0.9 Protein-ligand docking, including flexible receptor-flexible  (Page 8/9)

An example of a program that takes the approach of selecting a few degrees of freedom to represent protein flexibility is the program GOLD [14] . In GOLD, Jones et al. use a genetic algorithm (GA) to dock a flexible ligand to a semi-flexible protein. GAs are an optimization method that derives its behavior from a metaphor of the process of evolution. A solution to a problem is encoded in a chromosome and a fitness score is assigned to it based on the relative merit of the solution. A population of chromosomes then goes through a process of evolution in which only the fittest solutions survive. This program takes into account not only the position and conformation of the ligand but also the hydrogen bonding network in the binding site. This was achieved by encoding orientation information for donor hydrogen atoms and acceptors in the GA chromosome. This type of conformational information is very important because if the starting point for a docking study is a rigid crystallographic structure, the orientations of hydroxyl groups will be undetermined. Being able to model these orientations explicitly removes any bias that might result from positioning hydroxyl groups based upon a known ligand. One limitation of this work is that the binding site still remains essentially rigid because protein conformational changes are limited to a few terminal bonds. This program performed very well for hydrophilic ligands but encountered some difficulties when trying to dock hydrophobic ligands due to the reduced contribution of hydrogen bonding to the binding process.More information about GOLD can be found at the following link: http://www.ccdc.cam.ac.uk/prods/gold/

Multiple receptor structures

One possible way to represent a flexible receptor for drug design applications is the use of multiple static receptor structures (see Figure 8). This concept is supported by the currently accepted model that proteins in solution do not exist in a single minimum energy static conformation but are in fact constantly jumping between low energy conformational substates. In this way the best description for a protein structure is that of a conformational ensemble of slightly different protein structures coexisting in a low energy region of the potential energy surface. Moreover the binding process can be thought of as not exactly an induced fit model as described by Koshland in 1958 [11] but more like a selection of a particular substate from the conformational ensemble that best complements the shape of a specific ligand.

The use of multiple static conformations for docking gives rise to two critical questions. The first question is: How can we obtain a representative subset of the conformational ensemble typical of a given receptor? Currently there exist only a limited set of means to generate the three dimensional structure of macromolecules. The structures can be determined experimentally either from X-ray crystallography or NMR, or generated via computational methods such as Monte Carlo or molecular dynamics simulations. Simulations typically use as a starting point a structure determined by one of the experimental methods. Ideally we would like to use a sampling that provides the most extensive coverage of the structure space. Comparisons done between traditional molecular simulations and experimental techniques [15] , [16] seem to indicate that X-ray crystallography and NMR structures seem to provide better coverage. However this balance can potentially change due to advances in computational methods. Another limitation in choosing data sources is availability. Although experimental data is preferable, the monetary and time cost of determining multiple structures experimentally is significantly higher than obtaining the same amount of data computationally. The second critical question is: What is the best way of combining this large amount of structural information for a docking study? This question also remains open. Current approaches use diverse ways of combining multiple structures.