Konstantinos D. Tsirigos

The TOPCONS web server for consensus prediction of membrane protein topology and signal peptides

TOPCONS (http://topcons.net/) is a widely used web server for consensus prediction of membrane protein topology. We hereby present a major update to the server, with some substantial improvements, including the following: (i) TOPCONS can now efficiently separate signal peptides from transmembrane regions. (ii) The server can now differentiate more successfully between globular and membrane proteins. (iii) The server now is even slightly faster, although a much larger database is used to generate the multiple sequence alignments. For most proteins, the final prediction is produced in a matter of seconds. (iv) The user-friendly interface is retained, with the additional feature of submitting batch files and accessing the server programmatically using standard interfaces, making it thus ideal for proteome-wide analyses. Indicatively, the user can now scan the entire human proteome in a few days. (v) For proteins with homology to a known 3D structure, the homology-inferred topology is also displayed. (vi) Finally, the combination of methods currently implemented achieves an overall increase in performance by 4% as compared to the currently available best-scoring methods and TOPCONS is the only method that can identify signal peptides and still maintain a state-of-the-art performance in topology predictions.

A guideline to proteome-wide α-helical membrane protein topology predictions

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.

Inclusion of dyad-repeat pattern improves topology prediction of transmembrane β-barrel proteins

UNLABELLED : Accurate topology prediction of transmembrane β-barrels is still an open question. Here, we present BOCTOPUS2, an improved topology prediction method for transmembrane β-barrels that can also identify the barrel domain, predict the topology and identify the orientation of residues in transmembrane β-strands. The major novelty of BOCTOPUS2 is the use of the dyad-repeat pattern of lipid and pore facing residues observed in transmembrane β-barrels. In a cross-validation test on a benchmark set of 42 proteins, BOCTOPUS2 predicts the correct topology in 69% of the proteins, an improvement of more than 10% over the best earlier method (BOCTOPUS) and in addition, it produces significantly fewer erroneous predictions on non-transmembrane β-barrel proteins. AVAILABILITY AND IMPLEMENTATION BOCTOPUS2 webserver along with full dataset and source code is available at http://boctopus.bioinfo.se/ CONTACT : arne@bioinfo.se SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Improved topology prediction using the terminal hydrophobic helices rule

MOTIVATION The translocon recognizes sufficiently hydrophobic regions of a protein and inserts them into the membrane. Computational methods try to determine what hydrophobic regions are recognized by the translocon. Although these predictions are quite accurate, many methods still fail to distinguish marginally hydrophobic transmembrane (TM) helices and equally hydrophobic regions in soluble protein domains. In vivo, this problem is most likely avoided by targeting of the TM-proteins, so that non-TM proteins never see the translocon. Proteins are targeted to the translocon by an N-terminal signal peptide. The targeting is also aided by the fact that the N-terminal helix is more hydrophobic than other TM-helices. In addition, we also recently found that the C-terminal helix is more hydrophobic than central helices. This information has not been used in earlier topology predictors. RESULTS Here, we use the fact that the N- and C-terminal helices are more hydrophobic to develop a new version of the first-principle-based topology predictor, SCAMPI. The new predictor has two main advantages; first, it can be used to efficiently separate membrane and non-membrane proteins directly without the use of an extra prefilter, and second it shows improved performance for predicting the topology of membrane proteins that contain large non-membrane domains. AVAILABILITY AND IMPLEMENTATION The predictor, a web server and all datasets are available at http://scampi.bioinfo.se/ CONTACT arne@bioinfo.se SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Large Tilts in Transmembrane Helices Can Be Induced during Tertiary Structure Formation

While early structural models of helix-bundle integral membrane proteins posited that the transmembrane α-helices [transmembrane helices (TMHs)] were orientated more or less perpendicular to the membrane plane, there is now ample evidence from high-resolution structures that many TMHs have significant tilt angles relative to the membrane. Here, we address the question whether the tilt is an intrinsic property of the TMH in question or if it is imparted on the TMH during folding of the protein. Using a glycosylation mapping technique, we show that four highly tilted helices found in multi-spanning membrane proteins all have much shorter membrane-embedded segments when inserted by themselves into the membrane than seen in the high-resolution structures. This suggests that tilting can be induced by tertiary packing interactions within the protein, subsequent to the initial membrane-insertion step.

Creating a specialist protein resource network: a meeting report for the protein bioinformatics and community resources retreat

During 11–12 August 2014, a Protein Bioinformatics and Community Resources Retreat was held at the Wellcome Trust Genome Campus in Hinxton, UK. This meeting brought together the principal investigators of several specialized protein resources (such as CAZy, TCDB and MEROPS) as well as those from protein databases from the large Bioinformatics centres (including UniProt and RefSeq). The retreat was divided into five sessions: (1) key challenges, (2) the databases represented, (3) best practices for maintenance and curation, (4) information flow to and from large data centers and (5) communication and funding. An important outcome of this meeting was the creation of a Specialist Protein Resource Network that we believe will improve coordination of the activities of its member resources. We invite further protein database resources to join the network and continue the dialogue.

GWAR: robust analysis and meta-analysis of genome-wide association studies

Motivation: In the context of genome‐wide association studies (GWAS), there is a variety of statistical techniques in order to conduct the analysis, but, in most cases, the underlying genetic model is usually unknown. Under these circumstances, the classical Cochran‐Armitage trend test (CATT) is suboptimal. Robust procedures that maximize the power and preserve the nominal type I error rate are preferable. Moreover, performing a meta‐analysis using robust procedures is of great interest and has never been addressed in the past. The primary goal of this work is to implement several robust methods for analysis and meta‐analysis in the statistical package Stata and subsequently to make the software available to the scientific community. Results: The CATT under a recessive, additive and dominant model of inheritance as well as robust methods based on the Maximum Efficiency Robust Test statistic, the MAX statistic and the MIN2 were implemented in Stata. Concerning MAX and MIN2, we calculated their asymptotic null distributions relying on numerical integration resulting in a great gain in computational time without losing accuracy. All the aforementioned approaches were employed in a fixed or a random effects meta‐analysis setting using summary data with weights equal to the reciprocal of the combined cases and controls. Overall, this is the first complete effort to implement procedures for analysis and meta‐analysis in GWAS using Stata. Availability and Implementation: A Stata program and a web‐server are freely available for academic users at http://www.compgen.org/tools/GWAR Contact: pbagos@compgen.org Supplementary information: Supplementary data are available at Bioinformatics online.