Skip Navigation


Nucleic Acids Research Advance Access originally published online on May 8, 2009
Nucleic Acids Research 2009 37(Web Server issue):W465-W468; doi:10.1093/nar/gkp363
This Article
Right arrow Abstract Freely available
Right arrow Print PDF (1275K) Freely available
Right arrow Screen PDF (216K) Freely available
Right arrowOA All Versions of this Article:
37/suppl_2/W465    most recent
gkp363v1
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrow Commercial Re-use Guidelines
for Open Access NAR Content
Google Scholar
Right arrow Articles by Bernsel, A.
Right arrow Articles by Elofsson, A.
PubMed
Right arrow PubMed Citation
Right arrow Articles by Bernsel, A.
Right arrow Articles by Elofsson, A.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

Nucleic Acids Research, 2009, Vol. 37, No. suppl_2 W465-W468
© 2009 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Articles

TOPCONS: consensus prediction of membrane protein topology

Andreas Bernsel, Håkan Viklund, Aron Hennerdal and Arne Elofsson*

Center for Biomembrane Research, Department of Biochemistry and Biophysics, Stockholm University, SE-106 91 Sweden

*To whom correspondence should be addressed. Tel: +46 8 164276; Fax: +46 8 153679; Email: arne{at}bioinfo.se

Received December 13, 2008. Revised April 22, 2009. Accepted April 22, 2009.


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 METHODS
 BENCHMARK RESULTS
 THE TOPCONS AND SCAMPI...
 SCAMPI
 FUNDING
 REFERENCES
 
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.


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
METHODS
 BENCHMARK RESULTS
 THE TOPCONS AND SCAMPI...
 SCAMPI
 FUNDING
 REFERENCES
 
Genome-wide estimations indicate that alpha-helical transmembrane (TM) proteins comprise roughly 20–30% of the genes in a typical organism (1,2). These proteins are essential for vital biological functions such as cell communication and signaling, active and passive transport of molecules across the membrane, energy-transduction and cell–cell adhesion.

Prediction of membrane protein topology (i.e. the positions and in/out orientation of the membrane-spanning regions) serves to quickly obtain fundamental structural knowledge of TM proteins in silico. For TM proteins, computational methods are particularly important since structural knowledge is difficult to attain experimentally. Therefore, a correctly predicted topology provides an excellent template for further studies in the laboratory and might facilitate and improve functional and structural classification of protein sequences on a genomic level.

A number of different methods have been developed over the last decades that predict topology with high accuracy. Many of these methods are freely available as web servers, both individually (2,3) and combining the results from several methods (4). With prediction algorithms based on different principles, it is not a surprising observation that for a fair amount of proteins, different prediction methods disagree about the final result, causing uncertainty about the correct topology. Earlier studies have stated, for example, that topology predictions are more likely to be correct when individual methods agree in their prediction than when they do not (5), but so far only a few attempts have been made at combining individual topology predictions into one consensus prediction (6–8).

Here we present TOPCONS, a fundamental algorithm that 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.

We also present an implementation of TOPCONS as a web-server based on the individual topology prediction methods OCTOPUS (9), PRO-TMHMM and PRODIV-TMHMM (10), SCAMPI-single and SCAMPI-multi (11). During the development a large set of combinations using many different topology predictors as an input to TOPCONS was tested. However, no combination performed significantly better than the one used here and therefore we decided to only use methods developed in house in the current version of the TOPCONS webserver.


   METHODS
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
BENCHMARK RESULTS
 THE TOPCONS AND SCAMPI...
 SCAMPI
 FUNDING
 REFERENCES
 
TOPCONS algorithm
An overview of the different steps of the TOPCONS algorithm is presented in Figure 1. As input TOPCONS uses a set of topology predictions which are combined into a topology profile by letting each residue be represented by three values representing the fraction of methods that predict that residue to be situated in the membrane (M), on the inside of the membrane (i) or on the outside of the membrane (o). This topology profile is used as input to a dynamic programming algorithm similar to a hidden Markov model that has an alphabet consisting of the characters ‘M’, ‘i’ and ‘o’. The final topology corresponds to the highest scoring state path through this model using a Viterbi-like algorithm. In each state, the emission score for the structural category modeled by that state (i, o or M) is equal to 1.0 and for all other structural categories it equals 0.0. All transition probabilities are equal to 1.0. Thus, the final prediction equals the state path with the highest geometric mean score with respect to the topology profile and the grammar of the model.


Figure 1
View larger version (19K):
[in this window]
[in a new window]
[Download PowerPoint slide]
  		Figure 1. TOPCONS workflow: four of the topology predictors make use of multiple sequence information and require a sequence profile as input, created using BLAST (18), whereas the fifth method (SCAMPI-single) only requires the protein sequence. The topology predictions are used to construct a topology profile, which is fed into the TOPCONS hidden Markov model.

 
Reliability score
A reliability value is calculated for each residue in a sequence by taking the average over a 21 position window of the topology profile value for the consensus prediction of that position (i, o, M). A reliability score on the protein level is calculated by taking the minimum value as calculated above.

Dataset
A dataset of 163 sequences with known topology, compiled by combining the datasets from (10) and (3) and homology reducing at 30% sequence identity using cd-hit (12), was used to evaluate the performance of TOPCONS.


   BENCHMARK RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 BENCHMARK RESULTS
THE TOPCONS AND SCAMPI...
 SCAMPI
 FUNDING
 REFERENCES
 
Using a dataset of 163 sequences with known topology, the performance of TOPCONS was benchmarked against eight other topology prediction algorithms (Table 1). ConPredII (7) is an earlier consensus transmembrane topology prediction approach, and the other methods [OCTOPUS (9), MEMSAT3 (13), SCAMPI (11), HMMTOP (14) and PRO- and PRODIV-TMHMM (10)] are frequently used single topology prediction methods. TOPCONS outperforms the only other consensus prediction method ConPredII, partly because the underlying topology prediction methods are more recent, and achieves accuracy similar to that of the best available individual methods. However, the performance of TOPCONS is not significantly better than the best single server included in the predictions, possibly because the limit of prediction accuracy given the data available today is close to have been reached.


View this table:
[in this window]
[in a new window]

  		Table 1. Prediction accuracy for a benchmark set of 163 membrane proteins with known topology

 
Studying the reliability scores (Figure 2); it is evident that higher reliability scores (i.e. in principle the number of methods that agree for the sequence position with least agreement across the protein) correspond to higher probability that a prediction is correct. In our benchmark set, 71% of the sequences achieved reliability scores above 70, and among those sequences, the accuracy of TOPCONS is 93%, i.e. 10 percentage points higher than the overall accuracy for the complete dataset.


Figure 2
View larger version (9K):
[in this window]
[in a new window]
[Download PowerPoint slide]
  		Figure 2. Estimated probability of correct topology prediction as a function of reliability score. Of the sequences in the benchmark set, 71% have a reliability score >70, and among those, 93% of the predictions are correct. The probability of a correct topology prediction (y-axis) was estimated as the prediction accuracy among proteins with reliability score ± 10 from the value given by the x-axis.

 
Compared to an earlier derived reliability score for the individual prediction method TMHMM (15), the reliability score of TOPCONS is similar (although the overall prediction accuracy is higher). A z-value for the reliability scores were calculated using the Wilcoxon rank sum test to get a quantified comparison between the two reliability scores independent of the overall prediction accuracy. The z-value for TMHMM is –6.1 and for TOPCONS –4.8.


   THE TOPCONS AND SCAMPI WEBSERVERS
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 BENCHMARK RESULTS
 THE TOPCONS AND SCAMPI...
SCAMPI
 FUNDING
 REFERENCES
 
TOPCONS
To make TOPCONS available to a broad audience, a web server implementing the algorithm has been developed and can be freely accessed at http://topcons.net/. Given the amino-acid sequence of a putative membrane protein, the server outputs the predicted topology using the individual methods, as well as the consensus prediction (TOPCONS) (Figure 3). In addition, ZPRED (16) is used to predict the Z-coordinate (i.e. the distance to the membrane center) of each amino acid, and a scale describing the free-energy contributions of translocon-mediated membrane insertion (17) is used to predict a {Delta}G value for a window of 21 amino acids centered around each position in the sequence. Optionally, parts of the sequence can be constrained to a known Inside/Outside/Membrane-location, by using the Restrainment options, allowing for any combinations of restraints on one or more parts of the sequence. All results are both displayed graphically and are available for download in text format. The BLAST output, which is used as input to the methods, and high-resolution versions of the images are also available for download.


Figure 3
View larger version (26K):
[in this window]
[in a new window]
[Download PowerPoint slide]
  		Figure 3. Example output from the TOPCONS webserver, based on the Bacteriorhodopsin sequence from Halobacterium species (SwissProt-ID: BACR_HALS4). (A) Topologies predicted by the individual methods, predicted Z-coordinates, and predicted {Delta}G-values across the sequence. (B) The consensus prediction, which is based on the individual methods, and reliability score across the sequence.

 

   SCAMPI
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 BENCHMARK RESULTS
 THE TOPCONS AND SCAMPI...
 SCAMPI
FUNDING
 REFERENCES
 
Due to the computational limitations arising from the need to run BLAST (18) (Figure 1), only one sequence per query is allowed using TOPCONS, and the prediction typically takes 10–30 s. For large benchmark sets and full proteome scans, the SCAMPI server, implementing only the single-sequence version of SCAMPI (11), may be used instead, which is able to process around 20 000 protein sequences per minute (http://scampi.cbr.su.se/). Here, prediction results are made available in easily parsable text files.


   FUNDING
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 BENCHMARK RESULTS
 THE TOPCONS AND SCAMPI...
 SCAMPI
 FUNDING
REFERENCES
 
Swedish Research Council, the Foundation for Strategic Research, EU 6th Framework Program [Biosapiens, Contract LSHG-CT-2004-512092] and 7th Framework program [EDICT Contract No FP7-HEALTH-F4-2007-201924]. Funding for open access charge: EDICT program.

Conflict of interest statement. None declared.


   Footnotes
 
The authors wish it to be known that, in their opinion, the first two authors should be regarded as joint First Authors.


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 BENCHMARK RESULTS
 THE TOPCONS AND SCAMPI...
 SCAMPI
 FUNDING
 REFERENCES
 

  1. Wallin E, von Heijne G. Genome-wide analysis of integral membrane proteins from eubacterial, archaean, and eukaryotic organisms. Protein Sci. (1998) 7:1029–1038.[Web of Science][Medline]

  2. Krogh A, Larsson B, von Heijne G, Sonnhammer EL. Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. J. Mol. Biol. (2001) 305:567–580.[CrossRef][Web of Science][Medline]

  3. Käll L, Krogh A, Sonnhammer EL. A combined transmembrane topology and signal peptide prediction method. J. Mol. Biol. (2004) 338:1027–1036.[CrossRef][Web of Science][Medline]

  4. Amico M, Finelli M, Rossi I, Zauli A, Elofsson A, Viklund H, von Heijne G, Jones D, Krogh A, Fariselli P, et al. PONGO: a web server for multiple predictions of all-alpha transmembrane proteins. Nucleic Acids Res. (2006) 34:W169–W172.[Abstract/Free Full Text]

  5. Nilsson J, Persson B, von Heijne G. Consensus predictions of membrane protein topology. FEBS Lett. (2000) 486:267–269.[CrossRef][Web of Science][Medline]

  6. Parodi LA, Granatir CA, Maggiora GM. A consensus procedure for predicting the location of alpha-helical transmembrane segments in proteins. Comput. Appl. Biosci. (1994) 10:527–535.[Abstract/Free Full Text]

  7. Arai M, Mitsuke H, Ikeda M, Xia JX, Kikuchi T, Satake M, Shimizu T. ConPred II: a consensus prediction method for obtaining transmembrane topology models with high reliability. Nucleic Acids Res. (2004) 32:W390–W393.[Abstract/Free Full Text]

  8. Davis MJ, Zhang F, Yuan Z, Teasdale RD. MemO: a consensus approach to the annotation of a protein's membrane organization. In Silico Biol. (2006) 6:387–399.[Medline]

  9. Viklund H, Elofsson A. OCTOPUS: improving topology prediction by two-track ANN-based preference scores and an extended topological grammar. Bioinformatics. (2008) 24:1662–1668.[Abstract/Free Full Text]

  10. Viklund H, Elofsson A. Best alpha-helical transmembrane protein topology predictions are achieved using hidden Markov models and evolutionary information. Protein Sci. (2004) 13:1908–1917.[CrossRef][Web of Science][Medline]

  11. Bernsel A, Viklund H, Falk J, Lindahl E, von Heijne G, Elofsson A. Prediction of membrane-protein topology from first principles. Proc. Natl Acad. Sci. USA (2008) 105:7177–7181.[Abstract/Free Full Text]

  12. Li W, Godzik A. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics (2006) 22:1658–1659.[Abstract/Free Full Text]

  13. Jones DT. Improving the accuracy of transmembrane protein topology prediction using evolutionary information. Bioinformatics (2007) 23:538–544.[Abstract/Free Full Text]

  14. Tusnady GE, Simon I. The HMMTOP transmembrane topology prediction server. Bioinformatics (2001) 17:849–850.[Abstract/Free Full Text]

  15. Melen K, Krogh A, von Heijne G. Reliability measures for membrane protein topology prediction algorithms. J. Mol. Biol. (2003) 327:735–744.[CrossRef][Web of Science][Medline]

  16. Granseth E, Viklund H, Elofsson A. ZPRED: predicting the distance to the membrane center for residues in alpha-helical membrane proteins. Bioinformatics (2006) 22:e191–e196.[Abstract/Free Full Text]

  17. Hessa T, Meindl-Beinker NM, Bernsel A, Kim H, Sato Y, Lerch-Bader M, Nilsson I, White SH, von Heijne G. Molecular code for transmembrane-helix recognition by the Sec61 translocon. Nature (2007) 450:1026–1030.[CrossRef][Medline]

  18. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. J. Mol. Biol. (1990) 215:403–410.[CrossRef][Web of Science][Medline]


Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?


This article has been cited by other articles:


Home page
 J. Bacteriol.Home page
O. Neubauer, A. Alfandega, J. Schoknecht, U. Sternberg, A. Pohlmann, and T. Eitinger
Two Essential Arginine Residues in the T Components of Energy-Coupling Factor Transporters
J. Bacteriol., November 1, 2009; 191(21): 6482 - 6488.
[Abstract] [Full Text] [PDF]

Home page
 J. Biol. Chem.Home page
H. Roy and M. Ibba
Broad Range Amino Acid Specificity of RNA-dependent Lipid Remodeling by Multiple Peptide Resistance Factors
J. Biol. Chem., October 23, 2009; 284(43): 29677 - 29683.
[Abstract] [Full Text] [PDF]

This Article
Right arrow Abstract Freely available
Right arrow Print PDF (1275K) Freely available
Right arrow Screen PDF (216K) Freely available
Right arrowOA All Versions of this Article:
37/suppl_2/W465    most recent
gkp363v1
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrow Commercial Re-use Guidelines
for Open Access NAR Content
Google Scholar
Right arrow Articles by Bernsel, A.
Right arrow Articles by Elofsson, A.
PubMed
Right arrow PubMed Citation
Right arrow Articles by Bernsel, A.
Right arrow Articles by Elofsson, A.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?