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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. [ABSTRACT FROM AUTHOR]

Food-derived antihypertensive peptides have attracted increasing attention in functional foods for health promotion, due to their high biological activity, low toxicity and easy metabolism in the human body. Angiotensin converting enzyme (ACE) is a key enzyme that causes the increase in blood pressure in mammals. However, few reviews have summarized the current understanding of ACE inhibitory peptides and their knowledge gaps. This paper focuses on the food origins and production methods of ACE inhibitory peptides. Compared with conventional methods, the advanced technologies and emerging bioinformatics approaches have recently been applied for efficient and targeted release of ACE inhibitory peptides from food proteins. Furthermore, the transport and underlying mechanisms of ACE inhibitory peptides are emphatically described. Molecular modeling and the Michaelis-Menten equation can provide information on how ACE inhibitors function. Finally, we discuss the structure-activity relationships and other bio-functional properties of ACE inhibitory peptides. Molecular weight, hydrophobic amino acid residues, charge, amino acid composition and sequence (especially at the C-terminal and N-terminal) have a significant influence on ACE inhibitory activity. Some studies are required to increase productivity, improve bioavailability of peptides, evaluate their bio-accessibility and efficiency on reducing blood pressure to provide a reference for the development and application of health products and auxiliary treatment drugs. [ABSTRACT FROM AUTHOR]