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. 2015 Jul 1;43(W1):W401-7.
doi: 10.1093/nar/gkv485. Epub 2015 May 12.

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

Konstantinos D Tsirigos  1 , Christoph Peters  1 , Nanjiang Shu  2 , Lukas Käll  1 , Arne Elofsson  3
Affiliations

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

Konstantinos D Tsirigos et al. Nucleic Acids Res. .

Abstract

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.

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Figures

Figure 1.
Figure 1.
The TOPCONS workflow: four of the topology predictors (OCTOPUS, PolyPhobius, SPOCTOPUS and SCAMPI) use an MSA-derived sequence profile as input, whereas the fifth method (Philius) only requires the protein sequence. The topology predictions are used to construct a topology profile, which is fed into the TOPCONS Hidden Markov Model and the final consensus topology is created.
Figure 2.
Figure 2.
Distribution of time (in seconds) required for processing the proteins in all data sets we used in the benchmark. The increase in speed is substantial, since almost 80% of all proteins in total took less than 30 seconds.
Figure 3.
Figure 3.
Comparison of the topology prediction accuracy of the current TOPCONS implementation versus other topology prediction methods. Note that the performance drops for all predictors that predict both signal peptides and TM regions as opposed to methods specifically designed to predict the topology of membrane proteins.
Figure 4.
Figure 4.
Example output from the TOPCONS web server, based on the Bacteriorhodopsin sequence from Halobacterium sp. (UniProt-ID: BACR_HALS4). Topology predicted by TOPCONS, the individual methods and predicted ΔG values across the sequence.
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