Deterministic Global Optimization for Protein Structure Prediction

Abstract

Deterministic global optimization plays an essential role in the solution of many difficult problems with applications ranging from economics and operations research to computational chemistry and molecular biology. In this chapter we explore the application of deterministic global optimization approaches to problems related to protein structure prediction. Due to the complex nature of protein interactions, energy landscapes which model these systems display huge numbers of local minima often separated by high energy barriers. Since the number of local minima is vast, the corresponding formulation has earned the simple yet suggestive title of “multiple-minima” problem. Based on the complexity of the energy hypersurface, there is an obvious need for the development of effective global optimization techniques. In this work, we have focused on the development of such global optimization methods through the foundations of the αBB deterministic global optimization approach.