Amino Acids

, Volume 42, Issue 4, pp 1443–1454 | Cite as

Mito-GSAAC: mitochondria prediction using genetic ensemble classifier and split amino acid composition

  • Tariq Habib Afridi
  • Asifullah Khan
  • Yeon Soo Lee
Original Article


Mitochondria are all-important organelles of eukaryotic cells since they are involved in processes associated with cellular mortality and human diseases. Therefore, trustworthy techniques are highly required for the identification of new mitochondrial proteins. We propose Mito-GSAAC system for prediction of mitochondrial proteins. The aim of this work is to investigate an effective feature extraction strategy and to develop an ensemble approach that can better exploit the advantages of this feature extraction strategy for mitochondria classification. We investigate four kinds of protein representations for prediction of mitochondrial proteins: amino acid composition, dipeptide composition, pseudo amino acid composition, and split amino acid composition (SAAC). Individual classifiers such as support vector machine (SVM), k-nearest neighbor, multilayer perceptron, random forest, AdaBoost, and bagging are first trained. An ensemble classifier is then built using genetic programming (GP) for evolving a complex but effective decision space from the individual decision spaces of the trained classifiers. The highest prediction performance for Jackknife test is 92.62% using GP-based ensemble classifier on SAAC features, which is the highest accuracy, reported so far on the Mitochondria dataset being used. While on the Malaria Parasite Mitochondria dataset, the highest accuracy is obtained by SVM using SAAC and it is further enhanced to 93.21% using GP-based ensemble. It is observed that SAAC has better discrimination power for mitochondria prediction over the rest of the feature extraction strategies. Thus, the improved prediction performance is largely due to the better capability of SAAC for discriminating between mitochondria and non-mitochondria proteins at the N and C terminus and the effective combination capability of GP. Mito-GSAAC can be accessed at It is expected that the novel approach and the accompanied predictor will have a major impact to Molecular Cell Biology, Proteomics, Bioinformatics, System Biology, and Drug Development.


Mitochondrial protein Amino acid composition Random forest Genetic programming Dipeptide composition AdaBoost 



This work was supported by the Department of Computer and Information Sciences (DCIS) at Pakistan Institute of Engineering and Applied Science (PIEAS), Pakistan.

Conflict of interest

The authors declare that they have no conflict of interest.


  1. Bendtsen JD, Nielsen H, von Heijne G, Brunak S (2004) Improved prediction of signal peptides: SignalP 3.0. J Mol Biol 340:783–795PubMedCrossRefGoogle Scholar
  2. Breiman L (1996) Bagging predictors. Mach Learn 24:123–140Google Scholar
  3. Breiman L (2001) Random forests. Mach Learn 45:5–32CrossRefGoogle Scholar
  4. Cai YD, Zhou GP, Chou KC (2003) Support vector machines for predicting membrane protein types by using functional domain composition. Biophys J 84:3257–3263PubMedCrossRefGoogle Scholar
  5. Cameron JM, Hurd T, Robinson BH (2005) Computational identification of human mitochondrial proteins based on homology to yeast mitochondrially targeted proteins. Bioinformatics 21:1825–1830PubMedCrossRefGoogle Scholar
  6. Chou KC (2000) Prediction of protein subcellular locations by incorporating quasi-sequence-order effect. Biochem Biophys Res Commun 278:477–483PubMedCrossRefGoogle Scholar
  7. Chou KC (2001) Prediction of protein cellular attributes using pseudo amino acid composition. Proteins Struct Funct Genet 43:246–255, erratum 44:60Google Scholar
  8. Chou KC (2005a) Review: progress in protein structural class prediction and its impact to bioinformatics and proteomics. Curr Protein Pept Sci 6:423–436Google Scholar
  9. Chou KC (2005b) Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes. Bioinformatics 21:10–19Google Scholar
  10. Chou KC, Cai YD (2002) Using functional domain composition and support vector machines for prediction of protein subcellular location. J Biol Chem 277:45765–45769PubMedCrossRefGoogle Scholar
  11. Chou KC, Cai YD (2006) Predicting protein–protein interactions from sequences in a hybridization space. J Proteome Res 5:316–322PubMedCrossRefGoogle Scholar
  12. Chou KC, Elrod DW (1999) Protein subcellular location prediction. Protein Eng 12:107–118PubMedCrossRefGoogle Scholar
  13. Chou KC, Shen HB (2006a) Predicting protein subcellular location by fusing multiple classifiers. J Cell Biochem 99:517–527PubMedCrossRefGoogle Scholar
  14. Chou KC, Shen HB (2006b) Hum-PLoc: a novel ensemble classifier for predicting human protein subcellular localization. Biochem Biophys Res Commun 347:150–157PubMedCrossRefGoogle Scholar
  15. Chou KC, Shen HB (2007) Euk-mPLoc: a fusion classifier for large-scale eukaryotic protein subcellular location prediction by incorporating multiple sites. J Proteome Res 6:1728–1734PubMedCrossRefGoogle Scholar
  16. Chou KC, Shen HB (2008) Cell-PLoc: a package of web-servers for predicting subcellular localization of proteins in various organisms. Nat Protoc 3:153–162Google Scholar
  17. Claros MG, Vincens P (1996) Computational method to predict mitochondrial proteins and their targeting sequences. Eur J Biochem 241:779–786PubMedCrossRefGoogle Scholar
  18. Du P, Li Y (2006) Prediction of protein submitochondria locations by hybridizing pseudo-amino acid composition with various physicochemical features of segmented sequence. BMC Bioinform 7:518CrossRefGoogle Scholar
  19. Emanuelsson O, Nielsen H, Brunak S, von Heijne G (2000) Predicting subcellular localization of proteins based on their N-terminal amino acid sequence. J Mol Biol 300:1005–1016PubMedCrossRefGoogle Scholar
  20. Freund Y, Schapire RE (1996) Experiments with a new boosting algorithm. In: Proceedings of the Thirteenth International Conference on Machine Learning. Morgan Kaufmann, Massachusetts, pp 48–156Google Scholar
  21. Gerbitz KD, Gempel K, Brdiczka D (1996) Mitochondria and diabetes: genetic, biochemical, and clinical implications of the cellular energy circuit. Diabetes 45:113–126PubMedCrossRefGoogle Scholar
  22. Gottlieb RA (2000) Programmed cell death. Drug News Perspect 13:471–476PubMedGoogle Scholar
  23. Guda C, Fahy E, Subramaniam S (2004) MITOPRED: a genome-scale method for prediction of nucleus-encoded mitochondrial proteins. Bioinformatics 20:1785–1794PubMedCrossRefGoogle Scholar
  24. Guo YZ, Li M, Lu M, Wen Z, Wang K, Li G, Wu J (2006) Classifying GPCRs and NRs based on protein power spectrum from fast fourier transform. Amino Acids 30:397–402PubMedCrossRefGoogle Scholar
  25. Hayat M, Khan A (2010) Predicting membrane protein types by fusing composite protein sequence features into pseudo amino acid composition. J Theor Biol 271(1):10–17CrossRefGoogle Scholar
  26. Höglund A, Dönnes P, Blum T, Adolph HW, Kohlbacher O (2006) MultiLoc: prediction of protein subcellular localization using N-terminal targeting sequences, sequence motifs and amino acid composition. Bioinformatics 22:1158–1165PubMedCrossRefGoogle Scholar
  27. Horton P, Park KJ, Obayashi T, Nakai K (2006) Protein subcellular localization prediction with WoLF PSORT. In: Proceedings of the fourth Annual Asia Pacific Bioinformatics Conference APBC06, Taipei, Taiwan, pp 39–48Google Scholar
  28. Hu J, Fan Z (2009) Improving protein localization prediction using amino acid group based physiochemical encoding. BICoB 2009. LNBI 5462:248–258Google Scholar
  29. Hua SJ, Sun ZR (2001) Support vector machine approach for protein subcellular localization prediction. Bioinformatics 17:721–728PubMedCrossRefGoogle Scholar
  30. Huang Y, Li Y (2004) Prediction of protein subcellular locations using fuzzy k-NN method. Bioinformatics 20:21–28PubMedCrossRefGoogle Scholar
  31. Huang WL, Tung CW, Ho SW (2008) ProLoc-GO: utilizing informative gene ontology terms for sequences-based prediction of protein subcellular localization. BMC Bioinform 9:80CrossRefGoogle Scholar
  32. Hutchin T, Cortopassi GA (1995) A mitochondrial DNA clone is associated with increased risk for Alzheimer disease. Proc Natl Acad Sci USA 92:6892–6895PubMedCrossRefGoogle Scholar
  33. Jassem W, Fuggle SV, Rela M, Koo DD, Heaton ND (2002) The role of mitochondria in ischemia/reperfusion injury. Transplantation 73:493–499PubMedCrossRefGoogle Scholar
  34. Jiang L, Li ML, Wen ZN, Wang KL, Diao YB, Guo YZ, Liu LX (2006) Prediction of mitochondrial proteins using discrete wavelet transform. Protein J 25:241–249PubMedCrossRefGoogle Scholar
  35. Khan A, Mirza AM (2007) Genetic perceptual shaping: utilizing cover image and conceivable attack information using genetic programming. Inform Fus 8(4):354–365CrossRefGoogle Scholar
  36. Khan A, Majid A, Mirza AM (2005) Combination and optimization of classifiers in gender classification using genetic programming. Int J Knowl Based Intell Eng Syst 9:11Google Scholar
  37. Khan A, Khan MF, Choi TS (2008a) Proximity based GPCRs prediction in transform domain. Biochem Biophys Res Commun 371(3):411–415Google Scholar
  38. Khan A, Tahir SF, Majid A, Choi TS (2008b) Machine learning based adaptive watermark decoding in view of an anticipated attack. Pattern Recogn 41:2594–2610CrossRefGoogle Scholar
  39. Khan A, Majid A, Choi TS (2010) Predicting protein subcellular location: exploiting amino acid based sequence of feature spaces and fusion of diverse classifiers. Amino Acids 38:347–350PubMedCrossRefGoogle Scholar
  40. Koza JR (1992) Genetic programming: on the programming of computers by means of natural selection. MIT Press, CambridgeGoogle Scholar
  41. Kumar M, Verma R, Raghava GPS (2006) Prediction of mitochondrial proteins using support vector machine and hidden Markov model. J Biol Chem 281:5357–5363PubMedCrossRefGoogle Scholar
  42. Nanni L, Lumini A (2008a) Using ensemble of classifiers in bioinformatics. In: Peters H, Vogel M Machine Learning Research Progress. Nova publisher, New YorkGoogle Scholar
  43. Nanni L, Lumini A (2008b) Genetic programming for creating Chou’s pseudo amino acid based features for submitochondria localization. Amino Acids 34(4):653–660PubMedCrossRefGoogle Scholar
  44. Nanni L, Brahnam S, Lumini A (2010) High performance set of PseAAC and sequence based descriptors for protein classification. J Theor Biol 266(1):1–10PubMedCrossRefGoogle Scholar
  45. Rodríguez JJ, Ludmila IK, Carlos JA (2006) Rotation forest: a new classifier ensemble method. IEEE Trans Pattern Anal Mach Intell 28(10):1619–1630PubMedCrossRefGoogle Scholar
  46. Shen HB, Chou KC (2007) Hum-mPLoc: an ensemble classifier for large-scale human protein subcellular location prediction by incorporating samples with multiple sites. Biochem Biophys Res Commun 355:1006–1011PubMedCrossRefGoogle Scholar
  47. Tan F, Feng X, Fang Z, Li M, Guo Y, Jiang L (2006) Prediction of mitochondrial proteins based on genetic algorithm—partial least squares and support vector machine. Amino Acids (published online Oct 15 2006. doi:10.1007/s00726-006-0465-0)
  48. Vapnik VN (1998) Statistical learning theory. Wiley, New YorkGoogle Scholar
  49. Verma R, Varshney Grish C, Raghava GPS (2009) Prediction of mitochondrial proteins of malaria parasite using split amino acid composition and PSSM profile 39(1):101–110Google Scholar
  50. Wooten GF, Currie LJ, Bennett JP, Harrison MB, Trugman JM, Parker WD Jr (1997) Maternal inheritance in Parkinson’s disease. Ann Neurol 41:265–268PubMedCrossRefGoogle Scholar
  51. Xiao X, Shao S, Ding Y, Huang Z, Chen X, Chou KC (2005) An application of gene comparative image for predicting the effect on replication ratio by HBV virus gene missense mutation. J Theor Biol 235:555–565PubMedCrossRefGoogle Scholar
  52. Xiao X, Shao SH, Chou KC (2006a) A probability cellular automaton model for hepatitis B viral infections. Biochem Biophys Res Commun 342:605–610PubMedCrossRefGoogle Scholar
  53. Xiao X, Shao S, Ding Y, Huang Z, Chou KC (2006b) Using cellular automata images and pseudo amino acid composition to predict protein sub-cellular location. Amino Acids 30:49–54PubMedCrossRefGoogle Scholar
  54. Zhang CX, Zhang JS (2008) RotBoost: a technique for combining Rotation Forest and AdaBoost. Pattern Recogn Lett. doi: 10.1016/j.patrec.2008.03.006

Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  • Tariq Habib Afridi
    • 1
  • Asifullah Khan
    • 1
  • Yeon Soo Lee
    • 2
  1. 1.Department of Computer and Information SciencesPakistan Institute of Engineering and Applied SciencesIslamabadPakistan
  2. 2.Department of Biomedical Engineering, College of Medical ScienceCatholic University of DaeguGyungsanRepublic of Korea

Personalised recommendations