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G-protein coupled receptors (GPCRs) are a predominant class of cell-surface receptors in eukaryotic life. They are responsible for the perception of a broad range of ligands and involved in a multitude of physiological functions. GPCRs are therefore of crucial interest for biological and pharmaceutical research. Molecular analysis and functional characterisation of GPCRs is frequently hampered by challenges in efficient large-scale production, non-destructive purification and long-term stability. Cell-free protein synthesis (CFPS) provides new production platforms for GPCRs by extracting the protein synthesis machinery of the cell in an open system that allows target-oriented modulations of the synthesis process and direct access to the nascent polypeptide chain. CFPS is fast, reliable and highly adaptable. Unfortunately, highly productive cell-free synthesis of GPCRs is often opposed by low product quality. This thesis was aimed to adapt and improve some of the new possibilities for the cell-free production of GPCRs in high yield and quality for structural and pharmaceutical analysis. An E. coli based CFPS system was applied to synthesise various turkey and human Beta-adrenergic receptor (Beta1AR) derivatives as well as human Endothelin receptors type A and B (ETA and ETB) constructs. Both receptor families are important drug targets and pharmacologically addressed in the treatment of several cardiovascular diseases. CF-synthesis was mainly performed in presence of nanodiscs (ND), which are reconstituted high density lipoprotein particles forming discoidal bilayer patches with a diameter varyring from 6 to approx. 15 nm. The supplementation of ND in the CF-synthesis reaction caused the co-translational solubilisation of the freshly synthesised GPCRs. The fraction of the solubilised GPCR that was correctly folded was analysed by the competence to bind its ligand alprenolol or Endothelin-1, respectively. Both the solubilisation efficiency and the ability to fold in a ligand binding competent state was strongly affected by the lipid composition of the supplied ND. Best results were generally achieved with lipids having phosphoglycerol headgroups and unsaturated fatty acid chains with 18 carbon atoms. Furthermore, thermostabilisation by introduction of point mutations had a large positive impact on the folding efficiency of both Beta1AR and ETB receptor. Formation of a conserved disulphide bridge in the extracellular region was additionally found to be crucial for the function of the ETB receptor. Disulphide bridge formation could be enhanced by applying a glutathione-based redox system in the CFPS. Further improvements in the quality of ETB receptor could be made by the enrichment of heat-shock chaperones in the CF-reaction. Depending on the receptor type and DNA-template, roughly 10 – 30 nmol (350 – 1500 µg) of protein could be synthesised in 1 ml of CF-reaction mixture. After the applied optimisation steps, the fractions of correctly folded receptor could be improved by several orders of magnitude and were finally in between 35% for the thermostabilised turkey Beta1AR, 9% for the thermostabilised ETB receptor, 6.5% for the non-stabilised ETB receptor, 1 - 5% for non-stabilised turkey Beta1AR and for human Beta1AR isoforms and 0.1% for ETA receptor. Therefore, between 2 and 120 µg of GPCR could be synthesised in a ligand binding competent form, depending on the receptor and its modifications. Correctly folded turkey Beta1AR and ETB receptors were thermostable at 30°C and could be stored at 4°C for several weeks after purification. Yields of the thermostabilised turkey Beta1AR were sufficient to purify the receptor in a two-step process by ligand-binding chromatography to obtain pure and correctly folded receptor in the lipid bilayer of a ND. Furthermore, a lipid dependent ligand screen could be demonstrated with the turkey Beta1AR and significant alterations in binding affinities to currently in-use pharmaceuticals were found. The established protocols are therefore suitable and highly competetive for a variety of applications such as screening of GPCR ligands, analysis of lipid effects on GPCR function or for the systematical biochemical characterisation of GPCRs. Most promising for future approaches appears to address the suspected bottlenecks of intial insertion of the GPCR-polypeptide chain in the ND bilayer and the thermal stability of the receptors. Nevertheless, the estabilised protocols for the analysed targets in this thesis are already highly competitive to previously published production protocols either in cell-based or cell-free systems with regard to yield of functional protein, speediness and costs. Moreover, the direct accessibility and other general characteristics of cell-free synthesis open a large variety of possible applications and this work can therefore contribute to the molecular characterisation of this important receptor type and to the development of new pharmaceuticals.
Membrane proteins are biological macromolecules that are located in a cell’s membrane and are responsible for essential functions within an organism, which makes them to prominent drug targets. The extraction of membrane proteins from the hydrophobic membrane bilayer to determine high-resolution crystal structures is a difficult task and only 2% of all solved proteins structures are membrane proteins. Computational methods may help to gain deeper insights into membrane protein structures and their functions. This study will give an overview of such computational methods on a representative set of membrane proteins and will provide ideas for future computational and experimental research on membrane proteins.
In a first step (chapter 2), I updated an earlier, manually-curated data set of homologous membrane proteins (HOMEP) to more recent versions in 2010 (HOMEP2) and 2013 (HOMEP3) using an automated clustering approach. High-resolution structures of membrane proteins listed in the PDB_TM database were structurally aligned and subsequently clustered using structural similarity scores. Both data sets were used as a standard gold reference set for subsequent work.
Subsequently, I have updated and applied the sequence alignment program AlignMe to determine protein descriptors that are suitable for detecting evolutionary relationship between homologous a-helical membrane proteins. Single input descriptors were tested alone and in combination with each other in different modes of AlignMe by optimizing gap penalties on the HOMEP2 data set. Most accurate alignments and homology models on the HOMEP2 data set were observed when using position-specific substitution information (P), secondary structure propensities (S) and transmembrane propensities (T) in the AlignMe PST mode. An evaluation on an independent reference set of membrane protein sequence alignments from the BAliBASE collection showed that different modes of AlignMe are suitable for different sequence similarity levels. The AlignMe PST mode improved the alignment accuracy significantly for distantly related proteins, whereas for closely-related proteins from the BAliBASE set the AlignMe PS mode was more suitable. This work was published in March 2013 in PLOS ONE. In order to allow also an easier usage of the AlignMe program, I have implemented a web server of AlignMe (chapter 4) that provides the optimized settings and gap penalties for the AlignMe P, PS and PST modes. A comparison to other recent alignment web server shows that the alignments of AlignMe are similar or even more accurate than those of other methods, especially for very distantly related proteins for which the inclusion of membrane protein information has been shown to be suitable. This work was published in the NAR web server issue in July 2014.
Although membrane-specific information has been shown to be suitable for aligning distantly related membrane proteins on a sequence level, such information was not incorporated into structural alignment programs making it unclear which method is the most suitable for aligning membrane proteins. Thus, I compared 13 widely-used pairwise structural alignment methods on an updated reference set of homologous membrane protein structures (HOMEP3) and evaluated their accuracy by building models based on the underlying sequence alignments and used scoring functions (e.g., AL4 or CAD-score) to rate the model accuracy (chapter 5). The analysis showed that fragment-based approaches such as FR-TM-align are the most useful for aligning structures of membrane proteins that have undergone large conformational changes whereas rigid approaches were more suitable for proteins that were solved in the same or a similar state. However, no method showed a significant higher accuracy than any other. Additionally, all methods lack a measure to rate the reliability of the accuracy for a specific position within a structure alignment. In order to solve these problems, I propose a consensus-type approach that combines alignments from four different methods, namely FR-TM-align, DaliLite, MATT and FATCAT and assigns a confidence value to each position of the alignment that describes the agreement between the methods. This work has been published 2015 in the journal “PROTEINS: structure, function and bioinformatics”.
Consensus alignments were then generated for each pair of proteins of the HOMEP3 data set and subsequently analyzed for single evolutionary events within membrane spanning segments and for irregular structures (e.g., 310- and p-helices) (chapter 6). Interestingly, single insertions and deletions could be observed with the help of consensus alignments in the conserved membrane-spanning segments of membrane proteins in four protein families. The detection of such single InDels might help to identify crucial residues for a proteins function.