Cargando…

Identification of Helicobacter pylori Membrane Proteins Using Sequence-Based Features

Helicobacter pylori (H. pylori) is the most common risk factor for gastric cancer worldwide. The membrane proteins of the H. pylori are involved in bacterial adherence and play a vital role in the field of drug discovery. Thus, an accurate and cost-effective computational model is needed to predict...

Descripción completa

Detalles Bibliográficos
Autores principales: Liu, Mujiexin, Chen, Hui, Gao, Dong, Ma, Cai-Yi, Zhang, Zhao-Yue
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8769816/
https://www.ncbi.nlm.nih.gov/pubmed/35069791
http://dx.doi.org/10.1155/2022/7493834
_version_ 1784635225687457792
author Liu, Mujiexin
Chen, Hui
Gao, Dong
Ma, Cai-Yi
Zhang, Zhao-Yue
author_facet Liu, Mujiexin
Chen, Hui
Gao, Dong
Ma, Cai-Yi
Zhang, Zhao-Yue
author_sort Liu, Mujiexin
collection PubMed
description Helicobacter pylori (H. pylori) is the most common risk factor for gastric cancer worldwide. The membrane proteins of the H. pylori are involved in bacterial adherence and play a vital role in the field of drug discovery. Thus, an accurate and cost-effective computational model is needed to predict the uncharacterized membrane proteins of H. pylori. In this study, a reliable benchmark dataset consisted of 114 membrane and 219 nonmembrane proteins was constructed based on UniProt. A support vector machine- (SVM-) based model was developed for discriminating H. pylori membrane proteins from nonmembrane proteins by using sequence information. Cross-validation showed that our method achieved good performance with an accuracy of 91.29%. It is anticipated that the proposed model will be useful for the annotation of H. pylori membrane proteins and the development of new anti-H. pylori agents.
format Online
Article
Text
id pubmed-8769816
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Hindawi
record_format MEDLINE/PubMed
spelling pubmed-87698162022-01-20 Identification of Helicobacter pylori Membrane Proteins Using Sequence-Based Features Liu, Mujiexin Chen, Hui Gao, Dong Ma, Cai-Yi Zhang, Zhao-Yue Comput Math Methods Med Research Article Helicobacter pylori (H. pylori) is the most common risk factor for gastric cancer worldwide. The membrane proteins of the H. pylori are involved in bacterial adherence and play a vital role in the field of drug discovery. Thus, an accurate and cost-effective computational model is needed to predict the uncharacterized membrane proteins of H. pylori. In this study, a reliable benchmark dataset consisted of 114 membrane and 219 nonmembrane proteins was constructed based on UniProt. A support vector machine- (SVM-) based model was developed for discriminating H. pylori membrane proteins from nonmembrane proteins by using sequence information. Cross-validation showed that our method achieved good performance with an accuracy of 91.29%. It is anticipated that the proposed model will be useful for the annotation of H. pylori membrane proteins and the development of new anti-H. pylori agents. Hindawi 2022-01-12 /pmc/articles/PMC8769816/ /pubmed/35069791 http://dx.doi.org/10.1155/2022/7493834 Text en Copyright © 2022 Mujiexin Liu et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Liu, Mujiexin
Chen, Hui
Gao, Dong
Ma, Cai-Yi
Zhang, Zhao-Yue
Identification of Helicobacter pylori Membrane Proteins Using Sequence-Based Features
title Identification of Helicobacter pylori Membrane Proteins Using Sequence-Based Features
title_full Identification of Helicobacter pylori Membrane Proteins Using Sequence-Based Features
title_fullStr Identification of Helicobacter pylori Membrane Proteins Using Sequence-Based Features
title_full_unstemmed Identification of Helicobacter pylori Membrane Proteins Using Sequence-Based Features
title_short Identification of Helicobacter pylori Membrane Proteins Using Sequence-Based Features
title_sort identification of helicobacter pylori membrane proteins using sequence-based features
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8769816/
https://www.ncbi.nlm.nih.gov/pubmed/35069791
http://dx.doi.org/10.1155/2022/7493834
work_keys_str_mv AT liumujiexin identificationofhelicobacterpylorimembraneproteinsusingsequencebasedfeatures
AT chenhui identificationofhelicobacterpylorimembraneproteinsusingsequencebasedfeatures
AT gaodong identificationofhelicobacterpylorimembraneproteinsusingsequencebasedfeatures
AT macaiyi identificationofhelicobacterpylorimembraneproteinsusingsequencebasedfeatures
AT zhangzhaoyue identificationofhelicobacterpylorimembraneproteinsusingsequencebasedfeatures