De novo structure prediction of helical transmembrane proteins using RosettaTMH and pseudocontact shift NMR data

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Membrane proteins constitute nearly 30% of all cellular proteins and more than 50% of all known drug targets. They play a key role in a plethora of physiological processes including signal transduction, transport of metabolites and neurotransmission, but are also involved in the development of pathological situations as cancer, diabetes and obesity. Despite of their enormous medical relevance, membrane proteins make up less than 2% of all protein structures present in the Protein Data Bank. Despite significant advances in methods of structural biology as X-ray crystallography and nuclear magnetic resonance spectroscopy (NMR), membrane protein structure determination remains a formidable challenge often yielding limited experimental data. Computational methods can provide structural models even in the absence of experimental data. In this way, structural models of soluble proteins can be generated; however, computational methods for the structure prediction of membrane proteins are in their infancy. Structures of homolog proteins as template for a comparative modeling are missing, for which reason a de novo folding, starting from the amino acid sequence, is necessary. The success of these methods, e.g. Rosetta-Membrane, is so far limited to small proteins (<150 amino acids), but can be drastically improved by the incorporation of a limited amount of experimental structural information.The aim of the proposed research project is the development of a novel, innovative algorithm for the de novo structure prediction of alpha-helical membrane proteins using a limited set of pseudocontact shift (PCS) NMR data. PCSs are caused by paramagnetic lanthanide ions and give rise to long range distance and orientation information that can guide the structure prediction algorithm and verify the generated structural models. The project aims to develop a computational framework for the integration of PCSs into RosettaTMH. This algorithm will assemble predicted transmembrane helices (TMHs) into membrane protein topologies. Subsequent peptide fragment insertion completes loop regions and exhaustively samples the protein conformational space. The performance of the developed method, termed RosettaTMH+PCS, will be evaluated on a benchmark set comprising membrane proteins of different sizes (2-15 TMHs) and folds. Furthermore, RosettaTMH+PCS will be tested experimentally through the structure determination of the integral membrane protein CNIH1, a member of the chornicon protein family. Thereby, the project aims to develop an experimental protocol for the lanthanide tagging of membrane proteins and the time efficient collection of PCS data using TROSY-based NMR experiments, which will speed up the structure prediction process. The proposed method is conceived as a general route for membrane protein structure prediction and is expected to produce more accurate structural models, especially for large membrane proteins, than previous algorithms.