Membrane topology

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Topology of a transmembrane protein refers to locations of N- and C-termini of membrane-spanning polypeptide chain with respect to the inner or outer sides of the biological membrane occupied by the protein. [1]

Group I and II transmembrane proteins have opposite final topologies. Group I proteins have the N terminus on the far side and C terminus on the cytosolic side. Group II proteins have the C terminus on the far side and N terminus in the cytosol. However final topology is not the only criterion for defining transmembrane protein groups, rather location of topogenic determinants and mechanism of assembly is considered in the classification Group 1 and 2 transmembrane protein.png
Group I and II transmembrane proteins have opposite final topologies. Group I proteins have the N terminus on the far side and C terminus on the cytosolic side. Group II proteins have the C terminus on the far side and N terminus in the cytosol. However final topology is not the only criterion for defining transmembrane protein groups, rather location of topogenic determinants and mechanism of assembly is considered in the classification

Several databases provide experimentally determined topologies of membrane proteins. They include Uniprot, TOPDB, [3] [4] [5] OPM, and ExTopoDB. [6] [7] There is also a database of domains located conservatively on a certain side of membranes, TOPDOM. [8]

Several computational methods were developed, with a limited success, for predicting transmembrane alpha-helices and their topology. Pioneer methods utilized the fact that membrane-spanning regions contain more hydrophobic residues than other parts of the protein, however applying different hydrophobic scales altered the prediction results. Later, several statistical methods were developed to improve the topography prediction and a special alignment method was introduced. [9] According to the positive-inside rule, [10] cytosolic loops near the lipid bilayer contain more positively-charged amino acids. Applying this rule resulted in the first topology prediction methods. There is also a negative-outside rule in transmembrane alpha-helices from single-pass proteins, although negatively charged residues are rarer than positively charged residues in transmembrane segments of proteins. [11] As more structures were determined, machine learning algorithms appeared. Supervised learning methods are trained on a set of experimentally determined structures, however, these methods highly depend on the training set. [12] [13] [14] [15] Unsupervised learning methods are based on the principle that topology depends on the maximum divergence of the amino acid distributions in different structural parts. [16] [17] It was also shown that locking a segment location based on prior knowledge about the structure improves the prediction accuracy. [18] This feature has been added to some of the existing prediction methods. [17] [14] The most recent methods use consensus prediction (i.e. they use several algorithm to determine the final topology) [19] and automatically incorporate previously determined experimental informations. [20] HTP database [21] [22] provides a collection of topologies that are computationally predicted for human transmembrane proteins.

Discrimination of signal peptides and transmembrane segments is an additional problem in topology prediction treated with a limited success by different methods. [23] Both signal peptides and transmembrane segments contain hydrophobic regions which form α-helices. This causes the cross-prediction between them, which is a weakness of many transmembrane topology predictors. By predicting signal peptides and transmembrane helices simultaneously (Phobius [14] ), the errors caused by cross-prediction are reduced and the performance is substantially increased. Another feature used to increase the accuracy of the prediction is the homology (PolyPhobius).”

It is also possible to predict beta-barrel membrane proteins' topology. [24] [25]

See also

Related Research Articles

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<span class="mw-page-title-main">Protein secondary structure</span> General three-dimensional form of local segments of proteins

Protein secondary structure is the local spatial conformation of the polypeptide backbone excluding the side chains. The two most common secondary structural elements are alpha helices and beta sheets, though beta turns and omega loops occur as well. Secondary structure elements typically spontaneously form as an intermediate before the protein folds into its three dimensional tertiary structure.

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<span class="mw-page-title-main">Membrane protein</span> Proteins that are part of, or interact with, biological membranes

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<span class="mw-page-title-main">Transmembrane protein</span> Protein spanning across a biological membrane

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<span class="mw-page-title-main">Gunnar von Heijne</span> Swedish scientist

Professor Nils Gunnar Hansson von Heijne, born 10 June 1951 in Gothenburg, is a Swedish scientist working on signal peptides, membrane proteins and bioinformatics at the Stockholm Center for Biomembrane Research at Stockholm University.

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<span class="mw-page-title-main">David T. Jones (scientist)</span>

David Tudor Jones is a Professor of Bioinformatics, and Head of Bioinformatics Group in the University College London. He is also the director in Bloomsbury Center for Bioinformatics, which is a joint Research Centre between UCL and Birkbeck, University of London and which also provides bioinformatics training and support services to biomedical researchers. In 2013, he is a member of editorial boards for PLoS ONE, BioData Mining, Advanced Bioinformatics, Chemical Biology & Drug Design, and Protein: Structure, Function and Bioinformatics.

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<span class="mw-page-title-main">SLC46A3</span> Protein-coding gene in the species Homo sapiens

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<span class="mw-page-title-main">TMEM106C</span> Protein-coding gene in the species Homo sapiens

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References

  1. ↑ von Heijne, Gunnar (December 2006). "Membrane-protein topology". Nature Reviews Molecular Cell Biology. 7 (12): 909–918. doi:10.1038/nrm2063. PMID   17139331. S2CID   22218266.
  2. ↑ Goder, Veit; Spiess, Martin (31 August 2001). "Topogenesis of membrane proteins: determinants and dynamics". FEBS Letters. 504 (3): 87–93. doi:10.1016/S0014-5793(01)02712-0. PMID   11532438.
  3. ↑ Tusnády, Gábor E.; Kalmár, Lajos; Simon, István (1 January 2008). "TOPDB: topology data bank of transmembrane proteins". Nucleic Acids Research. 36 (suppl_1): D234–D239. doi: 10.1093/nar/gkm751 . PMC   2238857 . PMID   17921502.
  4. ↑ Dobson, László; Langó, Tamás; Reményi, István; Tusnády, Gábor E. (28 January 2015). "Expediting topology data gathering for the TOPDB database". Nucleic Acids Research. 43 (D1): D283–D289. doi: 10.1093/nar/gku1119 . PMC   4383934 . PMID   25392424.
  5. ↑ ".:TOPDB:". topdb.enzim.hu.
  6. ↑ Tsaousis, Georgios N.; Tsirigos, Konstantinos D.; Andrianou, Xanthi D.; Liakopoulos, Theodore D.; Bagos, Pantelis G.; Hamodrakas, Stavros J. (1 October 2010). "ExTopoDB: a database of experimentally derived topological models of transmembrane proteins". Bioinformatics. 26 (19): 2490–2492. doi: 10.1093/bioinformatics/btq362 . PMID   20601677.
  7. ↑ "ExTopoDB - Index". bioinformatics.biol.uoa.gr.
  8. ↑ ".:TOPDOM:". topdom.enzim.hu.
  9. ↑ "DAS".
  10. ↑ von Heijne, Gunnar (November 1986). "The distribution of positively charged residues in bacterial inner membrane proteins correlates with the trans-membrane topology". The EMBO Journal. 5 (11): 3021–3027. doi:10.1002/j.1460-2075.1986.tb04601.x. PMC   1167256 . PMID   16453726.
  11. ↑ Baker, James Alexander; Wong, Wing-Cheong; Eisenhaber, Birgit; Warwicker, Jim; Eisenhaber, Frank (2017). "Charged residues next to transmembrane regions revisited: "Positive-inside rule" is complemented by the "negative inside depletion/outside enrichment rule"". BMC Biology. 15 (1): 66. doi:10.1186/s12915-017-0404-4. PMC   5525207 . PMID   28738801.
  12. ↑ Krogh, Anders; Larsson, Björn; von Heijne, Gunnar; Sonnhammer, Erik L.L (January 2001). "Predicting transmembrane protein topology with a hidden markov model: application to complete genomes11Edited by F. Cohen" (PDF). Journal of Molecular Biology. 305 (3): 567–580. doi:10.1006/jmbi.2000.4315. PMID   11152613. S2CID   15769874. Archived from the original (PDF) on 2020-08-04.
  13. ↑ "TMHMM-2.0 - redirect". www.cbs.dtu.dk.
  14. 1 2 3 "Phobius". phobius.sbc.su.se.
  15. ↑ "OCTOPUS server".
  16. ↑ Tusnády, Gábor E.; Simon, István (October 1998). "Principles governing amino acid composition of integral membrane proteins: application to topology prediction 1 1Edited by J. Thornton" (PDF). Journal of Molecular Biology. 283 (2): 489–506. doi:10.1006/jmbi.1998.2107. PMID   9769220. S2CID   15027232. Archived from the original (PDF) on 2020-02-08.
  17. 1 2 "HMMTOP". www.enzim.hu.
  18. ↑ Tusnady, G. E.; Simon, I. (1 September 2001). "The HMMTOP transmembrane topology prediction server". Bioinformatics. 17 (9): 849–850. doi:10.1093/bioinformatics/17.9.849. PMID   11590105.
  19. ↑ "TOPCONS: Consensus prediction of membrane protein topology and signal peptides". topcons.net.
  20. ↑ "CCTOP". cctop.ttk.hu.
  21. ↑ Dobson, László; Reményi, István; Tusnády, Gábor E. (28 May 2015). "The human transmembrane proteome". Biology Direct. 10 (1): 31. doi: 10.1186/s13062-015-0061-x . PMC   4445273 . PMID   26018427.
  22. ↑ The human transmembrane proteome database
  23. ↑ E. Tusnady, Gabor; Simon, Istvan (1 November 2010). "Topology Prediction of Helical Transmembrane Proteins: How Far Have We Reached?" (PDF). Current Protein & Peptide Science. 11 (7): 550–561. doi:10.2174/138920310794109184. PMID   20887261. S2CID   6431228. Archived from the original (PDF) on 7 March 2019.
  24. ↑ Tsirigos, Konstantinos D.; Elofsson, Arne; Bagos, Pantelis G. (1 September 2016). "PRED-TMBB2: improved topology prediction and detection of beta-barrel outer membrane proteins". Bioinformatics. 32 (17): i665–i671. doi: 10.1093/bioinformatics/btw444 . PMID   27587687.
  25. ↑ Savojardo, Castrense; Fariselli, Piero; Casadio, Rita (15 February 2013). "BETAWARE: a machine-learning tool to detect and predict transmembrane beta-barrel proteins in prokaryotes". Bioinformatics. 29 (4): 504–505. doi: 10.1093/bioinformatics/bts728 . PMID   23297037.
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