Abstract
Protein-protein interactions (PPI) control most of the biological processes in a living cell. In order to fully understand protein functions, a knowledge of protein-protein interactions is necessary. Prediction of PPI is challenging, especially when the three-dimensional structure of interacting partners is not known. Recently, a novel prediction method was proposed by exploiting physical interactions of constituent domains. We propose here a novel knowledge-based prediction method, namely PPI_SVM, which predicts interactions between two protein sequences by exploiting their domain information. We trained a two-class support vector machine on the benchmarking set of pairs of interacting proteins extracted from the Database of Interacting Proteins (DIP). The method considers all possible combinations of constituent domains between two protein sequences, unlike most of the existing approaches. Moreover, it deals with both single-domain proteins and multi domain proteins; therefore it can be applied to the whole proteome in high-throughput studies. Our machine learning classifier, following a brainstorming approach, achieves accuracy of 86%, with specificity of 95%, and sensitivity of 75%, which are better results than most previous methods that sacrifice recall values in order to boost the overall precision. Our method has on average better sensitivity combined with good selectivity on the benchmarking dataset. The PPI_SVM source code, train/test datasets and supplementary files are available freely in the public domain at: http://code.google.com/p/cmater-bioinfo/.
Electronic Supplementary Material
The online version of this article (doi: 10.2478/s11658-011-0008-x contains supplementary material, which is available to authorized users.
Key words: Protein-protein interaction, Domain-frequency values, Domaindomain interaction affinity value, Proteome, Interactome, Brainstorming, Machine learning, Consensus, DIP, Protein domains, Sequences, Structures, Protein-protein complexes
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Abbreviations used
- AP
appearance probability
- BiFC
biomolecular fluorescence complementation
- BIND
Biomolecular Interaction Network Database
- DIP
Database of Interacting Proteins
- DPI
dual polarization interferometry
- FN
false negatives
- FP
false positives
- FPR
false positive rate
- FRET
fluorescence resonance energy transfer
- HMMs
hidden Markov models
- IgG
Immunoglobulin G
- IntAct
open source molecular interaction database
- MINT
Molecular Interactions Database
- MIPS
Mammalian Protein-Protein Interaction Database
- PID
interacting domain pairs
- PPI
protein-protein interactions
- RBF
radial basis function
- ROC
receiver operator curve
- SVM
support vector machine
- TAP
tandem affinity purification
- TN
true negatives
- TP
true positives
- TPR
true positive rate
References
- 1.Ito T., Tashiro K., Muta S., Ozawa R., Chiba T., Nishizawa M., Yamamoto K., Kuhara S., Sakaki Y. 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. 2000;97:1143–1147. doi: 10.1073/pnas.97.3.1143. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Plewczynski D., Basu S. AMS 3.0: prediction of post-translational modifications. BMC Bioinformatics. 2010;11:210. doi: 10.1186/1471-2105-11-210. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Gharakhanian E., Takahashi J., Clever J., Kasamatsu H. In vitro assay for protein-protein interaction: carboxyl-terminal 40 residues of simian virus 40 structural protein VP3 contain a determinant for interaction with VP1. Proc. Natl. Acad. Sci. USA. 1998;85:6607–6611. doi: 10.1073/pnas.85.18.6607. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Hu C.D., Chinenov Y., Kerppola T.K. Visualization of interactions among bZIP and Rel family proteins in living cells using bimolecular fluorescence complementation. Mol. Cell. 2002;9:789–798. doi: 10.1016/s1097-2765(02)00496-3. [DOI] [PubMed] [Google Scholar]
- 5.Rigaut G., Shevchenko A., Rutz B., Wilm M., Mann M., Seraphin B. A generic protein purification method for protein complex characterization and proteome exploration. Nat. Biotechnol. 1999;17:1030–1032. doi: 10.1038/13732. [DOI] [PubMed] [Google Scholar]
- 6.Klingström, T. and Plewczynski D. Protein-protein interaction and pathway databases, a graphical review. Brief. Bioinform. (2010) DOI: 10.1093/bib/bbq064. [DOI] [PubMed]
- 7.Salwinski L., Miller C.S., Smith A.J., Pettit F.K., Bowie J.U., Eisenberg E. The Database of Interacting Proteins: 2004 update. Nucleic Acids Res. 2004;32:449–451. doi: 10.1093/nar/gkh086. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Pagel P., Kovac S., Oesterheld M., Brauner B., Dunger-Kaltenbach I., Frishman G., Montrone C., Mark P., Stümpflen V., Mewes H.W., Ruepp A., Frishman D. The MIPS mammalian protein-protein interaction database. Bioinformatics. 2005;21:832–834. doi: 10.1093/bioinformatics/bti115. [DOI] [PubMed] [Google Scholar]
- 9.Bader G.D., Betel D., Hogue C.W. BIND: the Biomolecular Interaction Network Database. Nucleic Acids Res. 2003;31:248–250. doi: 10.1093/nar/gkg056. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Aranda B., Achuthan P., Alam-Faruque Y., Armean I., Bridge A., Derow C., Feuermann M., Ghanbarian A.T., Kerrien S., Khadake J., Kerssemakers J., Leroy C., Menden M., Michaut M., Montecchi-Palazzi L., Neuhauser L.N., Orchard S., Perreau V., Roechert B., van Eijk K., Hermjakob H. The IntAct molecular interaction database in 2010. Nucleic Acids Res. 2009;38:525–531. doi: 10.1093/nar/gkp878. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Ceol A., Chatr, Aryamontri A., Licata L., Peluso D., Briganti L., Perfetto L., Castagnoli L., Cesareni G. MINT, the molecular interaction database: 2009 update. Nucleic Acids Res. 2010;38:532–539. doi: 10.1093/nar/gkp983. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Plewczynski D., Łaźniewski M., Augustyniak R., Ginalski K. Can we trust docking results? Evaluation of seven commonly used programs on PDBbind database. J. Comput. Chem. 2011;32:742–755. doi: 10.1002/jcc.21643. [DOI] [PubMed] [Google Scholar]
- 13.Plewczynski D., Łaźniewski M., von Grotthuss M., Rychlewski L., Ginalski K. VoteDock: Consensus docking method for prediction of protein-ligand interactions. J. Comput. Chem. 2011;32:568–581. doi: 10.1002/jcc.21642. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Bock J.R., Gough A.D. A. Predicting protein-protein interactions from primary structure. Bioinformatics. 2001;17:455–460. doi: 10.1093/bioinformatics/17.5.455. [DOI] [PubMed] [Google Scholar]
- 15.Gomez S.M., Noble W.S., Rzhetsky A. Learning to predict protein-protein interactions from protein sequences. Bioinformatics. 2003;19:1875–1881. doi: 10.1093/bioinformatics/btg352. [DOI] [PubMed] [Google Scholar]
- 16.Zaki N. Prediction of protein-protein interactions using pairwise alignment and inter-domain linker region. Engin. Letter. 2008;16:505–511. [Google Scholar]
- 17.Wojcik J., Schachter V. Protein-protein interaction map inference using interacting domain profile pairs. Bioinformatics. 2001;17:296–305. doi: 10.1093/bioinformatics/17.suppl_1.s296. [DOI] [PubMed] [Google Scholar]
- 18.Kim W.K., Park J., Suh J.K. Large scale statistical prediction of protein-protein interaction by potentially interacting domain (PID) pair. Genome Inform. 2002;13:42–50. [PubMed] [Google Scholar]
- 19.Alashwal H., Deris S., Othman R.M. One-class support vector machines for protein-protein interactions prediction. J. Biomed. Sci. 2006;1:120–127. [Google Scholar]
- 20.Chen X.W., Liu M. Domain-based predictive models for proteinprotein interaction prediction. Eurasip Jasp. 2006;1:1–8. [Google Scholar]
- 21.Han D.S., Kim H.S., Jang W.H., Lee S.D., Suh J.K. PreSPI: a domain combination based prediction system for protein-protein interaction. Nucleic Acids Res. 2004;132:6312–6320. doi: 10.1093/nar/gkh972. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Alashwal H., Deris S., Othman R.M. A Bayesian kernel for the Prediction of Protein-Protein Interactions. World Academy of Science, Engineering and Technology. 2009;51:928–933. [Google Scholar]
- 23.Vapnik V. The nature of statistical learning theory. New York: Springer-Verlag; 1995. [Google Scholar]
- 24.Xenarios I., Salwinski L., Duan X.J., Higney P., Kim S.M., Eisenberg D. DIP, the Database of Interacting Proteins: a research tool for studying cellular networks of protein interactions. Nucleic Acids Res. 2002;30:303–305. doi: 10.1093/nar/30.1.303. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Joachims T. Making Large-Scale SVM Learning Practical. In: Schölkopf B., Burges C., Smola A., editors. Advances in Kernel Methods — Support Vector Learning. Cambridge: MIT Press; 1999. pp. 169–284. [Google Scholar]
- 26.Plewczynski D., Ginalski K. The interactome: Predicting the proteinprotein interactions in cells. Cell. Mol. Biol. Lett. 2009;14:1–22. doi: 10.2478/s11658-008-0024-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Plewczynski D. Brainstorming: weighted voting prediction of inhibitors for protein targets. J. Mol. Model. (2010) DOI 10.1007/s00894-010-0854-x. [DOI] [PMC free article] [PubMed]
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