Cargando…

A Deep Convolutional Neural Network for Prediction of Peptide Collision Cross Sections in Ion Mobility Spectrometry

Most frequently, the identification of peptides in mass spectrometry-based proteomics is carried out using high-resolution tandem mass spectrometry. In order to increase the accuracy of analysis, additional information on the peptides such as chromatographic retention time and collision cross sectio...

Descripción completa

Detalles Bibliográficos
Autores principales: Samukhina, Yulia V., Matyushin, Dmitriy D., Grinevich, Oksana I., Buryak, Aleksey K.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8699202/
https://www.ncbi.nlm.nih.gov/pubmed/34944547
http://dx.doi.org/10.3390/biom11121904
_version_ 1784620458164879360
author Samukhina, Yulia V.
Matyushin, Dmitriy D.
Grinevich, Oksana I.
Buryak, Aleksey K.
author_facet Samukhina, Yulia V.
Matyushin, Dmitriy D.
Grinevich, Oksana I.
Buryak, Aleksey K.
author_sort Samukhina, Yulia V.
collection PubMed
description Most frequently, the identification of peptides in mass spectrometry-based proteomics is carried out using high-resolution tandem mass spectrometry. In order to increase the accuracy of analysis, additional information on the peptides such as chromatographic retention time and collision cross section in ion mobility spectrometry can be used. An accurate prediction of the collision cross section values allows erroneous candidates to be rejected using a comparison of the observed values and the predictions based on the amino acids sequence. Recently, a massive high-quality data set of peptide collision cross sections was released. This opens up an opportunity to apply the most sophisticated deep learning techniques for this task. Previously, it was shown that a recurrent neural network allows for predicting these values accurately. In this work, we present a deep convolutional neural network that enables us to predict these values more accurately compared with previous studies. We use a neural network with complex architecture that contains both convolutional and fully connected layers and comprehensive methods of converting a peptide to multi-channel 1D spatial data and vector. The source code and pre-trained model are available online.
format Online
Article
Text
id pubmed-8699202
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-86992022021-12-24 A Deep Convolutional Neural Network for Prediction of Peptide Collision Cross Sections in Ion Mobility Spectrometry Samukhina, Yulia V. Matyushin, Dmitriy D. Grinevich, Oksana I. Buryak, Aleksey K. Biomolecules Article Most frequently, the identification of peptides in mass spectrometry-based proteomics is carried out using high-resolution tandem mass spectrometry. In order to increase the accuracy of analysis, additional information on the peptides such as chromatographic retention time and collision cross section in ion mobility spectrometry can be used. An accurate prediction of the collision cross section values allows erroneous candidates to be rejected using a comparison of the observed values and the predictions based on the amino acids sequence. Recently, a massive high-quality data set of peptide collision cross sections was released. This opens up an opportunity to apply the most sophisticated deep learning techniques for this task. Previously, it was shown that a recurrent neural network allows for predicting these values accurately. In this work, we present a deep convolutional neural network that enables us to predict these values more accurately compared with previous studies. We use a neural network with complex architecture that contains both convolutional and fully connected layers and comprehensive methods of converting a peptide to multi-channel 1D spatial data and vector. The source code and pre-trained model are available online. MDPI 2021-12-19 /pmc/articles/PMC8699202/ /pubmed/34944547 http://dx.doi.org/10.3390/biom11121904 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Samukhina, Yulia V.
Matyushin, Dmitriy D.
Grinevich, Oksana I.
Buryak, Aleksey K.
A Deep Convolutional Neural Network for Prediction of Peptide Collision Cross Sections in Ion Mobility Spectrometry
title A Deep Convolutional Neural Network for Prediction of Peptide Collision Cross Sections in Ion Mobility Spectrometry
title_full A Deep Convolutional Neural Network for Prediction of Peptide Collision Cross Sections in Ion Mobility Spectrometry
title_fullStr A Deep Convolutional Neural Network for Prediction of Peptide Collision Cross Sections in Ion Mobility Spectrometry
title_full_unstemmed A Deep Convolutional Neural Network for Prediction of Peptide Collision Cross Sections in Ion Mobility Spectrometry
title_short A Deep Convolutional Neural Network for Prediction of Peptide Collision Cross Sections in Ion Mobility Spectrometry
title_sort deep convolutional neural network for prediction of peptide collision cross sections in ion mobility spectrometry
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8699202/
https://www.ncbi.nlm.nih.gov/pubmed/34944547
http://dx.doi.org/10.3390/biom11121904
work_keys_str_mv AT samukhinayuliav adeepconvolutionalneuralnetworkforpredictionofpeptidecollisioncrosssectionsinionmobilityspectrometry
AT matyushindmitriyd adeepconvolutionalneuralnetworkforpredictionofpeptidecollisioncrosssectionsinionmobilityspectrometry
AT grinevichoksanai adeepconvolutionalneuralnetworkforpredictionofpeptidecollisioncrosssectionsinionmobilityspectrometry
AT buryakalekseyk adeepconvolutionalneuralnetworkforpredictionofpeptidecollisioncrosssectionsinionmobilityspectrometry
AT samukhinayuliav deepconvolutionalneuralnetworkforpredictionofpeptidecollisioncrosssectionsinionmobilityspectrometry
AT matyushindmitriyd deepconvolutionalneuralnetworkforpredictionofpeptidecollisioncrosssectionsinionmobilityspectrometry
AT grinevichoksanai deepconvolutionalneuralnetworkforpredictionofpeptidecollisioncrosssectionsinionmobilityspectrometry
AT buryakalekseyk deepconvolutionalneuralnetworkforpredictionofpeptidecollisioncrosssectionsinionmobilityspectrometry