DeepDigest: Prediction of Protein Proteolytic Digestion with Deep Learning

作者：Jinghan Yang, Zhiqiang Gao, Xiuhan Ren, Jie Sheng, Ping Xu, Cheng Chang, Yan Fu · 发表于：Analytical Chemistry · 年份：2021 · DOI：10.1021/acs.analchem.0c04704 · 被引用次数：51 · 研究领域：Machine Learning in Bioinformatics、Advanced Proteomics Techniques and Applications、Protein Hydrolysis and Bioactive Peptides

Proteolytic digestion of proteins by one or more proteases is a key step in shotgun proteomics, in which the proteolytic products, i.e., peptides, are taken as the surrogates of their parent proteins for further qualitative or quantitative analysis. The proteases generally cleave proteins at specific amino acid residue sites, but digestion is hardly complete (wide existence of missed cleavage sites). Therefore, it would be of great help to improve the prior experimental design and the posterior data analysis if the digestion behaviors of proteases can be accurately modeled and predicted. At present, systematic studies about the commonly used proteases in proteomics are insufficient, and there is a lack of easy-to-use tools to predict the cleavage sites of different proteases. Here, we propose a novel sequence-based deep learning algorithm-DeepDigest, which integrates convolutional neural networks and long short-term memory networks for protein digestion prediction. DeepDigest can predict the cleavage probability of each potential cleavage site on the protein sequences for eight popular proteases including trypsin, ArgC, chymotrypsin, GluC, LysC, AspN, LysN, and LysargiNase. We compared DeepDigest with three traditional machine learning algorithms, i.e., logistic regression, random forest, and support vector machine. On the eight training data sets, the 10-fold cross-validation accuracies (AUCs) of DeepDigest were 0.956-0.982, significantly higher than those of the three tradi...