ABSTRACT The large scale comparison of protein-ligand binding sites is problematic, in that measures of structural similarity are difficult to quantify and are not easily understood in terms of statistical similarity that can ultimately be related to structure and function. We present a binding site matching score the Poisson Index (PI) based upon a well defined statistical model. PI requires only the number of matching atoms between two sites and the size of the two sites -the same information used by the Tanimoto Index (TI), a comparable and widely used measure for molecular similarity. We apply PI and TI to a previously automatically extracted set of binding sites to determine the robustness and usefulness of both scores. Results: We found that PI outperforms TI; moreover site similarity is poorly defined for TI at values around the 99.5% confidence level for which PI is well defined. A difference map at this confidence level shows that PI gives much more meaningful information than TI. We show individual examples where TI fails to distinguish either a false or a true site paring in contrast to PI, which performs much better. TI cannot handle large or small sites very well, or the comparison of large and small sites, in contrast to PI which is shown to be much more robust. Despite the difficulty of determining a biological "ground truth" for binding site similarity we conclude that PI is a suitable measure of binding site similarity and could form the basis for a binding site classification scheme comparable to existing protein domain classification schema. Availability: PI is implemented in SitesBase www.modelling.leeds.ac.uk/sb/. Contact: r.m.jackson@leeds.ac.uk
In REF , the authors introduced a matching score for binding sites based on a probabilistic model and compare their metric with the Tanimoto Index.
The Poisson Index: a new probabilistic model for protein-ligand binding site similarity
Bioinformatics
Bioinformatics
Computer Science Mathematics Medicine