The Molecular Switching Mechanism at the Conserved D(E)RY Motif in Class-A GPCRs.

The disruption of ionic and H-bond interactions between the cytosolic ends of transmembrane helices TM3 and TM6 of class-A (rhodopsin-like) G protein-coupled receptors (GPCRs) is a hallmark for their activation by chemical or physical stimuli. In the bovine photoreceptor rhodopsin, this is accompanied by proton uptake at Glu(134) in the class-conserved D(E)RY motif. Studies on TM3 model peptides proposed a crucial role of the lipid bilayer in linking protonation to stabilization of an active state-like conformation. However, the molecular details of this linkage could not be resolved and have been addressed in this study by molecular dynamics (MD) simulations on TM3 model peptides in a bilayer of 1,2-dioleoyl-sn-glycero-3-phosphocholine (DOPC). We show that protonation of the conserved glutamic acid alters the peptide insertion depth in the membrane, its side-chain rotamer preferences, and stabilizes the C-terminal helical structure. These factors contribute to the rise of the side-chain pKa (> 6) and to reduced polarity around the TM3 C terminus as confirmed by fluorescence spectroscopy. Helix stabilization requires the protonated carboxyl group; unexpectedly, this stabilization could not be evoked with an amide in MD simulations. Additionally, time-resolved Fourier transform infrared (FTIR) spectroscopy of TM3 model peptides revealed a different kinetics for lipid ester carbonyl hydration, suggesting that the carboxyl is linked to more extended H-bond clusters than an amide. Remarkably, this was seen as well in DOPC-reconstituted Glu(134)- and Gln(134)-containing bovine opsin mutants and demonstrates that the D(E)RY motif is a hydrated microdomain. The function of the D(E)RY motif as a proton switch is suggested to be based on the reorganization of the H-bond network at the membrane interface.

Reliability of precision data obtained from interlaboratory studies.

Precision data, such as laboratory-to-laboratory SD (SL) and repeatability SD, obtained from interlaboratory tests are needed to assess analytical test methods. These precision data describing random error are subject to random variation. In order to avoid distorted assessments of test methods, interlaboratory tests must fulfill minimal requirements for achieving, e.g., a desired reliability in S(L). In 2009, McClure and Lee considered reliability of S(L) as a characteristic of an interlaboratory study. They developed an approach to approximate that reliability to make it possible to adapt the study design of an interlaboratory study to a desired reliability in S(L). The McClure and Lee approach introduces the "margin of relative error" to arrive at the magnitude of the uncertainty in S(L). This article discusses their approach and presents a generalized approach. The limitations of McClure and Lee's approximation are shown to result in underestimation of the actual variability of S(L) due to the disregard of the inherent negative bias of S(L). This bias corresponds to the fact that the expected value of the obtained S(L) lies below the true value sigmaL one would obtain in an interlaboratory study with an infinite number of laboratories and replicates. In order to achieve the reported level of reliability in S(L), the actual number of laboratories required is typically approximately 25% higher than that calculated by McClure and Lee. We present a generalized approach using "margins of relative random error," which takes the impact of the bias of the S(L) into account, resulting in a more realistic estimation of the variability of the precision parameter S(L).

3D profile-based approach to proteome-wide discovery of novel human chemokines.

Chemokines are small secreted proteins with important roles in immune responses. They consist of a conserved three-dimensional (3D) structure, so-called IL8-like chemokine fold, which is supported by disulfide bridges characteristic of this protein family. Sequence- and profile-based computational methods have been proficient in discovering novel chemokines by making use of their sequence-conserved cysteine patterns. However, it has been recently shown that some chemokines escaped annotation by these methods due to low sequence similarity to known chemokines and to different arrangement of cysteines in sequence and in 3D. Innovative methods overcoming the limitations of current techniques may allow the discovery of new remote homologs in the still functionally uncharacterized fraction of the human genome. We report a novel computational approach for proteome-wide identification of remote homologs of the chemokine family that uses fold recognition techniques in combination with a scaffold-based automatic mapping of disulfide bonds to define a 3D profile of the chemokine protein family. By applying our methodology to all currently uncharacterized human protein sequences, we have discovered two novel proteins that, without having significant sequence similarity to known chemokines or characteristic cysteine patterns, show strong structural resemblance to known anti-HIV chemokines. Detailed computational analysis and experimental structural investigations based on mass spectrometry and circular dichroism support our structural predictions and highlight several other chemokine-like features. The results obtained support their functional annotation as putative novel chemokines and encourage further experimental characterization. The identification of remote homologs of human chemokines may provide new insights into the molecular mechanisms causing pathologies such as cancer or AIDS, and may contribute to the development of novel treatments. Besides, the genome-wide applicability of our methodology based on 3D protein family profiles may open up new possibilities for improving and accelerating protein function annotation processes.

Are the results of customary methods for analyzing dioxin and dioxin-like compound congener profiles court-proof?

The congener profile of samples contaminated with dioxin and dioxin-like compounds allows identifying sources of contamination. This article studies the statistical methods of congener profile analysis reported in the literature with respect to the reliability of obtained results. The performance of customary analysis methods regarding raw data transformation and applied TEF (toxic equivalency factor) values is discussed. In particular, the method of principal component analysis and k-means cluster is taken as an example and examined in detail. Reasons for occurring inconsistencies such as the dependence of results on raw data transformation and the disregard of measurement uncertainty are described, and it is shown that they also explain inconsistencies in other methods of cluster analysis such as hierarchical cluster analysis and neural networks. It is concluded that these methods cannot be employed to reach court-proof decisions, i.e. decisions which meet court evidentiary standards. An alternative approach to analyzing congener profiles based on mathematical statistics is briefly presented, allowing reliable, court-proof decisions.