DeepIso: A Deep Learning Model for Peptide Feature Detection from LC-MS map

Liquid chromatography with tandem mass spectrometry (LC-MS/MS) based quantitative proteomics provides the relative different protein abundance in healthy and disease-afflicted patients, which offers the information for molecular interactions, signaling pathways, and biomarker identification to serve...

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Bibliographic Details
Published in:Scientific reports 2019-11, Vol.9 (1), p.17168-13, Article 17168
Main Authors: Zohora, Fatema Tuz, Rahman, M. Ziaur, Tran, Ngoc Hieu, Xin, Lei, Shan, Baozhen, Li, Ming
Format: Article
Language:English
Subjects:
631/114/1305
631/114/2784
Biomarkers - chemistry
Chromatography, Liquid - methods
Deep Learning
Humanities and Social Sciences
Liquid chromatography
Mass spectrometry
Mass spectroscopy
multidisciplinary
Neural networks
Neural Networks, Computer
Neurons - metabolism
Peptides
Peptides - chemistry
Proteins - chemistry
Proteomics
Proteomics - methods
Science
Science (multidisciplinary)
Tandem Mass Spectrometry - methods
Workflow
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Summary:Liquid chromatography with tandem mass spectrometry (LC-MS/MS) based quantitative proteomics provides the relative different protein abundance in healthy and disease-afflicted patients, which offers the information for molecular interactions, signaling pathways, and biomarker identification to serve the drug discovery and clinical research. Typical analysis workflow begins with the peptide feature detection and intensity calculation from LC-MS map. We are the first to propose a deep learning based model, DeepIso, that combines recent advances in Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) to detect peptide features of different charge states, as well as, estimate their intensity. Existing tools are designed with limited engineered features and domain-specific parameters, which are hardly updated despite a huge amount of new coming proteomic data. On the other hand, DeepIso consisting of two separate deep learning based modules, learns multiple levels of representation of high dimensional data itself through many layers of neurons, and adaptable to newly acquired data. The peptide feature list reported by our model matches with 97.43% of high quality MS/MS identifications in a benchmark dataset, which is higher than the matching produced by several widely used tools. Our results demonstrate that novel deep learning tools are desirable to advance the state-of-the-art in protein identification and quantification.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-019-52954-4