Predicting transcription factor affinities to DNA from a biophysical model.
BACKGROUND
Theoretical efforts to understand the regulation of gene expression are traditionally centered around the identification of transcription factor binding sites at specific DNA positions. More recently these efforts have been supplemented by experimental data for relative binding affinities of proteins to longer intergenic sequences. The question arises to what extent these two approaches converge. In this paper, we adopt a physical binding model to predict the relative binding affinity of a transcription factor for a given sequence.


RESULTS
We find that a significant fraction of genome-wide binding data in yeast can be accounted for by simple count matrices and a physical model with only two parameters. We demonstrate that our approach is both conceptually and practically more powerful than traditional methods, which require selection of a cutoff. Our analysis yields biologically meaningful parameters, suitable for predicting relative binding affinities in the absence of experimental binding data.


BACKGROUND
The C source code for our TRAP program is freely available for non-commercial use at http://www.molgen.mpg.de/~manke/papers/TFaffinities/
Roider
Helge G
HG
Max-Planck-Institute for Molecular Genetics Ihnestrasse 73, 14195 Berlin, Germany.
Kanhere
Aditi
A
Manke
Thomas
T
Vingron
Martin
M
D000465:Algorithms; D001665:Binding Sites; D001703:Biophysics / Q000379:methods; D003198:Computer Simulation; D004247:DNA / Q000737:chemistry* / Q000235:genetics*; D008954:Models, Biological; D008956:Models, Chemical; D011485:Protein Binding; D017422:Sequence Analysis, DNA / Q000379:methods*; D014157:Transcription Factors / Q000737:chemistry* / Q000235:genetics*; D014158:Transcription, Genetic / Q000235:genetics
D014157:Transcription Factors; D004247:DNA