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Ligand Docking (ligand + docking)
Selected AbstractsComparison of the inward- and outward-open homology models and ligand binding of human P-glycoproteinFEBS JOURNAL, Issue 23 2009Ilza K. Pajeva An homology model of human P-glycoprotein, based on the X-ray structure of the recently resolved mouse P-glycoprotein, is presented. The model corresponds to the inward-facing conformation competent for drug binding. From the model, the residues involved in the protein-binding cavity are identified and compared with those in the outward-facing conformation of human P-glycoprotein developed previously based on the Sav1866 structure. A detailed analysis of the interactions of the cyclic peptides QZ59- RRR and QZ59- SSS is presented in both the X-ray structures of mouse P-glycoprotein and the human P-glycoprotein model generated by ligand docking. The results confirm the functional role of transmembrane domains TM4, TM6, TM10 and TM12 as entrance gates to the protein cavity, and also imply differences in their functions. The analysis of the cavities in both models suggests that the ligands remain bound to the same residues during the transition from the inward- to the outward-facing conformations. The analysis of the ligand,protein interactions in the X-ray complexes shows differences in the residues involved, as well as in the specific interactions performed by the same ligand within the same protein. This observation is supported by docking of the QZ59 ligands into human P-glycoprotein, thus aiding in the understanding of the complex behavior of P-glycoprotein substrates and inhibitors. The results confirm the possibility for multispecific drug interactions of the protein, and are important for elucidating the P-glycoprotein function and ligand interactions. [source] SODOCK: Swarm optimization for highly flexible protein,ligand dockingJOURNAL OF COMPUTATIONAL CHEMISTRY, Issue 2 2007Hung-Ming Chen Abstract Protein,ligand docking can be formulated as a parameter optimization problem associated with an accurate scoring function, which aims to identify the translation, orientation, and conformation of a docked ligand with the lowest energy. The parameter optimization problem for highly flexible ligands with many rotatable bonds is more difficult than that for less flexible ligands using genetic algorithm (GA)-based approaches, due to the large numbers of parameters and high correlations among these parameters. This investigation presents a novel optimization algorithm SODOCK based on particle swarm optimization (PSO) for solving flexible protein,ligand docking problems. To improve efficiency and robustness of PSO, an efficient local search strategy is incorporated into SODOCK. The implementation of SODOCK adopts the environment and energy function of AutoDock 3.05. Computer simulation results reveal that SODOCK is superior to the Lamarckian genetic algorithm (LGA) of AutoDock, in terms of convergence performance, robustness, and obtained energy, especially for highly flexible ligands. The results also reveal that PSO is more suitable than the conventional GA in dealing with flexible docking problems with high correlations among parameters. This investigation also compared SODOCK with four state-of-the-art docking methods, namely GOLD 1.2, DOCK 4.0, FlexX 1.8, and LGA of AutoDock 3.05. SODOCK obtained the smallest RMSD in 19 of 37 cases. The average 2.29 Å of the 37 RMSD values of SODOCK was better than those of other docking programs, which were all above 3.0 Å. © 2006 Wiley Periodicals, Inc. J Comput Chem 28: 612,623, 2007 [source] Modeling and Selection of Flexible Proteins for Structure-Based Drug Design: Backbone and Side Chain Movements in p38 MAPKCHEMMEDCHEM, Issue 2 2008Jyothi Subramanian Abstract Receptor rearrangement upon ligand binding (induced fit) is a major stumbling block in docking and virtual screening. Even though numerous studies have stressed the importance of including protein flexibility in ligand docking, currently available methods provide only a partial solution to the problem. Most of these methods, being computer intensive, are often impractical to use in actual drug discovery settings. We had earlier shown that ligand-induced receptor side-chain conformational changes could be modeled statistically using data on known receptor,ligand complexes. In this paper, we show that a similar approach can be used to model more complex changes like backbone flips and loop movements. We have used p38 MAPK as a test case and have shown that a few simple structural features of ligands are sufficient to predict the induced variation in receptor conformations. Rigorous validation, both by internal resampling methods and on an external test set, corroborates this finding and demonstrates the robustness of the models. We have also compared our results with those from an earlier molecular dynamics simulation study on DFG loop conformations of p38 MAPK, and found that the results matched in the two cases. Our statistical approach enables one to predict the final ligand-induced conformation of the active site of a protein, based on a few ligand properties, prior to docking the ligand. We can do this without having to trace the step-by-step process by which this state is arrived at (as in molecular dynamics simulations), thereby drastically reducing computational effort. [source] | ||||||||||