The identification of novel binding-site conformations can greatly assist the progress of structure-based ligand design projects. Diverse pocket shapes drive medicinal chemistry to explore a broader chemical space and thus present additional opportunities to overcome key drug discovery issues such as potency, selectivity, toxicity, and pharmacokinetics.
We report a ne
w automated approach to diverse pocket selection, PocketAnalyzer
PCA,
which applies principal component analysis and clustering to the output of a grid-based pocket detection algorithm. Since the approach
works directly
with pocket shape descriptors, it is free from some of the problems hampering methods that are based on proxy shape descriptors, e.g. a set of atomic positional coordinates. The approach is technically straightfor
ward and allo
ws simultaneous analysis of mutants, isoforms, and protein structures derived from multiple sources
with different residue numbering schemes. The PocketAnalyzer
PCA approach is illustrated by the compilation of diverse sets of pocket shapes for aldose reductase and viral neuraminidase. In both cases this allo
ws identification of novel computationally derived binding-site conformations that are yet to be observed crystallographically. Indeed, kno
wn inhibitors capable of exploiting these novel binding-site conformations are subsequently identified, thereby demonstrating the utility of PocketAnalyzer
PCA for rationalizing and improving the understanding of the molecular basis of protein鈥搇igand interaction and bioactivity. A Python program implementing the PocketAnalyzer
PCA approach is available for do
wnload under an open-source license (
http://sourceforge.net/projects/papca/ or wnloads" class="extLink">http://cpclab.uni-duesseldorf.de/downloads).