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ABSTRACT: A novel and robust automated docking method that predicts the bound conformations of flexible ligands to macromolecular targets has been developed and tested, in combination with a new scoring function that estimates the free energy change upon binding. Interestingly, this method applies a Lamarckian model of genetics, in which environmental adaptations of an individual's phenotype are reverse transcribed into its genotype and become heritable traits (sic). We consider three search methods, Monte Carlo simulated annealing, a traditional genetic algorithm, and the Lamarckian genetic algorithm, and compare their performance in dockings of seven protein-ligand test systems having known three dimensional structure. We show that both the traditional and Lamarckian genetic algorithms can handle ligands with more degrees of freedom than the simulated annealing method used in earlier versions of AutoDock, and that the Lamarckian genetic algorithm is the most efficient, most reliable and most successful of the three. The empirical free energy function was calibrated using a set of 30 structurally-known protein-ligand complexes with experimentally-determined binding constants. Linear regression analysis of the observed binding constants in terms of a wide variety of structure-derived molecular properties was performed. The final model had a residual standard error of 9.11 kJ mol-1 (2.177 kcal mol-1) and was chosen as the new energy function. The new search methods and empirical free energy function are available in AutoDock version 3.0.
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