Application of graph-based analysis of genomic sequence context for characterization of drug targets.

We show that analyzing individual genes of target proteins in terms of multiplicities of possible realizations of position-dependent thermodynamic states of their DNA molecules constitutes a new bioinformatics paradigm. It provides information that is unique and complementary to results of existing methods of sequence analysis. Using this graph-theory based approach, we developed informative and computationally immensely tractable tool to gain insight into intricate details of properties of drug targets. We present validation of our method by processing seventeen target genes for approved drugs in complexes with known 3D structures. Our novel method can identify coding segments that form important parts of the active and binding sites (individual significance estimated by p-values < or = 0.001). We discuss limitations and advantages of the methodology. Because of its generality, this approach can be used for novel quantitative target-drug assessment and it is applicable to analysis of coding as well as non-coding regions. We also propose the application of this method in quantitative sequence-activity models.