Research And Application On Protein-protein Docking Algorithms

Most proteins interact with other proteins or molecules to perform their biological functions. The details of protein-protein interactions need 3D structures of complexes. However, it is relatively difficult to measure the structures of protein complexes experimentally and thus the number of available complex structures is still limited in comparison with monomer protein structures. Therefore, it is helpful to use computational approaches to predict structures of protein complexes. Shape complementary is the most important scoring function for many protein-protein doking algorithms, thus it will be helpful for many doking algorithms if the shape complementary model could be improved. Correct biological information helps to predicted structures of complexes, however, incorrect information will lead to a failure for predition, thus the unreliable biological information is difficult to utilize.We have developed a new Fast Fourier Transform (FFT) docking algorithm:ASPDock,which is based on Atomic Solvation Parameters (ASP) model instead of pure shape complementary. ASP model in FFT method is similar to shape complementary in practical calculation but illustrates the binding mechanism from a clearer view of physics. Shape complementary, which plays the most important role in almost all the docking algorithms, mainly focuses on the geometric feature of protein complexes. ASP model has made great success in calculations of binding free energy of proteins. We used ASPDock to calculate ASP scores of protein complexes and test it on 21 complexes with the experimentally determined binding free energies. The results show that the ASP score has a much stronger correlation (r≈0.69) with binding energy than shape complementary score (r≈0.48) on 1.0? grid space. This test indicates that, comparing with pure shape complementary, the ASP model is more reliable for docking. ASPDock has also been tested on a large dataset, benchmark3.0, which contain 124 complexes and it also performs better than that based on pure shape complementary.Reliable biological information helps to predict protein-protein complex structures. Several methods have been proposed to predict protein binding site theoretically. However, theoretical predicted binding sites is usually unreliable. We developed a softly restricting method (SRM) to use the unreliable binding site information. Based on ASPDock, SRM enhances the weight of the expected binding site residues and softly constrain receptor and ligand to bind around the expected binding site. This SRM algorithm is proved to be sensitive to correct binding information and insensitive to wrong binding site information, which should decrease the risk of theoretical binding site information. We performed SRM on 99 complexes. It shows that using theoretical predicted binding sites information, SRM enhanced the hit count number and success rate.ASPDock and SRM have been proved to be successful in CAPRI round 18 and 19. Combining ASP docking method and softly restricting method, we got high quality hits for CAPRI T40 and T41, and the best LRMSD are 2.35 and 1.41?, respectively.