  Side chain flexibility is an important factor in ligand binding. In order to
determine the extent to which side chain flexibility is involved in ligand
binding, a knowledge-based approach was taken. A database composed of examples
of protein structures in the presence or absence of a given ligand is used to
analyze which side chains undergo side chain conformational changes. Such an
analysis has determined that up to 40% of binding site do not present side
chain conformational changes. A total of three residues undergoing side chain
conformational changes encompass approximately 85% of the binding sites
studied. When analyzing the propensities of different amino acids to undergo
side chain conformational changes we find that there are considerable
differences between different amino acids. A support vector machine learning
approach was used to create a classifier system utilizing information about the
solvent accessible area as well as flexibility scale value of each specific
side chain to be predicted together with its neighboring side chains. An
accuracy level of 70% is reached using this approach. The fact that a small
number of residues undergo side chain conformational changes in the majority of
binding sites makes it feasible to introduce side chain flexibility in docking
simulations. An algorithm has been developed for introducing side chain
flexibility utilizing a hybrid genetic-algorithm/exhaustive-search procedure
and a surface complementarity based scoring function. This approach is
implemented in the software tool FlexAID. FlexAID utilizes a rotamer library to
create alternative conformations for a list of residues that are exhaustively
searched during the docking simulation. The performance of FlexAID in rigid
local as well as global simulations falls in the 70-80% range for both local
and global simulations.
