Prediction of aptamer-target interacting pairs with pseudo-amino acid composition.

Aptamers are oligonucleic acid or peptide molecules that bind to specific target molecules. As a novel and powerful class of ligands, aptamers are thought to have excellent potential for applications in the fields of biosensing, diagnostics and therapeutics. In this study, a new method for predictin...

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Autores principales:	Bi-Qing Li, Yu-Chao Zhang, Guo-Hua Huang, Wei-Ren Cui, Ning Zhang, Yu-Dong Cai
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/99567d08686b439da4d85fad57683688
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spelling	oai:doaj.org-article:99567d08686b439da4d85fad576836882021-11-18T08:36:21ZPrediction of aptamer-target interacting pairs with pseudo-amino acid composition.1932-620310.1371/journal.pone.0086729https://doaj.org/article/99567d08686b439da4d85fad576836882014-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24466214/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203Aptamers are oligonucleic acid or peptide molecules that bind to specific target molecules. As a novel and powerful class of ligands, aptamers are thought to have excellent potential for applications in the fields of biosensing, diagnostics and therapeutics. In this study, a new method for predicting aptamer-target interacting pairs was proposed by integrating features derived from both aptamers and their targets. Features of nucleotide composition and traditional amino acid composition as well as pseudo amino acid were utilized to represent aptamers and targets, respectively. The predictor was constructed based on Random Forest and the optimal features were selected by using the maximum relevance minimum redundancy (mRMR) method and the incremental feature selection (IFS) method. As a result, 81.34% accuracy and 0.4612 MCC were obtained for the training dataset, and 77.41% accuracy and 0.3717 MCC were achieved for the testing dataset. An optimal feature set of 220 features were selected, which were considered as the ones that contributed significantly to the interacting aptamer-target pair predictions. Analysis of the optimal feature set indicated several important factors in determining aptamer-target interactions. It is anticipated that our prediction method may become a useful tool for identifying aptamer-target pairs and the features selected and analyzed in this study may provide useful insights into the mechanism of interactions between aptamers and targets.Bi-Qing LiYu-Chao ZhangGuo-Hua HuangWei-Ren CuiNing ZhangYu-Dong CaiPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 1, p e86729 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Bi-Qing Li Yu-Chao Zhang Guo-Hua Huang Wei-Ren Cui Ning Zhang Yu-Dong Cai Prediction of aptamer-target interacting pairs with pseudo-amino acid composition.
description	Aptamers are oligonucleic acid or peptide molecules that bind to specific target molecules. As a novel and powerful class of ligands, aptamers are thought to have excellent potential for applications in the fields of biosensing, diagnostics and therapeutics. In this study, a new method for predicting aptamer-target interacting pairs was proposed by integrating features derived from both aptamers and their targets. Features of nucleotide composition and traditional amino acid composition as well as pseudo amino acid were utilized to represent aptamers and targets, respectively. The predictor was constructed based on Random Forest and the optimal features were selected by using the maximum relevance minimum redundancy (mRMR) method and the incremental feature selection (IFS) method. As a result, 81.34% accuracy and 0.4612 MCC were obtained for the training dataset, and 77.41% accuracy and 0.3717 MCC were achieved for the testing dataset. An optimal feature set of 220 features were selected, which were considered as the ones that contributed significantly to the interacting aptamer-target pair predictions. Analysis of the optimal feature set indicated several important factors in determining aptamer-target interactions. It is anticipated that our prediction method may become a useful tool for identifying aptamer-target pairs and the features selected and analyzed in this study may provide useful insights into the mechanism of interactions between aptamers and targets.
format	article
author	Bi-Qing Li Yu-Chao Zhang Guo-Hua Huang Wei-Ren Cui Ning Zhang Yu-Dong Cai
author_facet	Bi-Qing Li Yu-Chao Zhang Guo-Hua Huang Wei-Ren Cui Ning Zhang Yu-Dong Cai
author_sort	Bi-Qing Li
title	Prediction of aptamer-target interacting pairs with pseudo-amino acid composition.
title_short	Prediction of aptamer-target interacting pairs with pseudo-amino acid composition.
title_full	Prediction of aptamer-target interacting pairs with pseudo-amino acid composition.
title_fullStr	Prediction of aptamer-target interacting pairs with pseudo-amino acid composition.
title_full_unstemmed	Prediction of aptamer-target interacting pairs with pseudo-amino acid composition.
title_sort	prediction of aptamer-target interacting pairs with pseudo-amino acid composition.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/99567d08686b439da4d85fad57683688
work_keys_str_mv	AT biqingli predictionofaptamertargetinteractingpairswithpseudoaminoacidcomposition AT yuchaozhang predictionofaptamertargetinteractingpairswithpseudoaminoacidcomposition AT guohuahuang predictionofaptamertargetinteractingpairswithpseudoaminoacidcomposition AT weirencui predictionofaptamertargetinteractingpairswithpseudoaminoacidcomposition AT ningzhang predictionofaptamertargetinteractingpairswithpseudoaminoacidcomposition AT yudongcai predictionofaptamertargetinteractingpairswithpseudoaminoacidcomposition
_version_	1718421576541536256

Prediction of aptamer-target interacting pairs with pseudo-amino acid composition.

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