Prediction of Peptide Detectability Based on CapsNet and Convolutional Block Attention Module

According to proteomics technology, as impacted by the complexity of sampling in the experimental process, several problems remain with the reproducibility of mass spectrometry experiments, and the peptide identification and quantitative results continue to be random. Predicting the detectability ex...

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Autores principales:	Minzhe Yu, Yushuai Duan, Zhong Li, Yang Zhang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	peptide detectability CapsNet CBAM physicochemical properties of residues amino acid composition dipeptide composition Biology (General) QH301-705.5 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/635dc325918c412dbe74adb441b89d99
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spelling	oai:doaj.org-article:635dc325918c412dbe74adb441b89d992021-11-11T17:28:15ZPrediction of Peptide Detectability Based on CapsNet and Convolutional Block Attention Module10.3390/ijms2221120801422-00671661-6596https://doaj.org/article/635dc325918c412dbe74adb441b89d992021-11-01T00:00:00Zhttps://www.mdpi.com/1422-0067/22/21/12080https://doaj.org/toc/1661-6596https://doaj.org/toc/1422-0067According to proteomics technology, as impacted by the complexity of sampling in the experimental process, several problems remain with the reproducibility of mass spectrometry experiments, and the peptide identification and quantitative results continue to be random. Predicting the detectability exhibited by peptides can optimize the mentioned results to be more accurate, so such a prediction is of high research significance. This study builds a novel method to predict the detectability of peptides by complying with the capsule network (CapsNet) and the convolutional block attention module (CBAM). First, the residue conical coordinate (RCC), the amino acid composition (AAC), the dipeptide composition (DPC), and the sequence embedding code (SEC) are extracted as the peptide chain features. Subsequently, these features are divided into the biological feature and sequence feature, and separately inputted into the neural network of CapsNet. Moreover, the attention module CBAM is added to the network to assign weights to channels and spaces, as an attempt to enhance the feature learning and improve the network training effect. To verify the effectiveness of the proposed method, it is compared with some other popular methods. As revealed from the experimentally achieved results, the proposed method outperforms those methods in most performance assessments.Minzhe YuYushuai DuanZhong LiYang ZhangMDPI AGarticlepeptide detectabilityCapsNetCBAMphysicochemical properties of residuesamino acid compositiondipeptide compositionBiology (General)QH301-705.5ChemistryQD1-999ENInternational Journal of Molecular Sciences, Vol 22, Iss 12080, p 12080 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	peptide detectability CapsNet CBAM physicochemical properties of residues amino acid composition dipeptide composition Biology (General) QH301-705.5 Chemistry QD1-999
spellingShingle	peptide detectability CapsNet CBAM physicochemical properties of residues amino acid composition dipeptide composition Biology (General) QH301-705.5 Chemistry QD1-999 Minzhe Yu Yushuai Duan Zhong Li Yang Zhang Prediction of Peptide Detectability Based on CapsNet and Convolutional Block Attention Module
description	According to proteomics technology, as impacted by the complexity of sampling in the experimental process, several problems remain with the reproducibility of mass spectrometry experiments, and the peptide identification and quantitative results continue to be random. Predicting the detectability exhibited by peptides can optimize the mentioned results to be more accurate, so such a prediction is of high research significance. This study builds a novel method to predict the detectability of peptides by complying with the capsule network (CapsNet) and the convolutional block attention module (CBAM). First, the residue conical coordinate (RCC), the amino acid composition (AAC), the dipeptide composition (DPC), and the sequence embedding code (SEC) are extracted as the peptide chain features. Subsequently, these features are divided into the biological feature and sequence feature, and separately inputted into the neural network of CapsNet. Moreover, the attention module CBAM is added to the network to assign weights to channels and spaces, as an attempt to enhance the feature learning and improve the network training effect. To verify the effectiveness of the proposed method, it is compared with some other popular methods. As revealed from the experimentally achieved results, the proposed method outperforms those methods in most performance assessments.
format	article
author	Minzhe Yu Yushuai Duan Zhong Li Yang Zhang
author_facet	Minzhe Yu Yushuai Duan Zhong Li Yang Zhang
author_sort	Minzhe Yu
title	Prediction of Peptide Detectability Based on CapsNet and Convolutional Block Attention Module
title_short	Prediction of Peptide Detectability Based on CapsNet and Convolutional Block Attention Module
title_full	Prediction of Peptide Detectability Based on CapsNet and Convolutional Block Attention Module
title_fullStr	Prediction of Peptide Detectability Based on CapsNet and Convolutional Block Attention Module
title_full_unstemmed	Prediction of Peptide Detectability Based on CapsNet and Convolutional Block Attention Module
title_sort	prediction of peptide detectability based on capsnet and convolutional block attention module
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/635dc325918c412dbe74adb441b89d99
work_keys_str_mv	AT minzheyu predictionofpeptidedetectabilitybasedoncapsnetandconvolutionalblockattentionmodule AT yushuaiduan predictionofpeptidedetectabilitybasedoncapsnetandconvolutionalblockattentionmodule AT zhongli predictionofpeptidedetectabilitybasedoncapsnetandconvolutionalblockattentionmodule AT yangzhang predictionofpeptidedetectabilitybasedoncapsnetandconvolutionalblockattentionmodule
_version_	1718432066747498496

Prediction of Peptide Detectability Based on CapsNet and Convolutional Block Attention Module

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