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

• Published in: Biomolecules (Basel, Switzerland) Vol. 11; no. 12; p. 1904
• Main Authors: Samukhina, Yulia V, Matyushin, Dmitriy D, Grinevich, Oksana I, Buryak, Aleksey K
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 19.12.2021; MDPI
• Subjects: Accuracy; Amino acids; Chromatography; Datasets; Deep Learning; Ion Mobility Spectrometry; Ions - chemistry; Machine learning; Mass spectrometry; Mass spectroscopy; Mobility; Neural networks; Neural Networks, Computer; Peptides; Peptides - chemistry; Predictions; Proteomics; Proteomics - methods; Scientific imaging; Tandem Mass Spectrometry; ion mobility spectrometry; deep learning; peptides; proteomics
• ISSN: 2218-273X; 2218-273X
• DOI: 10.3390/biom11121904

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.