RPI-Pred: predicting ncRNA-protein interaction using sequence and structural information

Abstract

RNA-protein complexes are essential in mediating important fundamental cellular processes, such as transport and localization. In particular, ncRNA-protein interactions play an important role in post-transcriptional gene regulation like mRNA localization, mRNA stabilization, poly-adenylation, splicing and translation. The experimental methods to solve RNA-protein interaction prediction problem remain expensive and time-consuming. Here, we present the RPI-Pred (RNA-protein interaction predictor), a new support-vector machine-based method, to predict protein-RNA interaction pairs, based on both the sequences and structures. The results show that RPI-Pred can correctly predict RNA-protein interaction pairs with ∼94% prediction accuracy when using sequence and experimentally determined protein and RNA structures, and with ∼83% when using sequences and predicted protein and RNA structures. Further, our proposed method RPI-Pred was superior to other existing ones by predicting more experimentally validated ncRNA-protein interaction pairs from different organisms. Motivated by the improved performance of RPI-Pred, we further applied our method for reliable construction of ncRNA-protein interaction networks. The RPI-Pred is publicly available at: http://ctsb.is.wfubmc.edu/projects/rpi-pred.

References 69

1. 1.

   This reference’s wording can’t be shown under the paper’s licence.

   Not in our library10.1016/j.semcdb.2011.02.016

2. 2.

   This reference’s wording can’t be shown under the paper’s licence.

   Not in our library10.4161/rna.8.2.15391

3. 3.

   This reference’s wording can’t be shown under the paper’s licence.

   Not in our library10.4161/rna.8.2.14991

4. 4.

   This reference’s wording can’t be shown under the paper’s licence.

   Not in our library10.1007/978-1-60327-563-7_2

5. 5.

   This reference’s wording can’t be shown under the paper’s licence.

   Not in our library10.5483/bmbrep.2009.42.3.125

6. 6.
   Silencing and Innate Immunity in Plant Defense Against Viral and Non-Viral Pathogens · 2012

   No notice
7. 7.

   This reference’s wording can’t be shown under the paper’s licence.

   Not in our library10.1093/bfgp/elq028

8. 8.

   This reference’s wording can’t be shown under the paper’s licence.

   Not in our library10.1038/nrg2673

9. 9.

   This reference’s wording can’t be shown under the paper’s licence.

   Not in our library

10. 10.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library10.3791/2034

11. 11.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library10.1038/nprot.2006.47

12. 12.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library10.1038/nbt.1550

13. 13.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library10.1038/nature11233

14. 14.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library10.1101/gr.132159.111

15. 15.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

16. 16.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library10.1038/nature03001

17. 17.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library10.1016/j.cell.2007.05.022

18. 18.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library10.1016/j.molcel.2010.08.011

19. 19.
    A Chromosomal Memory Triggered by Xist Regulates Histone Methylation in X Inactivation · 2004

    No notice
20. 20.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library10.1038/nsmb.2480

21. 21.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library10.1093/nar/gkt1057

22. 22.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library10.1093/nar/29.13.2860

23. 23.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library10.1006/jmbi.1999.2659

24. 24.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library10.1021/bi982362d

25. 25.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library10.1093/nar/gkq1069

26. 26.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library10.1038/nature12311

27. 27.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library10.1073/pnas.0906940106

28. 28.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library10.1089/10665270050081478

29. 29.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library10.1016/s0022-2836(05)80360-2

30. 30.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library10.1038/ng0393-266

31. 31.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library10.1093/nar/28.1.263

32. 32.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library10.1093/nar/30.1.276

33. 33.
    Pfam: the protein families database · 2013

    No notice
34. 34.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library10.1093/nar/gkr160

35. 35.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library10.1038/nmeth.1611

36. 36.
    Predicting RNA-Protein Interactions Using Only Sequence Information · 2011

    No notice
37. 37.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library10.1073/pnas.0607879104

38. 38.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library10.1039/c2mb25292a

39. 39.
    Computational prediction of associations between long non-coding RNAs and proteins · 2013

    No notice
40. 40.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library10.1146/annurev.biophys.37.032807.125957

Cited by papers we hold 2

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