Production of membrane proteins in yeast

Background Yeast is an important and versatile organism for studying membrane proteins. It is easy to cultivate and can perform higher eukaryote-like post-translational modifications. S. cerevisiae has a fully-sequenced genome and there are several collections of deletion strains available, whilst P. pastoris can produce very high cell densities (230 g/l). Results We have used both S. cerevisiae and P. pastoris to over-produce the following His6 and His10 carboxyl terminal fused membrane proteins. CD81 – 26 kDa tetraspanin protein (TAPA-1) that may play an important role in the regulation of lymphoma cell growth and may also act as the viral receptor for Hepatitis C-Virus. CD82 – 30 kDa tetraspanin protein that associates with CD4 or CD8 cells and delivers co-stimulatory signals for the TCR/CD3 pathway. MC4R – 37 kDa seven transmembrane G-protein coupled receptor, present on neurons in the hypothalamus region of the brain and predicted to have a role in the feast or fast signalling pathway. Adt2p – 34 kDa six transmembrane protein that catalyses the exchange of ADP and ATP across the yeast mitochondrial inner membrane. Conclusion We show that yeasts are flexible production organisms for a range of different membrane proteins. The yields are such that future structure-activity relationship studies can be initiated via reconstitution, crystallization for X-ray diffraction or NMR experiments.

A predictor of membrane class:discriminating α-helical and β-barrel membrane proteins from non-membranous proteins

Accurate protein structure prediction remains an active objective of research in bioinformatics. Membrane proteins comprise approximately 20% of most genomes. They are, however, poorly tractable targets of experimental structure determination. Their analysis using bioinformatics thus makes an important contribution to their on-going study. Using a method based on Bayesian Networks, which provides a flexible and powerful framework for statistical inference, we have addressed the alignment-free discrimination of membrane from non-membrane proteins. The method successfully identifies prokaryotic and eukaryotic α-helical membrane proteins at 94.4% accuracy, β-barrel proteins at 72.4% accuracy, and distinguishes assorted non-membranous proteins with 85.9% accuracy. The method here is an important potential advance in the computational analysis of membrane protein structure. It represents a useful tool for the characterisation of membrane proteins with a wide variety of potential applications.

Alpha helical trans-membrane proteins:enhanced prediction using a Bayesian approach

Membrane proteins, which constitute approximately 20% of most genomes, are poorly tractable targets for experimental structure determination, thus analysis by prediction and modelling makes an important contribution to their on-going study. Membrane proteins form two main classes: alpha helical and beta barrel trans-membrane proteins. By using a method based on Bayesian Networks, which provides a flexible and powerful framework for statistical inference, we addressed alpha-helical topology prediction. This method has accuracies of 77.4% for prokaryotic proteins and 61.4% for eukaryotic proteins. The method described here represents an important advance in the computational determination of membrane protein topology and offers a useful, and complementary, tool for the analysis of membrane proteins for a range of applications.