Identification of Helicobacter pylori Membrane Proteins Using Sequence-Based Features.
Comput Math Methods Med
; 2022: 7493834, 2022.
Article
em En
| MEDLINE
| ID: mdl-35069791
ABSTRACT
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.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Proteínas de Bactérias
/
Helicobacter pylori
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Proteínas de Membrana
Idioma:
En
Ano de publicação:
2022
Tipo de documento:
Article