AptaNet as a deep learning approach for aptamer–protein interaction prediction

Abstract Aptamers are short oligonucleotides (DNA/RNA) or peptide molecules that can selectively bind to their specific targets with high specificity and affinity. As a powerful new class of amino acid ligands, aptamers have high potentials in biosensing, therapeutic, and diagnostic fields. Here, we...

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Autores principales:	Neda Emami, Reza Ferdousi
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/f9b26603b44f4b3da984cc4e94310d8c
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spelling	oai:doaj.org-article:f9b26603b44f4b3da984cc4e94310d8c2021-12-02T16:31:02ZAptaNet as a deep learning approach for aptamer–protein interaction prediction10.1038/s41598-021-85629-02045-2322https://doaj.org/article/f9b26603b44f4b3da984cc4e94310d8c2021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-85629-0https://doaj.org/toc/2045-2322Abstract Aptamers are short oligonucleotides (DNA/RNA) or peptide molecules that can selectively bind to their specific targets with high specificity and affinity. As a powerful new class of amino acid ligands, aptamers have high potentials in biosensing, therapeutic, and diagnostic fields. Here, we present AptaNet—a new deep neural network—to predict the aptamer–protein interaction pairs by integrating features derived from both aptamers and the target proteins. Aptamers were encoded by using two different strategies, including k-mer and reverse complement k-mer frequency. Amino acid composition (AAC) and pseudo amino acid composition (PseAAC) were applied to represent target information using 24 physicochemical and conformational properties of the proteins. To handle the imbalance problem in the data, we applied a neighborhood cleaning algorithm. The predictor was constructed based on a deep neural network, and optimal features were selected using the random forest algorithm. As a result, 99.79% accuracy was achieved for the training dataset, and 91.38% accuracy was obtained for the testing dataset. AptaNet achieved high performance on our constructed aptamer-protein benchmark dataset. The results indicate that AptaNet can help identify novel aptamer–protein interacting pairs and build more-efficient insights into the relationship between aptamers and proteins. Our benchmark dataset and the source codes for AptaNet are available in: https://github.com/nedaemami/AptaNet .Neda EmamiReza FerdousiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-19 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Neda Emami Reza Ferdousi AptaNet as a deep learning approach for aptamer–protein interaction prediction
description	Abstract Aptamers are short oligonucleotides (DNA/RNA) or peptide molecules that can selectively bind to their specific targets with high specificity and affinity. As a powerful new class of amino acid ligands, aptamers have high potentials in biosensing, therapeutic, and diagnostic fields. Here, we present AptaNet—a new deep neural network—to predict the aptamer–protein interaction pairs by integrating features derived from both aptamers and the target proteins. Aptamers were encoded by using two different strategies, including k-mer and reverse complement k-mer frequency. Amino acid composition (AAC) and pseudo amino acid composition (PseAAC) were applied to represent target information using 24 physicochemical and conformational properties of the proteins. To handle the imbalance problem in the data, we applied a neighborhood cleaning algorithm. The predictor was constructed based on a deep neural network, and optimal features were selected using the random forest algorithm. As a result, 99.79% accuracy was achieved for the training dataset, and 91.38% accuracy was obtained for the testing dataset. AptaNet achieved high performance on our constructed aptamer-protein benchmark dataset. The results indicate that AptaNet can help identify novel aptamer–protein interacting pairs and build more-efficient insights into the relationship between aptamers and proteins. Our benchmark dataset and the source codes for AptaNet are available in: https://github.com/nedaemami/AptaNet .
format	article
author	Neda Emami Reza Ferdousi
author_facet	Neda Emami Reza Ferdousi
author_sort	Neda Emami
title	AptaNet as a deep learning approach for aptamer–protein interaction prediction
title_short	AptaNet as a deep learning approach for aptamer–protein interaction prediction
title_full	AptaNet as a deep learning approach for aptamer–protein interaction prediction
title_fullStr	AptaNet as a deep learning approach for aptamer–protein interaction prediction
title_full_unstemmed	AptaNet as a deep learning approach for aptamer–protein interaction prediction
title_sort	aptanet as a deep learning approach for aptamer–protein interaction prediction
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/f9b26603b44f4b3da984cc4e94310d8c
work_keys_str_mv	AT nedaemami aptanetasadeeplearningapproachforaptamerproteininteractionprediction AT rezaferdousi aptanetasadeeplearningapproachforaptamerproteininteractionprediction
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AptaNet as a deep learning approach for aptamer–protein interaction prediction

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