Predicting protein-protein interactions from primary protein sequences using a novel multi-scale local feature representation scheme and the random forest

PLoS ONE

Volumn 10, Issue 5, 2015, Pages

  You, Zhu Hong ^a,b   Chan, Keith C C ^a   Hu, Pengwei ^a  

^a HONG KONG POLYTECHNIC UNIVERSITY   (Hong Kong)

^b TONGJI UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

AMINO ACID; BACTERIAL PROTEIN; FUNGAL PROTEIN; PROTEOME;

AMINO ACID SEQUENCE; ARTICLE; CONTROLLED STUDY; HELICOBACTER PYLORI; MACHINE LEARNING; MULTI SCALE LOCAL DESCRIPTOR PROTEIN FEATURE REPRESENTATION; NONHUMAN; PREDICTION; PROTEIN BINDING; PROTEIN PROTEIN INTERACTION; PROTEOMICS; RANDOM FOREST; SACCHAROMYCES; SENSITIVITY AND SPECIFICITY; ALGORITHM; BIOLOGY; GENETICS; METABOLISM; PROCEDURES; PROTEIN ANALYSIS; SACCHAROMYCES CEREVISIAE; SEQUENCE ANALYSIS; SUPPORT VECTOR MACHINE;

HELICOBACTER PYLORI; SACCHAROMYCES CEREVISIAE;

ALGORITHMS; AMINO ACID SEQUENCE; BACTERIAL PROTEINS; COMPUTATIONAL BIOLOGY; FUNGAL PROTEINS; HELICOBACTER PYLORI; PROTEIN INTERACTION MAPPING; PROTEIN INTERACTION MAPS; PROTEOME; PROTEOMICS; SACCHAROMYCES CEREVISIAE; SEQUENCE ANALYSIS, PROTEIN; SUPPORT VECTOR MACHINE;

EID: 84929094094     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0125811     Document Type: Article

References (50)

1. Fernandes LP, Annibale A, Kleinjung J, Coolen AC, Fraternali F. Protein networks reveal detection bias and species consistency when analysed by information-theoretic methods. PloS One. 2010; 5: e12083. doi: 10.1371/journal.pone.0012083 PMID: 20805870
2. Lu HC, Fornili A, Fraternali F. Protein-protein interaction networks studies and importance of 3D structure knowledge. Expert Review of Proteomics. 2013; 10(6):511-20. doi: 10.1586/14789450.2013. 856764 PMID: 24206225
3. Gavin AC, Bosche M, Krause R, Grandi P, Marzioch M, Bauer A, et al. Functional organization of the yeast proteome by systematic analysis of protein complexes. Nature. 2002; 415: 141-147. PMID: 11805826
4. Ito T, Chiba T, Ozawa R, Yoshida M, Hattori M, Sakaki Y. A comprehensive two-hybrid analysis to explore the yeast protein interactome. Proceedings of the National Academy of Sciences of the United States of America. 2001; 98: 4569-4574. PMID: 11283351
5. Ho Y, Gruhler A, Heilbut A, Bader GD, Moore L, Adams SL, et al. Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry. Nature. 2002; 415: 180-183. PMID: 11805837
6. Krogan NJ, Cagney G, Yu HY, Zhong GQ, Guo XH, Ignatchenko A, et al. Global landscape of protein complexes in the yeast Saccharomyces cerevisiae. Nature. 2006; 440: 637-643. PMID: 16554755
7. Jiao QJ, Zhang YK, Li LN, Shen HB. Bintree seeking: a novel approach to mine both bi-sparse and cohesive modules in protein interaction networks. PloS One. 2011; 6: e27646. doi: 10.1371/journal.pone.0027646 PMID: 22140454
8. You ZH, Lei YK, Gui J, Huang DS, Zhou XB. Using manifold embedding for assessing and predicting protein interactions from high-throughput experimental data. Bioinformatics. 2010; 26: 2744-2751. doi: 10.1093/bioinformatics/btq510 PMID: 20817744
9. Zhou F, Olman V, Xu Y. Large-Scale Analyses of Glycosylation in Cellulases. Genomics, Proteomics and Bioinformatics. 2009; 7: 194-199. doi: 10.1016/S1672-0229(08)60049-2 PMID: 20172492
10. Luo X, You ZH, Zhou MC, Li S, Leung H, Xia YN, et al. A Highly Efficient Approach to Protein Interactome Mapping Based on Collaborative Filtering Framework. Scientific Reports. 2015; 5: 7702. doi: 10.1038/srep07702 PMID: 25572661
11. You ZH, Yin Z, Han K, Huang DS, Zhou X. A semi-supervised learning approach to predict synthetic genetic interactions by combining functional and topological properties of functional gene network. BMC Bioinformatics. 2010; 11:343. doi: 10.1186/1471-2105-11-343 PMID: 20573270
12. Shen JW, Zhang J, Luo XM, Zhu WL, Yu KQ, et al. Predictina protein-protein interactions based only on sequences information. Proceedings of the National Academy of Sciences of the United States of America. 2007; 104: 4337-4341. PMID: 17360525
13. Shoemaker BA, Panchenko AR. Deciphering protein-protein interactions. Part II. Computational methods to predict protein and domain interaction partners. Plos Computational Biology. 2007; 3: 595-601.
14. Zhao XM, Li X, Chen L, Aihara K. Protein classification with imbalanced data. Proteins-Structure Function and Bioinformatics. 2008; 70: 1125-1132. PMID: 18076026
15. Lam WWM, Chan KCC. Discovering functional interdependence relationship in PPI networks for protein complex identification. IEEE Transactions on Biomedical Engineering. 2012; 59: 899-908. doi: 10.1109/TBME.2010.2093524 PMID: 21095855
16. You ZH, Lei YK, Zhu L, Xia JF, Wang B. Prediction of protein-protein interactions from amino acid sequences with ensemble extreme learning machines and principal component analysis. BMC Bioinformatics. 2013; 14 (8):10.
17. Zhang QC, Petrey D, Norel R, Honig BH. Protein interface conservation across structure space. Proceedings of the National Academy of Sciences. 2010; 107: 10896. doi: 10.1073/pnas.1005894107 PMID: 20534496
18. Zhang QC, Petrey D, Deng L, Qiang L, Shi Y, et al. Structure-based prediction of protein-protein interactions on a genome-wide scale. Nature. 2012; 490:556-560. doi: 10.1038/nature11503 PMID: 23023127
19. Lei YK, You ZH, Ji Z, Zhu L, Huang DS. Assessing and predicting protein interactions by combining manifold embedding with multiple information integration. BMC Bioinformatics. 2012; 13(7):3. doi: 10. 1186/1471-2105-13-7 PMID: 22233443
20. Xia JF, Zhao XM, Huang DS. Predicting protein-protein interactions from protein sequences using meta predictor. Amino Acids. 2010; 39:1595-1599. doi: 10.1007/s00726-010-0588-1 PMID: 20386937
21. Zhao XM, Chen LN, Aihara K. A discriminative approach for identifying domain-domain interactions from protein-protein interactions. Proteins-Structure Function and Bioinformatics. 2010; 78: 1243-1253. doi: 10.1002/prot.22643 PMID: 20027642
22. Zhao XM, Cheung YM, Huang DS. A novel approach to extracting features from motif content and protein composition for protein sequence classification. Neural Networks. 2005; 18: 1019-1028. PMID: 16153801
23. Qi YJ, Klein-Seetharaman J, Bar-Joseph Z. Random forest similarity for protein-protein interaction prediction from multiple sources. Pac Symp Biocomput. 2005; 531-542.
24. Pandini A, Fornili A, Fraternali F, Kleinjung J. Detection of allosteric signal transmission by information-theoretic analysis of protein dynamics. The FASEB Journal. 2012; 26: 868-881. doi: 10.1096/fj.11-190868 PMID: 22071506
25. Autore F, Pfuhl M, Quan X, Williams A, Roberts RG, et al. Large-scale modelling of the divergent spectrin repeats in nesprins: giant modular proteins. Plos One. 2013; 8: e63633. doi: 10.1371/journal.pone.0063633 PMID: 23671687
26. Xia JF, Han K, Huang DS. Sequence-Based Prediction of Protein-Protein Interactions by Means of Rotation Forest and Autocorrelation Descriptor. Protein and Peptide Letters. 2010; 17: 137-145. PMID: 20214637
27. Yu HJ, Huang DS. Graphical representation for DNA sequences via joint diagonalization of matrix pencil. IEEE Journal of Biomedical and Health Informatics. 2013; 17: 503-511. PMID: 24592449
28. Huang DS, Yu HJ. Normalized feature vectors: a novel alignment-free sequence comparison method based on the numbers of adjacent amino acids. IEEE-ACM Transactions on Computational Biology and Bioinformatics. 2013; 10: 457-467. doi: 10.1109/TCBB.2013.10 PMID: 23929869
29. Zhang YN, Pan XY, Huang Y, Shen HB. Adaptive compressive learning for prediction of protein-protein interactions from primary sequence. Journal of Theoretical Biology. 2011; 283: 44-52. doi: 10.1016/j.jtbi.2011.05.023 PMID: 21635901
30. Pan XY, Zhang YN, Shen HB. Large-scale prediction of human protein-protein interactions from amino acid sequence based on latent topic features. Journal of Proteome Research. 2010; 9: 4992-5001. doi: 10.1021/pr100618t PMID: 20698572
31. Zhang YN, Pan XY, Huang Y, Shen HB. Adaptive compressive learning for prediction of protein-protein interactions from primary sequence. Journal of Theoretical Biology. 2011; 283: 44-52. doi: 10.1016/j.jtbi.2011.05.023 PMID: 21635901
32. Pitre S, Hooshyar M, Schoenrock A, Samanfar B, Jessulat M, et al. Short co-occurring polypeptide regions can predict global protein interaction maps. Scientific Reports. 2012; 2:239. doi: 10.1038/srep00239 PMID: 22355752
33. Wang H, Huang H, Ding C, Nie F. Predicting protein-protein interactions from multimodal biological data sources via nonnegative matrix tri-factorization. Journal of Computational Biology. 2013; 20: 344-358. doi: 10.1089/cmb.2012.0273 PMID: 23509857
34. Mei S, Zhu H. AdaBoost based multi-instance transfer learning for predicting proteome wide interactions between salmonella and human proteins. PloS One. 2014; 9: e110488. doi: 10.1371/journal.pone.0110488 PMID: 25330226
35. Guo Y, Yu L, Wen Z, Li M. Using support vector machine combined with auto covariance to predict protein- protein interactions from protein sequences. Nucleic Acids Research. 2008; 36: 3025-3030. doi:10.1093/nar/gkn159 PMID: 18390576
36. Shi MG, Xia JF, Li XL, Huang DS. Predicting protein-protein interactions from sequence using correlation coefficient and high-quality interaction dataset. Amino Acids. 2010; 38: 891-899. doi: 10.1007/s00726-009-0295-y PMID: 19387790
37. Salwinski L, Miller CS, Smith AJ, Pettit FK, Bowie JU, et al. The database of interacting proteins: 2004 update. Nucleic Acids Research. 2004; 32: 449-451.
38. Ben-Hur A, Noble WS. Choosing negative examples for the prediction of protein-protein interactions. BMC Bioinformatics. 2006; 7: S2. PMID: 17254304
39. Smialowski P, Pagel P, Wong P, Brauner B, Dunger I, et al. The negatome database: a reference set of non-interacting protein pairs. Nucleic Acids Research. 2010; 38: 540-544.
40. Veres DV, Gyurkó DM, Thaler B, Szalay KZ, Fazekas D, et al. ComPPI: a cellular compartment-specific database for protein-protein interaction network analysis. Nucleic Acids Research. 2014; doi: 10.1093/nar/gku1007
41. Martin S, Roe D, Faulon JL. Predicting protein-protein interactions using signature products. Bioinformatics. 2005; 21: 218-226. PMID: 15319262
42. Browne F, Wang H, Zheng H, Azuaje F. GRIP: A web-based system for constructing gold standard datasets for protein-protein interaction prediction. Source Code for Biology and Medicine. 2009; 4: 1-7. doi: 10.1186/1751-0473-4-1 PMID: 19146660
43. Zhou YZ, Gao Y, Zheng YY. Prediction of protein-protein interactions using local description of amino acid sequence. Advances in Computer Science and Education Applications. 2011; 254-262.
44. Yang L, Xia JF, Gui J. Prediction of protein-protein interactions from protein sequence using local descriptors. Protein and Peptide Letters. 2010; 17: 1085-1090. PMID: 20509850
45. Martin S, Roe D, Faulon JL. Predicting protein-protein interactions using signature products. Bioinformatics. 2005; 21: 218-226. PMID: 15319262
46. Bock JR, Gough DA. Whole-proteome interaction mining. Bioinformatics. 2003; 19: 125-134. PMID: 12499302
47. Nanni L. Hyperplanes for predicting protein-protein interactions. Neurocomputing. 2005; 69: 257-263.
48. Nanni L, Lumini A. An ensemble of K-local hyperplanes for predicting protein-protein interactions. Bioinformatics. 2006; 22: 1207-1210. PMID: 16481334
49. Matthews LR, Vaglio P, Reboul J, Ge H, Davis BP, et al. Identification of potential interaction networks using sequence-based searches for conserved protein-protein interactions or "interologs". Genome Research. 2001; 11: 2120-2126. PMID: 11731503
50. Dubchak I, Muchnik I, Holbrook SR, Kim SH. Prediction of protein-folding class using global description of amino acid sequence. Proceedings of the National Academy of Sciences of the United States of America. 1995; 92: 8700-8704. PMID: 7568000