  The nucleotide sequence representation of DNA can be inadequate for resolving
protein-DNA binding sites and regulatory substrates, such as those involved in
gene expression and horizontal gene transfer. Considering that sequence-like
representations are algorithmically very useful, here we fused over 60
currently available DNA physicochemical and conformational variables into
compact structural representations that can encode single DNA binding sites to
whole regulatory regions. We find that the main structural components reflect
key properties of protein-DNA interactions and can be condensed to the amount
of information found in a single nucleotide position. The most accurate
structural representations compress functional DNA sequence variants by 30% to
50%, as each instance encodes from tens to thousands of sequences. We show that
a structural distance function discriminates among groups of DNA substrates
more accurately than nucleotide sequence-based metrics. As this opens up a
variety of implementation possibilities, we develop and test a distance-based
alignment algorithm, demonstrating the potential of using the structural
representations to enhance sequence-based algorithms. Due to the bias of most
current bioinformatic methods to nucleotide sequence representations, it is
possible that considerable performance increases might still be achievable with
such solutions.
