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Predicting Tryptic Cleavage from Proteomics Data Using Decision Tree Ensembles
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DOI：10.1021/pr4001114.png)
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Abstract
En 中文
Trypsin is the workhorse protease in mass spectrometry-based proteomics experiments and is used to digest proteins into more readily analyzable peptides. To identify these peptides after mass spectrometric analysis, the actual digestion has to be mimicked as faithfully as possible in Aim In this paper we introduce CP-DT (Cleavage Prediction with Decision Trees), an algorithm based on a decision tree ensemble that was learned on publicly available peptide identification data from the PRIDE repository. We demonstrate that CP-DT is able to accurately predict tryptic cleavage: tests on three independent data sets show that CP-DT significantly outperforms the Keil rules that are currently used to predict tryptic cleavage. Moreover, the trees generated by CP-DT can make predictions efficiently and are interpretable by domain experts.
Keywords:
mass spectrometry
trypsin
PRIDE
machine learning
decision tree