prediction Models

In the last three decades, a wide range of protein features have been discovered to annotate a protein. Numerous attempts have been made to integrate these features in a software package/platform so that the user may compute a wide range of features from a single source. To complement the existing methods, we developed a method, Pfeature, for computing a wide range of protein features. Pfeature allows to compute more than 200,000 features required for predicting the overall function of a protein, residue-level annotation of a protein, and function of chemically modified peptides. It has six major modules, namely, composition, binary profiles, evolutionary information, structural features, patterns, and model building. Composition module facilitates to compute most of the existing compositional features, plus novel features. The binary profile of amino acid sequences allows to compute the fraction of each type of residue as well as its position. The evolutionary information module allows to compute evolutionary information of a protein in the form of a position-specific scoring matrix profile generated using Position-Specific Iterative Basic Local Alignment Search Tool (PSI-BLAST); fit for annotation of a protein and its residues. A structural module was developed for computing of structural features/descriptors from a tertiary structure of a protein. These features are suitable to predict the therapeutic potential of a protein containing non-natural or chemically modified residues. The model-building module allows to implement various machine learning techniques for developing classification and regression models as well as feature selection. Pfeature also allows the generation of overlapping patterns and features from a protein. A user-friendly Pfeature is available as a web server python library and stand-alone package.

Identification and virtual screening of novel anti-inflammatory peptides from broccoli fermented by Lactobacillus strains

Lactobacillus strains fermentation of broccoli as a good source of bioactive peptides has not been fully elucidated. In this work, the peptide composition of broccoli fermented by L. plantarum A3 and L. rhamnosus ATCC7469 was analyzed by peptidomics to study the protein digestion patterns after fermentation by different strains. Results showed that water-soluble proteins such as rubisco were abundant sources of peptides, which triggered the sustained release of peptides as the main target of hydrolysis. In addition, 17 novel anti-inflammatory peptides were identified by virtual screening. Among them, SIWYGPDRP had the strongest ability to inhibit the release of NO from inflammatory cells at a concentration of 25 μM with an inhibition rate of 52.32 ± 1.48%. RFR and KASFAFAGL had the strongest inhibitory effects on the secretion of TNF-α and IL-6, respectively. At a concentration of 25 μM, the corresponding inhibition rates were 74.61 ± 1.68% and 29.84 ± 0.63%, respectively. Molecular docking results showed that 17 peptides formed hydrogen bonds and hydrophobic interactions with inducible nitric oxide synthase (iNOS). This study is conducive to the high-value utilization of broccoli and reduction of the antibiotic use.

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.

PTPAMP: prediction tool for plant-derived antimicrobial peptides

The emergence of antimicrobial peptides (AMPs) as a potential alternative to conventional antibiotics has led to the development of efficient computational methods for predicting AMPs. Among all organisms, the presence of multiple genes encoding AMPs in plants demands the development of a plant-based prediction tool. To this end, we developed models based on multiple peptide features like amino acid composition, dipeptide composition, and physicochemical attributes for predicting plant-derived AMPs. The selected compositional models are integrated into a web server termed PTPAMP. The designed web server is capable of classifying a query peptide sequence into four functional activities, i.e., antimicrobial (AMP), antibacterial (ABP), antifungal (AFP), and antiviral (AVP). Our models achieved an average area under the curve of 0.95, 0.91, 0.85, and 0.88 for AMP, ABP, AFP, and AVP, respectively, on benchmark datasets, which were ~ 6.75% higher than the state-of-the-art methods. Moreover, our analysis indicates the abundance of cysteine residues in plant-derived AMPs and the distribution of other residues like G, S, K, and R, which differ as per the peptide structural family. Finally, we have developed a user-friendly web server, available at the URL: http://www.nipgr.ac.in/PTPAMP/ . We expect the substantial input of this predictor for high-throughput identification of plant-derived AMPs followed by additional insights into their functions.

Accurate Prediction of Anti-hypertensive Peptides Based on Convolutional Neural Network and Gated Recurrent unit

Hypertension (HT) is a general disease, and also one of the most ordinary and major causes of cardiovascular disease. Some diseases are caused by high blood pressure, including impairment of heart and kidney function, cerebral hemorrhage and myocardial infarction. Due to the limitations of laboratory methods, bioactive peptides for the treatment of HT need a long time to be identified. Therefore, it is of great immediate significance for the identification of anti-hypertensive peptides (AHTPs). With the prevalence of machine learning, it is suggested to use it as a supplementary method for AHTPs classification. Therefore, we develop a new model to identify AHTPs based on multiple features and deep learning. And the deep model is constructed by combining a convolutional neural network (CNN) and a gated recurrent unit (GRU). The unique convolution structure is used to reduce the feature dimension and running time. The data processed by CNN is input into the recurrent structure GRU, and important information is filtered out through the reset gate and update gate. Finally, the output layer adopts Sigmoid activation function. Firstly, we use Kmer, the deviation between the dipeptide frequency and the expected mean (DDE), encoding based on grouped weight (EBGW), enhanced grouped amino acid composition (EGAAC) and dipeptide binary profile and frequency (DBPF) to extract features. For Kmer, DDE, EBGW and EGAAC, it is widely used in the field of protein research. DBPF is a new feature representation method designed by us. It corresponds dipeptides to binary numbers, and finally obtains a binary coding file and a frequency file. Then these features are spliced together and input into our proposed model for prediction and analysis. After a tenfold cross-validation test, this model has a better competitive advantage than the previous methods, and the accuracy is 96.23% and 99.10%, respectively. From the results, compared with the previous methods, it has been greatly improved. It shows that the combination of convolution calculation and recurrent structure has a positive impact on the classification of AHTPs. The results show that this method is a feasible, efficient and competitive sequence analysis tool for AHTPs. Meanwhile, we design a friendly online prediction tool and it is freely accessible at http://ahtps.zhanglab.site/ .

SCMRSA: a New Approach for Identifying and Analyzing Anti-MRSA Peptides Using Estimated Propensity Scores of Dipeptides

Staphylococcus aureus is deemed to be one of the major causes of hospital and community-acquired infections, especially in methicillin-resistant S. aureus (MRSA) strains. Because antimicrobial peptides have captured attention as novel drug candidates due to their rapid and broad-spectrum antimicrobial activity, anti-MRSA peptides have emerged as potential therapeutics for the treatment of bacterial infections. Although experimental approaches can precisely identify anti-MRSA peptides, they are usually cost-ineffective and labor-intensive. Therefore, computational approaches that are able to identify and characterize anti-MRSA peptides by using sequence information are highly desirable. In this study, we present the first computational approach (termed SCMRSA) for identifying and characterizing anti-MRSA peptides by using sequence information without the use of 3D structural information. In SCMRSA, we employed an interpretable scoring card method (SCM) coupled with the estimated propensity scores of 400 dipeptides. Comparative experiments indicated that SCMRSA was more effective and could outperform several machine learning-based classifiers with an accuracy of 0.960 and Matthews correlation coefficient of 0.848 on the independent test data set. In addition, we employed the SCMRSA-derived propensity scores to provide a more in-depth explanation regarding the functional mechanisms of anti-MRSA peptides. Finally, in order to serve community-wide use of the proposed SCMRSA, we established a user-friendly webserver which can be accessed online at http://pmlabstack.pythonanywhere.com/SCMRSA. SCMRSA is anticipated to be an open-source and useful tool for screening and identifying novel anti-MRSA peptides for follow-up experimental studies.

The current research status and strategies employed to modify food-derived bioactive peptides

The ability of bioactive peptides to exert biological functions has mainly contributed to their exploitation. The exploitation and utilization of these peptides have grown tremendously over the past two decades. Food-derived peptides from sources such as plant, animal, and marine proteins and their byproducts constitute a more significant portion of the naturally-occurring peptides that have been documented. Due to their high specificity and biocompatibility, these peptides serve as a suitable alternative to pharmacological drugs for treating non-communicable diseases (such as cardiovascular diseases, obesity, and cancer). They are helpful as food preservatives, ingredients in functional foods, and dietary supplements in the food sector. Despite their unique features, the application of these peptides in the clinical and food sector is to some extent hindered by their inherent drawbacks such as toxicity, bitterness, instability, and susceptibility to enzymatic degradation in the gastrointestinal tract. Several strategies have been employed to eliminate or reduce the disadvantages of peptides, thus enhancing the peptide bioactivity and broadening the opportunities for their applications. This review article focuses on the current research status of various bioactive peptides and the strategies that have been implemented to overcome their disadvantages. It will also highlight future perspectives regarding the possible improvements to be made for the development of bioactive peptides with practical uses and their commercialization.

Production and characterization of bioactive peptides in novel functional soybean chhurpi produced using Lactobacillus delbrueckii WS4

A novel soy chhurpi product was developed by fermentation of soymilk using proteolytic Lactobacillus delbrueckii strains isolated from traditional chhurpi production of Sikkim Himalaya. Soymilk fermentation by L. delbrueckii WS4 was associated with the hydrolysis of globulin proteins, with observed antioxidant, and ACE-inhibitory activity which further increased upon simulated in vitro gastrointestinal digestion. Peptidomics analysis of soy chhurpi and its gastrointestinal digest resulted in the identification of bioactive peptides with ACE-inhibitory and antioxidant properties. In silico antihypertensive property prediction followed by molecular docking study demonstrated strong binding affinity of selected peptides with ACE. The glycinin-derived peptide, SVIKPPTDE escaped gastrointestinal digestion and demonstrated strong non-bond interactions with ACE catalytic residues. QSAR models predicted an ACE-inhibitory IC50 of 21.29 µM for SVIKPPTDE. This is the first report on the production of novel functional soy chhurpi cheese using defined starter strains and the identification of bioactive peptides in undigested and gastrointestinal digested soy chhurpi.

Food protein-derived bioactive peptides for the management of nutrition related chronic diseases

Dietary intervention via modifications of dietary pattern or supplementations of naturally derived bioactive compounds has been considered as an efficient approach in management of nutrition related chronic diseases. Food protein-derived bioactive peptide is representative of natural compounds which show the potential to prevent or mitigate nutrition related chronic diseases. In the past decades, substantial research has been conducted concentrating on the characterization, bioavailability, and activity assessment of bioactive peptides. Although various activities of bioactive peptides have been reported, the activity testes of most peptides were only conducted in cells and animal models. Some clinical trials of bioactive peptides were also reported but only limited to antihypertensive peptides, antidiabetic peptides and peptides modulating blood lipid profile. Hereby, clinical evidence of bioactive peptides in management of nutrition-related chronic diseases is summarized in this chapter, which aims at providing implications for the clinical studies of bioactive peptides in the future.

Formation and inhibition mechanism of novel angiotensin I converting enzyme inhibitory peptides from Chouguiyu

Angiotensin I converting enzyme (ACE) inhibitory peptides from fermented foods exhibit great potential to alleviate hypertension. In this study, the peptide extract from Chouguiyu exhibited a good inhibition effect on ACE, and the inhibition rate was significantly enhanced after fermentation for 8 days. The ACE inhibitory peptides were further identified, followed by their inhibition and formation mechanisms using microbiome technology and molecular docking. A total of 356 ACE inhibitory peptides were predicted using in silico, and most ACE inhibitory peptides increased after fermentation. These peptides could be hydrolyzed from 94 kinds of precursor proteins, mainly including muscle-type creatine kinase, nebulin, and troponin I. P1 (VEIINARA), P2 (FAVMVKG), P4 (EITWSDDKK), P7 (DFDDIQK), P8 (IGDDPKF), P9 (INDDPKIL), and P10 (GVDNPGHPFI) were selected as the core ACE inhibitory peptides according to their abundance and docking energy. The salt bridge and conventional hydrogen bond connecting unsaturated oxygen atoms in the peptides contributed most to the ACE inhibition. The cleavage proteases from the microbial genera in Chouguiyu for preparing these 7 core ACE inhibitory peptides were further analyzed by hydrolysis prediction and Pearson's correlation. The correlation network showed that P7, P8, and P9 were mainly produced by the proteases from LAB including Lactococcus, Enterococcus, Vagococcus, Peptostreptococcus, and Streptococcus, while P1, P2, P4, and P10 were mainly Produced by Aeromonas, Bacillus, Escherichia, and Psychrobacter. This study is helpful in isolating the proteases and microbial strains to directionally produce the responding ACE inhibitory peptides.

Artificial intelligence and machine-learning approaches in structure and ligand-based discovery of drugs affecting central nervous system

CNS disorders are indications with a very high unmet medical needs, relatively smaller number of available drugs, and a subpar satisfaction level among patients and caregiver. Discovery of CNS drugs is extremely expensive affair with its own unique challenges leading to extremely high attrition rates and low efficiency. With explosion of data in information age, there is hardly any aspect of life that has not been touched by data driven technologies such as artificial intelligence (AI) and machine learning (ML). Drug discovery is no exception, emergence of big data via genomic, proteomic, biological, and chemical technologies has driven pharmaceutical giants to collaborate with AI oriented companies to revolutionise drug discovery, with the goal of increasing the efficiency of the process. In recent years many examples of innovative applications of AI and ML techniques in CNS drug discovery has been reported. Research on therapeutics for diseases such as schizophrenia, Alzheimer's and Parkinsonism has been provided with a new direction and thrust from these developments. AI and ML has been applied to both ligand-based and structure-based drug discovery and design of CNS therapeutics. In this review, we have summarised the general aspects of AI and ML from the perspective of drug discovery followed by a comprehensive coverage of the recent developments in the applications of AI/ML techniques in CNS drug discovery.

Hypertension: Constraining the Expression of ACE-II by Adopting Optimal Macronutrients Diet Predicted via Support Vector Machine

The recent elevation of cases infected from novel COVID-19 has placed the human life in trepidation mode, especially for those suffering from comorbidities. Most of the studies in the last few months have undeniably raised concerns for hypertensive patients that face greater risk of fatality from COVID-19. Furthermore, one of the recent WHO reports has estimated a total of 1.13 billion people are at a risk of hypertension of which two-thirds live in low and middle income countries. The gradual escalation of the hypertension problem andthe sudden rise of COVID-19 cases have placed an increasingly higher number of human lives at risk in low and middle income countries. To lower the risk of hypertension, most physicians recommend drugs that have angiotensin-converting enzyme (ACE) inhibitors. However, prolonged use of such drugs is not recommended due to metabolic risks and the increase in the expression of ACE-II which could facilitate COVID-19 infection. In contrast, the intake of optimal macronutrients is one of the possible alternatives to naturally control hypertension. In the present study, a nontrivial feature selection and machine learning algorithm is adopted to intelligently predict the food-derived antihypertensive peptide. The proposed idea of the paper lies in reducing the computational power while retaining the performance of the support vector machine (SVM) by estimating the dominant pattern in the features space through feature filtering. The proposed feature filtering algorithm has reported a trade-off performance by reducing the chances of Type I error, which is desirable when recommending a dietary food to patients suffering from hypertension. The maximum achievable accuracy of the best performing SVM models through feature selection are 86.17% and 85.61%, respectively.

A Deep Ensemble Predictor for Identifying Anti-Hypertensive Peptides Using Pretrained Protein Embedding

Hypertension (HT), or high blood pressure is one of the most common and main causes in cardiovascular diseases, which is also related to a series of detrimental diseases in humans. Deficiencies in effective treatment in HT are often associated with a series of diseases including multi-infarct dementia, amputation, and renal failure. Therefore, identifying anti-hypertension peptides has the vital realistic significance. Although many bioactive peptides have been developed to reduce blood pressure, they are time-consuming and laborious. In views of the obstacles of the intrinsic methods in antihypertensive peptide (AHTP) classification, computational methods are suggested as a supplement to identify AHTPs. In this study, we develop a comprehensive feature representation algorithm based on pretrained model and convolutional neural network and apply the deep ensemble model to construct the prediction model. The new predictor is used to identify AHTPs in benchmark and independent datasets. It has been shown in the independent test set that the performance is better than the recent methods. Comparative results indicate that our model can shed some light on hypertension therapy and gains more insights of classifying AHTPs. The implements and codes can be found in https://github.com/yuanying566/AHPred-DE.

Characterization and impact of peptide physicochemical properties on oral and subcutaneous delivery

Peptides, an emerging modality within the biopharmaceutical industry, are often delivered subcutaneously with evolving prospects on oral delivery. Barrier biology within the subcutis or gastrointestinal tract is a significant challenge in limiting absorption or otherwise disrupting peptide disposition. Aspects of peptide pharmacokinetic performance and ADME can be mitigated with careful molecular design that tailors for properties such as effective size, hydrophobicity, net charge, proteolytic stability, and albumin binding. In this review, we endeavor to highlight effective techniques in qualifying physicochemical properties of peptides and discuss advancements of in vitro models of subcutaneous and oral delivery. Additionally, we will delineate empirical findings around the relationship of these physicochemical properties and in vivo (animal or human) impact. We conclude that robust peptide characterization methods and in vitro techniques with demonstrated correlations to in vivo data are key routines to incorporate in the drug discovery and development to improve the probability of technical and commercial success of peptide therapeutics.

Bioactive Peptides from Algae: Traditional and Novel Generation Strategies, Structure-Function Relationships, and Bioinformatics as Predictive Tools for Bioactivity

Over the last decade, algae have been explored as alternative and sustainable protein sources for a balanced diet and more recently, as a potential source of algal-derived bioactive peptides with potential health benefits. This review will focus on the emerging processes for the generation and isolation of bioactive peptides or cryptides from algae, including: (1) pre-treatments of algae for the extraction of protein by physical and biochemical methods; and (2) methods for the generation of bioactive including enzymatic hydrolysis and other emerging methods. To date, the main biological properties of the peptides identified from algae, including anti-hypertensive, antioxidant and anti-proliferative/cytotoxic effects (for this review, anti-proliferative/cytotoxic will be referred to by the term anti-cancer), assayed in vitro and/or in vivo, will also be summarized emphasizing the structure–function relationship and mechanism of action of these peptides. Moreover, the use of in silico methods, such as quantitative structural activity relationships (QSAR) and molecular docking for the identification of specific peptides of bioactive interest from hydrolysates will be described in detail together with the main challenges and opportunities to exploit algae as a source of bioactive peptides.

Production and characterization of bioactive peptides from rice beans using Bacillus subtilis

A bioprocess was developed for production of bioactive peptides on microbial fermentation of rice beans using proteolytic Bacillus subtilis strains. The peptides produced were identified by LC-MS/MS analysis, revealing the presence of many unique peptide sequences to individual hydrolysates. On functional properties prediction, antihypertensive peptides (3.90%) were found to be higher in comparison to other bioactive peptides. Among different strains, B. subtilis KN2B fermented hydrolysate exhibited highest angiotensin converting enzyme (ACE)-inhibitory activity (45.73%). Furthermore, 19 selected peptides, including the common and unique peptides were examined for their affinity towards the binding cavity of ACE using molecular docking. The results showed a common peptide PFPIPFPIPIPLP, and another IPFPPIPFLPPI unique to B. subtilis KN2B fermented hydrolysate exhibited promising binding at the ACE binding site with substantial free binding energy. The process developed can be used for the production of bioactive peptides from rice bean for application in nutraceutical industries.

Ensemble-AHTPpred: A Robust Ensemble Machine Learning Model Integrated With a New Composite Feature for Identifying Antihypertensive Peptides

Hypertension or elevated blood pressure is a serious medical condition that significantly increases the risks of cardiovascular disease, heart disease, diabetes, stroke, kidney disease, and other health problems, that affect people worldwide. Thus, hypertension is one of the major global causes of premature death. Regarding the prevention and treatment of hypertension with no or few side effects, antihypertensive peptides (AHTPs) obtained from natural sources might be useful as nutraceuticals. Therefore, the search for alternative/novel AHTPs in food or natural sources has received much attention, as AHTPs may be functional agents for human health. AHTPs have been observed in diverse organisms, although many of them remain underinvestigated. The identification of peptides with antihypertensive activity in the laboratory is time- and resource-consuming. Alternatively, computational methods based on robust machine learning can identify or screen potential AHTP candidates prior to experimental verification. In this paper, we propose Ensemble-AHTPpred, an ensemble machine learning algorithm composed of a random forest (RF), a support vector machine (SVM), and extreme gradient boosting (XGB), with the aim of integrating diverse heterogeneous algorithms to enhance the robustness of the final predictive model. The selected feature set includes various computed features, such as various physicochemical properties, amino acid compositions (AACs), transitions, n-grams, and secondary structure-related information; these features are able to learn more information in terms of analyzing or explaining the characteristics of the predicted peptide. In addition, the tool is integrated with a newly proposed composite feature (generated based on a logistic regression function) that combines various feature aspects to enable improved AHTP characterization. Our tool, Ensemble-AHTPpred, achieved an overall accuracy above 90% on independent test data. Additionally, the approach was applied to novel experimentally validated AHTPs, obtained from recent studies, which did not overlap with the training and test datasets, and the tool could precisely predict these AHTPs.

Characterization of ACE inhibitory and antioxidant peptides in yak and cow milk hard chhurpi cheese of the Sikkim Himalayan region

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Yak and cow hard chhurpi cheese of Sikkim Himalaya.

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ACE-inhibitory activities of undigested and GI digested yak and cow hard chhurpi.

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Bioactive peptides identified by LC-MS/MS peptidomics of yak and cow hard chhurpi.

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In silico prediction and molecular docking of potential ACE-inhibitory peptides.

In this study, simulated in vitro GI digestion of the Himalayan hard chhurpi cheese resulted in the increase of hydrolyzed protein content, antioxidant and ACE-inhibitory activities. LC-MS/MS-based peptidomics revealed a total of 1473 peptides in the samples originating from different milk proteins, including α-S1-casein, α-S2-casein, β-casein, κ-casein, α-lactalbumin, and β-lactoglobulin, out of which 60 peptides have been reported for different functional properties. A total of 101 peptides were predicted to be antihypertensive using the bioactivity prediction web servers, AHTpin and mAHTPred. In silico molecular docking studies predicted 20 antihypertensive peptides, exhibiting non-bond interactions between hard chhurpi peptides and ACE catalytic residues. A peptide, SLVYPFPGPI, identified in GI digested cow hard chhurpi and undigested, and GI digested samples of yak hard chhurpi, showed a stronger binding affinity towards ACE. Identifying antioxidant and ACE inhibitory peptides in hard cheese products adds value to them as functional foods of the Himalayan region.

A hepatic antimicrobial peptide, hepcidin from Indian major carp, Catla catla: molecular identification and functional characterization

Background

Increase of antibiotic resistance in pathogenic microbes necessitated novel molecules for curing infection. Antimicrobial peptides (AMPs) are the gene-encoded evolutionarily conserved small molecules with therapeutic value. AMPs are considered as an alternative drug for conventional antibiotics. Hepcidin, the cysteine-rich antimicrobial peptide, is an important component in innate immune response. In this study, we identified and characterized hepcidin gene from the fish, Catla catla (Indian major carp) and termed it as Cc-Hep.

Results

Open reading frame of Cc-Hep consists of 261 base pair that encodes 87 amino acids. Cc-Hep is synthesized as a prepropeptide consisting of 24 amino acid signal peptide, 36 amino acid propeptide, and 26 amino acid mature peptide. Sequence analysis revealed that Cc-Hep shared sequence similarity with hepcidin from Sorsogona tuberculata. Phylogenetic analysis indicated that Cc-Hep was grouped with HAMP2 family. Structure analysis of mature Cc-Hep identified two antiparallel beta sheets stabilized by four disulphide bonds and a random coil. The mature peptide region of Cc-Hep has a charge of + 2, isoelectric value 8.23 and molecular weight 2.73 kDa.

Conclusion

Functional characterization predicted antibacterial, antioxidant, and anticancer potential of Cc-Hep, which can be explored in aquaculture or human health care.

Screening of Novel Bioactive Peptides from Goat Casein: In Silico to In Vitro Validation

Food-derived bioactive peptides are of great interest to science and industry due to evolving drivers of food product innovation, including health and wellness. This study aims to draw attention through a critical study on how bioinformatics analysis is employed in the identification of bioactive peptides in the laboratory. An in silico analysis (PeptideRanker, BIOPEP, AHTpin, and mAHTPred) of a list of peptides from goat casein hydrolysate was performed to predict which sequences could potentially be bioactive. To validate the predictions, the in vitro antihypertensive potential of the five peptides with the highest potential was first measured. Then, for three of these, gastrointestinal digestion was simulated in vitro, followed by the analysis of the resulting ACE inhibitory activity as well as antioxidant capacity. We thus observed that the use of new computational biology technologies to predict peptide sequences is an important research tool, but they should not be used alone and complementarity with various in vitro and in vivo assays is essential.

Large-scale comparative review and assessment of computational methods for phage virion proteins identification

Phage virion proteins (PVPs) are effective at recognizing and binding to host cell receptors while having no deleterious effects on human or animal cells. Understanding their functional mechanisms is regarded as a critical goal that will aid in rational antibacterial drug discovery and development. Although high-throughput experimental methods for identifying PVPs are considered the gold standard for exploring crucial PVP features, these procedures are frequently time-consuming and labor-intensive. Thusfar, more than ten sequence-based predictors have been established for the in silico identification of PVPs in conjunction with traditional experimental approaches. As a result, a revised and more thorough assessment is extremely desirable. With this purpose in mind, we first conduct a thorough survey and evaluation of a vast array of 13 state-of-the-art PVP predictors. Among these PVP predictors, they can be classified into three groups according to the types of machine learning (ML) algorithms employed (i.e. traditional ML-based methods, ensemble-based methods and deep learning-based methods). Subsequently, we explored which factors are important for building more accurate and stable predictors and this included training/independent datasets, feature encoding algorithms, feature selection methods, core algorithms, performance evaluation metrics/strategies and web servers. Finally, we provide insights and future perspectives for the design and development of new and more effective computational approaches for the detection and characterization of PVPs.

Integration of Molecular Docking Analysis and Molecular Dynamics Simulations for Studying Food Proteins and Bioactive Peptides

In silico tools, such as molecular docking, are widely applied to study interactions and binding affinity of biological activity of proteins and peptides. However, restricted sampling of both ligand and receptor conformations and use of approximated scoring functions can produce results that do not correlate with actual experimental binding affinities. Molecular dynamics simulations (MDS) can provide valuable information in deciphering functional mechanisms of proteins/peptides and other biomolecules, overcoming the rigid sampling limitations in docking analysis. This review will discuss the information related to the traditional use of in silico models, such as molecular docking, and its application for studying food proteins and bioactive peptides, followed by an in-depth introduction to the theory of MDS and description of why these molecular simulation techniques are important in the theoretical prediction of structural and functional dynamics of food proteins and bioactive peptides. Applications, limitations, and future prospects of MDS will also be discussed.

Autoregressive Modeling and Prediction of the Activity of Antihypertensive Peptides

Naturally derived bioactive peptides with antihypertensive activities serve as promising alternatives to pharmaceutical drugs. There are few relevant reports on the mapping relationship between the EC₅₀ value of antihypertensive peptide activity (AHTPA-EC₅₀) and its corresponding amino acid sequence (AAS) at present. In this paper, we have constructed two group series based on sorting natural logarithm of AHTPA-EC₅₀ or sorting its corresponding AAS encoding number. One group possesses two series, and we find that there must be a random number series in any group series. The random number series manifests fractal characteristics, and the constructed series of sorting natural logarithm of AHTPA-EC₅₀ shows good autocorrelation characteristics. Therefore, two non-linear autoregressive models with exogenous input (NARXs) were established to describe the two series. A prediction method is further designed for AHTPA-EC₅₀ prediction based on the proposed model. Two dynamic neural networks for NARXs (NARXNNs) are designed to verify the two series characteristics. Dipeptides and tripeptides are used to verify the proposed prediction method. The results show that the mean square error (MSE) of prediction is about 0.5589 for AHTPA-EC₅₀ prediction when the classification of AAS is correct. The proposed method provides a solution for AHTPA-EC₅₀ prediction.