Structure, prediction, evolution and genome wide studies of membrane proteins

α-helical membrane proteins constitute 20-30% of all proteins in a cell and are involved in many essential cellular functions. The structure is only known for a few hundred of them, which makes structural models important. The most common structural model of a membrane protein is the topology which is a two-dimensional representation of the structure. This thesis is focused on three different aspects of membrane protein structure: improving structural predictions of membrane proteins, improving the level of detail of structural models and the concept of dual topology. It is possible to improve topology models of membrane proteins by including experimental information in computer predictions. This was first performed in Escherichia coli and, by using homology, it was possible to extend the results to 225 prokaryotic organisms. The improved models covered ~80% of the membrane proteins in E. coli and ~30% of other prokaryotic organisms. However, the traditional topology concept is sometimes too simple for complex membrane protein structures, which create a need for more detailed structural models. We created two new machine learning methods, one that predicts more structural features of membrane proteins and one that predicts the distance to the membrane centre for the amino acids. These methods improve the level of detail of the structural models. The final topic of this thesis is dual topology and membrane protein evolution. We have studied a class of membrane proteins that are suggested to insert either way into the membrane, i.e. have a dual topology. These protein families might explain the frequent occurrence of internal symmetry in membrane protein structures