Computational approaches to understanding the protein structure

Official URL: http://risc01.sabanciuniv.edu/record=b1063518 (Table of Contents)

This thesis is composed of two different parts, aiming to predict and understand the protein structure from their contact maps. In the first part, residue contacts of a protein are predicted using neural networks in order to obtain structural constraints for the three-dimensional structure. Physical and chemical properties of residues and their primary sequence neighbors are used for the prediction. Our predictor can predict 11% of the contacting residuees with a false positive ratio of 2% and it performs 7 times better than a random predictor. In the second part, a new method is developed to model a protein as a network of its interacting residues. Small-world network concept is utilized to interpret the parameters of residue networks. It is concluded that proteins are neither regular nor randomly packed but between these two extremes. Such a structure gives the proteins the ability for fast information relay between their residues. They can undergo necessary conformational changes for their functions on very short time scales. Also, residuee networks are shown to obey a truncated power-law degree distribution instead of being scale-free. This shows that proteins have fewer structurally weak points, whose failure would be total damage for the system. This finding conforms to evolutionary plasticity of proteins: Having a low number of weak points makes the mild DNA mutations to be tranlated into the protein structure as highly tolerable.