Skip to main content
SpringerLink
Log in

Predicting protein–protein interactions from protein sequences using meta predictor

  • Short Communication
  • Published:
Amino Acids Aims and scope Submit manuscript

Abstract

A novel method is proposed for predicting protein–protein interactions (PPIs) based on the meta approach, which predicts PPIs using support vector machine that combines results by six independent state-of-the-art predictors. Significant improvement in prediction performance is observed, when performed on Saccharomyces cerevisiae and Helicobacter pylori datasets. In addition, we used the final prediction model trained on the PPIs dataset of S. cerevisiae to predict interactions in other species. The results reveal that our meta model is also capable of performing cross-species predictions. The source code and the datasets are available at http://home.ustc.edu.cn/~jfxia/Meta_PPI.html.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

References

  • Ben-Hur A, Noble WS (2005) Kernel methods for the predicting protein–protein interactions. Bioinformatics 21:i38–i46

    Article  CAS  PubMed  Google Scholar 

  • Bock JR, Gough DA (2003) Whole-proteome interaction mining. Bioinformatics 19:125–134

    Article  CAS  PubMed  Google Scholar 

  • Bujnicki J, Elofsson A, Fischer D, Rychlewski L (2001) Structure prediction meta server. Bioinformatics 8:750–751

    Article  Google Scholar 

  • Chang CC, Lin CJ (2001) LIBSVM: a library for support vector machines. Software available at http://www.csie.ntu.edu.tw/cjlin/libsvm

  • Chen X, Liu M (2005) Prediction of protein–protein interactions using random decision forest framework. Bioinformatics 21:4394–4400

    Article  CAS  PubMed  Google Scholar 

  • Chou KC, Cai YD (2006) Predicting protein–protein interactions from sequences in a hybridization space. J Proteome Res 5:316–322

    Article  CAS  PubMed  Google Scholar 

  • Chou KC, Shen HB (2007a) Euk-mPLoc: a fusion classifier for large-scale eukaryotic protein subcellular location prediction by incorporating multiple sites. J Proteome Res 6:1728–1734

    Article  CAS  PubMed  Google Scholar 

  • Chou KC, Shen HB (2007b) Using ensemble of classifier to identify membrane protein types. Amino Acids 32:483–488

    Article  PubMed  Google Scholar 

  • Enright A, Iliopoulos I, Kyrpides N, Ouzounis C (1999) Protein interaction maps for complete genomes based on gene fusion events. Nature 402:86–90

    Article  CAS  PubMed  Google Scholar 

  • Feng Z, Zhang C (2000) Prediction of membrane protein types based on the hydrophobic index of amino acids. J Protein Chem 19:269–275

    Article  CAS  PubMed  Google Scholar 

  • Guo Y, Yu L, Wen Z, Li M (2008) Using support vector machine combined with auto covariance to predict protein–protein interactions from protein sequences. Nucleic Acids Res 36:3025–3030

    Article  CAS  PubMed  Google Scholar 

  • Ishida T, Kinoshita K (2008) Prediction of disordered regions in proteins based on the meta approach. Bioinformatics 24:1344

    Article  CAS  PubMed  Google Scholar 

  • Jansen R et al (2003) A Bayesian Networks approach for predicting protein–protein interactions from genomic data. Science 302:449–453

    Article  CAS  PubMed  Google Scholar 

  • Lin N et al (2004) Information assessment on predicting protein–protein interactions. BMC Bioinformatics 5:154

    Article  PubMed  Google Scholar 

  • Liu KH, Xia JF, Li XL (2008) Efficient ensemble schemes for protein secondary structure prediction. Protein Pept Lett 15(5):488–493

    Article  PubMed  Google Scholar 

  • Lu L, Lu H, Skolnick J (2002) MULTIPROSPECTOR: an algorithm for the prediction of protein–protein interactions by multimeric threading. Proteins Struct Funct Genet 49:350

    Article  CAS  PubMed  Google Scholar 

  • Martin S, Roe D, Faulon J (2005) Predicting protein–protein interactions using signature products. Bioinformatics 21:218–226

    Article  CAS  PubMed  Google Scholar 

  • Nanni L (2005) Hyperplanes for predicting protein–protein interactions. Neurocomputing 69:257–263

    Article  Google Scholar 

  • Nanni L, Lumini A (2006) An ensemble of k-local hyperplane for predicting protein–protein interactions. Bioinformatics 22:1207–1210

    Article  CAS  PubMed  Google Scholar 

  • Pitre S, Alamgir M, Green J, Dumontier M, Dehne F, Golshani A (2008) Computational methods for predicting protein–protein interactions. Adv Biochem Eng Biotechnol 110:247–267

    CAS  PubMed  Google Scholar 

  • Saini H, Fischer D (2005) Meta-DP: domain prediction meta-server. Bioinformatics 21:2917–2920

    Article  CAS  PubMed  Google Scholar 

  • Shen HB, Chou KC (2007) Virus-PLoc: A fusion classifier for predicting the subcellular localization of viral proteins within host and virus-infected cells. Biopolymers 85:233–240

    Article  CAS  PubMed  Google Scholar 

  • Shen JW et al (2007) Predicting protein–protein interactions based only on sequences information. Proc Natl Acad Sci USA 104:4337–4341

    Article  CAS  PubMed  Google Scholar 

  • Shi MG, Xia JF, Li XL, Huang DS (2010) Predicting protein–protein interactions from sequence using correlation coefficient and high-quality interaction dataset. Amino Acids 38(3):891–899

    Article  CAS  PubMed  Google Scholar 

  • Sokal R, Thomson B (2006) Population structure inferred by local spatial autocorrelation: an example from an Amerindian tribal population. Am J Phys Anthropol 129:121–131

    Article  PubMed  Google Scholar 

  • Vapnik V (1999) An overview of statistical learning theory. IEEE Trans Neural Netw 10:988–999

    Article  CAS  PubMed  Google Scholar 

  • Wang J, Li C, Wang E, Wang X (2009) Uncovering the rules for protein–protein interactions from yeast genomic data. Proc Natl Acad Sci USA 106:3752–3757

    Article  CAS  PubMed  Google Scholar 

  • Xia JF, Wang B, Huang DS (2007) Inferring strengths of protein–protein interaction using artificial neural network. In: Proceedings of the international joint conference on neural networks (IJCNN), Orlando, FL, pp 2471–2475

  • Xia JF, Han K, Huang DS (2010) Sequence-based prediction of protein–protein interactions by means of rotation forest and autocorrelation descriptor. Protein Pept Lett 17(1):137–145

    Article  CAS  PubMed  Google Scholar 

  • Yang L, Xia JF, Gui J, and Huang DS (2010) Prediction of protein–protein interactions from protein sequence using local descriptors. Protein Pept Lett (in press)

  • Zhao XM, Wang R, Chen L, Aihara K (2008a) Uncovering signal transduction networks from high-throughput data by integer linear programming. Nucleic Acids Res 36(9):e48

    Article  PubMed  Google Scholar 

  • Zhao XM, Wang Y, Chen L, Aihara K (2008b) Gene function prediction using labeled and unlabeled data. BMC Bioinformatics 9:57

    Article  PubMed  Google Scholar 

  • Zhao XM, Chen L, Aihara K (2008c) Protein function prediction with high-throughput data. Amino Acids 35(3):517–530

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgments

This work is supported by the grants of the National Science Foundation of China (30700161, 60905023, 30900321 and 60975005), the National Basic Research Program of China (2007CB311002), the Young Talent Grant of Hefei Institutes of Physical Science (0823A16121), the National High Technology Research and Development Program of China (2006AA02Z309), Innovation Program of Shanghai Municipal Education Commission (10YZ01), and Shanghai Rising-Star Program (10QA1402700).

Author information

Authors and Affiliations

  1. Intelligent Computing Laboratory, Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, P.O. Box 1130, Hefei, Anhui, 230031, China

    Jun-Feng Xia & De-Shuang Huang

  2. School of Life Science, University of Science and Technology of China, Hefei, Anhui, 230027, China

    Jun-Feng Xia

  3. Institute of System Biology, Shanghai University, Shanghai, 200444, China

    Xing-Ming Zhao

Authors
  1. Jun-Feng Xia
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xing-Ming Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. De-Shuang Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to De-Shuang Huang.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOC 50 kb)

Supplementary material 2 (DOC 143 kb)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Xia, JF., Zhao, XM. & Huang, DS. Predicting protein–protein interactions from protein sequences using meta predictor. Amino Acids 39, 1595–1599 (2010). https://doi.org/10.1007/s00726-010-0588-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00726-010-0588-1

Keywords

Search

Navigation