NR-2l: A two-level predictor for identifying nuclear receptor subfamilies based on sequence-derived features

PLoS ONE

Volumn 6, Issue 8, 2011, Pages

  Wang, Pu ^a   Xiao, Xuan ^a,b   Chou, Kuo Chen ^b  

^a JINGDEZHEN CERAMIC INSTITUTE   (China)

^b GORDON LIFE SCIENCE INSTITUTE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CELL NUCLEUS RECEPTOR; ESTROGEN LIKE RECEPTOR; FUSHI TARAZU F1 LIKE RECEPTOR; GERM CELL NUCLEAR FACTOR LIKE RECEPTOR; HEPATOCYTE NUCLEAR FACTOR 4 LIKE RECEPTOR; KNIRPS LIKE RECEPTOR; NERVE GROWTH FACTOR IB LIKE RECEPTOR; THYROID HORMONE LIKE RECEPTOR; UNCLASSIFIED DRUG; CELL RECEPTOR;

ACCESS TO INFORMATION; AMINO ACID COMPOSITION; AMINO ACID SEQUENCE; ARTICLE; CLIENT SERVER APPLICATION; COMPUTER PROGRAM; FOURIER ANALYSIS; FUZZY SYSTEM; INTERNET; JACKKNIFE TEST; K NEAREST NEIGHBOR; NUCLEAR RECEPTOR SUBFAMILY; PHYSICAL CHEMISTRY; PREDICTION; PROTEIN ANALYSIS; PROTEIN DATABASE; PROTEIN FAMILY; ALGORITHM; ANIMAL; BIOLOGY; CLASSIFICATION; GENETICS; HUMAN; METHODOLOGY; MOLECULAR GENETICS; REPRODUCIBILITY;

ANIMALIA;

ALGORITHMS; AMINO ACID SEQUENCE; ANIMALS; COMPUTATIONAL BIOLOGY; DATABASES, PROTEIN; HUMANS; INTERNET; MOLECULAR SEQUENCE DATA; RECEPTORS, CYTOPLASMIC AND NUCLEAR; REPRODUCIBILITY OF RESULTS;

EID: 80051676719     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0023505     Document Type: Article

References (32)

1. Altucci L, Gronemeyer H, (2001) Nuclear receptors in cell life and death. Trends in Endocrinology and Metabolism 12: 460-468.
2. Mangelsdorf DJ, Thummel C, Beato M, Herrlich P, Schutz G, et al. (1995) The nuclear receptor superfamily: the second decade. Cell 83: 835-839.
3. Robinson-Rechavi M, Garcia HE, Laudet V, (2003) The nuclear receptor superfamily. J Cell Sci 116: 585-586.
4. Florence H, Gerrit V, Fred EC, (2001) Collecting and harvesting biological data: the GPCRDB and NucleaRDB information systems. Nucleic Acids Research 29: 346-349.
5. Altschul SF, Madden TL, Schäffer AA, Zhang J, Zhang Z, et al. (1997) Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res 25: 3389-3402.
6. Chou KC, (2001) Prediction of protein cellular attributes using pseudo-amino acid composition. Proteins 43: 246-255.
7. Bhasin M, Raghava GPS, (2004) Classification of Nuclear Receptors Based on Amino Acid Composition and Dipeptide Composition. Journal of Biological Chemistry 279: 23262-23266.
8. Gao QB, Jin ZC, Ye XF, Wu C, He J, (2009) Prediction of nuclear receptors with optimal pseudo amino acid composition. Analytical Biochemistry 387: 54-59.
9. 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.
10. Li W, Godzik A, (2006) Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 22: 1658-1659.
11. Chou KC, (1995) A novel approach to predicting protein structural classes in a (20-1)-D amino acid composition space. Proteins: Structure, Function, and Bioinformatics 21: 319-344.
12. Nakashima H, Nishikawa K, Ooi T, (1986) The folding type of a protein is relevant to the amino acid composition. J Biochem 99: 153-162.
13. Zhou GP, (1998) An intriguing controversy over protein structural class prediction. Journal of Protein Chemistry 17: 729-738.
14. Zhou GP, Doctor K, (2003) Subcellular location prediction of apoptosis proteins. PROTEINS: Structure, Function, and Genetics 50: 44-48.
15. Liu W, Chou KC, (1999) Protein secondary structural content prediction. Protein Engineering 12: 1041-1050.
16. Chou KC, (1993) A vectorized sequence-coupling model for predicting HIV protease cleavage sites in proteins. Journal of Biological Chemistry 268: 16938-16948.
17. Chou KC, (2000) Review: Prediction of tight turns and their types in proteins. Analytical Biochemistry 286: 1-16.
18. Xiao X, Shao SH, Huang ZD, Chou KC, (2006) Using pseudo amino acid composition to predict protein structural classes: approached with complexity measure factor. Journal of Computational Chemistry 27: 478-482.
19. Gusev VD, Nemytikova LA, Chuzhanova NA, (1999) On the complexity measures of genetic sequences. Bioinformatics 15: 994-999.
20. Chou KC, (1988) Review: Low-frequency collective motion in biomacromolecules and its biological functions. Biophysical Chemistry 30: 3-48.
21. Cover T, Hart P, (1967) Nearest neighbor pattern classification. IEEE transactions on information theory 13: 21-27.
22. 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.
23. Keller JM, Gray MR, Givens JAJ, (1985) A frezzy K-nearest neighbor algorithm. IEEE transactions on systems, man, and cybernetics 15: 580-585.
24. Mahalanobis PC, (1936) On the generalized distance in statistics. Proc Natl Inst Sci India 2: 49-55.
25. Chou KC, Zhang CT, (1995) Review: Prediction of protein structural classes. Critical Reviews in Biochemistry and Molecular Biology 30: 275-349.
26. Liu T, Jia C, (2010) A high-accuracy protein structural class prediction algorithm using predicted secondary structural information. Journal of Theoretical Biology 267: 272-275.
27. Masso M, Vaisman II, (2010) Knowledge-based computational mutagenesis for predicting the disease potential of human non-synonymous single nucleotide polymorphisms. Journal of Theoretical Biology 266: 560-568.
28. Wang T, Xia T, Hu XM, (2010) Geometry preserving projections algorithm for predicting membrane protein types. Journal of Theoretical Biology 262: 208-213.
29. Joshi RR, Sekharan S, (2010) Characteristic peptides of protein secondary structural motifs. Protein & Peptide Letters 17: 1198-1206.
30. Kandaswamy KK, Pugalenthi G, Moller S, Hartmann E, Kalies KU, et al. (2010) Prediction of Apoptosis Protein Locations with Genetic Algorithms and Support Vector Machines Through a New Mode of Pseudo Amino Acid Composition. Protein and Peptide Letters 17: 1473-1479.
31. Liu T, Zheng X, Wang C, Wang J, (2010) Prediction of Subcellular Location of Apoptosis Proteins using Pseudo Amino Acid Composition: An Approach from Auto Covariance Transformation. Protein & Peptide Letters 17: 1263-1269.
32. Mohabatkar H, (2010) Prediction of cyclin proteins using Chou's pseudo amino acid composition. Protein & Peptide Letters 17: 1207-1214.