| Summary: | Proteins form the very basis of life. If we were to open up any living cell, we would find, apart from DNA and RNA molecules whose primary role is to store genetic information, a large number of different proteins that comprise the cell itself (for example the cell membrane and organelles), as well as a diverse set of enzymes that catalyze various metabolic reactions. If enzymes were absent, the cell would not be able to function, since a number of metabolic reactions would not be possible. Functions of proteins are the consequences of their functional 3D shape. Therefore, to control these versatile properties, we need to be able to predict the 3D shape of proteins; in other words, solve the protein folding problem. The prediction of a protein’s conformation from its amino-acid sequence is currently one of the most prominent problems in molecular biology, biochemistry and bioinformatics. In this thesis, we address the protein folding problem and the closely-related problem of identifying folding pathways. The leading research objective for this work was to design efficient heuristic search algorithms for these problems, to empirically study these new methods and to compare them with existing algorithms. This thesis makes the following contributions: (1) we show that biologically inspired approaches based on the notion of stigmergy--where a collection of agents modifies the environment, and those changes in turn affect the decision process of each agent (particularly artificial colonies of ants that give rise to such properties as self-organization and cooperation also observed in proteins) is a promising field of study for the protein folding problem; (2) we develop a novel adaptive search framework that is used to identify and to bin promising candidate solutions and to adaptively retrieve solutions when the search progress is unsatisfactory; (3) we develop a new method that efficiently explores large search neighbourhoods by performing biased iterated solution construction for identifying folding pathways; and (4) we show that our algorithms efficiently search the vast search landscapes encountered and are able to capture important aspects of the process of protein folding for some widely accepted computational models. === Science, Faculty of === Computer Science, Department of === Graduate
|
|---|
