TYLER, a fast method that accurately predicts cyclin-dependent proteins by using computation-based motifs and sequence-derived features

Cyclins and related cyclin-dependent kinases play vital roles in regulating the progression in the cell cycle. Understanding the intrinsic mechanisms of cyclins promises knowledge about cell uncontrolled proliferation and prevention of cancer cells. Therefore, accurate recognition of cyclins is impo...

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Autores principales:	Jian Zhang, Xingchen Liang, Feng Zhou, Bo Li, Yanling Li
Formato:	article
Lenguaje:	EN
Publicado:	AIMS Press 2021
Materias:	cyclin-dependent proteins sequence motifs machine learning go analysis Biotechnology TP248.13-248.65 Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/9dadfab7b444406a9311a61dbffdb647
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spelling	oai:doaj.org-article:9dadfab7b444406a9311a61dbffdb6472021-11-11T01:26:51ZTYLER, a fast method that accurately predicts cyclin-dependent proteins by using computation-based motifs and sequence-derived features10.3934/mbe.20213181551-0018https://doaj.org/article/9dadfab7b444406a9311a61dbffdb6472021-07-01T00:00:00Zhttps://www.aimspress.com/article/doi/10.3934/mbe.2021318?viewType=HTMLhttps://doaj.org/toc/1551-0018Cyclins and related cyclin-dependent kinases play vital roles in regulating the progression in the cell cycle. Understanding the intrinsic mechanisms of cyclins promises knowledge about cell uncontrolled proliferation and prevention of cancer cells. Therefore, accurate recognition of cyclins is important for the investigation of tumor cells and biomedical engineering. This study proposes a novel sequence-based predictor named TYLER (predicT cYcLin-dEpendent pRoteins) for addressing the long challenge problem of predicting cyclin-dependent proteins (CDPs). We use information theory to compute selectively enriched CDP-related motifs and build the motif-based model. For those proteins without sharing enriched motifs, we compute sequence-derived features and construct machine learning-based models. We optimize the weights of two different models to build a more accurate predictor. We estimate these two types of models by using 5-fold cross-validations on the TRAINING dataset. We prove that the combination of two models and optimization of the corresponding weights promises decent and robust results on both TRAINING and independent TEST dataset. The empirical test demonstrates that TYLER is robust predictor and statistically significantly better than current methods. The runtime assessment reveals TYLER is a high-throughput effective method. We use TYLER to make predictions on the human proteome, and use the results to hypothesize CDPs. The latest experimental verified CDPs and GO analysis proves that some of our novel predictions shall be potential CDPs. TYLER is implemented as a public user-friendly web server at http://www.inforstation.com/webservers/TYLER/. We share all data and source code that used in this research at https://github.com/biocomputinglab/TYLER.git.Jian ZhangXingchen LiangFeng Zhou Bo Li Yanling LiAIMS Pressarticlecyclin-dependent proteinssequence motifsmachine learninggo analysisBiotechnologyTP248.13-248.65MathematicsQA1-939ENMathematical Biosciences and Engineering, Vol 18, Iss 5, Pp 6410-6429 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	cyclin-dependent proteins sequence motifs machine learning go analysis Biotechnology TP248.13-248.65 Mathematics QA1-939
spellingShingle	cyclin-dependent proteins sequence motifs machine learning go analysis Biotechnology TP248.13-248.65 Mathematics QA1-939 Jian Zhang Xingchen Liang Feng Zhou Bo Li Yanling Li TYLER, a fast method that accurately predicts cyclin-dependent proteins by using computation-based motifs and sequence-derived features
description	Cyclins and related cyclin-dependent kinases play vital roles in regulating the progression in the cell cycle. Understanding the intrinsic mechanisms of cyclins promises knowledge about cell uncontrolled proliferation and prevention of cancer cells. Therefore, accurate recognition of cyclins is important for the investigation of tumor cells and biomedical engineering. This study proposes a novel sequence-based predictor named TYLER (predicT cYcLin-dEpendent pRoteins) for addressing the long challenge problem of predicting cyclin-dependent proteins (CDPs). We use information theory to compute selectively enriched CDP-related motifs and build the motif-based model. For those proteins without sharing enriched motifs, we compute sequence-derived features and construct machine learning-based models. We optimize the weights of two different models to build a more accurate predictor. We estimate these two types of models by using 5-fold cross-validations on the TRAINING dataset. We prove that the combination of two models and optimization of the corresponding weights promises decent and robust results on both TRAINING and independent TEST dataset. The empirical test demonstrates that TYLER is robust predictor and statistically significantly better than current methods. The runtime assessment reveals TYLER is a high-throughput effective method. We use TYLER to make predictions on the human proteome, and use the results to hypothesize CDPs. The latest experimental verified CDPs and GO analysis proves that some of our novel predictions shall be potential CDPs. TYLER is implemented as a public user-friendly web server at http://www.inforstation.com/webservers/TYLER/. We share all data and source code that used in this research at https://github.com/biocomputinglab/TYLER.git.
format	article
author	Jian Zhang Xingchen Liang Feng Zhou Bo Li Yanling Li
author_facet	Jian Zhang Xingchen Liang Feng Zhou Bo Li Yanling Li
author_sort	Jian Zhang
title	TYLER, a fast method that accurately predicts cyclin-dependent proteins by using computation-based motifs and sequence-derived features
title_short	TYLER, a fast method that accurately predicts cyclin-dependent proteins by using computation-based motifs and sequence-derived features
title_full	TYLER, a fast method that accurately predicts cyclin-dependent proteins by using computation-based motifs and sequence-derived features
title_fullStr	TYLER, a fast method that accurately predicts cyclin-dependent proteins by using computation-based motifs and sequence-derived features
title_full_unstemmed	TYLER, a fast method that accurately predicts cyclin-dependent proteins by using computation-based motifs and sequence-derived features
title_sort	tyler, a fast method that accurately predicts cyclin-dependent proteins by using computation-based motifs and sequence-derived features
publisher	AIMS Press
publishDate	2021
url	https://doaj.org/article/9dadfab7b444406a9311a61dbffdb647
work_keys_str_mv	AT jianzhang tylerafastmethodthataccuratelypredictscyclindependentproteinsbyusingcomputationbasedmotifsandsequencederivedfeatures AT xingchenliang tylerafastmethodthataccuratelypredictscyclindependentproteinsbyusingcomputationbasedmotifsandsequencederivedfeatures AT fengzhou tylerafastmethodthataccuratelypredictscyclindependentproteinsbyusingcomputationbasedmotifsandsequencederivedfeatures AT boli tylerafastmethodthataccuratelypredictscyclindependentproteinsbyusingcomputationbasedmotifsandsequencederivedfeatures AT yanlingli tylerafastmethodthataccuratelypredictscyclindependentproteinsbyusingcomputationbasedmotifsandsequencederivedfeatures
_version_	1718439609031983104

TYLER, a fast method that accurately predicts cyclin-dependent proteins by using computation-based motifs and sequence-derived features

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