Protein Alpha Shape (PAS) Dock: A new gaussian-based score function suitable for docking in homology modelled protein structures

Abstract

Protein Alpha Shape (PAS) Dock is a new empirical score function suitable for virtual library screening using homology modelled protein structures. Here, the score function is used in combination with the geometry search method Tabu search. A description of the protein binding site is generated using gaussian property fields like in Protein Alpha Shape Similarity Analysis (PASSA). Gaussian property fields are also used to describe the ligand properties. The overlap between the receptor and ligand hydrophilicity and lipophilicity fields is maximised, while minimising steric clashes. Gaussian functions introduce a smoothing of the property fields. This makes the score function robust against small structural variations, and therefore suitable for use with homology models. This also makes it less critical to include protein flexibility in the docking calculations. We use a fast and simplified version of the score function in the geometry search, while a more detailed version is used for the final prediction of the binding free energies. This use of a two-level scoring makes PAS-Dock computationally efficient, and well suited for virtual screening. The PAS-Dock score function is trained on 218 X-ray structures of protein– ligand complexes with experimental binding affinities. The performance of PAS-Dock is compared to two other docking methods, AutoDock and MOE-Dock, with respect to both accuracy and computational efficiency. According to this study, PAS-Dock is more computationally efficient than both AutoDock and MOE-Dock, and gives a better prediction of the free energies of binding. PAS-Dock is also more robust against structural variations than AutoDock.