Prediction of Bioluminescent Proteins Using Auto Covariance Transformation of Evolutional Profiles

Bioluminescent proteins are important for various cellular processes, such as gene expression analysis, drug discovery, bioluminescent imaging, toxicity determination, and DNA sequencing studies. Hence, the correct identification of bioluminescent proteins is of great importance both for helping gen...

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Main Authors:	Zhao, Xiaowei, Li, Jiakui, Huang, Yanxin, Ma, Zhiqiang, Yin, Minghao
Format:	Online
Language:	English
Published:	Molecular Diversity Preservation International (MDPI) 2012
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3317733/

id	pubmed-3317733
recordtype	oai_dc
spelling	pubmed-33177332012-04-09 Prediction of Bioluminescent Proteins Using Auto Covariance Transformation of Evolutional Profiles Zhao, Xiaowei Li, Jiakui Huang, Yanxin Ma, Zhiqiang Yin, Minghao Article Bioluminescent proteins are important for various cellular processes, such as gene expression analysis, drug discovery, bioluminescent imaging, toxicity determination, and DNA sequencing studies. Hence, the correct identification of bioluminescent proteins is of great importance both for helping genome annotation and providing a supplementary role to experimental research to obtain insight into bioluminescent proteins’ functions. However, few computational methods are available for identifying bioluminescent proteins. Therefore, in this paper we develop a new method to predict bioluminescent proteins using a model based on position specific scoring matrix and auto covariance. Tested by 10-fold cross-validation and independent test, the accuracy of the proposed model reaches 85.17% for the training dataset and 90.71% for the testing dataset respectively. These results indicate that our predictor is a useful tool to predict bioluminescent proteins. This is the first study in which evolutionary information and local sequence environment information have been successfully integrated for predicting bioluminescent proteins. A web server (BLPre) that implements the proposed predictor is freely available. Molecular Diversity Preservation International (MDPI) 2012-03-19 /pmc/articles/PMC3317733/ /pubmed/22489173 http://dx.doi.org/10.3390/ijms13033650 Text en © 2012 by the authors; licensee Molecular Diversity Preservation International, Basel, Switzerland. http://creativecommons.org/licenses/by/3.0 This article is an open-access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Zhao, Xiaowei Li, Jiakui Huang, Yanxin Ma, Zhiqiang Yin, Minghao
spellingShingle	Zhao, Xiaowei Li, Jiakui Huang, Yanxin Ma, Zhiqiang Yin, Minghao Prediction of Bioluminescent Proteins Using Auto Covariance Transformation of Evolutional Profiles
author_facet	Zhao, Xiaowei Li, Jiakui Huang, Yanxin Ma, Zhiqiang Yin, Minghao
author_sort	Zhao, Xiaowei
title	Prediction of Bioluminescent Proteins Using Auto Covariance Transformation of Evolutional Profiles
title_short	Prediction of Bioluminescent Proteins Using Auto Covariance Transformation of Evolutional Profiles
title_full	Prediction of Bioluminescent Proteins Using Auto Covariance Transformation of Evolutional Profiles
title_fullStr	Prediction of Bioluminescent Proteins Using Auto Covariance Transformation of Evolutional Profiles
title_full_unstemmed	Prediction of Bioluminescent Proteins Using Auto Covariance Transformation of Evolutional Profiles
title_sort	prediction of bioluminescent proteins using auto covariance transformation of evolutional profiles
description	Bioluminescent proteins are important for various cellular processes, such as gene expression analysis, drug discovery, bioluminescent imaging, toxicity determination, and DNA sequencing studies. Hence, the correct identification of bioluminescent proteins is of great importance both for helping genome annotation and providing a supplementary role to experimental research to obtain insight into bioluminescent proteins’ functions. However, few computational methods are available for identifying bioluminescent proteins. Therefore, in this paper we develop a new method to predict bioluminescent proteins using a model based on position specific scoring matrix and auto covariance. Tested by 10-fold cross-validation and independent test, the accuracy of the proposed model reaches 85.17% for the training dataset and 90.71% for the testing dataset respectively. These results indicate that our predictor is a useful tool to predict bioluminescent proteins. This is the first study in which evolutionary information and local sequence environment information have been successfully integrated for predicting bioluminescent proteins. A web server (BLPre) that implements the proposed predictor is freely available.
publisher	Molecular Diversity Preservation International (MDPI)
publishDate	2012
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3317733/
_version_	1611518641042358272

Prediction of Bioluminescent Proteins Using Auto Covariance Transformation of Evolutional Profiles

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