Membrane proteins:

A good introduction into membrane proteins and their structural organization can be found in the course "Principles of Protein Structure Using the Internet" at the Birkbeck University of London.

Membrane proteins constitute a significant portion of all proteins. The first complete genome sequences allowed to estimate the percentage of cytoplasmic membrane proteins; for Escherichia coli a value of 20 % was predicted (Boyd et al., 1998). Erik Wallin and Gunnar von Heijne (1998) estimated that 20 to 30 % of all Open Reading Frames (ORFs) code for integral alpha-helical membrane proteins (cytoplasmic membrane proteins). Similar values were suggested by David T. Jones (1998) upon analysis of five genomes using MEMSAT. Fifteen single cell organisms were analyzed using the program SOSUI and led to the prediction of 15-20 % polytopic membrane proteins (Mitaku et al., 1999). Die Verfügbarkeit ganzer Genomsequenzen erlaubte auch die Beantwortung der Frage, ob komplexe Organismen einen höheren Anteil an Membranproteinen aufweisen als niedere Organismen. Diese Frage konnte verneint werden (Stevens and Arkin, 2000).

Membrane proteins fulfill a multitude of functions, such as signal transduction or transport of substrates. Milton Saier suggested a procedure to classify transport systems (Saier, 1998). Von Ian Paulsen und Mitarbeitern stammt ein genomischer Vergleich von Membrantransportsystemen (Paulsen et al., 2000).

Prediction of transmembrane helices:

The following websites predict membrane-spannening protein segments, using different algorithms. The lab of Gunnar von Heijne developed several algoriths to predict the topology of cytoplasmic membrane proteins and tested the corresponding rules experimentally. Several comparisons of different algorithms are found below in the list of references.

Several studies have shown that the combination of different algorithms can improve the prediction reliability:

• Johan Nilsson, Bengt Persson, and Gunnar von Heijne (2000)
  Consensus prediction of membrane protein topology
  FEBS Lett. 486: 267-269
  (TMHMM, HMMTOP, MEMSAT, TopPred, PHD)

• Masami Ikeda, Masafumi Arai, Demelo M Lao, and Toshio Shimizu (2002)
  Transmembrane topology prediction methods: a re-assessment and improvement by a consensus method using a dataset of experimentally-characterized transmembrane topologies
  In Silico Biol. 2: 19-33
  (KKD, TMpred, TopPred II, DAS, TMAP, MEMSAT 2, SOSUI, PRED-TMR2, TMHMM 2.0 and HMMTOP 2.0)

• Paul D. Taylor, Teresa K. Attwood, and Darren R. Flower (2003)
  BPROMPT: A consensus server for membrane protein prediction
  Nucleic Acids Res. 31: 3698-3700
  (DAS, HMMTOP2, TopPred II, TMpred, SOSUI)



• Two "classic algorithms" from the beginning of the 1980ies (Hopp and Woods sowie Kyte and Doolittle) und eine Fourier-Transformation der Hydrophobizitätsanalyse findet man auf einem Server am Weizmann-Institut in Israel. Ein ähnlicher Service zur Darstellung von Aminosäureeigenschaften steht bei ExPASy und bei NPS@ can be found at the IBCP in Lyon.

  Thomas P. Hopp and Kenneth R. Woods (1981)
  Prediction of protein antigenic determinants from amino acid sequences
  Proc. Natl. Acad. Sci. USA 78: 3824-3828

  Jack Kyte and Russell F. Doolittle (1982)
  A simple method for displaying the hydropathic character of a protein
  J. Mol. Biol. 157: 105-132

• TopPred 2
  Gunnar von Heijne (1992)
  Membrane protein structure prediction, hydrophobicity analysis and the positive-inside rule
  J. Mol. Biol. 225: 487-494

• TMpred
  K. Hofmann and W. Stoffel (1993)
  TMbase - A database of membrane spanning proteins segments
  Biol. Chem. Hoppe-Seyler 347: 166 ff

• SPLIT
• SPLIT 4.0
  D. Juretic, B. Lee, N. Trinajstic, and R.W. Williams (1993)
  Conformational preference functions for predicting helices in membrane proteins
  Biopolymers 33: 255-273

• MEMSAT
  D.T. Jones, W.R. Taylor, and J.M. Thornton (1994)
  A model recognition approach to the prediction of all-helical membrane protein structure and topology
  Biochemistry 33: 3038-3049

• TMAP, basierend auf einer einzelnen Sequenz
• TMAP, basierend auf einem multiplen Sequenzalignment
  B. Persson and P. Argos (1994)
  Prediction of transmembrane segments in proteins utilising multiple sequence alignments
  J. Mol. Biol. 237 (2) 182-192
  und
  B. Persson and P. Argos (1996)
  Topology prediction of membrane proteins
  Protein Sci. 5 (2) 363-371

• TransMem (for download)
  P. Aloy, J. Cedano, B. Oliva, F.X. Aviles, and E. Querol (1997)
  'TransMem': a neural network implemented in Excel spreadsheets for predicting transmembrane domains of proteins
  Comput. Appl. Biosci. 13: 231-234

• DAS: Dense Alignment Surface-Methode
  M. Cserzo, E. Wallin, I. Simon, G. von Heijne, and A. Elofsson (1997)
  Prediction of transmembrane alpha-helices in prokaryotic membrane proteins: the dense alignment surface method
  Protein Eng. 10 (6) 673-676

• SOSUI ("hydrophob sein" auf japanisch)
  Takatsugu Hirokawa, Seah Boon-Chieng, and Shigeki Mitaku (1998)
  SOSUI: classification and secondary structure prediction system for membrane proteins
  Bioinformatics 14 (4) 378-379
  Einführung

• TMHMM 1.0
  Erik L.L. Sonnhammer, Gunnar von Heijne, and Anders Krogh (1998)
  A hidden Markov model for predicting transmembrane helices in protein sequences
  In Proc. of Sixth Int. Conf. on Intelligent Systems for Molecular Biology, p 175-182 Ed J. Glasgow, T. Littlejohn, F. Major, R. Lathrop, D. Sankoff, and C. Sensen Menlo Park, CA: AAAI Press, 1998
  Einführung

• TMHMM 2.0
  Anders Krogh, B. Larsson, Gunnar von Heijne, and Erik L.L. Sonnhammer (2001)
  Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes
  J. Mol. Biol. 305 (3) 567-580
  Einführung

• HMMTOP 1.1
  Gabor E. Tusnády and Istvan Simon (1998)
  Principles governing amino acid composition of integral membrane proteins: Applications to topology prediction
  J. Mol. Biol. 283: 489-506

• HMMTOP 2.0
  Gabor E. Tusnády and Istvan Simon (2001)
  The HMMTOP transmembrane topology prediction server
  Bioinformatics 17: 849-850

• PRED-TMR 1.0
  Claude Pasquier, V.J. Promponas, G.A. Palaios, J.S. Hamodrakas, and Stavros J. Hamodrakas (1999)
  A novel method for predicting transmembrane segments in proteins based on a statistical analysis of the SwissProt database: the PRED-TMR algorithm
  Protein Eng. 12: 381-385

• PRED-TMR 2.0
  Claude Pasquier and Stavros J. Hamodrakas (1999)
  An hierarchical artificial neural network system for the classification of transmembrane proteins
  Protein Eng. 12: 631-634

• OrienTM
  Theodore D. Liakopoulos, Claude Pasquier, and Stavros J. Hamodrakas (2001)
  A novel tool for the prediction of transmembrane protein topology based on a statistical analysis of the SwissProt database: the OrienTM algorithm
  Protein Eng. 14: 387-390

• TM Finder (Login)
  Charles M. Deber, Chen Wang, Li-Ping Liu, Andrew S. Prior, Shuchi Agrawal, Brenda L. Muskat, and A. Jamie Cuticchia (2001)
  TM Finder: a prediction program for transmembrane protein segments using a combination of hydrophobicity and nonpolar phase helicity scales
  Protein Sci. 10: 212-219

• MPEx
  Sajith Jayasinghe, Kalina Hristova, and Stephen H. White (2001)
  Energetics, stability, and prediction of transmembrane helices
  J. Mol. Biol. 312: 927-934

• IRENE (Login)
• IRENE (Run)
  P.L. Martelli, P. Fariselli, G. Tasco, and R. Casadio (2003)
  Membrane protein structure and genome structural annotation
  Comparative and Functional Genomics 4: 406-409

• MaxSubSeq: an algorithm for finding all the maximal scoring subsequences with constrained segment lenghts
• MaxSubSeqKD: Transmembrane Helix Predictions with MaxSubSeq and Kyte-Doolittle's scale
  Piero Fariselli, Michele Finelli, Davide Marchignoli, Pier Luigi Martelli, Ivan Rossi I, and Rita Casadio (2003)
  MaxSubSeq: an algorithm for segment-length optimization. The case study of the transmembrane spanning segments
  Bioinformatics 19: 500-505

• TMHMMfix: Topology prediction with optional fixing and reliability score calculation
  Karin Melén, Anders Krogh, and Gunnar von Heijne (2003)
  Reliability measures for membrane protein topology prediction algorithms
  J. Mol. Biol. 327: 735-744

• HTMR: Predictor of Transmembrane Alpha Helices in Membrane Proteins (NN based)
  BioDec

• BPROMPT: Bayesian PRediction Of Membrane Protein Topology
  Paul D. Taylor, Teresa K. Attwood, and Darren R. Flower (2003)
  BPROMPT: A consensus server for membrane protein prediction
  Nucleic Acids Res. 31: 3698-3700

• SVMtm
  Zheng Yuan, John S. Mattick, Rohan D. Teasdale (2004)
  SVMtm: Support vector machines to predict transmembrane segments
  J. Comput. Chem. 25: 632-636.

• Phobius
  Phobius
  Lukas Käll, Anders Krogh, and Erik L. Sonnhammer (2004)
  A combined transmembrane topology and signal peptide prediction method
  J. Mol. Biol. 338: 1027-1036

• PRODIV-TMHMM, PRO-TMHMM, and S-TMHMM
  Håkan Viklund and Arne Elofsson (2004)
  Best α-helical transmembrane protein topology predictions are achieved using hidden Markov models and evolutionary information
  Protein Sci. 13: 1908-1917

• PolyPhobius
  Lukas Käll, Anders Krogh, and Erik L. Sonnhammer (2005)
  An HMM posterior decoder for sequence feature prediction that includes homology information
  Bioinformatics 21: i251-i257

• PONGO
  Mauro Amico, Michele Finelli, Ivan Rossi, Andrea Zauli, Arne Elofsson, Håkan Viklund, Gunnar von Heijne, David Jones, Anders Krogh, Piero Fariselli, Pier Luigi Martelli, and Rita Casadio (2006)
  PONGO: a web server for multiple predictions of all-alpha transmembrane proteins
  Nucleic Acids Res. 34: W169-W272

• SCAMPI
  Andreas Bernsel, Håkan Viklund, Jenny Falk, Erik Lindahl, Gunnar von Heijne, and Arne Elofsson (2008)
  Prediction of membrane-protein topology from first principles
  Proc. Natl. Acad. Sci. USA 105: 7177-7181

• OCTOPUS
  Håkan Viklund and Arne Elofsson (2008)
  OCTOPUS: improving topology prediction by two-track ANN-based preference scores and an extended topological grammar
  Bioinformatics 24: 1662-1668

• SPOCTOPUS
  Håkan Viklund, Andreas Bernsel, M. Skwark, and Arne Elofsson (2008)
  SPOCTOPUS: a combined predictor of signal peptides and membrane protein topology
  Bioinformatics 24: 2928-2929

• TOPCONS
  Andreas Bernsel, Håkan Viklund, Aron Hennerdal, and Arne Elofsson (2009)
  TOPCONS: consensus prediction of membrane protein topology
  Nucleic Acids Res. 37: W465-W468

Prediction of transmembrane β strands:

The polypeptide chain of cytoplasmic membrane proteins traverses the lipid bilayer in the form of α helices. By contrast, the polypeptide chain of bacterial outer membrane proteins traverses the lipid bilayer in the form of β strands (Koebnik et al., 2000). Estimations using the program Hunter suggest that outer membrane proteins constitute 1.5 % to 2.4 % of the proteome of Gram-negative bacteria (Casadio et al., 2003).

Membrane-spanning β strands are less easy to predict and existing algorithms are less precise for this class of membrane proteins. Still, some of them are listet below:

Horst Vogel and Fritz Jähnig (1986)
Models for the structure of outer-membrane proteins of Escherichia coli derived from raman spectroscopy and prediction methods
J. Mol. Biol. 190: 191-199

Tilman Schirmer and Sandra W. Cowan (1993)
Prediction of membrane-spanning beta-strands and its application to maltoporin
Protein Sci. 2: 1361-1363

Andrew F. Neuwald, Jun S. Liu, and Charles E. Lawrence (1995)
Gibbs motif sampling: detection of bacterial outer membrane protein repeats
Protein Sci. 4: 1618-1632

M. Michael Gromiha, R. Majumdar, and P.K. Ponnuswamy (1997)
Identification of membrane spanning beta strands in bacterial porins
Protein Eng. 10: 497-500

• Omp_topo_predict (NN)
  Kay Diederichs, Jörg Freigang, Stephan Umhau, Kornelius Zeth, and Jason Breed (1998)
  Prediction by a neural network of outer membrane beta-strand protein topology
  Protein Sci. 7: 2413-2420

• B2TMPRED (Login)
• B2TMPRED (Run)
  Irene Jacoboni, Pier Luigi Martelli, Piero Fariselli, Vito De Pinto, and Rita Casadio (2001)
  Prediction of the transmembrane regions of beta-barrel membrane proteins with a neural network-based predictor
  Protein Science 10: 779-787

  William C. Wimley (2002)
  Toward genomic identification of beta-barrel membrane proteins: composition and architecture of known structures
  Protein Sci. 11: 301-312

• B2TMR / HMM-B2TMR: Topologievorhersage von Außenmembranproteinen (NN/HMM) (Home)
• B2TMR / HMM-B2TMR (Login)
  Pier Luigi Martelli, Piero Fariselli, Anders Krogh, and Rita Casadio (2002)
  A sequence-profile-based HMM for predicting and discriminating beta barrel membrane proteins
  Bioinformatics 18: S46-S53

  BBF: Beta-Barrel Finder
  Yufeng Zhai and Milton H. Saier, Jr. (2002)
  The beta-barrel finder (BBF) program, allowing identification of outer membrane beta-barrel proteins encoded within prokaryotic genomes
  Protein Sci. 11: 2196-2207

• TMBETA (NN)
  M. Michael Gromiha, Shandar Ahmad, and Makiko Suwa (2004)
  Neural network-based prediction of transmembrane beta-strand segments in outer membrane proteins
  J. Comp. Chem. 25: 762-767
  M. Michael Gromiha and Makiko Suwa (2005)
  A simple statistical method for discriminating outer membrane proteins with better accuracy
  Bioinformatics 21: 961-968
  M. Michael Gromiha, Shandar Ahmad, and Makiko Suwa (2005)
  Application of residue distribution along the sequence for discriminating outer membrane proteins
  Comput. Biol Chem. 29: 135-142

• TMBETA-NET (NN)
  M. Michael Gromiha, Shandar Ahmad, and Makiko Suwa (2005)
  TMBETA-NET: discrimination and prediction of membrane spanning beta-strands in outer membrane proteins
  Nucleic Acids Res. 33: W164-W167

• TBBpred (SVM)
  N.K. Natt, H. Kaur, and G.P. Raghava (2004)
  Prediction of transmembrane regions of beta-barrel proteins using ANN- and SVM-based methods
  Proteins 56: 11-18

• ProfTMB (HMM)
  Henry R. Bigelow, Donald S. Petrey, Jinfeng Liu, Dariusz Przybylski, and Burkhard Rost (2004)
  Predicting transmembrane beta-barrels in proteomes
  Nucleic Acids Res. 32: 2566-2577

Identification of signal sequences for secretion across the cytoplasmic membrane:

To some extent, identification of classic N-terminal signal sequences for secretion across the cytoplasmic membrane is a variation of predicting membrane-spanning protein segments. These sequence segments are as well hydrophobic but are enzymatically cleaved by the signal peptidase upon transport and are thus not present in the mature protein. The specific recognition of the cleavage site has been described by the "(-3, -1) rule" (von Heijne, 1983; Perlman and H.O. Halvorson, 1983), i.e. positions -3 und -1 upstream of the cleavage site are strongly enriched in certain amino acids (-3: Ala, Gly, Ser, Leu, Val, Ile; -1: Ala, Gly, Ser).

General Secretion Pathway:
• SignalP 1.1 (OUTDATED)
  Henrik Nielsen, Jacob Engelbrecht, Søren Brunak, and Gunnar von Heijne (1997)
  Identification of prokaryotic and eukaryotic signal peptides and prediction of their cleavage sites
  Protein Eng. 10: 1-6

• SignalP 2.0 beta-Version 2
  Henrik Nielsen, Søren Brunak, and Gunnar von Heijne (1999)
  Machine learning approaches to the prediction of signal peptides and other protein sorting signals
  Protein Eng. 12: 3-9

• SignalP 3.0
  Jannick Dyrløv Bendtsen, Henrik Nielsen, Gunnar von Heijne, and Søren Brunak (2004)
  Improved prediction of signal peptides - SignalP 3.0
  J. Mol. Biol. 340: 783-795
  • Scientific background

• Phobius
  Phobius
  Lukas Käll, Anders Krogh, and Erik L. Sonnhammer (2004)
  A combined transmembrane topology and signal peptide prediction method
  J. Mol. Biol. 338: 1027-1036

• SOSUI
  Takatsugu Hirokawa, Seah Boon-Chieng, and Shigeki Mitaku (1998)
  SOSUI: classification and secondary structure prediction system for membrane proteins
  Bioinformatics 14 (4) 378-379

• ACNpredictor
  B. Jagla and J. Schuchhardt (2000)
  Adaptive encoding neural networks for the recognition of human signal peptide cleavage sites
  Bioinformatics 16: 245-250

• Human Signal Peptide Prediction HMM (for Download)
  Zemin Zhang and William J. Henzel (2004)
  Signal peptide prediction based on analysis of experimentally verified cleavage sites
  Protein Sci. 13: 2819-2824

• PolyPhobius
  Lukas Käll, Anders Krogh, and Erik L. Sonnhammer (2005)
  An HMM posterior decoder for sequence feature prediction that includes homology information
  Bioinformatics 21: i251-i257

• SPOCTOPUS
  Håkan Viklund, Andreas Bernsel, M. Skwark, and Arne Elofsson (2008)
  SPOCTOPUS: a combined predictor of signal peptides and membrane protein topology
  Bioinformatics 24: 2928-2929
  Lipoproteins:

• SPEPlip (Login)
• SPEPLip (Run)
  Piero Fariselli, Giacomo Finocchiaro, and Rita Casadio (2003)
  SPEPlip: the detection of signal peptide and lipoprotein cleavage sites
  Bioinformatics 19: 2498-2499

• LipoP
  Agnieszka S. Juncker, Hanni Willenbrock, Gunnar Von Heijne, Søren Brunak, Henrik Nielsen, Anders Krogh (2003)
  Prediction of lipoprotein signal peptides in Gram-negative bacteria
  Protein Sci. 12: 1652-1662
  Twin-Arginine Pathway:

• Twin Arginine Translocation: A Way Out for Halophilic Proteins
  R. Wesley Rose, Thomas Brüser, Jessica C. Kissinger, and Mechthild Pohlschröder (2002)
  Adaptation of proteins secretion to extremely high salt concentrations by extensive use of the twin arginine translocation pathway
  Mol. Microbiol. 45: 943-950

• Predicted Tat Substrates
  K. Dilks, R. W. Rose, and M. Pohlschröder (2003)
  Prokaryotic use of the Twin arginine translocation pathway: a genomic survey
  J. Bacteriol. 185: 1478-1483

• TATscan (C++ program, no web server)
  Haiming Li, Pierre-Étienne Jacques, Mariana Gabriela Ghinet, Ryszard Brzezinski, and Rolf Morosoli (2005)
  Determining the functionality of putative Tat-dependent signal peptides in Streptomyces coelicolor A3(2) by using two different reporter proteins
  Microbiol. 151: 2189-2198

• TatP
  Jannick Dyrløv Bendtsen, Henrik Nielsen, David Widdick, Tracy Palmer, and Søren Brunak (2005)
  Prediction of twin-arginine signal peptides
  BMC Bioinformatics 6: 167
  Eukaryotic Secretory Pathway:

• SecretomeP 1.0 (Mammalian secretory proteins)
  Jannick Dyrløv Bendtsen, Lars Juhl Jensen, Nikolaj Blom, Gunnar von Heijne, and Søren Brunak (2004)
  Feature based prediction of non-classical and leaderless protein secretion
  Protein Eng. Des. Sel. 17: 349-356

Databases of membrane proteins:
• TMbase: Membrane protein database at the EMBnet (Switzerland)

• Collection of membrane proteins in a database at the University of Tokio (Japan) (OUTDATED)

• Collection of well-characterized membrane proteins (available via FTP)
  Steffen Möller, Evgenia V. Kriventseva, and Rolf Apweiler (2000)
  A collection of well characterised integral membrane proteins
  Bioinformatics 16: 1159-1160

• MPtopo database
  Sajith Jayasinghe, Kalina Hristova, and Stephen H. White (2001)
  MPtopo: A database of membrane protein topology
  Protein Sci. 10: 455-458

• DOLOP (Database Of bacterial LipOProteins)
  M. Madan Babu and K. Sankaran (2002)
  DOLOP—database of bacterial lipoproteins
  Bioinformatics 18: 641-643

• TMPDB database
  Masami Ikeda, Masafumi Arai, Toshikatsu Okuno, and Toshio Shimizu (2003)
  TMPDB: a database of experimentally-characterized transmembrane topologies
  Nucleic Acids Res. 31: 406-409

• PDB_TM database
  GáborTusnády, ZsuzsannaDosztányi, and István Simon (2004)
  Transmembrane proteins in the Protein Data Bank: identification and classification
  Bioinformatics 20: 406-409

Further web resources:
• RbDe: Residue-based Diagram editor - Online construction of topology models of alpha-helical transmembrane proteins
  K. Konvicka, F. Campagne, and H. Weinstein (2000)
  Interactive construction of residue-based diagrams of proteins: the RbDe web service
  Protein Eng. 13: 395-396

• CoPreTHi: Combined Prediction of Transmembrane Helices
  V.J. Promponas, G.A. Palaios, C.M. Pasquier, J.S. Hamodrakas, and S.J. Hamodrakas (1999)
  CoPreTHi: a Web tool which combines transmembrane protein segment prediction methods
  In Silico Biol. 1: 159-162

Sequence examples:
• Database of amino acid sequences via Entrez
• Bacteriorhodopsin from Halobacterium salinarium: Seven-helix bundle protein
• LacY: Lactose permease from Escherichia coli
• MalE: Maltose-binding protein from Escherichia coli: Protein with N-terminal signal sequence for secretion into the periplasmic space
• MalF: Subunit of the maltose permease from Escherichia coli
• MalG: Subunit of the maltose permease from Escherichia coli
• MalK: ATPase subunit of the maltose transport system from Escherichia coli
• OmpA from Escherichia coli: Two-domain protein: the N-terminal protein domain is embedded into the outer membrane in form of an 8-stranded β barrel while the C-terminal protein domain is found in the periplasmic space
• TonB from Escherichia coli: Protein with N-terminal transmembrane helix
• TolA from Escherichia coli: Protein with N-terminal transmembrane helix

• Example of a multiple sequence alignment in MSF format

References

Latest update: October 14, 2009

Ralf Koebnik
Institut de recherche pour le dèveloppement
UMR 5096, CNRS-UP-IRD
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Phone: +33 (0)4 67 41 62 28
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