TMM@: a web application for the analysis of transmembrane helix mobility

Background: To understand the mechanism by which a protein transmits a signal through the cell membrane, an understanding of the flexibility of its transmembrane (TM) region is essential. Normal Mode Analysis (NMA) has become the method of choice to investigate the slowest motions in macromolecular systems. It has been widely used to study transmembrane channels and pumps. It relies on the hypothesis that the vibrational normal modes having the lowest frequencies (also named soft modes) describe the largest movements in a protein and are the ones that are functionally relevant. In particular NMA can be used to study dynamics of TM regions, but no tool making this approach available for non-experts, has been available so far. Results: We developed the web-application TMM@ (TransMembrane α-helical Mobility analyzer). It uses NMA to characterize the propensity of transmembrane α-helices to be displaced. Starting from a structure file at the PDB format, the server computes the normal modes of the protein and identifies which helices in the bundle are the most mobile. Each analysis is performed independently from the others and results can be visualized using only a web browser. No additional plug-in or software is required. For users who would like to further analyze the output data with their favourite software, raw results can also be downloaded. Conclusion: We built a novel and unique tool, TMM@, to study the mobility of transmembrane α-helices. The tool can be applied to for example membrane transporters and provides biologists studying transmembrane proteins with an approach to investigate which α-helices are likely to undergo the largest displacements, and hence which helices are most likely to be involved in the transportation of molecules in and out of the cell

Molecular Dynamics Simulations

figurasGroEL is an ATP dependent molecular chaperone that promotes the folding of a large number of substrate proteins in E. coli. Large-scale conformational transitions occurring during the reaction cycle have been characterized from extensive crystallographic studies. However, the link between the observed conformations and the mechanisms involved in the allosteric response to ATP and the nucleotide-driven reaction cycle are not completely established. Here we describe extensive (in total long) unbiased molecular dynamics (MD) simulations that probe the response of GroEL subunits to ATP binding. We observe nucleotide dependent conformational transitions, and show with multiple 100 ns long simulations that the ligand-induced shift in the conformational populations are intrinsically coded in the structure-dynamics relationship of the protein subunit. Thus, these simulations reveal a stabilization of the equatorial domain upon nucleotide binding and a concomitant “opening” of the subunit, which reaches a conformation close to that observed in the crystal structure of the subunits within the ADP-bound oligomer. Moreover, we identify changes in a set of unique intrasubunit interactions potentially important for the conformational transition.The Norwegian Research Council is acknowledged for CPU resources granted through the NOTUR supercomputing program (http://www.notur.no/) and Bergen Center for Computational Science for providing powerful computer facilities (http://www.bccs.uni.no/). Work at CSIC/UPV/EHU was financed by MICINN (Grant BUF2007-64452). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Peer reviewe

Pregnancy complications in last pregnancy and mothers’ long-term cardiovascular mortality: does the relation differ from that of complications in first pregnancy? A population-based study

Background Women who experience complications in first pregnancy are at increased risk of cardiovascular disease (CVD) later in life. Little corresponding knowledge is available for complications in later pregnancies. Therefore, we assessed complications (preeclampsia, preterm birth, and offspring small for gestational age) in first and last pregnancies and the risk of long-term maternal CVD death, taking women´s complete reproduction into account. Data and methods We linked data from the Medical Birth Registry of Norway to the national Cause of Death Registry. We followed women whose first birth took place during 1967–2013, from the date of their last birth until death, or December 31st 2020, whichever occurred first. We analysed risk of CVD death until 69 years of age according to any complications in last pregnancy. Using Cox regression analysis, we adjusted for maternal age at first birth and level of education. Results Women with any complications in their last or first pregnancy were at higher risk of CVD death than mothers with two-lifetime births and no pregnancy complications (reference). For example, the adjusted hazard ratio (aHR) for women with four births and any complications only in the last pregnancy was 2.85 (95% CI, 1.93–4.20). If a complication occurred in the first pregnancy only, the aHR was 1.74 (1.24–2.45). Corresponding hazard ratios for women with two births were 1.82 (CI, 1.59–2.08) and 1.41 (1.26–1.58), respectively. Conclusions The risk for CVD death was higher among mothers with complications only in their last pregnancy compared to women with no complications, and also higher compared to mothers with a complication only in their first pregnancy.publishedVersio

The Peripheral Binding of 14-3-3γ to Membranes Involves Isoform-Specific Histidine Residues

Mammalian 14-3-3 protein scaffolds include seven conserved isoforms that bind numerous phosphorylated protein partners and regulate many cellular processes. Some 14-3-3-isoforms, notably γ, have elevated affinity for membranes, which might contribute to modulate the subcellular localization of the partners and substantiate the importance of investigating molecular mechanisms of membrane interaction. By applying surface plasmon resonance we here show that the binding to phospholipid bilayers is stimulated when 14-3-3γ is complexed with its partner, a peptide corresponding to the Ser19-phosphorylated N-terminal region of tyrosine hydroxylase. Moreover, membrane interaction is dependent on salts of kosmotropic ions, which also stabilize 14-3-3γ. Electrostatic analysis of available crystal structures of γ and of the non-membrane-binding ζ-isoform, complemented with molecular dynamics simulations, indicate that the electrostatic potential distribution of phosphopeptide-bound 14-3-3γ is optimal for interaction with the membrane through amphipathic helices at the N-terminal dimerization region. In addition, His158, and especially His195, both specific to 14-3-3γ and located at the convex lateral side, appeared to be pivotal for the ligand induced membrane interaction, as corroborated by site-directed mutagenesis. The participation of these histidine residues might be associated to their increased protonation upon membrane binding. Overall, these results reveal membrane-targeting motifs and give insights on mechanisms that furnish the 14-3-3γ scaffold with the capacity for tuned shuffling from soluble to membrane-bound states.This work was supported by grants from the Norwegian Cancer Society (to ØH), Junta de Andalucía, grant CVI-02483 (to JMSR), The Research Council of Norway (grant 185181 to A.M.), the Western Norway Health Authorities (grant 911618 to A.M.) and The Kristian Gerhard Jebsen Foundation (to AM)

Comparative Protein Structure Analysis with Bio3D

Bio3D 1 is an R package that provides interactive tools for the analysis of bimolecular structure, sequence and simulation data. The aim of this document, termed a vignette 2 in R parlance, is to provide a brief task-oriented introduction to facilities for analyzing protein structure data with Bio3D (Grant et al., 2006). Requirements: Detailed instructions for obtaining and installing the Bio3D package on various platforms can be found in the Installing Bio3D vignette available both online and from within the Bio3D package. To see available vignettes use the command: Note that to follow along with this vignette the MUSCLE multiple sequence alignment program and the DSSP secondary structure assignment program must be installed on your system and in the search path for executables. Please see the installation vignette for full details. Getting Started Start R, load the Bio3D package and use the command demo("pdb") and then demo("pca") to get a quick feel for some of the tasks that we will be introducing in the following sections. Side-note: You will be prompted to hit the RETURN key at each step of the demos as this will allow you to see the particular functions being called. Also note that detailed documentation and example code for each function can be accessed via the help() and example() commands (e.g. help(read.pdb)). You can also copy and paste any of the example code from the documentation of a particular function, or indeed this vignette, directly into your R session to see how things work. You can also find this documentation online. Working with single PDB structures The code snippet below calls the read.pdb() with a single input argument, the four letter Protein Data Bank (PDB) identifier code "1tag". This will cause the read.pdb() function to read directly from the online RCSB PDB database and return a new object pdb for further manipulation. 1 The latest version of the package, full documentation and further vignettes (including detailed installation instructions) can be obtained from the main Bio3D website: http://thegrantlab.org/bio3d/ 2 This vignette contains executable examples, see help(vignette) for further details

Perinatal death and exposure to dental amalgam fillings during pregnancy in the population-based MoBa cohort

Objectives: The aim was to gain knowledge regarding the risk of perinatal death related to exposure to dental amalgam fillings in the mother. Design: Population-based observational cohort study. Setting: The Norwegian Mother and Child Cohort Study, a Norwegian birth cohort of children born in 1999–2008 conducted by the Norwegian Institute of Public Health. Participants: 72,038 pregnant women with data on the number of teeth filled with dental amalgam. Main outcome measures: Data on perinatal death (stillbirth ≥ 22 weeks plus early neonatal death 0–7 days after birth) were obtained from the Medical Birth Registry of Norway. Results: The absolute risk of perinatal death ranged from 0.20% in women with no amalgam-filled teeth to 0.67% in women with 13 or more teeth filled with amalgam. Analyses including the number of teeth filled with amalgam as a continuous variable indicated an increased risk of perinatal death by increasing number of teeth filled with dental amalgam (crude OR 1.065, 95% CI 1.034 to 1.098, p<0.001). After adjustment for potential confounders (mothers' age, education, body mass index, parity, smoking during pregnancy, alcohol consumption during pregnancy) included as categorical variables, there was still an increased risk for perinatal death associated with increasing number of teeth filled with amalgam (ORadj 1.041, 95% CI 1.008 to 1.076, p = 0.015). By an increased exposure from 0 to 16 teeth filled with amalgam, the model predicted an almost doubled odds ratio (ORadj 1.915, 95% CI 1.12 to 3.28). In groups with 1 to 12 teeth filled with amalgam the adjusted odds ratios were slightly, but not significantly, increased. The group with the highest exposure (participants with 13 or more teeth filled with amalgam) had an adjusted OR of 2.34 (95% CI 1.27 to 4.32; p = 0.007). Conclusion: The current findings suggest that the risk of perinatal death could increase in a dose-dependent way based on the mother’s number of teeth filled with dental amalgam. However, we cannot exclude that the relatively modest odds ratios could be a result of residual confounding. Additional studies on the relationship between exposure to dental amalgam fillings during pregnancy and perinatal death are warranted

TMM@: a web application for the analysis of transmembrane helix mobility-0

<p><b>Copyright information:</b></p><p>Taken from "TMM@: a web application for the analysis of transmembrane helix mobility"</p><p>http://www.biomedcentral.com/1471-2105/8/232</p><p>BMC Bioinformatics 2007;8():232-232.</p><p>Published online 2 Jul 2007</p><p>PMCID:PMC1949839.</p><p></p>cium pump around their own axis