Identification of anti-hypertensive peptides based on multi-source features and bidirectional gated recurrent units

Abstract

In order to develop a fast, efficient and intelligent tool to recognize anti-hypertensive peptides (AHTPs), an identification model based on multi-source characteristics and deep learning was constructed for the recognition of AHTPs. Novel enhanced grouped amino acid composition (NEGAAC), reduced dipeptide composition (RDPC), dipeptide deviation from expected mean (DDE), amino acid physicochemical properties-based distance transformation (AAP-DT) and BLOSUM62 encoding were used for feature extraction of peptide sequences. In addition, bidirectional gated recurrent units (BiGRU) were used for deep learning of protein characteristics, so as to effectively identify AHTPs. Under 10-fold cross-validation, the recognition accuracy of the recognition model based on multi-source features and deep learning reaches 96.78% and 98.72% on the benchmark data set and independent data set.

Keywords

• anti-hypertensive peptides(ahtps)
• multi-source features
• deep learning
• bidirectional gated recurrent units(bigru)
• protein