Predicting Side-chain Packings in Proteins Using Evolution Strategies

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Project Summary

The application of evolutionary computation (EC) techniques to various optimization problems in molecular biology has become increasingly common. Of considerable interest is the prediction of side-chain conformations in proteins. Because of the high density of side-chains in the protein interior, side-chain conformations are determined by packing considerations, which involve steric effects with other side-chains. The solution is difficult because of the large number of combinations possible. Nevertheless, an efficient solution to this problem is essential for most ab initio protein structure prediction procedures. It will also aid in determining permissible mutation sites for increased protein stability and for a large scale protein re-design.

Every optimization problem has a search space of all possible solutions. Each unique point in this search space represents a unique solution; optimization algorithms are therefore search algorithms. EC algorithms are probabilistic optimization algorithms which conduct searchs using the principles of Darwinian evolution. For the side-chain packing problem (SCPP), each individual in a population represents a distinct side-chain conformation. During each generation (iteration of the EC algorithm), stochastic reproduction operators perturb existing conformations to produce new conformations. The optimization problem provides a measure of the solution's quality which is indicated by the individual's fitness. (In SCPP high fitness may represent a minimal van der Waals interaction energy.) Highly fit individuals will tend to survive and reproduce in future generations. Studies have shown that the EC algorithms are superior to Monte Carlo methods in molecular biology optimization problems.

Genetic Algorithms (GAs) have been the predominant EC algorithm applied to SCPP. Yet, there are certain aspects of GAs which suggests they may not be the best EC algorithm for molecular biology problems. This work will use another type of EC algorithm called the Evolutionary Strategy (ES) to find optimum packing arrangements for side-chains. Independently developed, the ES algorithm has never been used to find solutions to protein structure prediction problems. Nevertheless, the ES algorithm may well prove to be superior to the GA approach if the results of previous work with atomic clusters are any indication.

For this work the C-alpha positions of the protein backbone will be assumed fixed, but the peptide groups of the backbone will be assumed rotatable and their optimum orientations will be found as a part of the side-chain packing optimization problem. The bond lengths and angles will also be fixed so the EC must only identify the peptide orientation angles and the side-chain torsion angles. Fitness will be determined using an energy function that includes the van der Waals interaction, hydrogen bonding, and hydrophobic effects.

Once properly constructed, the ES will be used to search for low energy conformations of moderately sized peptide chains. The final structure will be compared against the crystal structure. A likely first test case will be a 61 residue immunoglobulin binding protein. 

Biography

Dr. Greenwood holds a PhD in Electrical Engineering from the University of Washington, Seattle, WA. From 1976 to 1981 he developed computer models of surface-to-air missiles while employed at the Naval Weapons Station, Seal Beach, CA. From 1981 to 1993 he worked as a computer engineer in private industry for companies such as Honeywell, Inc., The Eldec Corp., and SpaceLabs Medical, Inc. In 1993 he joined the faculty of Western Michigan University, Kalamazoo, MI where he is currently an assistant professor in the Department of Electrical & Computer Engineering with a joint appointment in the Department of Computer Science. His research interests include evolutionary computation and scientific computing.

Dr. Greenwood is a member of Tau Beta Pi, Eta Kappa Nu and is a registered professional engineer in the State of California.