Advertisement

Acta Biotheoretica

, Volume 61, Issue 2, pp 259–268 | Cite as

Using Over-Represented Tetrapeptides to Predict Protein Submitochondria Locations

  • Hao Lin
  • Wei Chen
  • Lu-Feng Yuan
  • Zi-Qiang Li
  • Hui Ding
Regular Article

Abstract

The mitochondrion is a key organelle of eukaryotic cell that provides the energy for cellular activities. Correctly identifying submitochondria locations of proteins can provide plentiful information for understanding their functions. However, using web-experimental methods to recognize submitochondria locations of proteins are time-consuming and costly. Thus, it is highly desired to develop a bioinformatics method to predict the submitochondria locations of mitochondrion proteins. In this work, a novel method based on support vector machine was developed to predict the submitochondria locations of mitochondrion proteins by using over-represented tetrapeptides selected by using binomial distribution. A reliable and rigorous benchmark dataset including 495 mitochondrion proteins with sequence identity ≤25 % was constructed for testing and evaluating the proposed model. Jackknife cross-validated results showed that the 91.1 % of the 495 mitochondrion proteins can be correctly predicted. Subsequently, our model was estimated by three existing benchmark datasets. The overall accuracies are 94.0, 94.7 and 93.4 %, respectively, suggesting that the proposed model is potentially useful in the realm of mitochondrion proteome research. Based on this model, we built a predictor called TetraMito which is freely available at http://lin.uestc.edu.cn/server/TetraMito.

Keywords

Submitochondria location Tetrapeptide Binomial distribution Support vector machine 

Notes

Acknowledgments

We are grateful to Dr. Loris Nanni for his help. This work was supported by the National Nature Scientific Foundation of China (No. 61202256, 61100092), the Project of Education Department in Sichuan (12ZA112), the Fundamental Research Funds for the Central Universities (ZYGX2012J113) and the Scientific Research Startup Foundation of UESTC.

References

  1. Chou KC, Shen HB (2007) Recent progress in protein subcellular location prediction. Anal Biochem 370:1–16CrossRefGoogle Scholar
  2. Chou KC, Shen HB (2008) Cell-PLoc: a package of Web servers for predicting subcellular localization of proteins in various organisms. Nat Protoc 3:153–162CrossRefGoogle Scholar
  3. Chou KC, Zhang CT (1995) Review: prediction of protein structural classes. Crit Rev Biochem Mol Biol 30:275–349CrossRefGoogle Scholar
  4. Ding H, Liu L, Guo FB, Huang J, Lin H (2011) Identify Golgi protein types with modified Mahalanobis discriminant algorithm and pseudo amino acid composition. Protein Pept Lett 18:58–63CrossRefGoogle Scholar
  5. 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
  6. Du P, Cao S, Li Y (2009) SubChlo: predicting protein subchloroplast locations with pseudo-amino acid composition and the evidence-theoretic K-nearest neighbor (ET-KNN) algorithm. J Theor Biol 261:330–335CrossRefGoogle Scholar
  7. Du P, Li T, Wang X (2011) Recent progress in predicting protein sub-subcellular locations. Expert Rev Proteomics 8:391–404CrossRefGoogle Scholar
  8. Fan GL, Li QZ (2012) Predicting protein submitochondria locations by combining different descriptors into the general form of Chou’s pseudo amino acid composition. Amino Acids 43:545–555Google Scholar
  9. Fan RE, Chen PH, Lin CJ (2005) Working set selection using the second order information for training SVM. J Mach Learn Res 6:1889–1918Google Scholar
  10. Feng Y, Luo L (2008) Use of tetrapeptide signals for protein secondary-structure prediction. Amino Acids 35:607–614CrossRefGoogle Scholar
  11. Henze K, Martin W (2003) Evolutionary biology: essence of mitochondria. Nature 426:127–128CrossRefGoogle Scholar
  12. Huang WL, Tung CW, Huang HL, Hwang SF, Ho SY (2007) ProLoc: prediction of protein subnuclear localization using SVM with automatic selection from physicochemical composition features. Biosystems 90:573–581CrossRefGoogle Scholar
  13. Huang WL, Tung CW, Ho SW, Hwang SF, Ho SY (2008) ProLoc-GO: utilizing informative Gene Ontology terms for sequence-based prediction of protein subcellular localization. BMC Bioinform 9:80CrossRefGoogle Scholar
  14. Huang WL, Tung CW, Huang HL, Ho SY (2009) Predicting protein subnuclear localization using GO-amino-acid composition features. Biosystems 98:73–79CrossRefGoogle Scholar
  15. Jiang X, Wei R, Zhao Y, Zhang T (2008) Using Chou’s pseudo amino acid composition based on approximate entropy and an ensemble of AdaBoost classifiers to predict protein subnuclear location. Amino Acids 34:669–675CrossRefGoogle Scholar
  16. Lei Z, Dai Y (2005) An SVM-based system for predicting protein subnuclear localizations. BMC Bioinform 6:291CrossRefGoogle Scholar
  17. Lei Z, Dai Y (2006) Assessing protein similarity with Gene Ontology and its use in subnuclear localization prediction. BMC Bioinform 7:491CrossRefGoogle Scholar
  18. Li FM, Li QZ (2008) Using pseudo amino acid composition to predict protein subnuclear location with improved hybrid approach. Amino Acids 34:119–125CrossRefGoogle Scholar
  19. Mei S (2012) Multi-kernel transfer learning based on Chou’s PseAAC formulation for protein submitochondria localization. J Theor Biol 293:121–130CrossRefGoogle Scholar
  20. Mei S, Fei W (2010) Amino acid classification based spectrum kernel fusion for protein subnuclear localization. BMC Bioinform Suppl 1:S17CrossRefGoogle Scholar
  21. Nanni L, Lumini A (2008) Genetic programming for creating Chou’s pseudo amino acid based features for submitochondria localization. Amino Acids 34:653–660CrossRefGoogle Scholar
  22. Polianskyte Z, Peitsaro N, Dapkunas A, Liobikas J, Soliymani R, Lalowski M, Speer O, Seitsonen J, Butcher S, Cereghetti GM, Linder MD, Merckel M, Thompson J, Eriksson O (2009) LACTB is a filament-forming protein localized in mitochondria. Proc Natl Acad Sci USA 106:18960–18965CrossRefGoogle Scholar
  23. Rackovsky S (1993) On the nature of protein folding code. Proc Natl Acad Sci USA 90:644–648CrossRefGoogle Scholar
  24. Shen HB, Chou KC (2005) Predicting protein subnuclear location with optimized evidence-theoretic K-nearest classifier and pseudo amino acid composition. Biochem Biophys Res Commun 337:752–756CrossRefGoogle Scholar
  25. Shen HB, Chou KC (2007) Nuc-PLoc: a new web-server for predicting protein subnuclear localization by fusing PseAA composition and PsePSSM. Protein Eng Des Sel 20:561–567CrossRefGoogle Scholar
  26. Shi Y (2002) A conserved tetrapeptide motif: potentiating apoptosis through IAP-binding. Cell Death Differ 9:93–95CrossRefGoogle Scholar
  27. Shi SP, Qiu JD, Sun XY, Huang JH, Huang SY, Suo SB, Liang RP, Zhang L (2011) Identify submitochondria and subchloroplast locations with pseudo amino acid composition: approach from the strategy of discrete wavelet transform feature extraction. Biochim Biophys Acta 1813:424–430CrossRefGoogle Scholar
  28. Stuart GW, Moffett K, Leader JJ (2002) A comprehensive vertebrate phylogeny using vector representations of protein sequences from whole genomes. Mol Biol Evol 19:554–562CrossRefGoogle Scholar
  29. UniProt Consortium (2012) Reorganizing the protein space at the Universal Protein Resource (UniProt). Nucleic Acids Res 40:D71–D75CrossRefGoogle Scholar
  30. van Dijk AD, Bosch D, ter Braak CJ, van der Krol AR, van Ham RC (2008) Predicting sub-Golgi localization of type II membrane proteins. Bioinformatics 24:1779–1786CrossRefGoogle Scholar
  31. Verhagen AM, Kratina TK, Hawkins CJ, Silke J, Ekert PG, Vaux DL (2007) Identification of mammalian mitochondrial proteins that interact with IAPs via N-terminal IAP binding motifs. Cell Death Differ 14:348–357CrossRefGoogle Scholar
  32. Wang G, Dunbrack RL Jr (2005) PISCES: recent improvements to a PDB sequence culling server. Nucleic Acids Res 33:W94–W98CrossRefGoogle Scholar
  33. Zakeri P, Moshiri B, Sadeghi M (2011) Prediction of protein submitochondria locations based on data fusion of various features of sequences. J Theor Biol 269:208–216CrossRefGoogle Scholar
  34. Zeng YH, Guo YZ, Xiao RQ, Yang L, Yu LZ, Li ML (2009) Using the augmented Chou’s pseudo amino acid composition for predicting protein submitochondria locations based on auto covariance approach. J Theor Biol 259:366–372CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.Key Laboratory for NeuroInformation of Ministry of Education, Center of Bioinformatics, School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina
  2. 2.Department of Physics, College of Sciences, Center for Genomics and Computational BiologyHebei United UniversityTangshanChina
  3. 3.School of Information and EngineeringSichuan Agricultural UniversityYaanChina
  4. 4.School of Computer Science and EngineeringUniversity of Electronic Science and Technology of ChinaChengduChina

Personalised recommendations