Investigation and Highly Accurate Prediction of Missed Tryptic Cleavages by Deep Learning

Abstract

Trypsin is one of the most important and widely used proteolytic enzymes in mass spectrometry (MS)-based proteomic research. It exclusively cleaves peptide bonds at the C-terminus of lysine and arginine. However, the cleavage is also affected by several factors, including specific surrounding amino acids, resulting in frequent incomplete proteolysis and subsequent issues in peptide identification and quantification. The accurate annotations on missed cleavages are crucial to database searching in MS analysis. Here, we present deep-learning predicting missed cleavages (dpMC), a novel algorithm for the prediction of missed trypsin cleavage sites. This algorithm provides a very high accuracy for predicting missed cleavages with area under the curves (AUCs) of cross-validation and holdout testing above 0.99, along with the mean F1 score and the Matthews correlation coefficient (MCC) of 0.9677 and 0.9349, respectively. We tested our algorithm on data sets from different species and different experimental conditions, and its performance outperforms other currently available prediction methods. In addition, the method also provides a better insight into the detailed rules of trypsin cleavages coupled with propensity and motif analysis. Moreover, our method can be integrated into database searching in the MS analysis to identify and quantify mass spectra effectively and efficiently.

Keywords: deep learning; mass spectrometry; missed cleavage; prediction; trypsin.