Deep learning enables de novo peptide sequencing from data-independent-acquisition mass spectrometry.
Ngoc Hieu TranRui QiaoLei XinXin ChenChuyi LiuXianglilan ZhangBaozhen ShanAli GhodsiMing LiPublished in: Nature methods (2018)
We present DeepNovo-DIA, a de novo peptide-sequencing method for data-independent acquisition (DIA) mass spectrometry data. We use neural networks to capture precursor and fragment ions across m/z, retention-time, and intensity dimensions. They are then further integrated with peptide sequence patterns to address the problem of highly multiplexed spectra. DIA coupled with de novo sequencing allowed us to identify novel peptides in human antibodies and antigens.
Keyphrases
- mass spectrometry
- single cell
- electronic health record
- neural network
- deep learning
- big data
- liquid chromatography
- endothelial cells
- high resolution
- gas chromatography
- machine learning
- capillary electrophoresis
- artificial intelligence
- quantum dots
- immune response
- ms ms
- convolutional neural network
- simultaneous determination