Protein flexibility upon ligand binding: Docking predictions and statistical analysis
Abstract
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.
Cite
@article{arxiv.1301.4564,
title = {Protein flexibility upon ligand binding: Docking predictions and statistical analysis},
author = {Rafael Najmanovich},
journal= {arXiv preprint arXiv:1301.4564},
year = {2013}
}
Comments
Thesis for the degree Doctor of Philosophy submitted to the scientific council of the Weizmann Institute of Science. Rehovot, Israel, May 2003. Work performed under the supervision of Prof. Meir Edelman and Dr. Vladimir Sobolev (Plant Sciences Dept.) and Prof. Eytan Domany (Dept. Physics of Complex Systems). The thesis was accepted after anonymous peer-review and the degree conferred in June 2004