Transmembrane alpha-helices in integral membrane proteins are recognized co-translationally and inserted into the membrane of the endoplasmic reticulum by the Sec61 translocon. A full quantitative description of this phenomenon, linking amino acid sequence to membrane insertion efficiency, is still lacking. Here, using in vitro translation of a model protein in the presence of dog pancreas rough microsomes to analyse a large number of systematically designed hydrophobic segments, we present a quantitative analysis of the position-dependent contribution of all 20 amino acids to membrane insertion efficiency, as well as of the effects of transmembrane segment length and flanking amino acids. The emerging picture of translocon-mediated transmembrane helix assembly is simple, with the critical sequence characteristics mirroring the physical properties of the lipid bilayer.

TOPCONS (http://topcons.net/) is a web server for consensus prediction of membrane protein topology. The underlying algorithm combines an arbitrary number of topology predictions into one consensus prediction and quantifies the reliability of the prediction based on the level of agreement between the underlying methods, both on the protein level and on the level of individual TM regions. Benchmarking the method shows that overall performance levels match the best available topology prediction methods, and for sequences with high reliability scores, performance is increased by ∼10 percentage points. The web interface allows for constraining parts of the sequence to a known inside/outside location, and detailed results are displayed both graphically and in text format.

A considerable fraction of life develops in the sea at temperatures lower than 15°C. Little is known about the adaptive features selected under those conditions. We present the analysis of the genome sequence of the fast growing Antarctica bacterium Pseudoalteromonas haloplanktis TAC125. We find that it copes with the increased solubility of oxygen at low temperature by multiplying dioxygen scavenging while deleting whole pathways producing reactive oxygen species. Dioxygen-consuming lipid desaturases achieve both protection against oxygen and synthesis of lipids making the membrane fluid. A remarkable strategy for avoidance of reactive oxygen species generation is developed by P. haloplanktis, with elimination of the ubiquitous molybdopterin-dependent metabolism. The P. haloplanktis proteome reveals a concerted amino acid usage bias specific to psychrophiles, consistently appearing apt to accommodate asparagine, a residue prone to make proteins age. Adding to its originality, P. haloplanktis further differs from its marine counterparts with recruitment of a plasmid origin of replication for its second chromosome.[Supplemental material is available online at www.genome.org. The sequence data from this study have been submitted to EMBL under accession nos. CR954246 and CR954247.

The current best membrane-protein topology-prediction methods are typically based on sequence statistics and contain hundreds of parameters that are optimized on known topologies of membrane proteins. However, because the insertion of transmembrane helices into the membrane is the outcome of molecular interactions among protein, lipids and water, it should be possible to predict topology by methods based directly on physical data, as proposed >20 years ago by Kyte and Doolittle. Here, we present two simple topology-prediction methods using a recently published experimental scale of position-specific amino acid contributions to the free energy of membrane insertion that perform on a par with the current best statistics-based topology predictors. This result suggests that prediction of membrane-protein topology and structure directly from first principles is an attainable goal, given the recently improved understanding of peptide recognition by the translocon.bioinformatics ͉ membrane insertion ͉ topology prediction ͉ translocon ͉ biological hydrophobicity scale P rediction of membrane-protein topology is a classic problem in bioinformatics (1). The very first prediction algorithms were based solely on hydrophobicity plots (2), but these early methods performed poorly in practice and were soon supplanted by machine-learning methods that extract statistical sequence preferences from databases of experimentally mapped topologies. Today, the best performing methods have been trained on extensive datasets and contain hundreds of free parameters that are optimized during the training session (3, 4). With the inclusion of information from aligned homologous sequences, one can expect modern methods to predict the correct topology for up to 80% of all multispanning membrane proteins (5, 6).Yet, from a basic science point of view, it is somewhat unsatisfying that the best methods use sequence statistics rather than physicochemical principles as the underlying basis for the prediction. After all, the cellular machineries (translocons) responsible for membrane-protein biogenesis do not have access to statistical data but rather exploit molecular interactions (lipid-protein, water-protein, and protein-protein) to ensure that membrane proteins attain their correct topology (7,8). In principle, therefore, it should be possible to match the performance of current machine-learning predictors by using methods based on the same physical properties that determine translocon-mediated membrane insertion.Despite years of biophysical studies of protein-lipid interactions (9-12), it is only recently that the first comprehensive dataset describing the insertion of transmembrane (TM) ␣-helices into the endoplasmic reticulum (ER) membrane in terms of free-energy contributions from individual amino acids in different positions along the membrane normal has been published (13). Here, we show that a simple additive free-energy model derived from these experimental data, when coupled with the ''positive-inside '' rule (14), predicts the topolog...