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.

In mammalian cells, most integral membrane proteins are initially inserted into the endoplasmic reticulum membrane by the so-called Sec61 translocon. However, recent predictions suggest that many transmembrane helices (TMHs) in multispanning membrane proteins are not sufficiently hydrophobic to be recognized as such by the translocon. In this study, we have screened 16 marginally hydrophobic TMHs from membrane proteins of known three-dimensional structure. Indeed, most of these TMHs do not insert efficiently into the endoplasmic reticulum membrane by themselves. To test if loops or TMHs immediately upstream or downstream of a marginally hydrophobic helix might influence the insertion efficiency, insertion of marginally hydrophobic helices was also studied in the presence of their neighboring loops and helices. The results show that flanking loops and nearest-neighbor TMHs are sufficient to ensure the insertion of many marginally hydrophobic helices. However, for at least two of the marginally hydrophobic helices, the local interactions are not enough, indicating that post-insertional rearrangements are involved in the folding of these proteins.

Summary: State-of-the-art methods for topology of α-helical membrane proteins are based on the use of time-consuming multiple sequence alignments obtained from PSI-BLAST or other sources. Here, we examine if it is possible to use the consensus of topology prediction methods that are based on single sequences to obtain a similar accuracy as the more accurate multiple sequence-based methods. Here, we show that TOPCONS-single performs better than any of the other topology prediction methods tested here, but ∼6% worse than the best method that is utilizing multiple sequence alignments.Availability and Implementation: TOPCONS-single is available as a web server from http://single.topcons.net/ and is also included for local installation from the web site. In addition, consensus-based topology predictions for the entire international protein index (IPI) is available from the web server and will be updated at regular intervals.Contact: arne@bioinfo.seSupplementary information: Supplementary data are avaliable at Bioinformatics online.

For current state‐of‐the‐art methods, the prediction of correct topology of membrane proteins has been reported to be above 80%. However, this performance has only been observed in small and possibly biased data sets obtained from protein structures or biochemical assays. Here, we test a number of topology predictors on an “unseen” set of proteins of known structure and also on four “genome‐scale” data sets, including one recent large set of experimentally validated human membrane proteins with glycosylated sites. The set of glycosylated proteins is also used to examine the ability of prediction methods to separate membrane from nonmembrane proteins. The results show that methods utilizing multiple sequence alignments are overall superior to methods that do not. The best performance is obtained by TOPCONS, a consensus method that combines several of the other prediction methods. The best methods to distinguish membrane from nonmembrane proteins belong to the “Phobius” group of predictors. We further observe that the reported high accuracies in the smaller benchmark sets are not quite maintained in larger scale benchmarks. Instead, we estimate the performance of the best prediction methods for eukaryotic membrane proteins to be between 60% and 70%. The low agreement between predictions from different methods questions earlier estimates about the global properties of the membrane proteome. Finally, we suggest a pipeline to estimate these properties using a combination of the best predictors that could be applied in large‐scale proteomics studies of membrane proteins.