Abstract
BackgroundAtomic Solvation Parameters (ASP) model has been proven to be a very successful method of calculating the binding free energy of protein complexes. This suggests that incorporating it into docking algorithms should improve the accuracy of prediction. In this paper we propose an FFT-based algorithm to calculate ASP scores of protein complexes and develop an ASP-based protein-protein docking method (ASPDock).ResultsThe ASPDock is first tested on the 21 complexes whose binding free energies have been determined experimentally. The results show that the calculated ASP scores have stronger correlation (r ≈ 0.69) with the binding free energies than the pure shape complementarity scores (r ≈ 0.48). The ASPDock is further tested on a large dataset, the benchmark 3.0, which contain 124 complexes and also shows better performance than pure shape complementarity method in docking prediction. Comparisons with other state-of-the-art docking algorithms showed that ASP score indeed gives higher success rate than the pure shape complementarity score of FTDock but lower success rate than Zdock3.0. We also developed a softly restricting method to add the information of predicted binding sites into our docking algorithm. The ASP-based docking method performed well in CAPRI rounds 18 and 19.ConclusionsASP may be more accurate and physical than the pure shape complementarity in describing the feature of protein docking.
Highlights
Atomic Solvation Parameters (ASP) model has been proven to be a very successful method of calculating the binding free energy of protein complexes
ASP model assumes that the solvation energy of an atom or an atom-group is proportional to its solvent accessible surface area (ASA)
In order to meet the speed of the Fast Fourier Transform (FFT)-based docking method, we propose an approximate FFT method to calculate the ASA and so ASP values
Summary
Atomic Solvation Parameters (ASP) model has been proven to be a very successful method of calculating the binding free energy of protein complexes This suggests that incorporating it into docking algorithms should improve the accuracy of prediction. The docking algorithms based on Fast Fourier Transform (FFT) are widely used and have made great success [1] because they can search 6D space in a very fast way. These programs include MolFit [2], 3D-Dock [3,4,5], GRAMM [6], ZDock [7,8], DOT [9], BiGGER [10] and HEX [11]. Besides the FFT-based algorithms, there are other well-known docking algorithms that
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