Inferring binding energies from selected binding sites.

We employ a biophysical model that accounts for the non-linear relationship between binding energy and the statistics of selected binding sites. The model includes the chemical potential of the transcription factor, non-specific binding affinity of the protein for DNA, as well as sequence-specific p...

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Autores principales:	Yue Zhao, David Granas, Gary D Stormo
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2009
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/c4a8e23f9168473e837a5d2b31dd130b
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spelling	oai:doaj.org-article:c4a8e23f9168473e837a5d2b31dd130b2021-11-25T05:42:48ZInferring binding energies from selected binding sites.1553-734X1553-735810.1371/journal.pcbi.1000590https://doaj.org/article/c4a8e23f9168473e837a5d2b31dd130b2009-12-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/19997485/pdf/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358We employ a biophysical model that accounts for the non-linear relationship between binding energy and the statistics of selected binding sites. The model includes the chemical potential of the transcription factor, non-specific binding affinity of the protein for DNA, as well as sequence-specific parameters that may include non-independent contributions of bases to the interaction. We obtain maximum likelihood estimates for all of the parameters and compare the results to standard probabilistic methods of parameter estimation. On simulated data, where the true energy model is known and samples are generated with a variety of parameter values, we show that our method returns much more accurate estimates of the true parameters and much better predictions of the selected binding site distributions. We also introduce a new high-throughput SELEX (HT-SELEX) procedure to determine the binding specificity of a transcription factor in which the initial randomized library and the selected sites are sequenced with next generation methods that return hundreds of thousands of sites. We show that after a single round of selection our method can estimate binding parameters that give very good fits to the selected site distributions, much better than standard motif identification algorithms.Yue ZhaoDavid GranasGary D StormoPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 5, Iss 12, p e1000590 (2009)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Yue Zhao David Granas Gary D Stormo Inferring binding energies from selected binding sites.
description	We employ a biophysical model that accounts for the non-linear relationship between binding energy and the statistics of selected binding sites. The model includes the chemical potential of the transcription factor, non-specific binding affinity of the protein for DNA, as well as sequence-specific parameters that may include non-independent contributions of bases to the interaction. We obtain maximum likelihood estimates for all of the parameters and compare the results to standard probabilistic methods of parameter estimation. On simulated data, where the true energy model is known and samples are generated with a variety of parameter values, we show that our method returns much more accurate estimates of the true parameters and much better predictions of the selected binding site distributions. We also introduce a new high-throughput SELEX (HT-SELEX) procedure to determine the binding specificity of a transcription factor in which the initial randomized library and the selected sites are sequenced with next generation methods that return hundreds of thousands of sites. We show that after a single round of selection our method can estimate binding parameters that give very good fits to the selected site distributions, much better than standard motif identification algorithms.
format	article
author	Yue Zhao David Granas Gary D Stormo
author_facet	Yue Zhao David Granas Gary D Stormo
author_sort	Yue Zhao
title	Inferring binding energies from selected binding sites.
title_short	Inferring binding energies from selected binding sites.
title_full	Inferring binding energies from selected binding sites.
title_fullStr	Inferring binding energies from selected binding sites.
title_full_unstemmed	Inferring binding energies from selected binding sites.
title_sort	inferring binding energies from selected binding sites.
publisher	Public Library of Science (PLoS)
publishDate	2009
url	https://doaj.org/article/c4a8e23f9168473e837a5d2b31dd130b
work_keys_str_mv	AT yuezhao inferringbindingenergiesfromselectedbindingsites AT davidgranas inferringbindingenergiesfromselectedbindingsites AT garydstormo inferringbindingenergiesfromselectedbindingsites
_version_	1718414482435211264

Inferring binding energies from selected binding sites.

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