A Fast Fourier Transform correlation approach to protein mapping: Development and applications

Computational protein mapping moves molecular probes - small organic molecules containing various functional groups - around the protein surface, finds favorable positions using empirical free energy functions, clusters the conformations, and ranks the clusters on the basis of the average free energy. Mapping is important for finding "hot spots", regions of the protein surface that are major contributors to the binding free energy making them prime targets in drug design. The FTMAP protein mapping algorithm performs a global search of the entire protein surface. The search is based on the extremely efficient Fast Fourier Transform (FFT) correlation approach which can sample billions of probe positions on dense translational and rotational grids. The novelty of the FTMAP algorithm is that it incorporates a detailed energy expression resulting in very accurate identification of low energy probe clusters. Overlapping clusters of different probes are defined as consensus sites. The largest consensus sites are generally located at the most important subsites of protein binding sites, and the nearby smaller consensus sites identify other important subsites. Mapping results are presented for a variety of proteins. The X-ray structures of porcine pancreatic elastase and thermolysin have been solved in aqueous solutions of several organic solvents, and FTMAP is shown to reproduce the experimental data with remarkable accuracy. The mapping of renin, a long standing pharmaceutical target for the treatment of hypertension, yields consensus sites that trace out the shape of the first approved renin inhibitor, aliskiren. Applying FTMAP to the influenza virus M2 proton channel identifies the potential binding regions for small molecules and predicts the correct binding pose for the drug amantadine. FTMAP is also shown to capture the critical binding "hot spots" in the interface regions of drug targets that participate in protein-protein interactions, including interleukin-2, Bcl-xL, MDM2, HPV 11 E2, ZipA, and TNF-alpha. For all these targets, the high ranked consensus sites identify the "hot spots" that can potentially bind small molecular inhibitors. The development of a new interaction potential which improves the accuracy of both mapping and docking results is also described.