The cleverSuite approach for protein characterization: Predictions of structural properties, solubility, chaperone requirements and RNA-binding abilities

Bioinformatics

Volumn 30, Issue 11, 2014, Pages 1601-1608

  Klus, Petr ^a   Bolognesi, Benedetta ^a   Agostini, Federico ^a   Marchese, Domenica ^a   Zanzoni, Andreas ^a   Tartaglia, Gian Gaetano ^a  

^a UNIVERSITAT POMPEU FABRA   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

CHAPERONE; INTRINSICALLY DISORDERED PROTEIN; PROTEIN; RNA BINDING PROTEIN;

ALGORITHM; ARTICLE; CHEMISTRY; COMPUTER PROGRAM; METABOLISM; PROTEIN SECONDARY STRUCTURE; SEQUENCE ANALYSIS; SOLUBILITY;

ALGORITHMS; INTRINSICALLY DISORDERED PROTEINS; MOLECULAR CHAPERONES; PROTEIN STRUCTURE, SECONDARY; PROTEINS; RNA-BINDING PROTEINS; SEQUENCE ANALYSIS, PROTEIN; SOFTWARE; SOLUBILITY;

EID: 84901297233     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btu074     Document Type: Article

References (79)

1. Agostini, F. et al. (2012) Sequence-based prediction of protein solubility. J. Mol. Biol., 421, 237-241
2. Alberti, S. et al. (2009) A systematic survey identifies prions and illuminates sequence features of prionogenic proteins. Cell, 137, 146-158
3. Andreeva, A. et al. (2008) Data growth and its impact on the SCOP database: New developments. Nucleic Acids Res., 36, D419-D425. (Pubitemid 351149760
4. Argos, P. et al. (1982) Structural prediction of membrane-bound proteins. Eur. J. Biochem., 128, 565-575
5. Babu, M.M. et al. (2011) Intrinsically disordered proteins: Regulation and disease. Curr. Opin. Struct. Biol., 21, 432-440
6. Bailey, T.L. et al. (2009) MEME Suite: Tools for motif discovery and searching. Nucleic Acids Res., 37, W202-W208
7. Baltz, A.G. et al. (2012) The mRNA-bound proteome and its global occupancy profile on protein-coding transcripts. Mol. Cell, 46, 674-690
8. Bellay, J. et al. (2011) Bringing order to protein disorder through comparative genomics and genetic interactions. Genome Biol., 12, R14
9. Bernstein, F.C. et al. (1977) The Protein Data Bank: A computer-based archival file for macromolecular structures. J. Mol. Biol., 112, 535-542
10. Bhaskaran, R. and Ponnuswamy, P.k. (1988) Positional flexibilities of amino acid residues in globular proteins. Int. J. Peptide Protein Res., 32, 241-255
11. Black, S.D. and Mould, D.R. (1991) Development of hydrophobicity parameters to analyze proteins which bear post- or cotranslational modifications. Anal. Biochem., 193, 72-82
12. Buchan, D.W.A. et al. (2013) Scalable web services for the PSIPRED protein analysis workbench. Nucleic Acids Res., 41, W349-W357.
13. Bull, H.B. and Breese, K. (1974) Surface tension of amino acid solutions: A hydrophobicity scale of the amino acid residues. Arch. Biochem. Biophys, 161, 665-670
14. Burgess, A. et al. (1974) Analysis of conformation of amino acid residues and prediction of backbone topography in proteins. Isr. J. Chem., 239-286
15. Cai, C.Z. et al. (2003) SVM-Prot: Web-based support vector machine software for functional classification of a protein from its primary sequence. Nucleic Acids Res., 31, 3692-3697. (Pubitemid 37442225
16. Calloni, G. et al. (2012) DnaK functions as a central hub in the E.coli chaperone network. Cell Reports, 1, 251-264
17. Campen, A. et al. (2008) TOP-IDP-scale: A new amino acid scale measuring propensity for intrinsic disorder. Protein Pept. Lett., 15, 956-63
18. Castello, A. et al. (2012) Insights into RNA biology from an atlas of mammalian mRNA-binding proteins. Cell, 149, 1393-1406
19. Chiti, F. et al. (2003) Rationalization of the effects of mutations on peptide and protein aggregation rates. Nature, 424, 805-808. (Pubitemid 37021713
20. Chothia, C. (1975) Structural invariants in protein folding. Nature, 254, 304-308
21. Chou, P.Y. and Fasman, G.D. (1978) Prediction of the secondary structure of proteins from their amino acid sequence. Adv. Enzymol. Relat. AreasMol. Biol., 47, 45-148
22. Cirillo, D. et al. (2014) Constitutive patterns of gene expression regulated by RNAbinding proteins. Genome Biol., 15, R13
23. Cirillo, D. et al. (2013) Neurodegenerative diseases: Quantitative predictions of protein- RNA interactions. RNA, 19, 129-140
24. Conchillo-Solé, O. et al. (2007) AGGRESCAN: A server for the prediction and evaluation of 'hot spots' of aggregation in polypeptides. BMC Bioinform., 8, 65
25. Deléage, G. and Roux, B. (1987) An algorithm for protein secondary structure prediction based on class prediction. Protein Eng., 1, 289-294. (Pubitemid 18024006
26. Dinkel, H. et al. (2013) The eukaryotic linear motif resource ELM: 10 years and counting. Nucleic Acids Res., 42, D259-D266
27. Dunker, A.K. et al. (2002) Intrinsic disorder and protein function. Biochemistry, 41, 6573-6582. (Pubitemid 34547347
28. Van Durme, J. et al. (2009) Accurate prediction of DnaK-peptide binding via homology modelling and experimental data. PLoS Comput. Biol., 5, e1000475
29. Eisenberg, D. et al. (1984) Analysis of membrane and surface protein sequences with the hydrophobic moment plot. J. Mol. Biol., 179, 125-142
30. Fauchere, J. and Pliska, V. (1983) Hydrophobic parameters pi of amino-Acid side chains from the partitioning of N-Acetyl-Amino-Acid amides. Eur. J. Med. Chem., 18, 369-375
31. Fernandez-Escamilla, A-M. et al. (2004) Prediction of sequence-dependent and mutational effects on the aggregation of peptides and proteins. Nat. Biotechnol., 22, 1302-1306
32. Fu, L. et al. (2012) CD-HIT: Accelerated for clustering the next-generation sequencing data. Bioinformatics, 28, 3150-3152
33. Gao, J. et al. (2010) Accurate prediction of protein folding rates from sequence and sequence-derived residue flexibility and solvent accessibility. Proteins, 78, 2114-2130
34. Hall, M. et al. (2009) The WEKA Data Mining Software: An Update. SIGKDD Explor. Newsl., 11, 10-18
35. Harpaz, Y. et al. (1994) Volume changes on protein folding. Structure, 2, 641-649
36. Harrow, J. et al. (2012) GENCODE: The reference human genome annotation for The ENCODE Project. Genome Res., 22, 1760-1774
37. Hartl, F.U. and Hayer-Hartl, M. (2002) Molecular chaperones in the cytosol: From nascent chain to folded protein. Science, 295, 1852-1858. (Pubitemid 34214115
38. Hawkins, D.M. (2004) The problem of overfitting. J. Chem. Inf. Comput. Sci., 44, 1-12
39. Heinig, M. and Frishman, D. (2004) STRIDE: A web server for secondary structure assignment from known atomic coordinates of proteins. Nucleic Acids Res., 32, W500-W502. (Pubitemid 38997383
40. Hlevnjak, M. et al. (2012) Sequence signatures of direct complementarity between mRNAs and cognate proteins on multiple levels. Nucleic Acids Res., 40, 8874-8882
41. Ho, Y.C. and Pepyne, D.L. (2002) Simple explanation of the no-free-lunch theorem and its implications. J. Optim. Theor. Appl., 115, 549-570
42. Isogai, Y. et al. (1980) Characterization of multiple bends in proteins. Biopolymers, 19, 1183-1210
43. Kanehisa, M.I. and Tsong, T.Y. (1980) Local hydrophobicity stabilizes secondary structures in proteins. Biopolymers, 19, 1617-1628
44. Kerner, M.J. et al. (2005) Proteome-wide analysis of chaperonin-dependent protein folding in Escherichia coli. Cell, 122, 209-20. (Pubitemid 41032985
45. Kishor, A. et al. (2013) Hsp70 is a novel posttranscriptional regulator of gene expression that binds and stabilizes selected mRNAs containing AU-rich elements. Mol. Cell Biol., 33, 71-84
46. Koonin, E.V. et al. (2002) The structure of the protein universe and genome evolution. Nature, 420, 218-223. (Pubitemid 35340133
47. Kumar, M. et al. (2011) SVM based prediction of RNA-binding proteins using binding residues and evolutionary information. J. Mol. Recognit., 24, 303-313
48. Levitt, M. (1978) Conformational preferences of amino acids in globular proteins. Biochemistry, 17, 4277-4285
49. Lewis, B.A. et al. (2011) PRIDB: A protein-RNA interface database. Nucleic Acids Res., 39, D277-D282
50. Li, J. and Fine, J.P. (2008) ROC analysis with multiple classes and multiple tests: Methodology and its application in microarray studies. Biostatistics, 9, 566-576. (Pubitemid 351882089
51. Monné, M. et al. (1999) Turns in transmembrane helices: Determination of the minimal length of a 'helical hairpin' and derivation of a fine-grained turn propensity scale. J. Mol. Biol., 293, 807-814
52. Muppirala, U.K. et al. (2011) Predicting RNA-protein interactions using only sequence information. BMC Bioinformatics, 12, 489
53. Niwa, T. et al. (2009) Bimodal protein solubility distribution revealed by an aggregation analysis of the entire ensemble of Escherichia coli proteins. Proc. Natl Acad. Sci. USA, 106, 4201-4206
54. Pedregosa, F. et al. (2011) Scikit-learn: Machine learning in python. J. Mach. Learn. Res., 12, 2825-2830
55. Petersen, B. et al. (2009) A generic method for assignment of reliability scores applied to solvent accessibility predictions. BMC Struct. Biol., 9, 51
56. Prabhakaran, M. (1990) The distribution of physical, chemical and conformational properties in signal and nascent peptides. Biochem. J., 269, 691-696
57. Prilusky, J. et al. (2005) FoldIndex: A simple tool to predict whether a given protein sequence is intrinsically unfolded. Bioinformatics, 21, 3435-3438. (Pubitemid 41222454
58. Rao, H.B. et al. (2011) Update of PROFEAT: A web server for computing structural and physicochemical features of proteins and peptides from amino acid sequence. Nucleic Acids Res., 39, W385-W390
59. Riley, K.J. and Steitz, J.A. (2013) The 'Observer Effect' in genome-wide surveys of protein-RNA interactions. Mol. Cell, 49, 601-604
60. Rose, G.D. et al. (1985) Hydrophobicity of amino acid residues in globular proteins. Science, 229, 834-838. (Pubitemid 16246583
61. Rost, B. (1996) PHD: Predicting one-dimensional protein structure by profile-based neural networks. Methods Enzymol., 266, 525-539
62. Shazman, S. and Mandel-Gutfreund, Y. (2008) Classifying RNA-binding proteins based on electrostatic properties. PLoS Comput. Biol., 4, e1000146
63. Sickmeier, M. et al. (2007) DisProt: The database of disordered proteins. Nucleic Acids Res., 35, D786-D793. (Pubitemid 46056310
64. Smialowski, P. et al. (2012) PROSO II - a new method for protein solubility prediction. FEBS J., 279, 2192-2200
65. Smialowski, P. et al. (2010) The Negatome database: A reference set of non-interacting protein pairs. Nucleic Acids Res., 38, D540-D544
66. Sweet, R.M. and Eisenberg, D. (1983) Correlation of sequence hydrophobicities measures similarity in three-dimensional protein structure. J. Mol. Biol, 171, 479-488
67. Tartaglia, G.G. et al. (2005) Organism complexity anti-correlates with proteomic beta-Aggregation propensity. Protein Sci., 14, 2735-2740. (Pubitemid 41395599
68. Tartaglia, G.G. et al. (2010) Physicochemical determinants of chaperone requirements. J. Mol. Biol, 400, 579-588
69. Tartaglia, G.G. et al. (2008) Prediction of aggregation-prone regions in structured proteins. J. Mol. Biol., 380, 425-436
70. Tartaglia, G.G. et al. (2004) The role of aromaticity, exposed surface, and dipole moment in determining protein aggregation rates. Protein Sci., 13, 1939-1941. (Pubitemid 38822134
71. Tartaglia, G.G. and Vendruscolo, M. (2010) Proteome-level interplay between folding and aggregation propensities of proteins. J. Mol. Biol., 402, 919-928
72. Tartaglia, G.G. and Vendruscolo, M. (2008) The Zyggregator method for predicting protein aggregation propensities. Chem. Soc. Rev., 37, 1395-1401
73. Terribilini, M. et al. (2006) Prediction of RNA binding sites in proteins from amino acid sequence. RNA, 12, 1450-1462. (Pubitemid 44141524
74. Wang, M. et al. (2012) PaxDb, a database of protein abundance averages across all three domains of life. Mol. Cell Proteom., 11, 492-500
75. Wertz, D.H. and Scheraga, H.A. (1978) Influence of water on protein structure. An analysis of the preferences of amino acid residues for the inside or outside and for specific conformations in a protein molecule. Macromolecules, 11, 9-15
76. Wilkins, M.R. et al. (1999) Protein identification and analysis tools in the ExPASy server. Methods Mol. Biol., 112, 531-552
77. Wolpert, D.H. (2002) The supervised learning no-free-lunch theorems. In: Soft Computing and Industry. Springer, London, pp. 25-42
78. Zanzoni, A. et al. (2013) Principles of self-organization in biological pathways: A hypothesis on the autogenous association of alpha-synuclein. Nucleic Acids Res., 41, 9987-9998
79. Zimmer, C. et al. (2001) Analysis of sequence-specific binding of RNA to Hsp70 and its various homologs indicates the involvement of N- and C-terminal interactions. RNA, 7, 1628-1637. (Pubitemid 33078937