A Hybrid Deep Learning Model for Predicting Protein Hydroxylation Sites

Abstract

Protein hydroxylation is one type of post-translational modifications (PTMs) playing critical roles in human diseases. It is known that protein sequence contains many uncharacterized residues of proline and lysine. The question that needs to be answered is: which residue can be hydroxylated, and which one cannot. The answer will not only help understand the mechanism of hydroxylation but can also benefit the development of new drugs. In this paper, we proposed a novel approach for predicting hydroxylation using a hybrid deep learning model integrating the convolutional neural network (CNN) and long short-term memory network (LSTM). We employed a pseudo amino acid composition (PseAAC) method to construct valid benchmark datasets based on a sliding window strategy and used the position-specific scoring matrix (PSSM) to represent samples as inputs to the deep learning model. In addition, we compared our method with popular predictors including CNN, iHyd-PseAAC, and iHyd-PseCp. The results for 5-fold cross-validations all demonstrated that our method significantly outperforms the other methods in prediction accuracy.

Deep Learning in Proteomics

“…Deep learning has been used in the prediction of PTM sites for phosphorylation, [96][97][98][99][100] ubiquitination, [101,102] acetylation, [103][104][105][106] glycosylation, [107] malonylation, [108,109] succinylation, [110,111] glycation, [112] nitration/nitrosylation, [113] crotonylation [114] and other modifications [115][116][117]224] as shown in Table 3. MusiteDeep, the first deep learning-based PTM prediction tool, provides both general phosphosite prediction and kinase-specific phosphosite prediction for five kinase families, each with more than 100 known substrates.…”

A Comparative Analysis of Novel Deep Learning and Ensemble Learning Models to Predict the Allergenicity of Food Proteins

“…Instead of cutting out a single text first, it converts text recognition into a sequence-dependent sequence learning problem. It has also been reported CRNN plays a role in the function prediction of proteins [ 20 ]. The novel ensemble learning model is becoming one of the mainstream methods to improve the performance of machine learning, which has shown superior performance compared to traditional classifiers in text classification [ 21 ], disease diagnosis [ 22 ], and other fields, and its application in the peptide sequence classification has been rarely seen.…”

A Comparative Analysis of Novel Deep Learning and Ensemble Learning Models to Predict the Allergenicity of Food Proteins

“…Instead of cutting out a single text first, it converts text recognition into a sequence-dependent sequence learning problem. It has also been reported CRNN plays a role in function prediction of proteins [17]. The novel ensemble learning model is becoming one of the mainstream methods to improve the performance of machine learning, which has shown superior performance compared to traditional classifiers in text classification [18], disease diagnosis [19] and other fields, and its application in the peptide sequence classification has been rarely seen.…”