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.

Current Trends and Applications of Food Derived Antihypertensive Peptides for the Management of Cardiovascular Disease

Food derived Antihypertensive peptides is considered as a natural supplement for controlling the hypertension. Food protein not only serve as a macronutrient but also act as raw material for biosynthesis of physiologically active peptides. Food sources like milk and milk products, animal protein such as meat, chicken, fish, eggs and plant derived proteins from soy, rice, wheat, mushroom, pumpkins contain high amount of antihypertensive peptides. The food derived antihypertensive peptides has ability to supress the action of rennin and Angiotesin converting enzyme (ACE) which is mainly involved in regulation of blood pressure by RAS. The biosynthesis of endothelial nitric oxide synthase is also improved by ACE inhibitory peptides which increase the production of nitric oxide in vascular walls and encourage vasodilation. Interaction between the angiotensin II and its receptor is also inhibited by the peptides which help to reduce hypertension. This review will explore the novel sources and applications of food derived peptides for the management of hypertension.

Antihypertensive Peptides from Ultrafiltration and Fermentation of the Ricotta Cheese Exhausted Whey: Design and Characterization of a Functional Ricotta Cheese

Aiming at valorizing the ricotta cheese exhausted whey (RCEW), one of the most abundant by-products from the dairy industry, a biotechnological protocol to obtain bioactive peptides with angiotensin-I-converting enzyme (ACE)--inhibitory activity was set up. The approach was based on the combination of membrane filtration and fermentation. A Lactobacillus helveticus strain selected to be used as starter for the fermentation of the ultrafiltration protein-rich retentate (R-UF) obtained from RCEW. The fermented R-UF was characterized by a high anti-ACE activity. Peptides responsible for the bioactivity were purified and identified through nano-LC–ESI–MS/MS. The sequences identified in the purified active fractions of the fermented R-UF showed partial or complete overlapping with previously reported κ-casein antihypertensive fragments. The fermented R-UF was spray-dried and used to enrich ricotta cheese at different fortification level (1 and 5% w/w). An integrated approach including the assessment of the microbiological, chemical, functional, textural, and sensory properties was used to characterize the fortified products. A significantly higher anti-ACE activity was found in the ricotta cheese fortified with fermented R-UF as compared to the control and to the samples obtained with the unfermented R-UF fraction at the same levels of fortification. In particular, a 100 g portion of the ricotta cheese produced at 5% fortification level contained circa 30 mg of bioactive peptides. The fortification led to a moderate acidification, increased hardness and chewiness, and decreased the milk odor and taste of the ricotta cheese as compared to the control, while flavor persistence and sapidity improved.

The Blood-Pressure-Lowering Effect of Food-Protein-Derived Peptides: A Meta-Analysis of Recent Clinical Trials

Although clinical trials of food-protein-derived peptides in the management of hypertension have been published, the results are controversial, which compelled us to conduct a meta-analysis to evaluate the pooled effect of peptide intervention. In this study, we searched for studies published between 2010 and 2021 and selected 12 eligible studies for a meta-analysis. The pooled effect of peptide intervention for systolic blood pressure (SBP) and diastolic blood pressure (DBP) was −3.28 mmHg (95% CI: −4.54, −2.03, p < 0.001) and −1.82 mmHg (95% CI: −3.46, −0.18, p = 0.03), respectively. Sub-group analyses showed that the reduction in BP in participants with higher basal BP (>140/85 mmHg) was greater (p = 0.007 for SBP and p = 0.01 for DBP), and the effect was stronger in Asian participants as compared with non-Asian participants (p = 0.01 for SBP and p = 0.04 for DBP). In addition, the effect of peptide intervention was more pronounced on SBP in participant groups with a lower ratio of male to female (≤0.5) as well as in participants with a mean age ≥50 years old. In conclusion, food-protein-derived antihypertensive peptides can significantly reduce BP in prehypertensive and hypertensive patients. Findings from this study could provide guidance for the design of clinical trials of antihypertensive peptides.

Optimization of Antihypertensive Peptide Preparation by Lactobacillus plantarum Fermentation of Soy Whey

To optimize the process conditions of preparation of antihypertensive peptides from the soybean whey fermented by Lactobacillus plantarum, based on the single factor, the response surface method was used and its antihypertensive activity in vitro was studied. The results of variance analysis showed that the order affecting the polypeptide yield was: the initial pH > fermentation time > fermentation temperature > glucose addition. The model regression analysis showed that the optimal conditions for the production of antihypertensive peptides by Lactobacillus plantarum were as follows: fermentation temperature was 37 °C, fermentation time was 17 h, the initial pH of fermentation was 6.4, the amount of glucose addition was 1.50%, the polypeptide yield was 62.53% and the predicted value was 61.61%, with no significant difference (P > 0.05). In vitro, antihypertensive activity results showed that the angiotensin-converting enzyme (ACE) inhibition rate of fermented soy whey peptides was 85.77%. The results provided the theoretical basis for the high-value development and utilization of soy whey.

A Novel Machine Learning Strategy for the Prediction of Antihypertensive Peptides Derived from Food with High Efficiency

Strategies to screen antihypertensive peptides with high throughput and rapid speed will doubtlessly contribute to the treatment of hypertension. Food-derived antihypertensive peptides can reduce blood pressure without side effects. In the present study, a novel model based on the eXtreme Gradient Boosting (XGBoost) algorithm was developed and compared with the dominating machine learning models. To further reflect on the reliability of the method in a real situation, the optimized XGBoost model was utilized to predict the antihypertensive degree of the k-mer peptides cutting from six key proteins in bovine milk, and the peptide–protein docking technology was introduced to verify the findings. The results showed that the XGBoost model achieved outstanding performance, with an accuracy of 86.50% and area under the receiver operating characteristic curve of 94.11%, which were better than the other models. Using the XGBoost model, the prediction of antihypertensive peptides derived from milk protein was consistent with the peptide–protein docking results, and was more efficient. Our results indicate that using the XGBoost algorithm as a novel auxiliary tool is feasible to screen for antihypertensive peptides derived from food, with high throughput and high efficiency.

Boosted Prediction of Antihypertensive Peptides Using Deep Learning

Heart attack and other heart-related diseases are among the main causes of fatalities in the world. These diseases and some other severe problems like kidney failure and paralysis are mainly caused by hypertension. Since bioactive peptides extracted from naturally existing food substances possess antihypertensive activity, these antihypertensive peptides (AHTP) can function as prospective replacements for existing pharmacological drugs with no or fewer side effects. Such naturally existing peptides can be identified using in-silico approaches. The in-silico methods have been proven to save huge amounts of time and money in the identification of effective peptides. The proposed methodology is a deep learning-based in-silico approach for the identification of antihypertensive peptides (AHTPs). An ensemble method is proposed that combines convolutional neural network (CNN) and support vector machine (SVM) classifiers. Amino acid composition (AAC) and g-gap dipeptide composition (DPC) techniques are used for feature extraction. The proposed methodology has been evaluated on two standard antihypertensive peptide sequence datasets. The model yields 95% accuracy on the benchmarking dataset and 88.9% accuracy on the independent dataset. Comparative analysis is provided to demonstrate that the proposed method outperforms existing state-of-the-art methods on both of the benchmarking and independent datasets.