INitro-Tyr: Prediction of nitrotyrosine sites in proteins with general pseudo amino acid composition

PLoS ONE

Volumn 9, Issue 8, 2014, Pages

  Xu, Yan ^a   Wen, Xin ^a   Wen, Li Shu ^b   Wu, Ling Yun ^c   Deng, Nai Yang ^d   Chou, Kuo Chen ^e,f  

^a UNIVERSITY OF SCIENCE AND TECHNOLOGY BEIJING   (China)

^b Liaoning Shiyou University   (China)

^c INSTITUTE OF APPLIED MATHEMATICS   (China)

^d CHINA AGRICULTURAL UNIVERSITY   (China)

^e KING ABDULAZIZ UNIVERSITY   (Saudi Arabia)

^f GORDON LIFE SCIENCE INSTITUTE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

3-NITROTYROSINE; AMINO ACID; PROTEIN; TYROSINE;

AMINO ACID SEQUENCE; ANALOGS AND DERIVATIVES; CHEMISTRY; INTERNET; MOLECULAR GENETICS;

AMINO ACID SEQUENCE; AMINO ACIDS; INTERNET; MOLECULAR SEQUENCE DATA; PROTEINS; TYROSINE;

EID: 84905982402     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0105018     Document Type: Article

References (77)

1. Casoni F, Basso M, Massignan T, Gianazza E, Cheroni C, et al. (2005) Protein nitration in a mouse model of familial amyotrophic lateral sclerosis: possible multifunctional role in the pathogenesis. J Biol Chem 280: 16295-16304. (Pubitemid 40616757)
2. Ghesquiere B, Colaert N, Helsens K, Dejager L, Vanhaute C, et al. (2009) In vitro and in vivo protein-bound tyrosine nitration characterized by diagonal chromatography. Mol Cell Proteomics 8: 2642-2652.
3. Zhan X, Du Y, Crabb JS, Gu X, Kern TS, et al. (2008) Targets of tyrosine nitration in diabetic rat retina. Mol Cell Proteomics 7: 864-874. (Pubitemid 351737088)
4. Jarmula A, Rode W (2013) Computational study of the effects of protein tyrosine nitrations on the catalytic activity of human thymidylate synthase. J Comput Aided Mol Des 27: 45-66.
5. Abello N, Kerstjens HA, Postma DS, Bischoff R (2009) Protein tyrosine nitration: selectivity, physicochemical and biological consequences, denitration, and proteomics methods for the identification of tyrosine-nitrated proteins. J Proteome Res 8: 3222-3238.
6. Feeney MB, Schoneich C (2013) Proteomic Approaches to Analyze Protein Tyrosine Nitration. Antioxid Redox Signal.
7. Dekker F, Abello N, Wisastra R, Bischoff R (2012) Enrichment and detection of tyrosine-nitrated proteins. Curr Protoc Protein Sci Chapter 14: Unit 14 13.
8. Chou KC (2011) Some remarks on protein attribute prediction and pseudo amino acid composition (50th Anniversary Year Review). Journal of Theoretical Biology 273: 236-247.
9. Chen W, Feng PM, Lin H (2013) iRSpot-PseDNC: identify recombination spots with pseudo dinucleotide composition Nucleic Acids Research 41: e69.
10. Min JL, Xiao X (2013) iEzy-Drug: A web server for identifying the interaction between enzymes and drugs in cellular networking. BioMed Research International 2013: 701317.
11. Xu Y, Shao XJ, Wu LY, Deng NY (2013) iSNO-AAPair: incorporating amino acid pairwise coupling into PseAAC for predicting cysteine S-nitrosylation sites in proteins. PeerJ 1: e171.
12. Xiao X, Min JL, Wang P (2013) iCDI-PseFpt: Identify the channel-drug interaction in cellular networking with PseAAC and molecular fingerprints. Journal of Theoretical Biology 337C: 71-79.
13. Fan YN, Xiao X, Min JL (2014) iNR-Drug: Predicting the interaction of drugs with nuclear receptors in cellular networking. Intenational Journal of Molecular Sciences 15: 4915-4937.
14. Guo SH, Deng EZ, Xu LQ, Ding H, Lin H, et al. (2014) iNuc-PseKNC: a sequence-based predictor for predicting nucleosome positioning in genomes with pseudo k-tuple nucleotide composition. Bioinformatics 30: 1522-1529.
15. Liu B, Zhang D, Xu R, Xu J, Wang X, et al. (2014) Combining evolutionary information extracted from frequency profiles with sequence-based kernels for protein remote homology detection. Bioinformatics 30: 472-479.
16. Qiu WR, Xiao X (2014) iRSpot-TNCPseAAC: Identify recombination spots with trinucleotide composition and pseudo amino acid components. Int J Mol Sci 15: 1746-1766.
17. Xu Y, Wen X, Shao XJ, Deng NY (2014) iHyd-PseAAC: Predicting hydroxyproline and hydroxylysine in proteins by incorporating dipeptide position-specific propensity into pseudo amino acid composition. International Journal of Molecular Sciences 15: 7594-7610.
18. Ding H, Deng EZ, Yuan LF, Liu L, Lin H, et al. (2014) iCTX-Type: A sequence-based predictor for identifying the types of conotoxins in targeting ion channels. BioMed Research International 2014: 286419.
19. Qiu WR, Xiao X, Lin WZ (2014) iMethyl-PseAAC: Identification of protein methylation sites via a pseudo amino acid composition approach. BioMed Research International 2014: ID 947416.
20. Chen W, Feng PM, Lin H (2014) iSS-PseDNC: identifying splicing sites using pseudo dinucleotide composition. Biomed Research International 2014: 623149.
21. Chen W, Feng PM, Deng EZ, Lin H (2014) iTIS-PseTNC: a sequence-based predictor for identifying translation initiation site in human genes using pseudo trinucleotide composition. Analytical Biochemistry 10.1016/j.ab.2014. 1006.1022.
22. Chou KC (1993) A vectorized sequence-coupling model for predicting HIV protease cleavage sites in proteins. Journal of Biological Chemistry 268: 16938-16948.
23. Chou KC (1996) Review: Prediction of human immunodeficiency virus protease cleavage sites in proteins. Analytical Biochemistry 233: 1-14.
24. Chou KC (1995) A sequence-coupled vector-projection model for predicting the specificity of GalNAc-transferase. Protein Science 4: 1365-1383.
25. Chou KC (2001) Prediction of signal peptides using scaled window. Peptides 22: 1973-1979.
26. Chou KC, Shen HB (2007) Review: Recent progresses in protein subcellular location prediction. Analytical Biochemistry 370: 1-16.
27. Liu Z, Cao J, Ma Q, Gao X, Ren J, et al. (2011) GPS-YNO2: computational prediction of tyrosine nitration sites in proteins. Mol Biosyst 7: 1197-1204.
28. Xu Y, Ding J, Wu LY (2013) iSNO-PseAAC: Predict cysteine S-nitrosylation sites in proteins by incorporating position specific amino acid propensity into pseudo amino acid composition PLoS ONE 8: e55844.
29. Chou JJ (1993) A formulation for correlating properties of peptides and its application to predicting human immunodeficiency virus protease-cleavable sites in proteins. Biopolymers 33: 1405-1414.
30. Wang M, Yang J, Xu ZJ (2005) SLLE for predicting membrane protein types. Journal of Theoretical Biology 232: 7-15. (Pubitemid 39388370)
31. Feng KY, Cai YD (2005) Boosting classifier for predicting protein domain structural class. Biochemical & Biophysical Research Communications 334: 213-217.
32. Feng PM, Chen W, Lin H (2013) iHSP-PseRAAAC: Identifying the heat shock protein families using pseudo reduced amino acid alphabet composition. Analytical Biochemistry 442: 118-125.
33. Kandaswamy KK, Martinetz T, Moller S, Suganthan PN, et al. (2011) AFP-Pred: A random forest approach for predicting antifreeze proteins from sequence-derived properties. Journal of Theoretical Biology 270: 56-62.
34. Chou KC, Shen HB (2006) Predicting eukaryotic protein subcellular location by fusing optimized evidence-theoretic K-nearest neighbor classifiers. Journal of Proteome Research 5: 1888-1897.
35. Shen HB (2009) A top-down approach to enhance the power of predicting human protein subcellular localization: Hum-mPLoc 2.0. Analytical Biochemistry 394: 269-274.
36. Xiao X, Min JL, Wang P (2013) iGPCR-Drug: A web server for predicting interaction between GPCRs and drugs in cellular networking. PLoS ONE 8: e72234.
37. Chou KC (2013) Some Remarks on Predicting Multi-Label Attributes in Molecular Biosystems. Molecular Biosystems 9: 1092-1100.
38. Chou KC (2001) Prediction of protein cellular attributes using pseudo amino acid composition. PROTEINS: Structure, Function, and Genetics (Erratum: ibid, 2001, Vol44, 60) 43: 246-255.
39. Lin SX, Lapointe J (2013) Theoretical and experimental biology in one. J Biomedical Science and Engineering (JBiSE) 6: 435-442.
40. Nanni L, Lumini A, Gupta D, Garg A (2012) Identifying Bacterial Virulent Proteins by Fusing a Set of Classifiers Based on Variants of Chou's Pseudo Amino Acid Composition and on Evolutionary Information. IEEE/ACM Trans Comput Biol Bioinform 9: 467-475.
41. Hajisharifi Z, Piryaiee M, Mohammad Beigi M, Behbahani M, Mohabatkar H (2014) Predicting anticancer peptides with Chou's pseudo amino acid composition and investigating their mutagenicity via Ames test. Journal of Theoretical Biology 341: 34-40.
42. Mei S (2012) Predicting plant protein subcellular multi-localization by Chou's PseAAC formulation based multi-label homolog knowledge transfer learning. Journal of Theoretical Biology 310: 80-87.
43. Chen YK, Li KB (2013) Predicting membrane protein types by incorporating protein topology, domains, signal peptides, and physicochemical properties into the general form of Chou's pseudo amino acid composition. Journal of Theoretical Biology 318: 1-12.
44. Georgiou DN, Karakasidis TE, Megaritis AC (2013) A short survey on genetic sequences, Chou's pseudo amino acid composition and its combination with fuzzy set theory. The Open Bioinformatics Journal 7: 41-48.
45. Mohabatkar H, Mohammad Beigi M, Esmaeili A (2011) Prediction of GABA(A) receptor proteins using the concept of Chou's pseudo-amino acid composition and support vector machine. Journal of Theoretical Biology 281: 18-23.
46. Khosravian M, Faramarzi FK, Beigi MM, Behbahani M, Mohabatkar H (2013) Predicting Antibacterial Peptides by the Concept of Chou's Pseudo-amino Acid Composition and Machine Learning Methods. Protein & Peptide Letters 20: 180-186.
47. Mohabatkar H, Beigi MM, Abdolahi K, Mohsenzadeh S (2013) Prediction of Allergenic Proteins by Means of the Concept of Chou's Pseudo Amino Acid Composition and a Machine Learning Approach. Medicinal Chemistry 9: 133-137.
48. Mohammad Beigi M, Behjati M, Mohabatkar H (2011) Prediction of metalloproteinase family based on the concept of Chou's pseudo amino acid composition using a machine learning approach. Journal of Structural and Functional Genomics 12: 191-197.
49. Zia Ur R, Khan A (2012) Identifying GPCRs and their Types with Chou's Pseudo Amino Acid Composition: An Approach from Multi-scale Energy Representation and Position Specific Scoring Matrix. Protein & Peptide Letters 19: 890-903.
50. Sun XY, Shi SP, Qiu JD, Suo SB, Huang SY, et al. (2012) Identifying protein quaternary structural attributes by incorporating physicochemical properties into the general form of Chou's PseAAC via discrete wavelet transform. Molecular BioSystems 8: 3178-3184.
51. Du P, Gu S, Jiao Y (2014) PseAAC-General: Fast building various modes of general form of Chou's pseudo-amino acid composition for large-scale protein datasets. International Journal of Molecular Sciences 15: 3495-3506.
52. Jiang Y, Huang T, Chen L, Gao YF, Cai Y, et al. (2013) Signal propagation in protein interaction network during colorectal cancer progression. Biomed Res Int 2013: 287019.
53. Du P, Wang X, Xu C, Gao Y (2012) PseAAC-Builder: A cross-platform stand-alone program for generating various special Chou's pseudo-amino acid compositions. Analytical Biochemistry 425: 117-119.
54. Cao DS, Xu QS, Liang YZ (2013) propy: a tool to generate various modes of Chou's PseAAC. Bioinformatics 29: 960-962.
55. Chou KC (2001) Using subsite coupling to predict signal peptides. Protein Engineering 14: 75-79.
56. Chou KC, Shen HB (2009) Review: recent advances in developing web-servers for predicting protein attributes. Natural Science 2: 63-92.
57. Chou KC (2001) Prediction of protein signal sequences and their cleavage sites. PROTEINS: Structure, Function, and Genetics 42: 136-139.
58. Chou KC, Wu ZC, Xiao X (2012) iLoc-Hum: Using accumulation-label scale to predict subcellular locations of human proteins with both single and multiple sites. Molecular Biosystems 8: 629-641.
59. Xiao X, Wu ZC (2011) iLoc-Virus: A multi-label learning classifier for identifying the subcellular localization of virus proteins with both single and multiple sites. Journal of Theoretical Biology 284: 42-51.
60. Wu ZC, Xiao X (2011) iLoc-Plant: a multi-label classifier for predicting the subcellular localization of plant proteins with both single and multiple sites. Molecular BioSystems 7: 3287-3297.
61. Lin WZ, Fang JA, Xiao X (2013) iLoc-Animal: A multi-label learning classifier for predicting subcellular localization of animal proteins Molecular BioSystems 9: 634-644.
62. Chou KC, Wu ZC, Xiao X (2011) iLoc-Euk: A Multi-Label Classifier for Predicting the Subcellular Localization of Singleplex and Multiplex Eukaryotic Proteins. PLoS One 6: e18258.
63. Xiao X, Wang P, Lin WZ, Jia JH (2013) iAMP-2L: A two-level multi-label classifier for identifying antimicrobial peptides and their functional types. Analytical Biochemistry 436: 168-177.
64. Chou KC, Zhang CT (1995) Review: Prediction of protein structural classes. Critical Reviews in Biochemistry and Molecular Biology 30: 275-349.
65. Chou KC, Shen HB (2008) Cell-PLoc: A package of Web servers for predicting subcellular localization of proteins in various organisms. Nature Protocols 3: 153-162.
66. Shen HB, Yang J, Liu XJ (2005) Using supervised fuzzy clustering to predict protein structural classes. Biochem Biophys Res Commun 334: 577-581. (Pubitemid 40982405)
67. Mei S (2012) Multi-kernel transfer learning based on Chou's PseAAC formulation for protein submitochondria localization. Journal of Theoretical Biology 293: 121-130.
68. Chen W, Lin H, Feng PM, Ding C, Zuo YC, et al. (2012) iNuc-PhysChem: A Sequence-Based Predictor for Identifying Nucleosomes via Physicochemical Properties. PLoS ONE 7: e47843.
69. Sahu SS, Panda G (2010) A novel feature representation method based on Chou's pseudo amino acid composition for protein structural class prediction. Computational Biology and Chemistry 34: 320-327.
70. Huang C, Yuan JQ (2013) Predicting protein subchloroplast locations with both single and multiple sites via three different modes of Chou's pseudo amino acid compositions. Journal of Theoretical Biology 335: 205-212.
71. Kong L, Zhang L, Lv J (2014) Accurate prediction of protein structural classes by incorporating predicted secondary structure information into the general form of Chou's pseudo amino acid composition. J Theor Biol 344: 12-18.
72. Jia C, Lin X, Wang Z (2014) Prediction of Protein S-Nitrosylation Sites Based on Adapted Normal Distribution Bi-Profile Bayes and Chou's Pseudo Amino Acid Composition. Int J Mol Sci 15: 10410-10423.
73. Liu W (1999) Protein secondary structural content prediction. Protein Engineering 12: 1041-1050.
74. Chou KC (1999) Using pair-coupled amino acid composition to predict protein secondary structure content. Journal of Protein Chemistry 18: 473-480.
75. Chou KC (2005) Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes. Bioinformatics 21: 10-19.
76. Chou KC (2002) Review: Prediction of protein signal sequences. Current Protein and Peptide Science 3: 615-622.
77. Chou KC, Shen HB (2007) Signal-CF: a subsite-coupled and window-fusing approach for predicting signal peptides. Biochem Biophys Res Comm 357: 633-640.