No abstract is provided for this article.
Sorting of mitochondrial inner membrane proteins is a complex process in which translocons and proteases function in a concerted way. Many inner membrane proteins insert into the membrane via the TIM23 translocon, and some are then further acted upon by the mitochondrial m-AAA protease, a molecular motor capable of dislocating proteins from the inner membrane. This raises the possibility that the threshold hydrophobicity for the retention of transmembrane segments in the inner membrane is different depending on whether they belong to membrane proteins that are m-AAA protease substrates or not. Here, using model transmembrane segments engineered into m-AAA protease-dependent proteins, we show that the threshold hydrophobicity for membrane retention measured in yeast cells in the absence of a functional m-AAA protease is markedly lower than that measured in its presence. Whether a given hydrophobic segment in a mitochondrial inner membrane protein will ultimately form a transmembrane helix may therefore depend on whether or not it will be exposed to the pulling force exerted by the m-AAA protease during biogenesis. Background: The m-AAA protease dislocates transmembrane segments from the mitochondrial inner membrane. Results: The presence of the m-AAA protease increases the hydrophobicity required for a transmembrane segment to remain in the membrane. Conclusion: The hydrophobicity thresholds for transmembrane segment retention in the mitochondrial inner membrane differ with or without the m-AAA protease. Significance: Retention of a transmembrane domain in the inner membrane depends on recognition by the m-AAA protease.
The DsbA and DsbB proteins of Escherichia coli are involved in facilitating the formation of disulfide bonds in periplasmic proteins. Here, we show that the rate of formation of a disulfide bond in the periplasmic domain of the inner membrane protein leader peptidase is reduced in dsbA and dsbB strains, whereas the rate of formation of a disulfide bond engineered into the membrane embedded domain of the same protein is completely unaffected by these mutations. We conclude that the Dsb proteins do not facilitate the formation of intramembraneous disulfides.
The net charge distribution in a region around the signal sequence cleavage site has been calculated for samples of 41 prokaryotic and 165 eukaryotic exported proteins. The results show that prokaryotic proteins in particular have a markedly higher incidence of acidic than of basic residues in this region. The possibility that a “dipolar” structure with a positive net charge difference between the N and C-terminal regions is important for signal sequence function in bacteria is suggested, and invoked to rationalize a number of known export-defective signal sequence mutations.
We have carried out detailed statistical analyses of integral membrane proteins of the helix‐bundle class from eubacterial, archaean, and eukaryotic organisms for which genome‐wide sequence data are available. Twenty to 30% of all ORFs are predicted to encode membrane proteins, with the larger genomes containing a higher fraction than the smaller ones. Although there is a general tendency that proteins with a smaller number of transmembrane segments are more prevalent than those with many, uni‐cellular organisms appear to prefer proteins with 6 and 12 transmembrane segments, whereas Caenorhabditis elegans and Homo sapiens have a slight preference for proteins with seven transmembrane segments. In all organisms, there is a tendency that membrane proteins either have manytransmembrane segments with short connecting loops or few transmembrane segments with large extra‐membraneous domains. Membrane proteins from all organisms studied, except possibly the archaeon Methanococcus jannaschii , follow the so‐called “positive‐inside” rule; i.e., they tend to have a higher frequency of positively charged residues in cytoplasmic than in extra‐cytoplasmic segments.
According to the signal hypothesis, a signal sequence, once having initiated export of a growing protein chain across the rough endoplasmic reticulum, is cleaved from the mature protein at a specific site. It has long been known that some part of the cleavage specificity resides in the last residue of the signal sequence, which invariably is one with a small, uncharged side‐chain, but no further specific patterns of amino acids near the point of cleavage have been discovered so far. In this paper, some such patterns, based on a sample of 78 eukaryotic signal sequences, are presented and discussed, and a first attempt at formulating rules for the prediction of cleavage sites is made.
Our knowledge of the complexity of the molecular mechanisms in biological systems and the processes that take place in living cells is expanding rapidly. This is due to a number of factors, among others the completion of many genome sequences and advances in structural biology. Structure determinations, primarily by X-ray crystallography but to a growing extent also by cryo-electron microscopy, have eliminated many of the experiments that were previously performed to get fragments of structural information. Now we can describe the mechanisms of many processes in atomic detail. The detailed descriptions of these, often large and complex systems, have ushered in a new phase of biology where experiments based on detailed structural descriptions are performed or where the interpretations of observations can be made from atomic structures. However, rarely do we understand these systems so well that we can predict what conformational changes will take place or how different molecules will interact in less well known parts of the processes. To understand the rates at which things happen, and to predict what are the rate determining steps, is usually well beyond the current capacities. Thus, our understanding in many instances remains descriptive. It is important to bring scientists of different fields of biology and physical and theoretical chemistry together in an attempt to catalyze the development towards a deeper understanding of biological phenomena. The Nobel Symposium 130 and this issue focus on some of the basic biological phenomena: replication, transcription, translation and transport where there are tremendous research activities and where the molecular systems and the functional mechanisms can now be described in atomic detail. These fields are now available for more fundamental research to give us a improved chemical understanding of the processes and the interactions, opening up the potential to predict what happens at steps not yet accessible to experimental research. This meeting was held 51 years after the structure of DNA was published and 45 years after the first protein structure was reported. These achievements form the basis for much of the current research and it was highly appropriate that the meeting (and this issue) opened with a memoir of F.H.C. Crick presented by Aaron Klug.
No abstract is provided for this article.
Protein translocation normally starts from an N-terminal signal peptide and proceeds in an N-to-C-terminal direction. However, in certain integral membrane proteins an N-terminal tail is translocated even though it is not preceded by a signal peptide. In eukaryotic cells this process involves the normal Sec-machinery. In contrast, recent studies in Escherichia coli show that translocation of such N-terminal tails occurs by a mechanism that does not appear to involve the Sec proteins and is most efficient for short tails lacking positively charged residues. These novel observations suggest that the Sec-machinery has an inherent N-to-C-terminal directionality and cannot work ‘in reverse’.
No abstract is provided for this article.
Membrane proteins are key molecules in the cell, and are important targets for pharmaceutical drugs. Few three‐dimensional structures of membrane proteins have been obtained, which makes computational prediction of membrane proteins crucial for studies of these key molecules. Here, seven membrane protein topology prediction methods based on different underlying algorithms, such as hidden Markov models, neural networks and support vector machines, have been used for analysis of the protein sequences from the 21 416 annotated genes in the human genome. The number of genes coding for a protein with predicted α‐helical transmembrane region(s) ranged from 5508 to 7651, depending on the method used. Based on a majority decision method, we estimate 5539 human genes to code for membrane proteins, corresponding to approximately 26% of the human protein‐coding genes. The largest fraction of these proteins has only one predicted transmembrane region, but there are also many proteins with seven predicted transmembrane regions, including the G‐protein coupled receptors. A visualization tool displaying the topologies suggested by the eight prediction methods, for all predicted membrane proteins, is available on the public Human Protein Atlas portal ( www.proteinatlas.org ).
This folder contains the raw data that was used for quantifications (and the quantifications in an Excel sheet). The raw data (.txt files called EasyQuant files) was extracted from autoradiographs of SDS-PAGE gels using the ImageGauge software from Fuji (associated with the Fuji gel scanner). This machine has since been discontinued (these data were collected 2014-2016). The .txt files were imported into EasyQuant and fitted to a Gaussian distribution automatically by the software (software developed in the Gunnar von Heijne lab by Dr. Rickard Hedman), and the Ffl (fraction full length) was calculated.
