The Poisson Index: a new probabilistic model for protein ligand binding site similarity.

  • Davies J
  • Jackson R
  • Mardia K
 et al. 
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MOTIVATION: 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 that 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.

Author-supplied keywords

  • Algorithms; Amino Acid Sequence; Binding Sites; Co
  • Amino Acid
  • Chemical; Models
  • Protein
  • Statistical; Molecular Sequence Data; Poisson Dis
  • chemistry; Sequence Analysis
  • methods; Proteins
  • methods; Sequence Homology

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  • J R Davies

  • R M Jackson

  • K V Mardia

  • C C Taylor

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