CiteULike: dpollard affinity

Use of an evolutionary model to provide evidence for a wide heterogeneity of required affinities between transcription factors and their binding sites in yeast.

RW Lusk — 2008-02-01T06:17:57-00:00

Pac Symp Biocomput (2008), pp. 489-500.

The identification of transcription factor binding sites commonly relies on the interpretation of scores generated by a position weight matrix. These scores are presumed to reflect on the affinity of the transcription factor for the bound sequence. In almost all applications, a cutoff score is chosen to distinguish between functional and non-functional binding sites. This cutoff is generally based on statistical rather than biological criteria. Furthermore, given the variety of transcription factors, it is unlikely that the use of a common statistical threshold for all transcription factors is appropriate. In order to incorporate biological information into the choice of cutoff score, we developed a simple evolutionary model that assumes that transcription factor binding sites evolve to maintain an affinity greater than some factor-specific threshold. We then compared patterns of substitution in binding sites predicted by this model at different thresholds to patterns of substitution observed at sites bound in vivo by transcription factors in S. cerevisiae. Assuming that the cutoff value that gives the best fit between the observed and predicted values will optimally distinguish functional and non-functional sites, we discovered substantial heterogeneity for appropriate cutoff values among factors. While commonly used thresholds seem appropriate for many factors, some factors appear to function at cutoffs satisfied commonly in the genome. This evidence was corroborated by local patterns of rate variation for examples of stringent and lenient p-value cutoffs. Our analysis further highlights the necessity of taking a factor-specific approach to binding site identification.

Structure-based prediction of C2H2 zinc-finger binding specificity: sensitivity to docking geometry.

Trevor W Siggers — 2007-02-09T01:33:17-00:00

Nucleic Acids Res (30 January 2007)

Predicting the binding specificity of transcription factors is a critical step in the characterization and computational identification and of cis-regulatory elements in genomic sequences. Here we use protein-DNA structures to predict binding specificity and consider the possibility of predicting position weight matrices (PWM) for an entire protein family based on the structures of just a few family members. A particular focus is the sensitivity of prediction accuracy to the docking geometry of the structure used. We investigate this issue with the goal of determining how similar two docking geometries must be for binding specificity predictions to be accurate. Docking similarity is quantified using our recently described interface alignment score (IAS). Using a molecular-mechanics force field, we predict high-affinity nucleotide sequences that bind to the second zinc-finger (ZF) domain from the Zif268 protein, using different C(2)H(2) ZF domains as structural templates. We identify a strong relationship between IAS values and prediction accuracy, and define a range of IAS values for which accurate structure-based predictions of binding specificity is to be expected. The implication of our results for large-scale, structure-based prediction of PWMs is discussed.

Dorsal gradient networks in the Drosophila embryo.

A Stathopoulos — 2007-02-05T20:05:06-00:00

Dev Biol, Vol. 246, No. 1. (1 June 2002), pp. 57-67.

Here, we describe one of the major maternal regulatory gradients, Dorsal, and threshold outputs of gene expression that result from the graded distribution of this transcription factor. The analysis of a large number of authentic and synthetic target genes suggests that the Dorsal gradient directly specifies at least four, and possibly as many as seven, different thresholds of gene activity and tissue differentiation. These thresholds initiate the differentiation of the three primary embryonic tissues: the mesoderm, neurogenic ectoderm, and dorsal ectoderm. Moreover, primary readouts of the Dorsal gradient create asymmetries that subdivide each tissue into multiple cell types during gastrulation. Dorsal patterning thresholds represent the culmination of one of the most complete gene regulation network known in development, which begins with the asymmetric positioning of the oocyte nucleus within the egg chamber and leads to the localized activation of the Toll-Dorsal signaling pathway in ventral regions of the early embryo.