TIM23-mediated insertion of transmembrane alpha-helices into the mitochondrial inner membrane

While overall hydrophobicity is generally recognized as the main characteristic of transmembrane (TM) alpha-helices, the only membrane system for which there are detailed quantitative data on how different amino acids contribute to the overall efficiency of membrane insertion is the endoplasmic reticulum (ER) of eukaryotic cells. Here, we provide comparable data for TIM23-mediated membrane protein insertion into the inner mitochondrial membrane of yeast cells. We find that hydrophobicity and the location of polar and aromatic residues are strong determinants of membrane insertion. These results parallel what has been found previously for the ER. However, we see striking differences between the effects elicited by charged residues flanking the TM segments when comparing the mitochondrial inner membrane and the ER, pointing to an unanticipated difference between the two insertion systems. Keywords: CoxVa , membrane protein , Mgm1p , mitochondria , TIM2

TIM23-mediated insertion of transmembrane α-helices into the mitochondrial inner membrane

This comprehensive analysis of the sequence determinants for TIM23-mediated insertion of mitochondrial inner membrane proteins reveals strikingly different requirements for protein insertion into the mitochondrial inner membrane and the endoplasmic reticulum

Protein Interactions from the Molecular to the Domain Level

The basic unit of life is the cell, from single-cell bacteria to the largest creatures on the planet. All cells have DNA, which contains the blueprint for proteins. This information is transported in the form of messenger RNA from the genome to ribosomes where proteins are produced. Proteins are the main functional constituents of the cell, they usually have one or several functions and are the main actors in almost all essential biological processes. Proteins are what make the cell alive. Proteins are found as solitary units or as part of large complexes. Proteins can be found in all parts of the cell, the most common place being the cytoplasm, a central space in all cells. They are also commonly found integrated into or attached to various membranes. Membranes define the cell architecture. Proteins integrated into the membrane have a wide number of responsibilities: they are the gatekeepers of the cell, they secrete cellular waste products, and many of them are receptors and enzymes. The main focus of this thesis is the study of protein interactions, from the molecular level up to the protein domain level. In paper I use reoccurring local protein structures to try and predict what sections of a protein interacts with another part using only sequence information. In papers II and III we use a randomization approach on a membrane protein motif that we know interacts with a sphingomyelin lipid to find other candidate proteins that interact with sphingolipids. These are then experimentally verified as sphingolipid-binding. In the last paper, paper IV, we look at how protein domain interaction networks overlap and can be evaluated.At the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper 3: Manuscript.</p

Method for recognizing local descriptors of protein structures using Hidden Markov Models

Being able to predict the sequence-structure relationship in proteins will extend the scope of many bioinformatics tools relying on structure information. Here we use Hidden Markov models (HMM) to recognize and pinpoint the location in target sequences of local structural motifs (local descriptors of protein structure, LDPS) These substructures are composed of three or more segments of amino acid backbone structures that are in proximity with each other in space but not necessarily along the amino acid sequence. We were able to align descriptors to their proper locations in 41.1% of the cases when using models solely built from amino acid information. Using models that also incorporated secondary structure information, we were able to assign 57.8% of the local descriptors to their proper location. Further enhancements in performance was yielded when threading a profile through the Hidden Markov models together with the secondary structure, with this material we were able assign 58,5% of the descriptors to their proper locations. Hidden Markov models were shown to be able to locate LDPS in target sequences, the performance accuracy increases when secondary structure and the profile for the target sequence were used in the models

Identification of novel sphingolipid-binding motifs in mammalian membrane proteins

AbstractSpecific interactions between transmembrane proteins and sphingolipids is a poorly understood phenomenon, and only a couple of instances have been identified. The best characterized example is the sphingolipid-binding motif VXXTLXXIY found in the transmembrane helix of the vesicular transport protein p24. Here, we have used a simple motif-probability algorithm (MOPRO) to identify proteins that contain putative sphingolipid-binding motifs in a dataset comprising proteomes from mammalian organisms. From these motif-containing candidate proteins, four with different numbers of transmembrane helices were selected for experimental study: i) major histocompatibility complex II Q alpha chain subtype (DQA1), ii) GPI-attachment protein 1 (GAA1), iii) tetraspanin-7 TSN7, and iv), metabotropic glutamate receptor 2 (GRM2). These candidates were subjected to photo-affinity labeling using radiolabeled sphingolipids, confirming all four candidate proteins as sphingolipid-binding proteins. The sphingolipid-binding motifs are enriched in the 7TM family of G-protein coupled receptors, predominantly in transmembrane helix 6. The ability of the motif-containing candidate proteins to bind sphingolipids with high specificity opens new perspectives on their respective regulation and function

S-Palmitoylation Sorts Membrane Cargo for Anterograde Transport in the Golgi

International audienceWhile retrograde cargo selection in the Golgi is known to depend on specific signals, it is unknown whether anterograde cargo is sorted, and anterograde signals have not been identified. We suggest here that S-palmitoylation of anterograde cargo at the Golgi membrane interface is an anterograde signal and that it results in concentration in curved regions at the Golgi rims by simple physical chemistry. The rate of transport across the Golgi of two S-palmitoylated membrane proteins is controlled by S-palmitoylation. The bulk of S-palmitoylated proteins in the Golgi behave analogously, as revealed by click chemistry-based fluorescence and electron microscopy. These palmitoylated cargos concentrate in the most highly curved regions of the Golgi membranes, including the fenestrated perimeters of cisternae and associated vesicles. A palmitoylated transmembrane domain behaves similarly in model systems

Using multi-data hidden Markov models trained on local neighborhoods of protein structure to predict residue–residue contacts

Motivation:Correct prediction of residue–residue contacts in proteins that lack good templates with known structure would take ab initio protein structure prediction a large step forward. The lack of correct contacts, and in particular long-range contacts, is considered the main reason why these methods often fail