Large-Scale Mining for Similar Protein Binding Pockets: With RAPMAD Retrieval on the Fly Becomes Real

Determination of structural similarities
between protein binding
pockets is an important challenge in in silico drug
design. It can help to understand selectivity considerations, predict
unexpected ligand cross-reactivity, and support the putative annotation
of function to orphan proteins. To this end, Cavbase was developed
as a tool for the automated detection, storage, and classification
of putative protein binding sites. In this context, binding sites
are characterized as sets of pseudocenters, which denote surface-exposed
physicochemical properties, and can be used to enable mutual binding
site comparisons. However, these comparisons tend to be computationally
very demanding and often lead to very slow computations of the similarity
measures. In this study, we propose RAPMAD (RApid Pocket MAtching
using Distances), a new evaluation formalism for Cavbase entries that
allows for ultrafast similarity comparisons. Protein binding sites
are represented by sets of distance histograms that are both generated
and compared with linear complexity. Attaining a speed of more than
20 000 comparisons per second, screenings across large data sets and
even entire databases become easily feasible. We demonstrate the discriminative
power and the short runtime by performing several classification and
retrieval experiments. RAPMAD attains better success rates than the
comparison formalism originally implemented into Cavbase or several
alternative approaches developed in recent time, while requiring only
a fraction of their runtime. The pratical use of our method is finally
proven by a successful prospective virtual screening study that aims
for the identification of novel inhibitors of the NMDA receptor.