Abstract
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.
Highlights
Hypertension is a clinical syndrome characterized by increased systemic arterial blood pressure, which can be accompanied by functional or organic damage of the heart, brain, kidney, and other organs
We further proposed a prediction method for AHTPA-EC50 based on two NARXNNs and Machine learning (ML) classification algorithms
2.1.7 Prediction Method for AHTPA-EC50 We further proposed a method for AHTPA-EC50 prediction
Summary
Hypertension is a clinical syndrome characterized by increased systemic arterial blood pressure, which can be accompanied by functional or organic damage of the heart, brain, kidney, and other organs. The ACE can convert inactive angiotensin I into angiotensin II with vasoconstriction, which indirectly increases blood pressure (Zhang et al, 2000). Antihypertensive active peptide is an effective ACE inhibitor (Tu et al, 2018a; Tu et al, 2018b; Wu et al, 2019), which has attracted great attention in the treatment and prevention of hypertension. In 2015, Kumar et al developed four different model types for predicting AHTPs with varied lengths using ML approaches (Kumar et al, 2015a; Kumar et al, 2015b). There is great uncertainty in the relationship between the AAS of antihypertensive peptides and its corresponding AHTPA-EC50. The mapping relationship between AHTPA-EC50 and its corresponding AAS has not been reported. It is difficult to establish a deterministic model between the AAS and AHTPA-EC50 directly
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