University of Oxford

Transmembrane Prediction Server

This page has been developed for use locally (at the moment) for the prediction of transmembrane helices in proteins. It is made available publically, but is currently unsupported. If you have a major problem contact benjamin.hall (at) (replace (at) with @) but the response is not guaranteed. This service was developed by John Cuthbertson and the following reference should be cited if used in published work.

Transmembrane helix prediction: a comparative evaluation and analysis.

Protein Eng Des Sel. 2005 Jun;18(6):295-308

This work was funded by the MRC: MRC

None of the original programs were produced or developed by me:

DAS   HMMTOP2.0   MEMSAT2.0   MPEX   PHD   PSORT (ALOM2)   SPLIT-4.0   TMAP   TM-Finder   TMHMM2.0   TMpred   TopPred2  

Enter your query protein sequence into the form below. The sequence should be in one letter code with no identifiers. A good site for converting between different sequence formats is READSEQ

The calculations will take several minutes. The window will remain blank in the meantime.

  1. DAS
  2. For a brief description of the method read the abstract.

    Please cite: M. Cserzo, E. Wallin, I. Simon, G. von Heijne and A. Elofsson: Prediction of transmembrane alpha-helices in procariotic membrane proteins: the Dense Alignment Surface method; Prot. Eng. vol. 10, no. 6, 673-676, 1997

    Send your comments to

  4. The method is described in "G.E Tusnády and I. Simon (1998) Principles Governing Amino Acid Composition of Integral Membrane Proteins: Applications to Topology Prediction." J. Mol. Biol. 283, 489-506. New features of HMMTOP 2.0 are described in "G.E Tusnády and I. Simon(2001). The HMMTOP transmembrane topology prediction server" Bioinformatics 17, 849-850

    Comments to be sent to

  5. MEMSATv1.0 (the current version available online is version 2)
  6. Jones, D. T., Taylor, W. R., Thornton, J. M. (1994) A Model Recognition Approach to the Prediction of All-Helical Membrane Protein Structure and Topology. Biochem. 33: 3038-3049

    Comments to be sent to

  7. MPEx
  8. White & Wimley (1999) Annu. Rev. Biophys. Biomolec. Struct. 28:319-365

    Comments to Stephen White ( or to Sajith Jayasinghe (

  9. PHD
  10. PHDhtm predicts the location and topology of transmembrane helices from multiple sequence alignments Transmembrane helices in integral membrane proteins are predicted by a system of neural networks. The shortcoming of the network system is that often too long helices are predicted. These are cut by an empirical filter. The final prediction (Rost et al., Protein Science, 1995, 4, 521-533) has an expected per-residue accuracy of about 95%. The number of false positives, i.e., transmembrane helices predicted in globular proteins, is about 2% (Rost et al. 1996). The neural network prediction of transmembrane helices (PHDhtm) is refined by a dynamic programming-like algorithm. This method resulted in correct predictions of all transmembrane helices for 89% of the 131 proteins used in a cross-validation test; more than 98% of the transmembrane helices were correctly predicted. The output of this method is used to predict topology, i.e., the orientation of the N-term with respect to the membrane. The expected accuracy of the topology prediction is > 86%. Prediction accuracy is higher than average for eukaryotic proteins and lower than average for prokaryotes. PHDtopology was more accurate than all other methods tested on identical data sets in 1996 (Rost, Casadio & Fariselli, 1996a and 1996b). B Rost: PHD: predicting one-dimensional protein structure by profile based neural networks. Methods in Enzymology, 266, 525-539, 1996. B Rost, P Fariselli, and R Casadio: Topology prediction for helical transmembrane proteins at 86% accuracy. Protein Science, 7, 1704-1718, 1996 Comments to be sent to

  11. ALOM2
  12. Please cite the following references when you publish the results of this program. Klein, P., Kanehisa, M., and De Lisi, C., Biochim. Biophys. Acta, 815, 468-476, 1985. (for the modification using two threshold parameters:) Nakai, K., and Kanehisa, M., Genomics 14, 897-911, 1992. Any comments to . Originally coded by Minoru Kanehisa

  13. SPLIT4.0
  14. Membrane Protein Secondary Structure Prediction Server

    The purpose of this server is to predict the transmembrane (TM) secondary structures of membrane proteins, using the method of preference functions. The method was invented by Davor Juretic, professor at the University of Split, Croatia. This server was written by Damir Zucic,at the University of Osijek , Croatia. Ana Jeroncic was involved both in development of the prediction program and in testing of this server. Click here to read more about Prof. Davor Juretic group. For comments contact prof. dr. Davor Juretic or

  15. TMAP
  16. This program predicts transmembrane segments in proteins, utilising the algorithm described in: "Persson, B. & Argos, P. (1994) Prediction of transmembrane segments in proteins utilsing multiple sequence alignments J. Mol. Biol. 237, 182-192."and "Persson, B. & Argos, P. (1996) Topology prediction of membrane proteins Prot. Sci. 5, 363-371" Users of this program are kindly asked to cite the above references in publications (or other types of presentation). Send your comments to

  17. TM-Finder
  18. Liu, L.-P. and Deber, C.M.: Guidelines for Membrane Protein Engineerin g Derived from de novo Designed Model Peptides. Biopolymers (Peptide Science) 47, 41-62 (1998). (Abstract)

    Liu, L.-P. and Deber, C.M.: Uncoupling Protein Hydrophobicity and Helicity in Nonpolar Environments. J. Biol. Chem 273, 23645-23648 (1998). (Abstract)

    Liu, L.-P. and Deber, C.M.: Combining Hydrophobicity and Helicity: A Novel Approach to Membrane Protein Structure Prediction. Bioorg & Med. Chem.7, 1-7 (1999). (Abstract) Feel free to send comments to

  19. TMHMM2.0
  20. TMHMM is described in:

    Anders Krogh and Bjorn Larsson, Gunnar von Heijne, and Erik L.L. Sonnhammer: Predicting Transmembrane Protein Topology with a Hidden Markov Model: Application to Complete Genomes. J. Mol. Biol. 305:567-580, 2001. and Erik L.L. Sonnhammer, Gunnar von Heijne, and Anders Krogh: A hidden Markov model for predicting transmembrane helices in protein sequences. In J. Glasgow et al., eds.: Proc. Sixth Int. Conf. on Intelligent Systems for Molecular Biology, pages 175-182. AAAI Press, 1998. Comments to be sent to Anders Krogh,

  21. TMpred
  22. The TMpred program makes a prediction of membrane-spanning regions and their orientation. The algorithm is based on the statistical analysis of TMbase, a database of naturally occuring transmembrane proteins. The prediction is made using a combination of several weight-matrices for scoring. K. Hofmann & W. Stoffel (1993) TMbase - A database of membrane spanning proteins segments Biol. Chem. Hoppe-Seyler 347,166

    For further information see the TMbase

    Comments to be sent to

  23. TopPred2
  24. Topology prediction of membrane proteins von Heijne, G. (1992) Membrane Protein Structure Prediction: Hydrophobicity Analysis and the 'Positive Inside' Rule. J.Mol.Biol. 225, 487-494. Claros, M.G., and von Heijne, G. (1994) TopPred II: An Improved Software For Membrane Protein Structure Predictions. CABIOS 10, 685-686. Deveaud and Schuerer (Pasteur Institute) new implementation of the original toppred program, based on G. von Heijne algorithm.

    Comments to be sent to