  The transcription of DNA into mRNA is initiated and aided by a number of
transcription factors (TFs), proteins with DNA-binding regions that attach
themselves to binding sites in the DNA (transcription factor binding sites,
TFBSs). As it has become apparent that both TFs and TFBSs are highly variable,
tools are needed to quantify the strength of the interaction resulting from a
certain TF variant binding to a certain TFBS. We used a simple way to predict
interactions between protein and DNA: given experimental cases from the
literature where the interaction strength between two sequences has been
quantified, we created training vectors for rule induction by regarding each
amino acid and nucleotide position as a single feature in the example vector.
The resulting interaction strength was used as the target class or value. These
training vectors were then used to build a rule induction model. We applied the
rule induction method to three protein families - transcription factors from
the NF-kappaB, the early-growth-response (EGR), and the paired domain groups -
and their corresponding DNA targets. These three prediction problems increase
in complexity with regard to model building, and thus gave us a good range for
validation. The main focus of the study was the most complex problem: paired
domain-DNA binding. For this problem, we also found sequence/binding strength
correlations using measures from information theory. Prediction results were
uniformly good: the rule induction approach was able to correctly order all of
nine unseen examples for one NF-kappaB protein, achieved a correlation
coefficient of 0.52 on unseen (and noisy) examples in the EGR case, and reached
a classification accuracy of 69.7% for the Paired domain as evaluated by
cross-validation.
