AcACS: Improving the prediction accuracy of protein subcellular locations and protein classification by incorporating the average chemical shifts composition

Scientific World Journal

Volumn 2014, Issue , 2014, Pages

  Fan, Guo Liang ^a   Liu, Yan Ling ^a   Zuo, Yong Chun ^a   Mei, Han Xue ^a   Rang, Yi ^a   Hou, Bao Yan ^a   Zhao, Yan ^a  

^a INNER MONGOLIA UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ARTICLE; AVERAGE CHEMICAL SHIFT; CELLULAR DISTRIBUTION; CHEMICAL COMPOSITION; CHEMICAL PHENOMENA; PREDICTION; PROTEIN ANALYSIS; PROTEIN LOCALIZATION; PROTEIN SECONDARY STRUCTURE; PROTEIN STRUCTURE, FUNCTION AND VARIABILITY; PROTON NUCLEAR MAGNETIC RESONANCE; BIOLOGICAL MODEL; CHEMICAL MODEL; CHEMISTRY; INTRACELLULAR SPACE; METABOLISM; PROTEIN TRANSPORT;

PROTEIN;

ALGORITHMS; INTRACELLULAR SPACE; MODELS, BIOLOGICAL; MODELS, CHEMICAL; PROTEIN STRUCTURE, SECONDARY; PROTEIN TRANSPORT; PROTEINS;

EID: 84904633769     PISSN: 23566140     EISSN: 1537744X     Source Type: Journal    
DOI: 10.1155/2014/864135     Document Type: Article

References (58)

1. Casadio R., Martelli P. L., Pierleoni A., The prediction of protein subcellular localization from sequence: a shortcut to functional genome annotation. Briefings in Functional Genomics and Proteomics 2008 7 1 63 73 10.1093/bfgp/eln003 2-s2.0-44249095907 (Pubitemid 351721151)
2. Shi J.-Y., Zhang S.-W., Pan Q., Cheng Y.-M., Xie J., Prediction of protein subcellular localization by support vector machines using multi-scale energy and pseudo amino acid composition. Amino Acids 2007 33 1 69 74 10.1007/s00726-006-0475-y 2-s2.0-34447106453 (Pubitemid 47101465)
3. Bakhtiarizadeh M. R., Moradi-Shahrbabak M., Ebrahimi M., Ebrahimie E., Neural network and SVM classifiers accurately predict lipid binding proteins, irrespective of sequence homology. Journal of Theoretical Biology 2014 356 213 222 10.1016/j.jtbi.2014.04.040
4. Campos T. D., Young N. D., Korhonen P. K., Identification of G protein-coupled receptors in Schistosoma haematobium and S. mansoni by comparative genomics. Parasites & Vectors 2014 7 1, article 242 10.1186/1756-3305-7-242
5. Chen Y. L., Li Q. Z., Prediction of apoptosis protein subcellular location using improved hybrid approach and pseudo-amino acid composition. Journal of Theoretical Biology 2007 248 2 377 381 10.1016/j.jtbi.2007.05.019 2-s2.0-34548009150 (Pubitemid 47284174)
6. Ding H., Guo S., Deng E., Yuan L., Guo F., Huang J., Rao N., Chen W., Lin H., Prediction of Golgi-resident protein types by using feature selection technique. Chemometrics and Intelligent Laboratory Systems 2013 124 9 13 10.1016/j.chemolab.2013.03.005 2-s2.0-84876306464
7. Mondal S., Pai P. P., Chou's pseudo amino acid composition improves sequence-based antifreeze protein prediction. Journal of Theoretical Biology 2014 356 30 35 10.1016/j.jtbi.2014.04.006
8. Zhang L., Liao B., Li D., Zhu W., A novel representation for apoptosis protein subcellular localization prediction using support vector machine. Journal of Theoretical Biology 2009 259 2 361 365 10.1016/j.jtbi.2009.03.025 2-s2.0-67349230921
9. Zuo Y. C., Peng Y., Liu L., Chen W., Yang L., Fan G. L., Predicting peroxidase subcellular location by hybridizing different descriptors of Chou' pseudo amino acid patterns. Analytical Biochemistry 2014 458 14 19
10. Ding H., Feng P.-M., Chen W., Lin H., Identification of bacteriophage virion proteins by the ANOVA feature selection and analysis. Molecular Biosystems 2014 10.1039/C4MB00316K
11. Ding H., Deng E.-Z., Yuan L.-F., iCTX-Type: a sequence-based predictor for identifying the types of conotoxins in targeting ion channels. BioMed Research International 2014 2014 10 286419 10.1155/2014/286419
12. Cai Y. D., Chou K. C., Using neural networks for prediction of subcellular location of prokaryotic and eukaryotic proteins. Molecular Cell Biology Research Communications 2000 4 3 172 173 10.1006/mcbr.2001.0269 2-s2.0-0034452415 (Pubitemid 32298899)
13. Li Q. Z., Lu Z. Q., The prediction of the structural class of protein: application of the measure of diversity. Journal of Theoretical Biology 2001 213 3 493 502 10.1006/jtbi.2001.2441 2-s2.0-0035824718 (Pubitemid 34014412)
14. Jin Y., Niu B., Feng K., Lu W., Cai Y., Li G., Predicting subcellular localization with AdaBoost learner. Protein and Peptide Letters 2008 15 3 286 289 10.2174/092986608783744234 2-s2.0-41449117926 (Pubitemid 351457378)
15. Cai Y. D., Chou K. C., Nearest neighbour algorithm for predicting protein subcellular location by combining functional domain composition and pseudo-amino acid composition. Biochemical and Biophysical Research Communications 2003 305 2 407 411 10.1016/S0006-291X(03)00775-7 2-s2.0-0038705861 (Pubitemid 36555484)
16. Chou K. C., Shen H. B., Predicting eukaryotic protein subcellular location by fusing optimized evidence-theoretic K-nearest neighbor classifiers. Journal of Proteome Research 2006 5 8 1888 1897 10.1021/pr060167c 2-s2.0-33747197197 (Pubitemid 44232701)
17. Nanni L., Lumini A., Genetic programming for creating Chou's pseudo amino acid based features for submitochondria localization. Amino Acids 2008 34 4 653 660 10.1007/s00726-007-0018-1 2-s2.0-43149097851
18. Chou K.-C., Shen H.-B., Predicting protein subcellular location by fusing multiple classifiers. Journal of Cellular Biochemistry 2006 99 2 517 527 10.1002/jcb.20879 2-s2.0-33748944288 (Pubitemid 44435568)
19. Emanuelsson O., Brunak S., von Heijne G., Nielsen H., Locating proteins in the cell using TargetP, SignalP and related tools. Nature Protocols 2007 2 4 953 971 10.1038/nprot.2007.131 2-s2.0-34248531753 (Pubitemid 46745592)
20. Höglund A., Dönnes P., Blum T., Adolph H., Kohlbacher O., MultiLoc: prediction of protein subcellular localization using N-terminal targeting sequences, sequence motifs and amino acid composition. Bioinformatics 2006 22 10 1158 1165 10.1093/bioinformatics/btl002 2-s2.0-33646861792 (Pubitemid 43779005)
21. Horton P., Park K., Obayashi T., Fujita N., Harada H., Adams-Collier C. J., Nakai K., WoLF PSORT: protein localization predictor. Nucleic Acids Research 2007 35 2 W585 W587 10.1093/nar/gkm259 2-s2.0-34547560275
22. Brady S., Shatkay H., EPILOC: a (working) text-based system for predicting protein subcellular location. Proceedings of the 13th Pacific Symposium on Biocomputing (PSB '08) January 2008 604 615 2-s2.0-40549096793
23. Fyshe A., Liu Y., Szafron D., Greiner R., Lu P., Improving subcellular localization prediction using text classification and the gene ontology. Bioinformatics 2008 24 21 2512 2517 10.1093/bioinformatics/btn463 2-s2.0-54949120643
24. Du P., Li Y., Prediction of protein submitochondria locations by hybridizing pseudo-amino acid composition with various physicochemical features of segmented sequence. BMC Bioinformatics 2006 7, article 518 10.1186/1471-2105-7-518 2-s2.0-33846010187 (Pubitemid 46040027)
25. Chou K. C., Cai Y. D., Prediction of protein subcellular locations by GO-FunD-PseAA predictor. Biochemical and Biophysical Research Communications 2004 320 4 1236 1239 10.1016/j.bbrc.2004.06.073 2-s2.0-3142779931 (Pubitemid 38916676)
26. Sibley A. B., Cosman M., Krishnan V. V., An empirical correlation between secondary structure content and averaged chemical shifts in proteins. Biophysical Journal 2003 84 2 I 1223 1227 10.1016/S0006-3495(03)74937-6 2-s2.0-0037304366 (Pubitemid 36133444)
27. Mielke S. P., Krishnan V. V., Protein structural class identification directly from NMR spectra using averaged chemical shifts. Bioinformatics 2003 19 16 2054 2064 10.1093/bioinformatics/btg280 2-s2.0-0242661071 (Pubitemid 37419907)
28. Spera S., Bax A., Empirical correlation between protein backbone conformation and C.alpha. and C.beta. 13C nuclear magnetic resonance chemical shifts. Journal of the American Chemical Society 1991 113 14 5490 5492 10.1021/ja00014a071
29. Zhao Y., Alipanahi B., Li S. C., Li M., Protein secondary structure prediction using NMR chemical shift data. Journal of Bioinformatics and Computational Biology 2010 8 5 867 884 10.1142/S0219720010004987 2-s2.0-77958544970
30. α chemical shifts in protein structure determination. Journal of Magnetic Resonance B 1995 109 2 229 233 10.1006/jmrb.1995.0016 2-s2.0-0001455222
31. Wishart D. S., Sykes B. D., Richards F. M., Relationship between nuclear magnetic resonance chemical shift and protein secondary structure. Journal of Molecular Biology 1991 222 2 311 333 10.1016/0022-2836(91)90214-Q 2-s2.0-0026410969 (Pubitemid 121004009)
32. Wishart D. S., Arndt D., Berjanskii M., Tang P., Zhou J., Lin G., CS23D: a web server for rapid protein structure generation using NMR chemical shifts and sequence data. Nucleic acids research 2008 36 W496 502 10.1093/nar/gkn305 2-s2.0-48449095850
33. Seavey B. R., Farr E. A., Westler W. M., Markley J. L., A relational database for sequence-specific protein NMR data. Journal of Biomolecular NMR 1991 1 3 217 236 10.1007/BF01875516 2-s2.0-0026223896
34. Shen Y., Bax A., Protein backbone chemical shifts predicted from searching a database for torsion angle and sequence homology. Journal of Biomolecular NMR 2007 38 4 289 302 10.1007/s10858-007-9166-6 2-s2.0-34547179849 (Pubitemid 47108511)
35. Han B., Liu Y., Ginzinger S. W., Wishart D. S., SHIFTX2: significantly improved protein chemical shift prediction. Journal of Biomolecular NMR 2011 50 1 43 57 10.1007/s10858-011-9478-4 2-s2.0-80051673221
36. Tian Y., Opella S. J., Marassi F. M., Improved chemical shift prediction by Rosetta conformational sampling. Journal of Biomolecular NMR 2012 54 3 237 243 10.1007/s10858-012-9677-7 2-s2.0-84868205509
37. Vila J. A., Villegas M. E., Baldoni H. A., Scheraga H. A., Predicting 13C α chemical shifts for validation of protein structures. Journal of Biomolecular NMR 2007 38 3 221 235 10.1007/s10858-007-9162-x 2-s2.0-34748921707
38. Markin C. J., Spyracopoulos L., Accuracy and precision of protein-ligand interaction kinetics determined from chemical shift titrations. Journal of Biomolecular NMR 2012 54 4 355 376 10.1007/s10858-012-9678-6 2-s2.0-84874486080
39. Markin C. J., Spyracopoulos L., Increased precision for analysis of protein-ligand dissociation constants determined from chemical shift titrations. Journal of Biomolecular NMR 2012 53 2 125 138 10.1007/s10858-012-9630-9 2-s2.0-84865185420
40. Chou K., Some remarks on protein attribute prediction and pseudo amino acid composition. Journal of Theoretical Biology 2011 273 1 236 247 10.1016/j.jtbi.2010.12.024 MR2974930 2-s2.0-79951518208
41. Du P., Wang X., Xu C., Gao Y., PseAAC-Builder: a cross-platform stand-alone program for generating various special Chou's pseudo-amino acid compositions. Analytical Biochemistry 2012 425 2 117 119 10.1016/j.ab.2012.03. 015 2-s2.0-84859932176
42. Du P., Gu S., Jiao Y., 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 2014 15 3 3495 3506
43. Berman H. M., Westbrook J., Feng Z., Gilliland G., Bhat T. N., Weissig H., Shindyalov I. N., Bourne P. E., The protein data bank. Nucleic Acids Research 2000 28 1 235 242 10.1093/nar/28.1.235 2-s2.0-0033954256 (Pubitemid 30047768)
44. Li W., Jaroszewski L., Godzik A., Clustering of highly homologous sequences to reduce the size of large protein databases. Bioinformatics 2001 17 3 282 283 10.1093/bioinformatics/17.3.282 2-s2.0-0035072551 (Pubitemid 32288218)
45. Kabsch W., Sander C., Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers 1983 22 12 2577 2637 10.1002/bip.360221211 2-s2.0-0020997912
46. Pollastri G., Martin A. J. M., Mooney C., Vullo A., Accurate prediction of protein secondary structure and solvent accessibility by consensus combiners of sequence and structure information. BMC Bioinformatics 2007 8 article 201 10.1186/1471-2105-8-201 2-s2.0-34547127137 (Pubitemid 47097303)
47. Pollastri G., McLysaght A., Porter: a new, accurate server for protein secondary structure prediction. Bioinformatics 2005 21 8 1719 1720 10.1093/bioinformatics/bti203 2-s2.0-17444397116 (Pubitemid 40542742)
48. Du P., Yu Y., SubMito-PSPCP: predicting protein submitochondrial locations by hybridizing positional specific physicochemical properties with pseudoamino acid compositions. BioMed Research International 2013 2013 7 263829 10.1155/2013/263829
49. Fan G. L., Li Q. Z., Predicting protein submitochondria locations by combining different descriptors into the general form of Chou's pseudo amino acid composition. Amino Acids 2012 43 2 545 555 10.1007/s00726-011-1143-4 2-s2.0-84864779292
50. Fan G. L., Li Q. Z., Predict mycobacterial proteins subcellular locations by incorporating pseudo-average chemical shift into the general form of Chou's pseudo amino acid composition. Journal of Theoretical Biology 2012 304 88 95 10.1016/j.jtbi.2012.03.017 2-s2.0-84859635404
51. Fan G. L., Li Q. Z., Discriminating bioluminescent proteins by incorporating average chemical shift and evolutionary information into the general form of Chou's pseudo amino acid composition. Journal of Theoretical Biology 2013 334 45 51 10.1016/j.jtbi.2013.06.003 2-s2.0-84879708816
52. Fan G. L., Li Q. Z., Zuo Y. C., Predicting acidic and alkaline enzymes by incorporating the average chemical shift and gene ontology informations into the general form of Chou's PseAAC. Process Biochemistry 2013 48 7 1048 1053 10.1016/j.procbio.2013.05.012 2-s2.0-84880069651
53. Rashid M., Saha S., Raghava G. P. S., Support Vector Machine-based method for predicting subcellular localization of mycobacterial proteins using evolutionary information and motifs. BMC Bioinformatics 2007 8, article 337 10.1186/1471-2105-8-337 2-s2.0-37249058502 (Pubitemid 350278741)
54. Liu B., Wang X., Lin L., Dong Q., A discriminative method for protein remote homology detection and fold recognition combining Top- n -grams and latent semantic analysis. BMC Bioinformatics 2008 9, article 510 10.1186/1471-2105-9-510 2-s2.0-58149345703
55. Liu B., Xu J., Zou Q., Using distances between Top-n-gram and residue pairs for protein remote homology detection. Bmc Bioinformatics 2014 15, article S3
56. Liu B., Zhang D., Xu R., Combining evolutionary information extracted from frequency profiles with sequence-based kernels for protein remote homology detection. Bioinformatics 2014 30 4 472 479 10.1093/bioinformatics/btt709
57. Liu B., Wang X., Zou Q., Dong Q., Chen Q., Protein remote homology detection by combining Chou's pseudo amino acid composition and profile-based protein representation. Molecular Informatics 2013 32 9-10 775 782 10.1002/minf.201300084
58. Liu B., Wang X., Chen Q., Dong Q., Lan X., Using amino acid physicochemical distance transformation for fast protein remote homology detection. PLoS ONE 2012 7 9 e46633 10.1371/journal.pone.0046633 2-s2.0-84867004398