Hybrid methods for protein loop modelling

Loops are often vital for protein function, and therefore accurate prediction of their structures is highly desirable. A particularly important example is the H3 loop of antibodies. Antibodies are proteins of the immune system that are able to bind to a huge variety of different substances, in order to initiate their removal from the body. The binding characteristics of an antibody are mainly determined by the six loops, or complementarity determining regions, that make up their binding site. The most important of these is the H3 loop - however, since it is extremely variable in structure, the accuracy of H3 structure prediction is often poor. Current loop modelling algorithms can mostly be divided into two categories: knowledge-based, where databases of fragments are searched to find suitable conformations; and ab initio, where conformations are generated computationally. In this thesis, we test the ability of such methods to predict H3 structures using one of each: the previously published, knowledge-based algorithm FREAD; and our own new ab initio method MECHANO. Existing knowledge-based methods only use fragments that are the same length as the target, even though loops of slightly different lengths may adopt similar conformations. We describe the development of a novel algorithm, Sphinx, which combines ab initio techniques with the potential extra structural information contained within loops of a different length to improve structure prediction. Finally we look at protein flexibility, by identifying loops for which there are multiple structures deposited in the PDB. We examine the outcome of performing structure prediction on loops with varying amounts of flexibility, and investigate differences between those loops that show a high degree of structural variability and those that do not.