Given the amino acid sequence of a putative alpha-helical transmembrane (TM)
protein, TOPCONS predicts the topology of the protein, i.e. a specification of
the membrane spanning segments and their IN/OUT orientation relative to the
membrane. The prediction is a consensus from five different topology prediction
algorithms: OCTOPUS, Philius, PolyPhobius, SCAMPI (multiple sequence mode) and
SPOCTOPUS . These five predictions are used as input to the TOPCONS Hidden
Markov Model (HMM), which gives a consensus prediction for the protein,
together with a reliability score based on the agreement of the included
methods across the sequence. In addition, the ΔG-scale is used to predict the
free energy of membrane insertion for a window of 19 amino acids centered
around each position in the sequence. For an explanation of the methods
included in the server, see the corresponding links.
The main improvement from the first version of the TOPCONS webserver is that
now the server can distinguish between TM and non-TM proteins more efficiently,
making it ideal for batch submission of proteins and whole-genome analyses.
Further, the server also predicts the presence of potential cleavable signal
peptides in the input sequence(s), thus prior scanning with an separate signal
peptide predictor is no longer required.
TOPCONS is supported by the EGI FedCloud
with the VO VO.NBIS.SE for computational resource.
Input to the server is an amino acid sequence in FASTA format. The user can either submit paste
one or more sequences in the text-area provided, or, alternatively,
upload a file for batch submission of sequences. A sequence profile is
created for the input sequence using BLAST, and this profile is used
as input to all the different methods (except Philius, where only the
query sequence is used).
>sp|O93740|BACR_HALS4 Bacteriorhodopsin Halobacterium sp.
The server outputs the topology predictions using all the individual
methods, as well as the consensus prediction (TOPCONS 2.0). In
addition, predicted ΔG-values and reliability scores are given for
each position in the sequence. The results are both displayed
graphically and are available for download in text format in the
TOPCONS result file. In the case of batch submission, the results
are only presented in raw text format. High-resolution versions of
the images are also available for download (single-sequence
The result page of an example job with a single sequence:
A job with one sequence
The result page of an example job with multiple sequences:
A job with multiple sequences
Please note that the result will be kept on the server for one month and then it will be deleted.
The TOPCONS web server for combined membrane protein topology and
signal peptide prediction.
Tsirigos KD*, Peters C*, Shu N*, Käll L and Elofsson A (2015) Nucleic
Acids Research 43 (Webserver issue), W401-W407. [PubMed]
TOPCONS: consensus prediction of membrane protein topology.
Bernsel A*, Viklund H*, Hennerdal A and Elofsson A (2009) Nucleic
Acids Research 37 (Webserver issue), W465-8. [PubMed]
A method that improves topology prediction for transmembrane proteins
by using two-track ANN-based preference scores and an improved
topological grammar. |
Viklund H and Elofsson A (2008) Bioinformatics. 24, 1662-1668.
Transmembrane topology and signal peptide prediction using dynamic
bayesian networks. |
Reynolds SM, Käll L, Riffle ME, Bilmes JA and Noble WS (2008) PLoS
Comput Biol. 11. [Pubmed]
An HMM posterior decoder for sequence feature prediction that includes
homology information. |
Käll L, Krogh A and Sonnhammer EL (2005) Bioinformatics. 21,
Prediction of membrane-protein topology from first principles. |
Bernsel A, Viklund H, Falk J, Lindahl E, von Heijne G and Elofsson A
(2008) Proc. Natl. Acad. Sci. USA. 105, 7177-7181. [Pubmed]
SPOCTOPUS: a combined predictor of signal peptides and membrane
protein topology. |
Viklund H, Bernsel A, Skwark M and Elofsson A. (2008) Bioinformatics.
24, 2928-2929. [Pubmed]
Molecular code for transmembrane-helix recognition by the Sec61
Hessa T, Meindl-Beinker NM, Bernsel A, Kim H, Sato Y, Lerch-Bader M,
Nilsson I, White SH and von Heijne G. (2007) Nature 450, 1026-1030.