Recognizing Proteins with Binding Function in Elymus nutans Based on Machine Learning Methods

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.