Motivation. Membrane proteins play essential roles in cellular processes of organisms. Photosynthesis, transport of ions and small molecules, signal transduction, and light harvesting are examples of processes which are realised by membrane proteins and contribute to a cell's specificity and functionality. The analysis of membrane proteins has shown to be an important part in the understanding of complex biological processes. Genome-wide investigations of membrane proteins have revealed a large number of short, distinct sequence motifs. Results. The in silico analysis of 32 membrane protein families with domains of unknown functions discussed in this study led to a novel approach which describes the separation of motifs by residue-specific distributions. Based on these distributions, the topology structure of the majority of motifs in hypothesised membrane proteins with unknown topology can be predicted. Conclusion. We hypothesise that short sequence motifs can be separated into structure-forming motifs on the one hand, as such motifs show high prediction accuracy in all investigated protein families. This points to their general importance in α-helical membrane protein structure formation and interaction mediation. On the other hand, motifs which show high prediction accuracies only in certain families can be classified as functionally important and relevant for family-specific functional characteristics. 1. Introduction Membrane proteins are essential for many fundamental biological processes within organisms. Active nutrient transport, signal and energy transduction, and ion flow are only a few of the numerous functions enabled by membrane proteins [1]. Membrane proteins obtain their specific functionality by individual folding and interactions with the hydrophobic membrane environment as well as, in many cases, by oligomeric complex formation and protein-protein interactions [1, 2]. The identification of such complexes and interactions is valuable, since, on the one hand, detailed information of the function of an unknown membrane protein can be obtained by analysing its interactions with proteins of known function. On the other hand, biological processes can be comprehended as a dynamically fluctuating system, whereby the biological role of the unknown membrane protein can be defined more precisely [3, 4]. Accordingly, destabilisation of the three-dimensional structure of a membrane protein caused by mutations or ligand interactions are triggers for numerous diseases, for example, diabetes insipidus, cystic fibrosis, hereditary deafness and
References
[1]
M. Luckey, Membrane Structural Biology, Cambrigde University Press, 2008.
[2]
M. H. Y. Lam and I. Stagljar, “Strategies for membrane interaction proteomics: proteomics: no mass spectrometry required,” Proteomics, vol. 12, no. 10, pp. 1519–1526, 2012.
[3]
D. Eisenberg, E. M. Marcotte, I. Xenarios, and T. O. Yeates, “Protein function the post-genomic era,” Nature, vol. 405, no. 6788, pp. 823–826, 2000.
[4]
N. Lan, G. T. Montelione, and M. Gerstein, “Ontologies for proteomics: towards a systematic definition of structure and function that scales to the genome level,” Current Opinion in Chemical Biology, vol. 7, no. 1, pp. 44–54, 2003.
[5]
A. Marsico, D. Labudde, T. Sapra, D. J. Muller, and M. Schroeder, “A novel pattern recognition algorithm to classify membrane protein unfolding pathways with high-throughput single-molecule force spectroscopy,” Bioinformatics, vol. 23, no. 2, pp. e231–e236, 2007.
[6]
M. Childers, G. Eckel, A. Himmel, and J. Caldwell, “A new model of cystic fibrosis pathology: lack of transport of glutathione and its thiocyanate conjugates,” Medical Hypotheses, vol. 68, no. 1, pp. 101–112, 2006.
[7]
S. M. Rowe, S. Miller, and E. J. Sorscher, “Cystic fibrosis,” The New England Journal of Medicine, vol. 352, no. 19, pp. 1992–2001, 2005.
[8]
S. Tan, T. T. Hwee, and M. C. M. Chung, “Membrane proteins and membrane proteomics,” Proteomics, vol. 8, no. 19, pp. 3924–3932, 2008.
[9]
G. C. Brito and D. W. Andrews, “Removing bias against membrane proteins in interaction networks,” BMC Systems Biology, vol. 5, article 169, 2011.
[10]
P. G. Sadowski, A. J. Groen, P. Dupree, and K. S. Lilley, “Subcellular localization of membrane proteins,” Proteomics, vol. 8, no. 19, pp. 3991–4011, 2008.
[11]
J. U. Bowie, “Solving the membrane protein folding problem,” Nature, vol. 438, no. 7068, pp. 581–589, 2005.
[12]
G. E. Tusnady, Z. Dosztanyi, and I. Simon, “Transmembrane proteins in the protein data bank: identification and classification,” Bioinformatics, vol. 20, no. 17, pp. 2964–2972, 2004.
[13]
G. E. Tusnady, Z. Dosztanyi, and I. Simon, “Pdbtm: selection and membrane localization of transmembrane proteins in the protein data bank,” Nucleic Acids Research, vol. 33, pp. D275–D278, 2005.
[14]
A. Krogh, B. Larsson, G. Von Heijne, and E. L. L. Sonnhammer, “Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes,” Journal of Molecular Biology, vol. 305, no. 3, pp. 567–580, 2001.
[15]
B. Rost, R. Casadio, P. Fariselli, and C. Sander, “Transmembrane helices predicted at 95% accuracy,” Protein Science, vol. 4, no. 3, pp. 521–533, 1995.
[16]
D. T. Jones, “Improving the accuracy of transmembrane protein topology prediction using evolutionary information,” Bioinformatics, vol. 23, no. 5, pp. 538–544, 2007.
[17]
S. Hayat and A. Elofsson, “Boctopus: improved topology prediction of transmembrane β-barrel proteins,” Bioinformatics, vol. 28, no. 4, pp. 516–522, 2012.
[18]
Y. Liu, D. M. Engelman, and M. Gerstein, “Genomic analysis of membrane protein families: abundance and conserved motifs,” Genome Biology, vol. 3, no. 10, research0054, 2002.
[19]
I. T. Arkin and A. T. Brunger, “Statistical analysis of predicted transmembrane alpha-helices,” Biochim Biophys Acta, vol. 1429, no. 1, pp. 113–128, 1998.
[20]
A. Senes, M. Gerstein, and D. M. Engelman, “Statistical analysis of amino acid patterns in transmembrane helices: the GxxxG motif occurs frequently and association with β-branched residues at neighboring positions,” Journal of Molecular Biology, vol. 296, no. 3, pp. 921–936, 2000.
[21]
W. P. Russ and D. M. Engelman, “The GxxxG motif: a framework for transmembrane helix-helix association,” Journal of Molecular Biology, vol. 296, no. 3, pp. 911–919, 2000.
[22]
A. Senes, D. E. Engel, and W. F. DeGrado, “Folding of helical membrane proteins: the role of polar, gxxxg-like and proline motifs,” Current Opinion in Structural Biology, vol. 14, no. 4, pp. 465–479, 2004.
[23]
R. A. Melnyk, S. Kim, A. R. Curran, D. M. Engelman, J. U. Bowie, and C. M. Deber, “The Affinity of GXXXG Motifs in Transmembrane Helix-Helix Interactions Is Modulated by Long-range Communication,” Journal of Biological Chemistry, vol. 279, no. 16, pp. 16591–16597, 2004.
[24]
D. Schneider, C. Finger, A. Prod？hl, and T. Volkmer, “From interactions of single transmembrane helices to folding of α-helical membrane proteins: analyzing transmembrane helix-helix interactions in bacteria,” Current Protein and Peptide Science, vol. 8, no. 1, pp. 45–61, 2007.
[25]
D. Schneider and D. M. Engelman, “Motifs of two small residues can assist but are not sufficient to mediate transmembrane helix interactions,” Journal of Molecular Biology, vol. 343, no. 4, pp. 799–804, 2004.
[26]
R. Jackups and J. Liang, “Combinatorial model for sequence and spatial motif discovery in short sequence fragments: examples from beta-barrel membrane proteins,” IEEE Engineering in Medicine and Biology Society, vol. 1, pp. 3470–3473, 2006.
[27]
M. Punta, P. C. Coggill, R. Y. Eberhardt, et al., “The pfam protein families database,” Nucleic Acids Research, vol. 40, pp. D290–D301, 2012.
[28]
M. Punta, P. C. Coggill, R. Y. Eberhardt, et al., “The Pfam protein families database,” Nucleic Acids Research Acids Res, vol. 40, pp. 290–301, 2012.
[29]
W. Li and A. Godzik, “Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences,” Bioinformatics, vol. 22, no. 13, pp. 1658–1659, 2006.
[30]
S. F. Altschul, W. Gish, W. Miller, E. W. Myers, and D. J. Lipman, “Basic local alignment search tool,” Journal of Molecular Biology, vol. 215, no. 3, pp. 403–410, 1990.
[31]
G. E. Crooks, G. Hon, J. M. Chandonia, and S. E. Brenner, “WebLogo: a sequence logo generator,” Genome Research, vol. 14, no. 6, pp. 1188–1190, 2004.
[32]
E. W. Sayers, et al., “Database resources of the National Center for Biotechnology Information,” Nucleic Acids Research, vol. 40, pp. 13–25, 2012.
[33]
R. R. Sokal and C. D. Michener, A Statistical Method for Evaluating Systematic Relationships, vol. 38 of University of Kansas Science Bulletin, 1958.
[34]
A. Wismueller, “A computational framework for nonlinear dimensionality reduction and clustering,” Lecture Notes in Computer Science, vol. 5629, pp. 334–343, 2009.
[35]
D. Langosch and J. Heringa, “Interaction of transmembrane helices by a knobsinto-holes packing characteristic of soluble coiled coils,” Proteins, vol. 31, no. 2, pp. 150–159, 1998.
[36]
M. C. Thomsen and M. Nielsen, “Seq2Logo: a method for construction and visualization of amino acid binding motifs and sequence profiles including sequence weighting, pseudo counts and two-sided representation of amino acid enrichment and depletion,” Nucleic Acids Research, vol. 40, pp. W281–W287, 2012.
[37]
L. Wilkinson and M. Friendly, “The History of the Cluster Heat Map,” 2008, www.cs.uic.edu/~wilkinson/Publications/heatmap.pdf.
[38]
E. L. Sonnhammer, G. von Heijne, and A. Krogh, “A hidden markov model for predicting transmembrane helices in protein sequences,” Proceedings of the International Conference on Intelligent Systems for Molecular Biology, vol. 6, pp. 175–182, 1998.
[39]
B. Carl and T. John, Introduction to Protein Structure, Taylor and Francis, Auflage, 2nd edition, 1998.
