MS2CNN: predicting MS/MS spectrum based on protein sequence using deep convolutional neural networks.

ABSTRACT:

Background

Tandem mass spectrometry allows biologists to identify and quantify protein samples in the form of digested peptide sequences. When performing peptide identification, spectral library search is more sensitive than traditional database search but is limited to peptides that have been previously identified. An accurate tandem mass spectrum prediction tool is thus crucial in expanding the peptide space and increasing the coverage of spectral library search.

Results

We propose MS²CNN, a non-linear regression model based on deep convolutional neural networks, a deep learning algorithm. The features for our model are amino acid composition, predicted secondary structure, and physical-chemical features such as isoelectric point, aromaticity, helicity, hydropho

SUBMITTER: Lin YM 

PROVIDER: S-EPMC6929458 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature