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, reliable, and 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 y1 y1 .

/pdf/automated-docking-using-a-lamarckian-genetic-algorithm-and-2t6y95r7fm.pdf

Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function

https://users.cs.duke.edu/~brd/Teaching/Bio/asmb/current/Papers/Lit4Docking/Gold_dock.pdf

Development and validation of a genetic algorithm for flexible docking.

Drug-like properties and the causes of poor solubility and poor permeability

A method is presented for analytically calculating a smooth, three-dimensional contour about a molecule. The molecular surface envelope may be drawn on either color raster computer displays or real-time vector computer graphics systems. Molecular areas and volumes may be computed analytically from this surface representation. Unlike most previous computer graphics representations of molecules, which imitate wire models or space-filling plastic spheres, this surface shows only the atoms that are accessible to solvent. This analytical method extends the earlier dot surface numerical algorithm, which has been applied in enzymology, rational drug design, immunology, and understanding DNA base sequence recognition.

/pdf/solvent-accessible-surfaces-of-proteins-and-nucleic-acids-3euqtz86k0.pdf

Solvent-Accessible Surfaces of Proteins and Nucleic Acids

A Fast Flexible Docking Method using an Incremental Construction Algorithm

Screening synthetic combinatorial libraries, such as mixtures of oligo(N-substituted)glycines, facilitates rapid drug lead discovery and optimization by vastly increasing the number of candidate molecules made and tested. Discovery efficiency and productivity can be further improved by using experimental design to maximize molecular diversity for a given library size or to bias the library with key features for a specific receptor. We describe new methods to quantify molecular diversity using descriptors that characterize lipophilicity, shape and branching, chemical functionality, and specific binding features. Experimental design methods select sets of side chains that are diverse in these properties, and "flower plots" allow the diversity to be graphically compared. We also quantify the overall diversity accessible to different families of combinatorial chemistry.

Measuring Diversity: Experimental Design of Combinatorial Libraries for Drug Discovery

Color-coded computer graphics representations of the electrostatic potentials of trypsin, trypsin-inhibitor, prealbumin and its thyroxine complex, fragments of double-helical DNA, and a netropsin--DNA complex illustrate the electrostatic and topographic complementarity in macromolecule-ligand interactions. This approach is powerful in revealing intermolecular specificity and shows promise of having predictive value in drug design.

https://www.pnas.org/content/pnas/79/12/3754.full.pdf

Electrostatic potential molecular surfaces

The biological activities of proteins depend on specific molecular recognition and binding. Computational methods for predicting binding modes can facilitate the discovery and design of ligands and yield information on the factors governing complementarity. The DOCK suite of programs has been applied to several systems; here, the degree of orientational sampling required to reproduce and identify known binding modes, with and without rigid-body energy minimization, is investigated for four complexes. There is a tradeoff between sampling and minimization. The known binding modes can be identified with intensive sampling alone (10,000 to 20,000 orientations generated per system) or with moderate sampling combined with minimization. Optimization improves energies significantly, particularly when steric clashes are present, and brings many orientations closer to the experimentally observed position. Whether or not minimization is performed, however, sampling must be sufficient to find at least one structure in the vicinity of the presumed true binding mode. Hybrid approaches combining docking and minimization are promising and will become more viable with the use of faster algorithms and the judicious selection of fewer orientations for minimization.

Orientational sampling and rigid-body minimization in molecular docking.

Many approaches have been developed for solving the ‘docking problem’: Predict the structure and binding free energy of a ligand-receptor complex given only the structures of the free ligand and receptor. We review major approaches for docking small-molecule ligands to receptors and focus on the successes and limitations of their application to drug design.

Jeffrey M. Blaney

Papers

Measuring Diversity: Experimental Design of Combinatorial Libraries for Drug Discovery

Electrostatic potential molecular surfaces

Orientational sampling and rigid-body minimization in molecular docking.

A good ligand is hard to find: Automated docking methods

Structure-activity relationships of dihydrofolated reductase inhibitors