Skip to main content
SpringerLink
Log in

Predicting protein–protein interactions from sequence using correlation coefficient and high-quality interaction dataset

  • Original Article
  • Published:
Amino Acids Aims and scope Submit manuscript

Abstract

Identifying protein–protein interactions (PPIs) is critical for understanding the cellular function of the proteins and the machinery of a proteome. Data of PPIs derived from high-throughput technologies are often incomplete and noisy. Therefore, it is important to develop computational methods and high-quality interaction dataset for predicting PPIs. A sequence-based method is proposed by combining correlation coefficient (CC) transformation and support vector machine (SVM). CC transformation not only adequately considers the neighboring effect of protein sequence but describes the level of CC between two protein sequences. A gold standard positives (interacting) dataset MIPS Core and a gold standard negatives (non-interacting) dataset GO-NEG of yeast Saccharomyces cerevisiae were mined to objectively evaluate the above method and attenuate the bias. The SVM model combined with CC transformation yielded the best performance with a high accuracy of 87.94% using gold standard positives and gold standard negatives datasets. The source code of MATLAB and the datasets are available on request under smgsmg@mail.ustc.edu.cn.

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

Fig. 1
Fig. 2

Similar content being viewed by others

References

  • Bader GD, Donaldson I, Wolting C, Ouellette BF, Pawson T, Hogue CW (2001) BIND-the biomolecular interaction network database. Nucleic Acids Res 29:242–245

    Article  CAS  PubMed  Google Scholar 

  • Baudat G, Anouar F (2000) Generalized discriminant analysis using a kernel approach. Neural Comput 12:2385–2404

    Article  CAS  PubMed  Google Scholar 

  • Ben-Hur A, Noble WS (2006) Choosing negative examples for the prediction of protein–protein interactions. BMC Bioinformatics 7:S2

    Article  PubMed  Google Scholar 

  • Brenner SE, Chothia C, Hubbard TJ (1998) Assessing sequence comparison methods with reliable structurally identified distant evolutionary relationships. Proc Natl Acad Sci USA 95:6073–6078

    Article  CAS  PubMed  Google Scholar 

  • Charton M, Charton BI (1982) The structural dependence of amino acid hydrophobicity parameters. J Theor Biol 99:629–644

    Article  CAS  PubMed  Google Scholar 

  • Chothia C (1976) The nature of the accessible and buried surfaces in proteins. J Mol Biol 105:1–12

    Article  CAS  PubMed  Google Scholar 

  • Deane CM, Salwinski L, Xenarios I, Eisenberg D (2002) Protein interactions: Two methods for assessment of the reliability of high throughput observations. Mol Cell Proteomics 1:349–356

    Article  CAS  PubMed  Google Scholar 

  • Efron B, Hastie T, Johnstone I, Tibshirani R (2004) Least angle regression. Ann Stat 32:407–499

    Article  Google Scholar 

  • Eisenberg D, McLachlan AD (1986) Solvation energy in protein folding and binding. Nature 319:199–203

    Article  CAS  PubMed  Google Scholar 

  • Fauchere JL (1988) Amino acid side chain parameters for correlation studies in biology and pharmacology. Int J Pept Protein Res 32:269–278

    CAS  PubMed  Google Scholar 

  • Faulon JL, Misra M, Martin S, Sale K, Sapra R (2008) Genome scale enzyme-metabolites and drug-target interaction predictions using the signature molecular descriptor. Bioinformatics 24:225–233

    Article  CAS  PubMed  Google Scholar 

  • Feng ZP, Zhang CT (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 

  • Friedman JH (2001) Greedy function approximation: a gradient boosting machine. Ann Stat 29:1189–1232

    Article  Google Scholar 

  • Garel JP (1973) Coefficients de partage d’aminoacides, nucleobases, nucleosides et nucleotides dans un systeme solvant salin. J Chromatogr 78:381–391

    CAS  PubMed  Google Scholar 

  • Gavin AC et al (2002) Functional organization of the yeast proteome by systematic analysis of protein complexes. Nature 415:141–147

    Article  CAS  PubMed  Google Scholar 

  • Giot L et al (2003) A protein interaction map of Drosophila melanogaster. Science 302:1727–1736

    Article  CAS  PubMed  Google Scholar 

  • Gomez SM, Noble WS, Rzhetsky A (2003) Learning to predict protein–protein interactions. Bioinformatics 19:1875–1881

    Article  CAS  PubMed  Google Scholar 

  • Grantham R (1974) Amino acid difference formula to help explain protein evolution. Science 185:862–864

    Article  CAS  PubMed  Google Scholar 

  • Guldener U, Munsterkotter M, Oesterheld M, Pagel P, Ruepp A, Mewes HW, Stumpflen V (2006) MPact: the MIPS protein interaction resource on yeast. Nucleic Acids Res 34:D436–D441

    Article  PubMed  Google Scholar 

  • Guo X et al (2006) Assessing semantic similarity measures for the characterization of human regulatory pathways. Bioinformatics 22:967–973

    Article  CAS  PubMed  Google Scholar 

  • Guo J, Wu XM, Zhang DY, Lin K (2008a) Genome-wide inference of protein interaction sites: lessons from the yeast high-quality negative protein–protein interaction dataset. Nucleic Acids Res 36:2002–2011

    Article  CAS  PubMed  Google Scholar 

  • Guo YZ, Yu LZ, Wen ZN, Li ML (2008b) 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 

  • Ho Y et al (2002) Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry. Nature 415:180–183

    Article  CAS  PubMed  Google Scholar 

  • Hopp TP, Woods KR (1981) Prediction of protein antigenic determinants from amino acid sequences. Proc Natl Acad Sci USA 78:3824–3828

    Article  CAS  PubMed  Google Scholar 

  • Horne DS (1988) Prediction of protein helix content from an autocorrelation analysis of sequence hydrophobicities. Biopolymers 27:451–477

    Article  CAS  PubMed  Google Scholar 

  • Hutchens JO (1970) Heat capacities, absolute entropies, and entropies of formation of amino acids and related compounds. In: Sober HA (ed) Handbook of biochemistry, 2nd edn. Chemical Rubber Co., Cleveland, pp B60–B61

    Google Scholar 

  • Ito T et al (2000) Toward a protein–protein interaction map of the budding yeast: a comprehensive system to examine two-hybrid interactions in all possible combinations between the yeast proteins. Proc Natl Acad Sci USA 97:1143–1147

    Article  CAS  PubMed  Google Scholar 

  • Ito T et al (2001) A comprehensive two-hybrid analysis to explore the yeast protein ineractome. Proc Natl Acad Sci USA 98:4569–4574

    Article  CAS  PubMed  Google Scholar 

  • Janin J (1979) Surface and inside volumes in globular proteins. Nature 277:491–492

    Article  CAS  PubMed  Google Scholar 

  • Jansen R, Gerstein M (2004) Analyzing protein function on a genomic scale: the importance of gold-standard positives and negatives for network prediction. Curr Opin Microbiol 7:535–545

    Article  CAS  PubMed  Google Scholar 

  • Koji T, William SN (2004) Learning kernels from biological networks by maximizing entropy. Bioinformatics 20:i326–i333

    Article  Google Scholar 

  • Krogan NJ et al (2006) Global landscape of protein complexes in the yeast Saccharomyces cerevisiae. Nature 440:637–643

    Article  CAS  PubMed  Google Scholar 

  • Leslie C, Eskin E, Noble WS (2002) The spectrum kernel: a string kernel for SVM protein classification. In: Proceedings of the Pacific symposium on biocomputing, New Jersey. World Scientific, Singapore, pp 564–575

  • Li S et al (2004) A map of the interactome network of the metazoan c elegans. Science 303:540–543

    Article  CAS  PubMed  Google Scholar 

  • Madaoui H, Guerois R (2008) Coevolution at protein complex interfaces can be detected by the complementarity trace with important impact for predictive docking. Proc Natl Acad Sci USA 105:7708–7713

    Article  CAS  PubMed  Google Scholar 

  • Manly KF, Nettleton D, Hwang JT (2004) Genomics, prior probability, and statistical tests of multiple hypotheses. Genome Res 14:997–1001

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  • Mewes HW et al (2006) MIPS: analysis and annotation of proteins from whole genomes in 2005. Nucleic Acids Res 34:D169–D172

    Article  CAS  PubMed  Google Scholar 

  • Prabhakaran M, Ponnuswamy PK (1982) Shape and surface features of globular proteins. Macromolecules 15:314–320

    Article  CAS  Google Scholar 

  • Rain JC et al (2001) The protein–protein interaction map of Helicobacter pylori. Nature 409:211–215

    Article  CAS  PubMed  Google Scholar 

  • Resnik P (1999) Semantic similarity in a taxonomy: an information-based measure and its application to problems of ambiguity in natural language. J Artif Intell Res 11:95–130

    Google Scholar 

  • Saito R et al (2003) Construction of reliable protein–protein interaction networks with a new interaction generality measure. Bioinformatics 19:756–763

    Article  CAS  PubMed  Google Scholar 

  • Scholkopf B, Smola A, Muller KR (1998) Nonlinear component analysis as a kernel eigenvalue problem. Neural Comput 10:1299–1319

    Article  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 

  • Sokal RR, Thomson BA (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 

  • Sprinzak E, Margalit H (2001) Correlated sequence-signatures as markers of protein–protein interaction. J Mol Biol 311:681–692

    Article  CAS  PubMed  Google Scholar 

  • Sweet RM, Eisenberg D (1983) Correlation of sequence hydrophobicities measures similarity in three-dimensional protein structure. J Mol Biol 171:479–488

    Article  CAS  PubMed  Google Scholar 

  • Uetz P et al (2000) A comprehensive analysis of protein–protein interactions in Saccharomyces cerevisiae. Nature 403:623–627

    Article  CAS  PubMed  Google Scholar 

  • Vapnik V (1998) Statistical learning theory. Wiley, New York

    Google Scholar 

  • Von Mering C, Krause R, Snel B, Cornell M, Oliver SG, Fields S, Bork P (2002) Comparative assessment of large scale data sets of protein–protein interactions. Nature 417:399–403

    Article  Google Scholar 

  • Wang JZ, Du ZD, Payattakool R, Yu PS, Chen CF (2007) A new method to measure the semantic similarity of GO terms. Bioinformatics 23:1274–1281

    Article  CAS  PubMed  Google Scholar 

  • Wiwatwattana N, Landau CM, Cope GJ, Harp GA, Kumar A (2007) Organelle DB: an updated resource of eukaryotic protein localization and function. Nucleic Acids Res 35:D810–D814

    Article  CAS  PubMed  Google Scholar 

  • Wold S et al (1993) DNA and peptide sequences and chemical processes mutlivariately modelled by principal component analysis and partial least-squares projections to latent structures. Anal Chim Acta 277:239–253

    Article  CAS  Google Scholar 

  • Wu X, Zhu L, Guo J, Zhang DY, Lin K (2006) Prediction of yeast protein–protein interaction network: insights from the gene ontology and annotations. Nucleic Acids Res 34:2137–2150

    Article  CAS  PubMed  Google Scholar 

  • Xenarios I et al (2002) Dip, the database of interacting proteins: a research tool for studying cellular networks of protein interactions. Nucleic Acids Res 30:303–305

    Article  CAS  PubMed  Google Scholar 

  • Yeang CH, Haussler D (2007) Detecting coevolution in and among protein domains. PLoS Comput Biol 3:e211

    Article  PubMed  Google Scholar 

  • Zhu H et al (2001) Global analysis of protein activities using proteome chips. Science 293:2101–2105

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgments

This work was supported by the grants of the National Science Foundation of China, Nos. 60472111 and 30570368, the grant from the National Basic Research Program of China (973 Program), No. 2007CB311002, the grants from the National High Technology Research and Development Program of China (863 Program), Nos. 2007AA01Z167 and 2006AA02Z309, the grant of Oversea Outstanding Scholars Fund of CAS, No. 2005-1-18, HFUT, No. 070403F and the Knowledge Innovation Program of the Chinese Academy of Sciences (0823A16121).

Author information

Authors and Affiliations

  1. Intelligent Computing Lab, Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, 230031, Hefei, China

    Ming-Guang Shi, Jun-Feng Xia, Xue-Ling Li & De-Shuang Huang

  2. Department of Automation, University of Science and Technology of China, 230026, Hefei, China

    Ming-Guang Shi

  3. School of Electric Engineering and Automation, Hefei University of Technology, 230009, Hefei, China

    Ming-Guang Shi

  4. School of Life Science, University of Science and Technology of China, 230026, Hefei, China

    Jun-Feng Xia

Authors
  1. Ming-Guang Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jun-Feng Xia
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xue-Ling Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. 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

Rights and permissions

Reprints and permissions

About this article

Cite this article

Shi, MG., Xia, JF., Li, XL. et al. Predicting protein–protein interactions from sequence using correlation coefficient and high-quality interaction dataset. Amino Acids 38, 891–899 (2010). https://doi.org/10.1007/s00726-009-0295-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00726-009-0295-y

Keywords

Search

Navigation