Quality assessment of protein models

Abstract: Proteins are crucial for all living organisms and they are involved in many different processes. The function of a protein is tightly coupled to its structure, yet to determine the structure experimentally is both non-trivial and expensive. Computational methods that are able to predict the structure are often the only possibility to obtain structural information for a particular protein. Structure prediction has come a long way since its inception. More advanced algorithms, refined mathematics and statistical analysis and use of machine learning techniques have improved this field considerably. Making a large number of protein models is relatively fast. The process of identifying and separating correct from less correct models, from a large set of plausible models, is also known as model quality assessment. Critical Assessment of Techniques for Protein Structure Prediction (CASP) is an international experiment to assess the various methods for structure prediction of proteins. CASP has shown the improvements of these different methods in model quality assessment, structure prediction as well as better model building.In the two studies done in this thesis, I have improved the model quality assessment part of this structure prediction problem for globular proteins, as well as trained the first such method dedicated towards membrane proteins. The work has resulted in a much-improved version of our previous model quality assessment program ProQ, and in addition I have also developed the first model quality assessment program specifically tailored for membrane proteins.