Impact of Protein Conformational Diversity on Structure-Based Prediction of Druggability

We have investigated the impact of conformational diversity on the prediction of druggability to see whether structural ensembles of protein targets offer more precise insights. The study is based on binding hot spot analyses performed on 37 binding sites from 33 proteins adapted from well-known druggability benchmark sets. Binding hot spots are regions on proteins that significantly influence binding free energy and hence can be used to predict druggability. Using fast Fourier transform-based algorithms, small organic probe molecules are docked to pinpoint these hot spots with denser probe clusters indicating higher binding affinity. The binding sites are mapped across the structural ensemble of protein crystal structures from the Protein Data Bank with 90% sequence identity. Druggability is analyzed according to the hot spot strength, connectivity, compactness, and maximum dimension. Our results show that a protein's druggability depends on consensus across the structural ensemble, requiring approximately 70% of structures to have a strong hot spot at the binding site of interest and approximately 50% of structures to meet all three druggability criteria. The ability to occasionally access a rare druggable conformation is not sufficient for a protein to be druggable in practice. Hot spot strength proves to be crucial for druggability. However, failing to meet the secondary druggability criteria of connectivity/compactness and maximum dimension for 30% or more structures indicates the inhomogeneity of the ensemble and signals the need for a more detailed analysis of mapping results. Such inhomogeneity may occur due to conformational differences caused by the binding of charged ligands or by the substantial flexibility of the binding site. The hot spots can be determined by the public server FTMove, and the codes for processing the server output for druggability analysis are available on GitHub.