基于多源特征和双向门控循环单元的抗高血压肽识别

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

为了开发快速、高效和智能的抗高血压肽(anti-hypertensive peptides,AHTPs)识别工具,针对AHTPs的识别,构建基于多源特征和深度学习的识别模型。利用新增强分组氨基酸组分(novel enhanced grouped amino acid composition,NEGAAC)、约简的二肽组分(reduced dipeptide composition,RDPC)、二肽频率与预期平均值之间的偏差(dipeptide deviation from expected mean,DDE)、氨基酸物理化学性质的距离变换(amino acid physicochemical properties-based distance transformation,AAP-DT)和BLOSUM62编码对肽序列进行特征提取。采用双向门控循环单元(bidirectional gated recurrent units,BiGRU)对蛋白质特征进行深度学习,进而有效识别AHTPs。在10-折交叉验证下,基于多源特征和深度学习的识别模型在基准数据集和独立数据集上的识别精度达到96.78%和98.72%。

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.