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Nucleic Acids Research 2005 33(17):e141; doi:10.1093/nar/gni139
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Published online 25 September 2005

© The Author 2005. Published by Oxford University Press. All rights reserved
The online version of this article has been published under an open access model. Users are entitled to use, reproduce, disseminate, or display the open access version of this article for non-commercial purposes provided that: the original authorship is properly and fully attributed; the Journal and Oxford University Press are attributed as the original place of publication with the correct citation details given; if an article is subsequently reproduced or disseminated not in its entirety but only in part or as a derivative work this must be clearly indicated. For commercial re-use, please contact journals.permissions{at}oxfordjournals.org

Methods Online

Combining SELEX with quantitative assays to rapidly obtain accurate models of protein–DNA interactions

Jiajian Liu and Gary D. Stormo*

Department of Genetics, Washington University School of Medicine 660 S Euclid, Box 8232, St Louis, MO 63110, USA

*To whom correspondence should be addressed. Tel: +1 314 747 5534; Fax: +1 314 362 2156; Email: stormo{at}genetics.wustl.edu

Received March 23, 2005. Revised April 25, 2005. Accepted August 26, 2005.

Models for the specificity of DNA-binding transcription factors are often based on small amounts of qualitative data and therefore have limited accuracy. In this study we demonstrate a simple and efficient method of affinity chromatography-SELEX followed by a quantitative binding (QuMFRA) assay to rapidly collect the data necessary for more accurate models. Using the zinc finger protein EGR as an e.g. we show that many bindings sites can be obtained efficiently with affinity chromatography-SELEX, but those sequences alone provide a weight matrix model with limited accuracy. Using a QuMFRA assay to determine the quantitative relative affinity for only a subset of the sequences obtained by SELEX leads to a much more accurate model. Application of this method to variants of a transcription factor would allow us to generate a large collection of quantitative data for modeling protein–DNA interactions that could facilitate the determination of recognition codes for different transcription factor families.


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