Cargando…

Predicting Gram-Positive Bacterial Protein Subcellular Location by Using Combined Features

There are a lot of bacteria in the environment, and Gram-positive bacteria are the most common ones. Some Gram-positive bacteria are very harmful to the human body, so it is significant to predict Gram-positive bacterial protein subcellular location. And identification of Gram-positive bacterial pro...

Descripción completa

Detalles Bibliográficos
Autores principales: Li, Feng-Min, Gao, Xiao-Wei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7421015/
https://www.ncbi.nlm.nih.gov/pubmed/32802888
http://dx.doi.org/10.1155/2020/9701734
_version_ 1783569975332569088
author Li, Feng-Min
Gao, Xiao-Wei
author_facet Li, Feng-Min
Gao, Xiao-Wei
author_sort Li, Feng-Min
collection PubMed
description There are a lot of bacteria in the environment, and Gram-positive bacteria are the most common ones. Some Gram-positive bacteria are very harmful to the human body, so it is significant to predict Gram-positive bacterial protein subcellular location. And identification of Gram-positive bacterial protein subcellular location is important for developing effective drugs. In this paper, a new Gram-positive bacterial protein subcellular location dataset was established. The amino acid composition, the gene ontology annotation information, the hydropathy dipeptide composition information, the amino acid dipeptide composition information, and the autocovariance average chemical shift information were selected as characteristic parameters, then these parameters were combined. The locations of Gram-positive bacterial proteins were predicted by the Support Vector Machine (SVM) algorithm, and the overall accuracy (OA) reached 86.1% under the Jackknife test. The overall accuracy (OA) in our predictive model was higher than those in existing methods. This improved method may be helpful for protein function prediction.
format Online
Article
Text
id pubmed-7421015
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher Hindawi
record_format MEDLINE/PubMed
spelling pubmed-74210152020-08-14 Predicting Gram-Positive Bacterial Protein Subcellular Location by Using Combined Features Li, Feng-Min Gao, Xiao-Wei Biomed Res Int Research Article There are a lot of bacteria in the environment, and Gram-positive bacteria are the most common ones. Some Gram-positive bacteria are very harmful to the human body, so it is significant to predict Gram-positive bacterial protein subcellular location. And identification of Gram-positive bacterial protein subcellular location is important for developing effective drugs. In this paper, a new Gram-positive bacterial protein subcellular location dataset was established. The amino acid composition, the gene ontology annotation information, the hydropathy dipeptide composition information, the amino acid dipeptide composition information, and the autocovariance average chemical shift information were selected as characteristic parameters, then these parameters were combined. The locations of Gram-positive bacterial proteins were predicted by the Support Vector Machine (SVM) algorithm, and the overall accuracy (OA) reached 86.1% under the Jackknife test. The overall accuracy (OA) in our predictive model was higher than those in existing methods. This improved method may be helpful for protein function prediction. Hindawi 2020-08-02 /pmc/articles/PMC7421015/ /pubmed/32802888 http://dx.doi.org/10.1155/2020/9701734 Text en Copyright © 2020 Feng-Min Li and Xiao-Wei Gao. http://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
Li, Feng-Min
Gao, Xiao-Wei
Predicting Gram-Positive Bacterial Protein Subcellular Location by Using Combined Features
title Predicting Gram-Positive Bacterial Protein Subcellular Location by Using Combined Features
title_full Predicting Gram-Positive Bacterial Protein Subcellular Location by Using Combined Features
title_fullStr Predicting Gram-Positive Bacterial Protein Subcellular Location by Using Combined Features
title_full_unstemmed Predicting Gram-Positive Bacterial Protein Subcellular Location by Using Combined Features
title_short Predicting Gram-Positive Bacterial Protein Subcellular Location by Using Combined Features
title_sort predicting gram-positive bacterial protein subcellular location by using combined features
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7421015/
https://www.ncbi.nlm.nih.gov/pubmed/32802888
http://dx.doi.org/10.1155/2020/9701734
work_keys_str_mv AT lifengmin predictinggrampositivebacterialproteinsubcellularlocationbyusingcombinedfeatures
AT gaoxiaowei predictinggrampositivebacterialproteinsubcellularlocationbyusingcombinedfeatures