Focus on composition and interaction potential of single-pass transmembrane domains

Transmembrane domains (TMD) connect the inner with the outer world of a living cell. Single TMD containing (bitopic) receptors are of particular interest, because their oligomerization seems to be a common activation mechanism in cell signaling. We analyzed the composition of TMDs in bitopic proteins within the proteomes of 12 model organisms. The average number of strongly polar and charged residues decreases during evolution, while the occurrence of a dimerization motif, GxxxG, remains unchanged. This may reflect the avoidance of unspecific binding within a growing receptor interaction network. In addition, we propose a new experimental approach for studying helix-helix interactions in giant plasma membrane vesicles using scanning fluorescence cross-correlation spectroscopy. Measuring eGFP/mRFP tagged versions of cytokine receptors confirms the homotypic interactions of the erythropoietin receptor in contrast to the Interleukin-4 receptor chains. As a proof of principle, by swapping the TMDs, the interaction potential of erythropoietin receptor was partially transferred to Interleukin-4 receptor α and vice versa. Non-interacting receptors can therefore serve as host molecules for TMDs whose oligomerization capability must be assessed. Computational analysis of the free energy gain resulting from TMD dimer formation strongly corroborates the experimental findings, potentially allowing in silico pre-screening of interacting pairs

GPU-accelerated computation of electron transfer

Electron transfer is a fundamental process that can be studied with the help of computer simulation. The underlying quantum mechanical description renders the problem a computationally intensive application. In this study, we probe the graphics processing unit (GPU) for suitability to this type of problem. Time-critical components are identified via profiling of an existing implementation and several different variants are tested involving the GPU at increasing levels of abstraction. A publicly available library supporting basic linear algebra operations on the GPU turns out to accelerate the computation approximately 50-fold with minor dependence on actual problem size. The performance gain does not compromise numerical accuracy and is of significant value for practical purposes