Structure-Based Identification of Small Molecule Binding Sites Using a Free Energy Model

We separately have shown that the maximal druglike affinity of a given binding site on a protein can be
calculated on the basis of the binding-site structure alone by using a desolvation-based free energy model
along with the notion that druglike ligands fall into certain physiochemical property ranges. Here, we present
an approach where we reformulate the calculated druggability affinity as an additive free energy to facilitate
the searching of whole protein surfaces for druglike binding sites. The highest-scoring patches in many
cases represent known ligand-binding sites for druggable targets, but not for difficult targets. This approach
differs from other approaches in that it does not simply identify pockets with the greatest volume but instead
identifies pockets that are likely to be amenable to druglike small-molecule binding. Combining the method
with a functional residue prediction method called SCA (statistical coupling analysis) results in the prediction
of potentially druggable allosteric binding sites on p38α kinase.