研究生: |
麥德倫 Mai, Te-Lun |
---|---|
論文名稱: |
TOG蛋白質超家族的演化與摺疊 Protein Evolution and Folding of the TOG Superfamily: Transporters, Opsins, and G protein-coupled Receptors |
指導教授: |
陳啟明
Chen, Chi-Ming |
學位類別: |
博士 Doctor |
系所名稱: |
物理學系 Department of Physics |
論文出版年: | 2016 |
畢業學年度: | 104 |
語文別: | 英文 |
論文頁數: | 89 |
中文關鍵詞: | 蛋白質演化 、蛋白質摺疊 、視蛋白 、G蛋白偶聯受體 |
英文關鍵詞: | Protein Evolution, Protein Folding, Opsins, G protein-coupled receptors |
DOI URL: | https://doi.org/10.6345/NTNU202204546 |
論文種類: | 學術論文 |
相關次數: | 點閱:241 下載:17 |
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蛋白質摺疊密碼,也是遺傳訊息密碼傳遞的第二部分,為分子生物學的重要研究課題之一。自從Christian Anfinsen提出了蛋白質能自發性折疊的實驗後,科學家們已研究這個問題超過數十年。其中,膜蛋白作為與各種生命現象相關之一類特殊蛋白質、且目前超過一半的藥物分子皆以其作為標靶,其摺疊過程於近十年特別受到注目。在膜蛋白之中,又以G protein-coupled receptors特別受到重視。這類蛋白質在各種感官現象如視覺、嗅覺、味覺都扮演著重要的角色,其中與視覺相關的膜蛋白尤其吸引我們,像是與色彩辨識相關的Cone Opsins及與夜間視覺相關的Rod Opsins。為了解這些蛋白質及其序列-結構-功能之間的關係,我們選擇了TOG(Transporter-Opsin-G protein-coupled receptor)蛋白質超家族來做為我們電腦模擬研究的目標。我們的研究主要分為三個方面:一、蛋白質相似度網路的分析。我們使用一個分群算法來分析蛋白質之間的關係。這個方法可以用來區別各個蛋白質家族間差異以及預測新定序列的功能。並且,藉由此算法所建立的網路結構,我們可以從中研究蛋白質之間可能的演化關係。二、膜蛋白的二級結構螺旋及其上扭角的預測。我們使用了胺基酸的親疏水指數來預測可能屬於穿膜螺旋與半穿膜螺旋的序列片段,以及使用結合分子動力學與摺疊識別演算法的方法來研究穿膜螺旋的結構特徵。三、與Rod Opsins相關的Microbial rhodopsins的折疊研究。我們建立了一個基於胺基酸物理性質的粗粒化模型來了解這些物理交互作用在蛋白質的折疊中扮演怎麼樣的角色,並使用平行回火的蒙地卡羅方法來尋找能量最小的結構。
The protein folding code, known as the second half of the genetic code, is one of the most important problems in molecular biology. Since Christian Anfinsen performed experiments concerning spontaneous folding of proteins, scientists have tried to solve this issue over several decades. It is estimated that 20-30% of all genes in most genomes are encoded to membrane proteins (MPs). MPs play key roles in living cells, and are the primary targets of more than 50% of drugs. In human genome, G protein-coupled receptors (GPCRs) constitute the largest family of integral MPs. GPCRs are involved in cell communication processes and mediate senses such as vision, taste, smell, hearing, and touch. In particular, we are interested in understanding the molecular mechanism for color vision in nervous systems. To achieve our goal, we chose the Transporter-Opsin-G protein-coupled receptor (TOG) superfamily to be the target of our computational studies of protein evolution and folding. In this dissertation, we have studied several aspects of the TOG superfamily, listed as followed: (1) The probable evolutionary relations of proteins could be investigated from protein similarity networks. We applied a clustering approach to provide a panoramic view of isofunctional groups of diverse protein superfamilies. This method is useful in predicting the structures and functions of novel protein sequences, and could explore possible evolutionary examples by visualizing the structures of our clustering networks. (2) Recognition of secondary structure helices and investigation of geometric structures of those helices were studied for MPs. We proposed a simplified algorithm to identify both transmembrane (TM) segments and half TM helices by finding the maximum sum of a hydropathic index with a variable window size, and then combined molecular dynamics simulations and a fold identification algorithm to fold the structural features of TM helices. These two methods provide good results in recognizing and folding TM helices. (3) The folding of molecular architecture for microbial rhodopsins was studied. This portion is the most important part of the protein folding code. We constructed a coarse-grained model based on the physical properties of amino acids, and then used replica-exchange Monte Carlo simulations to model their structures. This method has been used to construct protein models that are close to their experimental structures.
Bibliography
(1) Crick, F. Central dogma of molecular biology. Nature 1970, 227 (5258), 561.
(2) Lei, Q.; Li, C.; Zuo, Z.; Huang, C.; Cheng, H.; Zhou, R. Evolutionary Insights into RNA trans-Splicing in Vertebrates. Genome biology and evolution 2016, 8 (3), 562.
(3) Rackovsky, S. On the nature of the protein folding code. Proceedings of the National Academy of Sciences 1993, 90 (2), 644.
(4) Dill, K. A.; MacCallum, J. L. The protein-folding problem, 50 years on. Science 2012, 338 (6110), 1042.
(5) Tsou, C. Folding of the nascent peptide chain into a biologically active protein. Biochemistry 1988, 27 (6), 1809.
(6) Tsou, C. The second genetic code. Science Bulletin (Chinese edition) 2000, 45 (16), 1681.
(7) Anfinsen, C. Principles that govern the protein folding chains. Science 1973, 181, 233.
(8) Ellis, R. J. Molecular chaperones: pathways and networks. Current biology 1999, 9 (4), R137.
(9) Wynn, R.; Harkins, P. C.; Richards, F. M.; Fox, R. O. Mobile unnatural amino acid side chains in the core of staphylococcal nuclease. Protein science 1996, 5 (6), 1026.
(10) Prusiner, S. B. Molecular biology and pathogenesis of prion diseases. Trends in biochemical sciences 1996, 21 (12), 482.
(11) Tsirka, S.; Turck, C.; Coffino, P. Multiple active conformers of mouse ornithine decarboxylase. Biochemical Journal 1993, 293 (1), 289.
(12) Zhang, H. J.; Sheng, X. R.; Niu, W. D.; Pan, X. M.; Zhou, J. M. Evidence for at least two native forms of rabbit muscle adenylate kinase in equilibrium in aqueous solution. Journal of Biological Chemistry 1998, 273 (13), 7448.
(13) Huang, D.-B.; Ainsworth, C. F.; Stevens, F. J.; Schiffer, M. Three quaternary structures for a single protein. Proceedings of the National Academy of Sciences 1996, 93 (14), 7017.
(14) Zimmerman, S. B.; Minton, A. P. Macromolecular crowding: biochemical, biophysical, and physiological consequences. Annual review of biophysics and biomolecular structure 1993, 22 (1), 27.
(15) Khoury, G. A.; Baliban, R. C.; Floudas, C. A. Proteome-wide post-translational modification statistics: frequency analysis and curation of the swiss-prot database. Scientific reports 2011, 1.
(16) Kozma, D.; Simon, I.; Tusnády, G. E. PDBTM: Protein Data Bank of transmembrane proteins after 8 years. Nucleic acids research 2012, gks1169.
(17) Tastan, O.; Dutta, A.; Booth, P.; Klein-Seetharaman, J. Retinal proteins as model systems for membrane protein folding. Biochimica et Biophysica Acta (BBA)-Bioenergetics 2014, 1837 (5), 656.
(18) Saier, M. H.; Reddy, V. S.; Tamang, D. G.; Västermark, Å. The transporter classification database. Nucleic acids research 2013, gkt1097.
(19) Yee, D. C.; Shlykov, M. A.; Västermark, Å.; Reddy, V. S.; Arora, S.; Sun, E. I.; Saier, M. H. The transporter–opsin–G protein‐coupled receptor (TOG) superfamily. FEBS Journal 2013, 280 (22), 5780.
(20) Shalaeva, D. N.; Galperin, M. Y.; Mulkidjanian, A. Y. Eukaryotic G protein-coupled receptors as descendants of prokaryotic sodium-translocating rhodopsins. Biology direct 2015, 10 (1), 1.
(21) Trabanino, R. J.; Hall, S. E.; Vaidehi, N.; Floriano, W. B.; Kam, V. W.; Goddard, W. A. First principles predictions of the structure and function of g-protein-coupled receptors: validation for bovine rhodopsin. Biophysical journal 2004, 86 (4), 1904.
(22) Heng, B. C.; Aubel, D.; Fussenegger, M. An overview of the diverse roles of G-protein coupled receptors (GPCRs) in the pathophysiology of various human diseases. Biotechnology advances 2013, 31 (8), 1676.
(23) Moreira, I. S. Structural features of the G-protein/GPCR interactions. Biochimica et Biophysica Acta (BBA)-General Subjects 2014, 1840 (1), 16.
(24) Bockaert, J.; Pin, J. P. Molecular tinkering of G protein‐coupled receptors: an evolutionary success. The EMBO journal 1999, 18 (7), 1723.
(25) Fredriksson, R.; Lagerström, M. C.; Lundin, L.-G.; Schiöth, H. B. The G-protein-coupled receptors in the human genome form five main families. Phylogenetic analysis, paralogon groups, and fingerprints. Molecular pharmacology 2003, 63 (6), 1256.
(26) Imamoto, Y.; Shichida, Y. Cone visual pigments. Biochimica et Biophysica Acta (BBA)-Bioenergetics 2014, 1837 (5), 664.
(27) Hofmann, L.; Palczewski, K. Advances in understanding the molecular basis of the first steps in color vision. Progress in retinal and eye research 2015, 49, 46.
(28) Stenkamp, R.; Filipek, S.; Driessen, C.; Teller, D.; Palczewski, K. Crystal structure of rhodopsin: a template for cone visual pigments and other G protein-coupled receptors. Biochimica et Biophysica Acta (BBA)-Biomembranes 2002, 1565 (2), 168.
(29) Hartong, D. T.; Berson, E. L.; Dryja, T. P. Retinitis pigmentosa. The Lancet 2006, 368 (9549), 1795.
(30) Shintani, K.; Shechtman, D. L.; Gurwood, A. S. Review and update: current treatment trends for patients with retinitis pigmentosa. Optometry-Journal of the American Optometric Association 2009, 80 (7), 384.
(31) Berger, A.; Lorain, S.; Joséphine, C.; Desrosiers, M.; Peccate, C.; Voit, T.; Garcia, L.; Sahel, J.-A.; Bemelmans, A.-P. Repair of rhodopsin mRNA by spliceosome-mediated RNA trans-splicing: a new approach for autosomal dominant retinitis pigmentosa. Molecular Therapy 2015, 23 (5), 918.
(32) Engelman, D. M.; Chen, Y.; Chin, C.-N.; Curran, A. R.; Dixon, A. M.; Dupuy, A. D.; Lee, A. S.; Lehnert, U.; Matthews, E. E.; Reshetnyak, Y. K. Membrane protein folding: beyond the two stage model. Febs Letters 2003, 555 (1), 122.
(33) Klein-Seetharaman, J. Dual role of interactions between membranous and soluble portions of helical membrane receptors for folding and signaling. Trends in pharmacological sciences 2005, 26 (4), 183.
(34) Ernst, O. P.; Lodowski, D. T.; Elstner, M.; Hegemann, P.; Brown, L. S.; Kandori, H. Microbial and animal rhodopsins: structures, functions, and molecular mechanisms. Chemical reviews 2013, 114 (1), 126.
(35) Popot, J.-L.; Engelman, D. M. Membrane protein folding and oligomerization: the two-stage model. Biochemistry 1990, 29 (17), 4031.
(36) Albert, R.; Barabási, A.-L. Statistical mechanics of complex networks. Reviews of Modern Physics 2002, 74 (1), 47.
(37) Watts, D. J.; Strogatz, S. H. Collective dynamics of 'small-world' networks. Nature 1998, 393 (6684), 440.
(38) Börner, K.; Chen, C.; Boyack, K. W. Visualizing knowledge domains. Annual Review of Information Science and Technology 2003, 37 (1), 179.
(39) Chang, Y. F.; Chen, C. M. Classification and Visualization of the Social Science Network by the Minimum Span Clustering Method. J. Am. Soc. Inf. Sci. Technol. 2011, 62 (12), 2404.
(40) Apeltsin, L.; Morris, J. H.; Babbitt, P. C.; Ferrin, T. E. Improving the quality of protein similarity network clustering algorithms using the network edge weight distribution. Bioinformatics 2011, 27 (3), 326.
(41) Frey, B. J.; Dueck, D. Clustering by Passing Messages Between Data Points. Science 2007, 315 (5814), 972.
(42) Samoylenko, I.; Chao, T. C.; Liu, W. C.; Chen, C. M. Visualizing the scientific world and its evolution. J. Am. Soc. Inf. Sci. Technol. 2006, 57 (11), 1461.
(43) Camoglu, O.; Can, T.; Singh, A. K. Integrating multi-attribute similarity networks for robust representation of the protein space. Bioinformatics 2006, 22 (13), 1585.
(44) Noble, W. S.; Kuang, R.; Leslie, C.; Weston, J. Identifying remote protein homologs by network propagation. The FEBS journal 2005, 272 (20), 5119.
(45) Hakes, L.; Pinney, J. W.; Robertson, D. L.; Lovell, S. C. Protein-protein interaction networks and biology—what's the connection? Nature biotechnology 2008, 26 (1), 69.
(46) Jeong, H.; Tombor, B.; Albert, R.; Oltvai, Z. N.; Barabási, A.-L. The large-scale organization of metabolic networks. Nature 2000, 407 (6804), 651.
(47) MacNeil, L. T.; Walhout, A. J. Gene regulatory networks and the role of robustness and stochasticity in the control of gene expression. Genome research 2011, 21 (5), 645.
(48) Alberts, B. The cell as a collection of protein machines: Preparing the next generation of molecular biologists. Cell 1998, 92 (3), 291.
(49) Grabmeier, J.; Rudolph, A. Techniques of cluster algorithms in data mining. Data Min Knowl Disc 2002, 6 (4), 303.
(50) Jain, A. K.; Murty, M. N.; Flynn, P. J. Data clustering: A review. Acm Comput Surv 1999, 31 (3), 264.
(51) Kaufman, L.; Rousseeuw, P. J. Finding Groups in Data: An Introduction to Cluster Analysis; New York: John Wiley & Sons, Inc., 1990.
(52) Hartigan, J.; Wong, M. Algorithm AS136: A k-means clustering algorithm. Applied Statistics 1979, 28, 100.
(53) Atkinson, H. J.; Morris, J. H.; Ferrin, T. E.; Babbitt, P. C. Using Sequence Similarity Networks for Visualization of Relationships Across Diverse Protein Superfamilies. PLoS One 2009, 4 (2), 14.
(54) Uberto, R.; Moomaw, E. W. Protein Similarity Networks Reveal Relationships among Sequence, Structure, and Function within the Cupin Superfamily. PLoS One 2013, 8 (9), 10.
(55) Mashiyama, S. T.; Malabanan, M. M.; Akiva, E.; Bhosle, R.; Branch, M. C.; Hillerich, B.; Jagessar, K.; Kim, J.; Patskovsky, Y.; Seidel, R. D.et al. Large-Scale Determination of Sequence, Structure, and Function Relationships in Cytosolic Glutathione Transferases across the Biosphere. Plos Biol 2014, 12 (4).
(56) Enright, A. J.; Van Dongen, S.; Ouzounis, C. A. An efficient algorithm for large-scale detection of protein families. Nucleic acids research 2002, 30 (7), 1575.
(57) Vendruscolo, M.; Dokholyan, N. V.; Paci, E.; Karplus, M. Small-world view of the amino acids that play a key role in protein folding. Physical Review E 2002, 65 (6), 061910.
(58) Atilgan, A. R.; Akan, P.; Baysal, C. Small-World Communication of Residues and Significance for Protein Dynamics. Biophysical Journal 2004, 86 (1), 85.
(59) Krishnan, A.; Zbilut, J. P.; Tomita, M.; Giuliani, A. Proteins as networks: usefulness of graph theory in protein science. Current protein & peptide science 2008, 9 (1), 28.
(60) Song, N.; Joseph, J. M.; Davis, G. B.; Durand, D. Sequence similarity network reveals common ancestry of multidomain proteins. Plos Comput Biol 2008, 4 (5).
(61) Zhang, Y.; Zagnitko, O.; Rodionova, I.; Osterman, A.; Godzik, A. The FGGY Carbohydrate Kinase Family: Insights into the Evolution of Functional Specificities. Plos Comput Biol 2011, 7 (12).
(62) Gerlt, J. A.; Bouvier, J. T.; Davidson, D. B.; Imker, H. J.; Sadkhin, B.; Slater, D. R.; Whalen, K. L. Enzyme Function Initiative-Enzyme Similarity Tool (EFI-EST): A web tool for generating protein sequence similarity networks. Biochimica et Biophysica Acta (BBA)-Proteins and Proteomics 2015, 1854 (8), 1019.
(63) Paccanaro, A.; Casbon, J. A.; Saqi, M. A. Spectral clustering of protein sequences. Nucleic acids research 2006, 34 (5), 1571.
(64) Shi, L.; Ji, B.; Kolar-Znika, L.; Boskovic, A.; Jadeau, F.; Combet, C.; Grangeasse, C.; Franjevic, D.; Talla, E.; Mijakovic, I. Evolution of bacterial protein-tyrosine kinases and their relaxed specificity toward substrates. Genome Biol Evol 2014, 6 (4), 800.
(65) Wittkop, T.; Baumbach, J.; Lobo, F. P.; Rahmann, S. Large scale clustering of protein sequences with FORCE - A layout based heuristic for weighted cluster editing. BMC Bioinformatics 2007, 8, 12.
(66) Altschul, S. F.; Madden, T. L.; Schäffer, A. A.; Zhang, J.; Zhang, Z.; Miller, W.; Lipman, D. J. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic acids research 1997, 25 (17), 3389.
(67) Blume-Jensen, P.; Hunter, T. Oncogenic kinase signalling. Nature 2001, 411 (6835), 355.
(68) Forrest, A. R.; Ravasi, T.; Taylor, D.; Huber, T.; Hume, D. A.; Grimmond, S.; Group, R. G.; Members, G. S. L. Phosphoregulators: protein kinases and protein phosphatases of mouse. Genome Res 2003, 13 (6B), 1443.
(69) Lopez-Otin, C.; Bond, J. S. Proteases: Multifunctional Enzymes in Life and Disease. Journal of Biological Chemistry 2008, 283 (45), 30433.
(70) Lopez-Otin, C.; Hunter, T. The regulatory crosstalk between kinases and proteases in cancer. Nature reviews. Cancer 2010, 10 (4), 278.
(71) Overington, J. P.; Al-Lazikani, B.; Hopkins, A. L. How many drug targets are there? Nature reviews Drug discovery 2006, 5 (12), 993.
(72) Peters, J. M. Proteasomes - Protein-Degradation Machines of the Cell. Trends in Biochemical Sciences 1994, 19 (9), 377.
(73) Krebs, E. G.; Beavo, J. A. Phosphorylation-Dephosphorylation of Enzymes. Annu Rev Biochem 1979, 48, 923.
(74) King, R. W.; Deshaies, R. J.; Peters, J. M.; Kirschner, M. W. How proteolysis drives the cell cycle. Science 1996, 274 (5293), 1652.
(75) Ghodge, S. V.; Fedorov, A. A.; Fedorov, E. V.; Hillerich, B.; Seidel, R.; Almo, S. C.; Raushel, F. M. Structural and Mechanistic Characterization of l-Histidinol Phosphate Phosphatase from the Polymerase and Histidinol Phosphatase Family of Proteins. Biochemistry 2013, 52 (6), 1101.
(76) Cross, K. J.; Huq, N. L.; Reynolds, E. C. A bio-informatics study of the c25 cysteine protease family. Open Journal of Genetics 2013, 2, 18.
(77) Arnold Emerson, I.; Gothandam, K. M. Residue centrality in alpha helical polytopic transmembrane protein structures. Journal of theoretical biology 2012, 309, 78.
(78) White, S. H.; Wimley, W. C. Membrane protein folding and stability: physical principles. Annu Rev Biophys Biomol Struct 1999, 28, 319.
(79) Wu, H. H.; Chen, C. C.; Chen, C. M. Replica exchange Monte-Carlo simulations of helix bundle membrane proteins: rotational parameters of helices. Journal of computer-aided molecular design 2012, 26 (3), 363.
(80) Chen, C. C.; Wei, C. C.; Sun, Y. C.; Chen, C. M. Packing of transmembrane helices in bacteriorhodopsin folding: structure and thermodynamics. Journal of structural biology 2008, 162 (2), 237.
(81) Chen, C. C.; Chen, C. M. A dual-scale approach toward structure prediction of retinal proteins. Journal of structural biology 2009, 165 (1), 37.
(82) Huang, Y. H.; Chen, C. M. Statistical analyses and computational prediction of helical kinks in membrane proteins. Journal of computer-aided molecular design 2012, 26 (10), 1171.
(83) Mai, T. L.; Chen, C. M. Computational prediction of kink properties of helices in membrane proteins. Journal of computer-aided molecular design 2014, 28 (2), 99.
(84) Kruskal, J. B. On the shortest spanning subtree of a graph and the traveling salesman problem. Proceedings of the American Mathematical society 1956, 7 (1), 48.
(85) Bairoch, A. The ENZYME database in 2000. Nucleic acids research 2000, 28 (1), 304.
(86) Rawlings, N. D.; Waller, M.; Barrett, A. J.; Bateman, A. MEROPS: the database of proteolytic enzymes, their substrates and inhibitors. Nucleic acids research 2013, gkt953.
(87) Wang, G.; Dunbrack, R. L. PISCES: recent improvements to a PDB sequence culling server. Nucleic acids research 2005, 33 (suppl 2), W94.
(88) Wang, G.; Dunbrack, R. L. PISCES: a protein sequence culling server. Bioinformatics 2003, 19 (12), 1589.
(89) Zhang, Y.; Skolnick, J. TM-align: a protein structure alignment algorithm based on the TM-score. Nucleic acids research 2005, 33 (7), 2302.
(90) Xu, J.; Zhang, Y. How significant is a protein structure similarity with TM-score= 0.5? Bioinformatics 2010, 26 (7), 889.
(91) Frishman, D.; Argos, P. Knowledge-based protein secondary structure assignment. Proteins: structure, function, and genetics 1995, 23 (4), 566.
(92) Smoot, M. E.; Ono, K.; Ruscheinski, J.; Wang, P.-L.; Ideker, T. Cytoscape 2.8: new features for data integration and network visualization. Bioinformatics 2011, 27 (3), 431.
(93) Goldberg, D. S.; Roth, F. P. Assessing experimentally derived interactions in a small world. Proceedings of the National Academy of Sciences 2003, 100 (8), 4372.
(94) Torres, G. J.; Basnet, R. B.; Sung, A. H.; Mukkamala, S.; Ribeiro, B. M. A Similarity Measure for Clustering and its Applications. International Journal of Electrical, Computer & Systems Engineer 2009, 3 (3), 164.
(95) Moult, J.; Fidelis, K.; Kryshtafovych, A.; Schwede, T.; Tramontano, A. Critical assessment of methods of protein structure prediction (CASP)—round x. Proteins: Structure, Function, and Bioinformatics 2014, 82 (S2), 1.
(96) Sadowski, M. I.; Jones, D. T. The sequence–structure relationship and protein function prediction. Current Opinion in Structural Biology 2009, 19 (3), 357.
(97) Buller, A. R.; Townsend, C. A. Intrinsic evolutionary constraints on protease structure, enzyme acylation, and the identity of the catalytic triad. Proceedings of the National Academy of Sciences 2013, 110 (8), E653.
(98) Polgár, L. The catalytic triad of serine peptidases. Cell. Mol. Life Sci. 2005, 62 (19-20), 2161.
(99) Pelé, J.; Abdi, H.; Moreau, M.; Thybert, D.; Chabbert, M. Multidimensional Scaling Reveals the Main Evolutionary Pathways of Class A G-Protein-Coupled Receptors. PLoS One 2011, 6 (4), e19094.
(100) Archibald, J. M.; Keeling, P. J. Recycled plastids: a ‘green movement’in eukaryotic evolution. TRENDS in Genetics 2002, 18 (11), 577.
(101) Chen, Y.-h.; Hu, L.; Punta, M.; Bruni, R.; Hillerich, B.; Kloss, B.; Rost, B.; Love, J.; Siegelbaum, S. A.; Hendrickson, W. A. Homologue structure of the SLAC1 anion channel for closing stomata in leaves. Nature 2010, 467 (7319), 1074.
(102) Geiger, D.; Scherzer, S.; Mumm, P.; Stange, A.; Marten, I.; Bauer, H.; Ache, P.; Matschi, S.; Liese, A.; Al-Rasheid, K. A. S.et al. Activity of guard cell anion channel SLAC1 is controlled by drought-stress signaling kinase-phosphatase pair. Proceedings of the National Academy of Sciences 2009, 106 (50), 21425.
(103) Konc, J.; Cesnik, T.; Trykowska Konc, J.; Penca, M.; Janezic, D. ProBiS-Database: Precalculated Binding Site Similarities and Local Pairwise Alignments of PDB Structures. J Chem Inf Model 2012, 52 (2), 604.
(104) Drews, J. Drug discovery: a historical perspective. Science 2000, 287 (5460), 1960.
(105) Filmore, D. It’sa GPCR world. Modern drug discovery 2004, 7 (11), 24.
(106) Bowie, J. U. Solving the membrane protein folding problem. Nature 2005, 438 (7068), 581.
(107) Milik, M.; Skolnick, J. Spontaneous insertion of polypeptide chains into membranes: a Monte Carlo model. Proceedings of the National Academy of Sciences 1992, 89 (20), 9391.
(108) Dobbs, H.; Orlandini, E.; Bonaccini, R.; Seno, F. Optimal potentials for predicting inter‐helical packing in transmembrane proteins. Proteins: Structure, Function, and Bioinformatics 2002, 49 (3), 342.
(109) Floriano, W. B.; Vaidehi, N.; Goddard, W. A.; Singer, M. S.; Shepherd, G. M. Molecular mechanisms underlying differential odor responses of a mouse olfactory receptor. Proceedings of the National Academy of Sciences 2000, 97 (20), 10712.
(110) Kokubo, H.; Okamoto, Y. Self-assembly of transmembrane helices of bacteriorhodopsin by a replica-exchange Monte Carlo simulation. Chemical physics letters 2004, 392 (1), 168.
(111) Langelaan, D. N.; Wieczorek, M.; Blouin, C.; Rainey, J. K. Improved helix and kink characterization in membrane proteins allows evaluation of kink sequence predictors. J Chem Inf Model 2010, 50 (12), 2213.
(112) Wilman, H. R.; Shi, J.; Deane, C. M. Helix kinks are equally prevalent in soluble and membrane proteins. Proteins: Structure, Function, and Bioinformatics 2014, 82 (9), 1960.
(113) von Heijne, G. Proline kinks in transmembrane α-helices. Journal of Molecular Biology 1991, 218 (3), 499.
(114) Orzáez, M.; Salgado, J.; Giménez-Giner, A.; Pérez-Payá, E.; Mingarro, I. Influence of proline residues in transmembrane helix packing. Journal of molecular biology 2004, 335 (2), 631.
(115) Richardson, J. S.; Richardson, D. C. Amino acid preferences for specific locations at the ends of alpha helices. Science 1988, 240 (4859), 1648.
(116) Slepkov, E. R.; Signy, C.; Lemieux, M. J.; Fliegel, L. Proline residues in transmembrane segment IV are critical for activity, expression and targeting of the Na+/H+ exchanger isoform 1. Biochemical Journal 2004, 379 (1), 31.
(117) Bright, J. N.; Shrivastava, I. H.; Cordes, F. S.; Sansom, M. S. Conformational dynamics of helix S6 from Shaker potassium channel: simulation studies. Biopolymers 2002, 64 (6), 303.
(118) Jacob, J.; Duclohier, H.; Cafiso, D. S. The Role of Proline and Glycine in Determining the Backbone Flexibility of a Channel-Forming Peptide. Biophysical Journal 1999, 76 (3), 1367.
(119) Chakrabartty, A.; Baldwin, R. L. Stability of alpha-helices. Advances in protein chemistry 1995, 46, 141.
(120) Costantini, S.; Colonna, G.; Facchiano, A. M. Amino acid propensities for secondary structures are influenced by the protein structural class. Biochemical and Biophysical Research Communications 2006, 342 (2), 441.
(121) Gray, T. M.; Matthews, B. W. Intrahelical hydrogen bonding of serine, threonine and cysteine residues within α-helices and its relevance to membrane-bound proteins. Journal of Molecular Biology 1984, 175 (1), 75.
(122) Hall, S. E.; Roberts, K.; Vaidehi, N. Position of helical kinks in membrane protein crystal structures and the accuracy of computational prediction. Journal of Molecular Graphics & Modelling 2009, 27 (8), 944.
(123) Kyte, J.; Doolittle, R. F. A simple method for displaying the hydropathic character of a protein. Journal of molecular biology 1982, 157 (1), 105.
(124) Engelman, D.; Steitz, T.; Goldman, A. Identifying nonpolar transbilayer helices in amino acid sequences of membrane proteins. Annual review of biophysics and biophysical chemistry 1986, 15 (1), 321.
(125) Von Heijne, G. Membrane protein structure prediction: hydrophobicity analysis and the positive-inside rule. Journal of molecular biology 1992, 225 (2), 487.
(126) Ponnuswamy, P. Hydrophobic characteristics of folded proteins. Progress in biophysics and molecular biology 1993, 59 (1), 57.
(127) Jones, D.; Taylor, W.; Thornton, J. A model recognition approach to the prediction of all-helical membrane protein structure and topology. Biochemistry 1994, 33 (10), 3038.
(128) Chou, P. Y.; Fasman, G. D. Prediction of protein conformation. Biochemistry 1974, 13 (2), 222.
(129) Garnier, J.; Osguthorpe, D. J.; Robson, B. Analysis of the accuracy and implications of simple methods for predicting the secondary structure of globular proteins. Journal of molecular biology 1978, 120 (1), 97.
(130) Lio, P.; Vannucci, M. Wavelet change-point prediction of transmembrane proteins. Bioinformatics 2000, 16 (4), 376.
(131) Rost, B.; Sander, C.; Casadio, R.; Fariselli, P. Transmembrane helices predicted at 95% accuracy. Protein Science 1995, 4 (3), 521.
(132) Leman, J. K.; Mueller, R.; Karakas, M.; Woetzel, N.; Meiler, J. Simultaneous prediction of protein secondary structure and transmembrane spans. Proteins: Structure, Function, and Bioinformatics 2013, 81 (7), 1127.
(133) Sonnhammer, E. L.; Von Heijne, G.; Krogh, A. Ismb, 1998; p 175.
(134) Nugent, T.; Jones, D. T. Transmembrane protein topology prediction using support vector machines. BMC Bioinformatics 2009, 10 (1), 1.
(135) Riek, R. P.; Rigoutsos, I.; Novotny, J.; Graham, R. M. Non-alpha-helical elements modulate polytopic membrane protein architecture. J Mol Biol 2001, 306 (2), 349.
(136) Yohannan, S.; Faham, S.; Yang, D.; Whitelegge, J. P.; Bowie, J. U. The evolution of transmembrane helix kinks and the structural diversity of G protein-coupled receptors. Proceedings of the National Academy of Sciences of the United States of America 2004, 101 (4), 959.
(137) Cao, Z.; Bowie, J. U. Shifting hydrogen bonds may produce flexible transmembrane helices. Proceedings of the National Academy of Sciences of the United States of America 2012, 109 (21), 8121.
(138) Meruelo, A. D.; Samish, I.; Bowie, J. U. TMKink: a method to predict transmembrane helix kinks. Protein science : a publication of the Protein Society 2011, 20 (7), 1256.
(139) Kneissl, B.; Mueller, S. C.; Tautermann, C. S.; Hildebrandt, A. String kernels and high-quality data set for improved prediction of kinked helices in alpha-helical membrane proteins. J Chem Inf Model 2011, 51 (11), 3017.
(140) Werner, T.; Church, W. B. Kink characterization and modeling in transmembrane protein structures. J Chem Inf Model 2013, 53 (11), 2926.
(141) Kahn, T. W.; Engelman, D. M. Bacteriorhodopsin can be refolded from two independently stable transmembrane helixes and the complementary five-helix fragment. Biochemistry 1992, 31 (26), 6144.
(142) Peled-Zehavi, H.; Arkin, I. T.; Engelman, D. M.; Shai, Y. Coassembly of synthetic segments of shaker K+ channel within phospholipid membranes. Biochemistry 1996, 35 (21), 6828.
(143) Tusnády, G. E.; Dosztányi, Z.; Simon, I. PDB_TM: selection and membrane localization of transmembrane proteins in the protein data bank. Nucleic acids research 2005, 33 (suppl 1), D275.
(144) Lomize, M. A.; Lomize, A. L.; Pogozheva, I. D.; Mosberg, H. I. OPM: orientations of proteins in membranes database. Bioinformatics 2006, 22 (5), 623.
(145) Lomize, A. L.; Pogozheva, I. D.; Lomize, M. A.; Mosberg, H. I. Positioning of proteins in membranes: a computational approach. Protein Science 2006, 15 (6), 1318.
(146) Tusnády, G. E.; Dosztányi, Z.; Simon, I. Transmembrane proteins in the Protein Data Bank: identification and classification. Bioinformatics 2004, 20 (17), 2964.
(147) Bansal, M.; Kumart, S.; Velavan, R. HELANAL: a program to characterize helix geometry in proteins. Journal of Biomolecular Structure and Dynamics 2000, 17 (5), 811.
(148) Case, D. A.; Cheatham, T. E., 3rd; Darden, T.; Gohlke, H.; Luo, R.; Merz, K. M., Jr.; Onufriev, A.; Simmerling, C.; Wang, B.; Woods, R. J. The Amber biomolecular simulation programs. J Comput Chem 2005, 26 (16), 1668.
(149) Zhang, Y.; Skolnick, J. SPICKER: A clustering approach to identify near‐native protein folds. Journal of computational chemistry 2004, 25 (6), 865.
(150) Tsong, T. Y. Electrical modulation of membrane proteins: enforced conformational oscillations and biological energy and signal transductions. Annu Rev Biophys Biophys Chem 1990, 19, 83.
(151) Zhou, F.; Schulten, K. Molecular dynamics study of a membrane-water interface. The Journal of Physical Chemistry 1995, 99 (7), 2194.
(152) Chen, C. M. Lattice model of transmembrane polypeptide folding. Phys Rev E 2001, 63 (1), 010901.
(153) Chen, C. M.; Chen, C. C. Computer Simulations of membrane protein folding: Structure and dynamics. Biophys J 2003, 84 (3), 1902.
(154) Berendsen, H. J.; Postma, J. v.; van Gunsteren, W. F.; DiNola, A.; Haak, J. Molecular dynamics with coupling to an external bath. The Journal of chemical physics 1984, 81 (8), 3684.
(155) Hunenberger, P. Thermostat algorithms for molecular dynamics simulations. Adv Polym Sci 2005, 173, 105.
(156) Shortle, D.; Simons, K. T.; Baker, D. Clustering of low-energy conformations near the native structures of small proteins. Proceedings of the National Academy of Sciences 1998, 95 (19), 11158.
(157) Siew, N.; Elofsson, A.; Rychlewski, L.; Fischer, D. MaxSub: an automated measure for the assessment of protein structure prediction quality. Bioinformatics 2000, 16 (9), 776.
(158) Zemla, A. LGA: a method for finding 3D similarities in protein structures. Nucleic acids research 2003, 31 (13), 3370.
(159) Read, R. J.; Chavali, G. Assessment of CASP7 predictions in the high accuracy template-based modeling category. Proteins 2007, 69 Suppl 8, 27.
(160) Jones, D. T. Protein secondary structure prediction based on position-specific scoring matrices. Journal of molecular biology 1999, 292 (2), 195.
(161) Hu, G. M.; Mai, T. L.; Chen, C. M. Clustering and visualizing similarity networks of membrane proteins. Proteins: Structure, Function, and Bioinformatics 2015, 83 (8), 1450.
(162) Zvelebil, M.; Baum, J. Understanding bioinformatics; Garland Science, 2007.
(163) Schobert, B.; Cupp-Vickery, J.; Hornak, V.; Smith, S. O.; Lanyi, J. K. Crystallographic structure of the K intermediate of bacteriorhodopsin: conservation of free energy after photoisomerization of the retinal. Journal of molecular biology 2002, 321 (4), 715.
(164) Sapra, K. T.; Park, P. S.-H.; Palczewski, K.; Muller, D. J. Mechanical properties of bovine rhodopsin and bacteriorhodopsin: possible roles in folding and function. Langmuir 2008, 24 (4), 1330.
(165) Mendes, H. F.; van der Spuy, J.; Chapple, J. P.; Cheetham, M. E. Mechanisms of cell death in rhodopsin retinitis pigmentosa: implications for therapy. Trends in molecular medicine 2005, 11 (4), 177.
(166) Punta, M.; Forrest, L. R.; Bigelow, H.; Kernytsky, A.; Liu, J.; Rost, B. Membrane protein prediction methods. Methods 2007, 41 (4), 460.
(167) Nugent, T.; Jones, D. T. Membrane protein structural bioinformatics. Journal of structural biology 2012, 179 (3), 327.
(168) França, T. C. C. Homology modeling: an important tool for the drug discovery. Journal of Biomolecular Structure and Dynamics 2015, 33 (8), 1780.
(169) Vaidehi, N.; Floriano, W. B.; Trabanino, R.; Hall, S. E.; Freddolino, P.; Choi, E. J.; Zamanakos, G.; Goddard, W. A. Prediction of structure and function of G protein-coupled receptors. Proceedings of the National Academy of Sciences 2002, 99 (20), 12622.
(170) Godzik, A. Fold recognition methods. Structural Bioinformatics, Volume 44 2005, 525.
(171) Khoury, G. A.; Smadbeck, J.; Kieslich, C. A.; Floudas, C. A. Protein folding and de novo protein design for biotechnological applications. Trends in biotechnology 2014, 32 (2), 99.
(172) Epstein, C. J.; Goldberger, R. F.; Anfinsen, C. B. Cold Spring Harbor symposia on quantitative biology, 1963; p 439.
(173) Finkelstein, A.; Galzitskaya, O. Physics of protein folding. Physics of Life reviews 2004, 1 (1), 23.
(174) Baker, D.; Agard, D. A. Kinetics versus thermodynamics in protein folding. Biochemistry 1994, 33 (24), 7505.
(175) Skolnick, J.; Fetrow, J. S.; Kolinski, A. Structural genomics and its importance for gene function analysis. Nature biotechnology 2000, 18 (3), 283.
(176) Popot, J.-L.; Engelman, D. M. Helical membrane protein folding, stability, and evolution. Annu Rev Biochem 2000, 69 (1), 881.
(177) Huschilt, J.; Hodges, R.; Davis, J. Phase equilibria in an amphiphilic peptide-phospholipid model membrane by deuterium nuclear magnetic resonance difference spectroscopy. Biochemistry 1985, 24 (6), 1377.
(178) Subczynski, W. K.; Lewis, R. N.; McElhaney, R. N.; Hodges, R. S.; Hyde, J. S.; Kusumi, A. Molecular organization and dynamics of 1-palmitoyl-2-oleoylphosphatidylcholine bilayers containing a transmembrane α-helical peptide. Biochemistry 1998, 37 (9), 3156.
(179) Nina, M.; Roux, B.; Smith, J. C. Functional interactions in bacteriorhodopsin: a theoretical analysis of retinal hydrogen bonding with water. Biophysical journal 1995, 68 (1), 25.
(180) Baudry, J.; Crouzy, S.; Roux, B.; Smith, J. C. Simulation analysis of the retinal conformational equilibrium in dark-adapted bacteriorhodopsin. Biophysical journal 1999, 76 (4), 1909.
(181) Zhang, X.; Stevens, R. C.; Xu, F. The importance of ligands for G protein-coupled receptor stability. Trends in biochemical sciences 2015, 40 (2), 79.
(182) Hildebrand, P. W.; Goede, A.; Bauer, R. A.; Gruening, B.; Ismer, J.; Michalsky, E.; Preissner, R. SuperLooper—a prediction server for the modeling of loops in globular and membrane proteins. Nucleic acids research 2009, gkp338.
(183) Urano, R.; Okamoto, Y. Observation of helix associations for insertion of a retinal molecule and distortions of helix structures in bacteriorhodopsin. The Journal of chemical physics 2015, 143 (23), 235101.
(184) Popot, J.-L.; Gerchman, S.-E.; Engelman, D. M. Refolding of bacteriorhodopsin in lipid bilayers: a thermodynamically controlled two-stage process. Journal of molecular biology 1987, 198 (4), 655.