The Sec1/Munc18 protein Vps45 regulates cellular levels of its SNARE binding partners Tlg2 and Snc2 in Saccharomyces cerevisiae

Intracellular membrane trafficking pathways must be tightly regulated to ensure proper functioning of all eukaryotic cells. Central to membrane trafficking is the formation of specific SNARE (soluble N-ethylmeleimide-sensitive factor attachment protein receptor) complexes between proteins on opposing lipid bilayers. The Sec1/Munc18 (SM) family of proteins play an essential role in SNARE-mediated membrane fusion, and like the SNAREs are conserved through evolution from yeast to humans. The SM protein Vps45 is required for the formation of yeast endosomal SNARE complexes and is thus essential for traffic through the endosomal system. Here we report that, in addition to its role in regulating SNARE complex assembly, Vps45 regulates cellular levels of its SNARE binding partners: the syntaxin Tlg2 and the v-SNARE Snc2: Cells lacking Vps45 have reduced cellular levels of Tlg2 and Snc2; and elevation of Vps45 levels results in concomitant increases in the levels of both Tlg2 and Snc2. As well as regulating traffic through the endosomal system, the Snc v-SNAREs are also required for exocytosis. Unlike most vps mutants, cells lacking Vps45 display multiple growth phenotypes. Here we report that these can be reversed by selectively restoring Snc2 levels in vps45 mutant cells. Our data indicate that as well as functioning as part of the machinery that controls SNARE complex assembly, Vps45 also plays a key role in determining the levels of its cognate SNARE proteins; another key factor in regulation of membrane traffic

Four small puzzles that Rosetta doesn't solve

A complete macromolecule modeling package must be able to solve the simplest structure prediction problems. Despite recent successes in high resolution structure modeling and design, the Rosetta software suite fares poorly on deceptively small protein and RNA puzzles, some as small as four residues. To illustrate these problems, this manuscript presents extensive Rosetta results for four well-defined test cases: the 20-residue mini-protein Trp cage, an even smaller disulfide-stabilized conotoxin, the reactive loop of a serine protease inhibitor, and a UUCG RNA tetraloop. In contrast to previous Rosetta studies, several lines of evidence indicate that conformational sampling is not the major bottleneck in modeling these small systems. Instead, approximations and omissions in the Rosetta all-atom energy function currently preclude discriminating experimentally observed conformations from de novo models at atomic resolution. These molecular "puzzles" should serve as useful model systems for developers wishing to make foundational improvements to this powerful modeling suite.Comment: Published in PLoS One as a manuscript for the RosettaCon 2010 Special Collectio

A Novel Protein LZTFL1 Regulates Ciliary Trafficking of the BBSome and Smoothened

Many signaling proteins including G protein-coupled receptors localize to primary cilia, regulating cellular processes including differentiation, proliferation, organogenesis, and tumorigenesis. Bardet-Biedl Syndrome (BBS) proteins are involved in maintaining ciliary function by mediating protein trafficking to the cilia. However, the mechanisms governing ciliary trafficking by BBS proteins are not well understood. Here, we show that a novel protein, Leucine-zipper transcription factor-like 1 (LZTFL1), interacts with a BBS protein complex known as the BBSome and regulates ciliary trafficking of this complex. We also show that all BBSome subunits and BBS3 (also known as ARL6) are required for BBSome ciliary entry and that reduction of LZTFL1 restores BBSome trafficking to cilia in BBS3 and BBS5 depleted cells. Finally, we found that BBS proteins and LZTFL1 regulate ciliary trafficking of hedgehog signal transducer, Smoothened. Our findings suggest that LZTFL1 is an important regulator of BBSome ciliary trafficking and hedgehog signaling

Automated Alphabet Reduction for Protein Datasets

<p>Abstract</p> <p>Background</p> <p>We investigate automated and generic alphabet reduction techniques for protein structure prediction datasets. Reducing alphabet cardinality without losing key biochemical information opens the door to potentially faster machine learning, data mining and optimization applications in structural bioinformatics. Furthermore, reduced but informative alphabets often result in, e.g., more compact and human-friendly classification/clustering rules. In this paper we propose a robust and sophisticated alphabet reduction protocol based on mutual information and state-of-the-art optimization techniques.</p> <p>Results</p> <p>We applied this protocol to the prediction of two protein structural features: contact number and relative solvent accessibility. For both features we generated alphabets of two, three, four and five letters. The five-letter alphabets gave prediction accuracies statistically similar to that obtained using the full amino acid alphabet. Moreover, the automatically designed alphabets were compared against other reduced alphabets taken from the literature or human-designed, outperforming them. The differences between our alphabets and the alphabets taken from the literature were quantitatively analyzed. All the above process had been performed using a primary sequence representation of proteins. As a final experiment, we extrapolated the obtained five-letter alphabet to reduce a, much richer, protein representation based on evolutionary information for the prediction of the same two features. Again, the performance gap between the full representation and the reduced representation was small, showing that the results of our automated alphabet reduction protocol, even if they were obtained using a simple representation, are also able to capture the crucial information needed for state-of-the-art protein representations.</p> <p>Conclusion</p> <p>Our automated alphabet reduction protocol generates competent reduced alphabets tailored specifically for a variety of protein datasets. This process is done without any domain knowledge, using information theory metrics instead. The reduced alphabets contain some unexpected (but sound) groups of amino acids, thus suggesting new ways of interpreting the data.</p

Membrane Bridging and Hemifusion by Denaturated Munc18

Neuronal Munc18-1 and members of the Sec1/Munc18 (SM) protein family play a critical function(s) in intracellular membrane fusion together with SNARE proteins, but the mechanism of action of SM proteins remains highly enigmatic. During experiments designed to address this question employing a 7-nitrobenz-2-oxa-1,3-diazole (NBD) fluorescence de-quenching assay that is widely used to study lipid mixing between reconstituted proteoliposomes, we observed that Munc18-1 from squid (sMunc18-1) was able to increase the apparent NBD fluorescence emission intensity even in the absence of SNARE proteins. Fluorescence emission scans and dynamic light scattering experiments show that this phenomenon arises at least in part from increased light scattering due to sMunc18-1-induced liposome clustering. Nuclear magnetic resonance and circular dichroism data suggest that, although native sMunc18-1 does not bind significantly to lipids, sMunc18-1 denaturation at 37°C leads to insertion into membranes. The liposome clustering activity of sMunc18-1 can thus be attributed to its ability to bridge two membranes upon (perhaps partial) denaturation; correspondingly, this activity is hindered by addition of glycerol. Cryo-electron microscopy shows that liposome clusters induced by sMunc18-1 include extended interfaces where the bilayers of two liposomes come into very close proximity, and clear hemifusion diaphragms. Although the physiological relevance of our results is uncertain, they emphasize the necessity of complementing fluorescence de-quenching assays with alternative experiments in studies of membrane fusion, as well as the importance of considering the potential effects of protein denaturation. In addition, our data suggest a novel mechanism of membrane hemifusion induced by amphipathic macromolecules that does not involve formation of a stalk intermediate

Computation of Conformational Coupling in Allosteric Proteins

In allosteric regulation, an effector molecule binding a protein at one site induces conformational changes, which alter structure and function at a distant active site. Two key challenges in the computational modeling of allostery are the prediction of the structure of one allosteric state starting from the structure of the other, and elucidating the mechanisms underlying the conformational coupling of the effector and active sites. Here we approach these two challenges using the Rosetta high-resolution structure prediction methodology. We find that the method can recapitulate the relaxation of effector-bound forms of single domain allosteric proteins into the corresponding ligand-free states, particularly when sampling is focused on regions known to change conformation most significantly. Analysis of the coupling between contacting pairs of residues in large ensembles of conformations spread throughout the landscape between and around the two allosteric states suggests that the transitions are built up from blocks of tightly coupled interacting sets of residues that are more loosely coupled to one another

The binding of Varp to VAMP7 traps VAMP7 in a closed, fusogenically inactive conformation.

SNAREs provide energy and specificity to membrane fusion events. Fusogenic trans-SNARE complexes are assembled from glutamine-contributing SNAREs (Q-SNAREs) embedded in one membrane and an arginine-contributing SNARE (R-SNARE) embedded in the other. Regulation of membrane fusion events is crucial for intracellular trafficking. We identify the endosomal protein Varp as an R-SNARE-binding regulator of SNARE complex formation. Varp colocalizes with and binds to VAMP7, an R-SNARE that is involved in both endocytic and secretory pathways. We present the structure of the second ankyrin repeat domain of mammalian Varp in complex with the cytosolic portion of VAMP7. The VAMP7-SNARE motif is trapped between Varp and the VAMP7 longin domain, and hence Varp kinetically inhibits the ability of VAMP7 to form SNARE complexes. This inhibition will be increased when Varp can also bind to other proteins present on the same membrane as VAMP7, such as Rab32-GTP

Homology modelling and spectroscopy, a never-ending love story

Homology modelling is normally the technique of choice when experimental structure data are not available but three-dimensional coordinates are needed, for example, to aid with detailed interpretation of results of spectroscopic studies. Herein, the state of the art of homology modelling will be described in the light of a series of recent developments, and an overview will be given of the problems and opportunities encountered in this field. The major topic, the accuracy and precision of homology models, will be discussed extensively due to its influence on the reliability of conclusions drawn from the combination of homology models and spectroscopic data. Three real-world examples will illustrate how both homology modelling and spectroscopy can be beneficial for (bio)medical research

A quality metric for homology modeling: the H-factor

<p>Abstract</p> <p>Background</p> <p>The analysis of protein structures provides fundamental insight into most biochemical functions and consequently into the cause and possible treatment of diseases. As the structures of most known proteins cannot be solved experimentally for technical or sometimes simply for time constraints, <it>in silico </it>protein structure prediction is expected to step in and generate a more complete picture of the protein structure universe. Molecular modeling of protein structures is a fast growing field and tremendous works have been done since the publication of the very first model. The growth of modeling techniques and more specifically of those that rely on the existing experimental knowledge of protein structures is intimately linked to the developments of high resolution, experimental techniques such as NMR, X-ray crystallography and electron microscopy. This strong connection between experimental and <it>in silico </it>methods is however not devoid of criticisms and concerns among modelers as well as among experimentalists.</p> <p>Results</p> <p>In this paper, we focus on homology-modeling and more specifically, we review how it is perceived by the structural biology community and what can be done to impress on the experimentalists that it can be a valuable resource to them. We review the common practices and provide a set of guidelines for building better models. For that purpose, we introduce the H-factor, a new indicator for assessing the quality of homology models, mimicking the R-factor in X-ray crystallography. The methods for computing the H-factor is fully described and validated on a series of test cases.</p> <p>Conclusions</p> <p>We have developed a web service for computing the H-factor for models of a protein structure. This service is freely accessible at <url>http://koehllab.genomecenter.ucdavis.edu/toolkit/h-factor</url>.</p

In silico pathway reconstruction: Iron-sulfur cluster biogenesis in Saccharomyces cerevisiae

BACKGROUND: Current advances in genomics, proteomics and other areas of molecular biology make the identification and reconstruction of novel pathways an emerging area of great interest. One such class of pathways is involved in the biogenesis of Iron-Sulfur Clusters (ISC). RESULTS: Our goal is the development of a new approach based on the use and combination of mathematical, theoretical and computational methods to identify the topology of a target network. In this approach, mathematical models play a central role for the evaluation of the alternative network structures that arise from literature data-mining, phylogenetic profiling, structural methods, and human curation. As a test case, we reconstruct the topology of the reaction and regulatory network for the mitochondrial ISC biogenesis pathway in S. cerevisiae. Predictions regarding how proteins act in ISC biogenesis are validated by comparison with published experimental results. For example, the predicted role of Arh1 and Yah1 and some of the interactions we predict for Grx5 both matches experimental evidence. A putative role for frataxin in directly regulating mitochondrial iron import is discarded from our analysis, which agrees with also published experimental results. Additionally, we propose a number of experiments for testing other predictions and further improve the identification of the network structure. CONCLUSION: We propose and apply an iterative in silico procedure for predictive reconstruction of the network topology of metabolic pathways. The procedure combines structural bioinformatics tools and mathematical modeling techniques that allow the reconstruction of biochemical networks. Using the Iron Sulfur cluster biogenesis in S. cerevisiae as a test case we indicate how this procedure can be used to analyze and validate the network model against experimental results. Critical evaluation of the obtained results through this procedure allows devising new wet lab experiments to confirm its predictions or provide alternative explanations for further improving the models