Advertisement

Journal of Computer-Aided Molecular Design

, Volume 32, Issue 12, pp 1363–1373 | Cite as

Individually double minimum-distance definition of protein–RNA binding residues and application to structure-based prediction

  • Wen Hu
  • Liu Qin
  • Menglong Li
  • Xuemei Pu
  • Yanzhi Guo
Article
  • 30 Downloads

Abstract

Identifying protein–RNA binding residues is essential for understanding the mechanism of protein–RNA interactions. So far, rigid distance thresholds are commonly used to define protein–RNA binding residues. However, after investigating 182 non-redundant protein–RNA complexes, we find that it would be unsuitable for a certain amount of complexes since the distances between proteins and RNAs vary widely. In this work, a novel definition method was proposed based on a flexible distance cutoff. This method can fully consider the individual differences among complexes by setting a variable tolerance limit of protein–RNA interactions, i.e. the double minimum-distance by which different distance thresholds are achieved for different complexes. In order to validate our method, a comprehensive comparison between our flexible method and traditional rigid methods was implemented in terms of interface structure, amino acid composition, interface area and interaction force, etc. The results indicate that this method is more reasonable because it incorporates the specificity of different complexes by extracting the important residues lost by rigid distance methods and discarding some redundant residues. Finally, to further test our double minimum-distance definition strategy, we developed a classifier to predict those binding sites derived from our new method by using structural features and a random forest machine learning algorithm. The model achieved a satisfactory prediction performance and the accuracy on independent data sets reaches to 85.0%. To the best of our knowledge, it is the first prediction model to define positive and negative samples using a flexible cutoff. So the comparison analysis and modeling results have demonstrated that our method would be a very promising strategy for more precisely defining protein–RNA binding sites.

Keywords

Protein–RNA interactions Double minimum-distance cutoff RNA-binding residue definition Structural prediction 

Notes

Funding

This work was funded by the National Natural Science Foundation of China (Nos. 21675114, 21573151).

Compliance with ethical standards

Conflict of interest

The authors declare no competing financial interests.

Supplementary material

10822_2018_177_MOESM1_ESM.doc (296 kb)
Supplementary material 1 (DOC 296 KB)

References

  1. 1.
    Howard GC, Brown WE (2001) Modern protein chemistry: practical aspects. CRC press, Boca RatonGoogle Scholar
  2. 2.
    Hannigan GE, Dedhar S (1997) Protein kinase mediators of integrin signal transduction. J Mol Med (Berl) 75(1):35Google Scholar
  3. 3.
    Si J, Cui J, Cheng J, Wu R (2015) Computational prediction of RNA-binding proteins and binding sites. Int J Mol Sci 16(11):26303PubMedPubMedCentralGoogle Scholar
  4. 4.
    Noller HF (2005) RNA structure: reading the ribosome. Science 309(5740):1508PubMedGoogle Scholar
  5. 5.
    Nachtergaele S, He C (2017) The emerging biology of RNA post-transcriptional modifications. Nat Methods 14(2):156Google Scholar
  6. 6.
    Khalil AM, Rinn JL (2011) RNA-protein interactions in human health and disease. Semin Cell Dev Biol 22(4):359PubMedPubMedCentralGoogle Scholar
  7. 7.
    Bellucci M, Agostini F, Masin M, Tartaglia GG (2011) Predicting protein associations with long noncoding RNAs. Nat Methods 8(6):444PubMedGoogle Scholar
  8. 8.
    Suresh V, Liu L, Adjeroh D, Zhou X (2015) RPI-Pred: predicting ncRNA-protein interaction using sequence and structural information. Nucleic Acids Res 43(3):1370PubMedPubMedCentralGoogle Scholar
  9. 9.
    Cirillo D, Blanco M, Armaos A, Buness A, Avner P, Guttman M, Cerase A, Tartaglia GG (2016) Quantitative predictions of protein interactions with long noncoding RNAs. Nat Methods 14(1):5PubMedGoogle Scholar
  10. 10.
    Ponting CP, Oliver PL, Reik W (2009) Evolution and functions of long noncoding RNAs. Cell 136(4):629PubMedGoogle Scholar
  11. 11.
    Wang Y, Lin Y, Guo YZ, Pu XM, Li ML (2017) Functional dissection of human targets for KSHV-encoded miRNAs using network analysis. Sci Rep (7): 3159Google Scholar
  12. 12.
    Liu ZY, Guo YZ, Pu XM, Li ML (2016) Dissecting the regulation rules of cancer-related miRNAs based on network analysis. Sci Rep (6): 34172Google Scholar
  13. 13.
    Cheng CW, Su EC, Hwang JK, Sung TY, Hsu WL (2008) Predicting RNA-binding sites of proteins using support vector machines and evolutionary information. BMC Bioinformatics 9(Suppl 12):S6PubMedPubMedCentralGoogle Scholar
  14. 14.
    Tong J, Jiang P, Lu ZH (2008) RISP: a web-based server for prediction of RNA-binding sites in proteins. Comput Methods Programs Biomed 90(2):148PubMedGoogle Scholar
  15. 15.
    Murakami Y, Spriggs RV, Nakamura H, Jones S (2010) PiRaNhA: a server for the computational prediction of RNA-binding residues in protein sequences. Nucleic Acids Res 38(Web Server issue):W412PubMedPubMedCentralGoogle Scholar
  16. 16.
    Wang L, Huang C, Yang MQ, Yang JY (2010) BindN+ for accurate prediction of DNA and RNA-binding residues from protein sequence features. BMC Syst Biol 4(Suppl 1):S3PubMedPubMedCentralGoogle Scholar
  17. 17.
    Carson MB, Langlois R, Lu H (2010) NAPS: a residue-level nucleic acid-binding prediction server. Nucleic Acids Res 38(Web Server issue):W431PubMedPubMedCentralGoogle Scholar
  18. 18.
    Ma X, Guo J, Wu J, Liu H, Yu J, Xie J, Sun X (2011) Prediction of RNA-binding residues in proteins from primary sequence using an enriched random forest model with a novel hybrid feature. Proteins 79(4):1230PubMedGoogle Scholar
  19. 19.
    Fernandez M, Kumagai Y, Standley DM, Sarai A, Mizuguchi K, Ahmad S (2011) Prediction of dinucleotide-specific RNA-binding sites in proteins. BMC Bioinform 12(Suppl 13):S5Google Scholar
  20. 20.
    Puton T, Kozlowski L, Tuszynska I, Rother K, Bujnicki JM (2012) Computational methods for prediction of protein-RNA interactions. J Struct Biol 179(3):261PubMedGoogle Scholar
  21. 21.
    Walia RR, Xue LC, Wilkins K, El-Manzalawy Y, Dobbs D, Honavar V (2014) RNABindRPlus: a predictor that combines machine learning and sequence homology-based methods to improve the reliability of predicted RNA-binding residues in proteins. PLoS ONE 9(5):e97725PubMedPubMedCentralGoogle Scholar
  22. 22.
    Perez-Cano L, Fernandez-Recio J (2010) Optimal protein-RNA area, OPRA: a propensity-based method to identify RNA-binding sites on proteins. Proteins 78(1):25PubMedGoogle Scholar
  23. 23.
    Zhao H, Yang Y, Zhou Y (2011) Structure-based prediction of RNA-binding domains and RNA-binding sites and application to structural genomics targets. Nucleic Acids Res 39(8):3017PubMedGoogle Scholar
  24. 24.
    Towfic F, Caragea C, Gemperline DC, Dobbs D, Honavar V (2010) Struct-NB: predicting protein-RNA binding sites using structural features. Int J Data Min Bioinform 4(1):21PubMedPubMedCentralGoogle Scholar
  25. 25.
    Li S, Yamashita K, Amada KM, Standley DM (2014) Quantifying sequence and structural features of protein-RNA interactions. Nucleic Acids Res 42(15):10086PubMedPubMedCentralGoogle Scholar
  26. 26.
    Yang XX, Deng ZL, Liu R (2014) RBRDetector: improved prediction of binding residues on RNA-binding protein structures using complementary feature- and template-based strategies. Proteins 82(10):2455PubMedGoogle Scholar
  27. 27.
    Miao Z, Westhof E (2015) Prediction of nucleic acid binding probability in proteins: a neighboring residue network based score. 43(11):5340Google Scholar
  28. 28.
    Miao Z, Westhof E (2015) A large-scale assessment of nucleic acids binding site prediction programs. Nucleic Acids Res 11(12):e1004639Google Scholar
  29. 29.
    Dey S, Pal A, Guharoy M, Sonavane S, Chakrabarti P (2012) Characterization and prediction of the binding site in DNA-binding proteins: improvement of accuracy by combining residue composition, evolutionary conservation and structural parameters. Nucleic Acids Res 40(15):7150PubMedPubMedCentralGoogle Scholar
  30. 30.
    Pan X, Zhu L, Fan YX, Yan J (2014) Predicting protein-RNA interaction amino acids using random forest based on submodularity subset selection. Comput Biol Chem 53pb:324PubMedGoogle Scholar
  31. 31.
    Xiong D, Zeng J, Gong H (2015) RBRIdent: an algorithm for improved identification of RNA-binding residues in proteins from primary sequences. Structure 83(6):1068Google Scholar
  32. 32.
    Kirsanov DD, Zanegina ON, Aksianov EA, Spirin SA, Karyagina AS, Alexeevski AV (2013) NPIDB: nucleic acid-protein interaction database. Nucleic Acids Res 41(Database issue):D517PubMedGoogle Scholar
  33. 33.
    Zanegina O, Kirsanov D, Baulin E, Karyagina A, Alexeevski A, Spirin S (2016) An updated version of NPIDB includes new classifications of DNA-protein complexes and their families. Nucleic Acids Res 44(D1):D144PubMedGoogle Scholar
  34. 34.
    Bahadur RP, Zacharias M, Janin J (2008) Dissecting protein-RNA recognition sites. Nucleic Acids Res 36(8):2705PubMedPubMedCentralGoogle Scholar
  35. 35.
    Iwakiri J, Tateishi H, Chakraborty A, Patil P, Kenmochi N (2012) Dissecting the protein–RNA interface: the role of protein surface shapes and RNA secondary structures in protein–RNA recognition. Nucleic Acids Res 40(8):3299PubMedGoogle Scholar
  36. 36.
    Barik A, C N, Pilla SP, Bahadur RP (2015) Molecular architecture of protein-RNA recognition sites. J Biomol Struct Dyn 33(12):2738PubMedGoogle Scholar
  37. 37.
    Kim OT, Yura K, Go N (2006) Amino acid residue doublet propensity in the protein-RNA interface and its application to RNA interface prediction. Nucleic Acids Res 34(22):6450PubMedPubMedCentralGoogle Scholar
  38. 38.
    Wang G, Dunbrack RL Jr (2003) PISCES: a protein sequence culling server. Bioinformatics 19(12):1589PubMedGoogle Scholar
  39. 39.
    Krissinel E, Henrick K (2007) Inference of macromolecular assemblies from crystalline state. J Mol Biol 372(3):774PubMedGoogle Scholar
  40. 40.
    Kawashima S, Pokarowski P, Pokarowska M, Kolinski A, Katayama T, Kanehisa M (2008) AAindex: amino acid index database, progress report 2008. Nucleic Acids Res 36(Database issue):D202PubMedGoogle Scholar
  41. 41.
    Sun M, Wang X, Zou C, He Z, Liu W, Li H (2016) Accurate prediction of RNA-binding protein residues with two discriminative structural descriptors. BMC Bioinform 17(1):231Google Scholar
  42. 42.
    Heinig M, Frishman D (2004) STRIDE: a web server for secondary structure assignment from known atomic coordinates of proteins. Nucleic Acids Res 32(Web Server issue):W500PubMedPubMedCentralGoogle Scholar
  43. 43.
    Hubbard SJ, Thornton JM (1998) NACCESS: program for calculating accessibilities. Department of Biochemistry and Molecular Biology, University College of London, UKGoogle Scholar
  44. 44.
    Mihel J, Sikic M, Tomic S, Jeren B, Vlahovicek K (2008) PSAIA—protein structure and interaction analyzer. BMC Struct Biol 8:21PubMedPubMedCentralGoogle Scholar
  45. 45.
    Piovesan D, Minervini G, Tosatto SC (2016) The RING 2.0 web server for high quality residue interaction networks. Nucleic Acids Res 44(W1):W367PubMedPubMedCentralGoogle Scholar
  46. 46.
    Smoot ME, Ono K, Ruscheinski J, Wang PL, Ideker T (2011) Cytoscape 2.8: new features for data integration and network visualization. Bioinformatics 27(3):431PubMedGoogle Scholar
  47. 47.
    Baker NA, Sept D, Joseph S, Holst MJ, McCammon JA (2001) Electrostatics of nanosystems: application to microtubules and the ribosome. Proc Natl Acad Sci USA 98(18):10037PubMedGoogle Scholar
  48. 48.
    Dolinsky TJ, Czodrowski P, Li H, Nielsen JE, Jensen JH, Klebe G, Baker NA (2007) PDB2PQR: expanding and upgrading automated preparation of biomolecular structures for molecular simulations. Nucleic Acids Res 35(Web Server issue):W522PubMedPubMedCentralGoogle Scholar
  49. 49.
    Breiman L (2001) Random forests. Mach Learn 45:5Google Scholar
  50. 50.
    Luo JS, Guo YZ, Zhong Y, Ma D, Li WL, Li ML (2014) A functional feature analysis on diverse protein–protein interactions: application for the prediction of binding affinity. J Comput Mol Des 28(6):619Google Scholar
  51. 51.
    Luo JS, Li WL, Liu ZY, Guo YZ, Pu XM, Li ML (2015) A sequence-based two-level method for the prediction of type I secreted RTX proteins. Analyst 140(9):3048PubMedGoogle Scholar
  52. 52.
    Wang Y, Guo YZ, Kuang QF, Pu XM, Ji Y, Zhang ZH, Li ML (2015) A comparative study of family-specific protein–ligand complex affinity prediction based on random forest approach. J Comput Mol Des 29(4):349Google Scholar
  53. 53.
    Wang Y, Guo YZ, Pu XM, Li ML (2017) Effective prediction of bacterial type IV secreted effectors by combined features of both C-termini and N-termini. J Comput Mol Des 3(11):1Google Scholar
  54. 54.
    Qiu H, Guo YZ, Yu LZ, Pu XM, Li ML (2018) Predicting protein lysine methylation sites by incorporating single-residue structural features into Chou’s pseudo components. Chemometr Intell Lab Sys 179(1):31Google Scholar
  55. 55.
    Liu ZP, Wu LY, Wang Y, Zhang XS, Chen L (2010) Prediction of protein–RNA binding sites by a random forest method with combined features. Bioinformatics 26(13):1616PubMedGoogle Scholar
  56. 56.
    Jones S, Daley DT, Luscombe NM, Berman HM, Thornton JM (2001) Protein–RNA interactions: a structural analysis. Nucleic Acids Res 29(4):943PubMedPubMedCentralGoogle Scholar
  57. 57.
    El-Manzalawy Y, Abbas M, Malluhi Q, Honavar V (2016) Fastrnabindr: fast and accurate prediction of protein-RNA interface residues. Plos ONE 11(7):e0158445PubMedPubMedCentralGoogle Scholar
  58. 58.
    Allers J, Shamoo Y (2001) Structure-based analysis of protein–RNA interactions using the program ENTANGLE. J Mol Biol 311(1):75PubMedGoogle Scholar
  59. 59.
    Xie W, Liu X, Huang RH (2003) Chemical trapping and crystal structure of a catalytic tRNA guanine transglycosylase covalent intermediate. Nat Struct Biol 10(10):781PubMedGoogle Scholar
  60. 60.
    Yamashita S, Martinez A, Tomita K (2015) Measurement of acceptor-TPsiC helix length of tRNA for terminal A76-addition by A-adding enzyme. Nucleic Acids Res 23(5):830Google Scholar
  61. 61.
    Tsuchiya Y, Kinoshita K, Nakamura H (2005) PreDs: a server for predicting dsDNA-binding site on protein molecular surfaces. Bioinformatics 21(8):1721PubMedGoogle Scholar
  62. 62.
    Li T, Li QZ, Liu S, Fan GL, Zuo YC et al (2013) PreDNA: accurate prediction of DNA-binding sites in proteins by integrating sequence and geometric structure information. Bioinformatics 29(6):678PubMedGoogle Scholar
  63. 63.
    Liu R, Hu J (2013) DNABind: a hybrid algorithm for structure-based prediction of DNA-binding residues by combining machine learning- and template-based approaches. Proteins 81(11):1885PubMedGoogle Scholar
  64. 64.
    Yan J, Friedrich S, Kurgan L (2015) A comprehensive comparative review of sequence-based predictors of DNA- and RNA-binding residues. Brief Bioinformatics 17(1):88PubMedGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Wen Hu
    • 1
  • Liu Qin
    • 1
  • Menglong Li
    • 1
  • Xuemei Pu
    • 1
  • Yanzhi Guo
    • 1
  1. 1.College of ChemistrySichuan UniversityChengduPeople’s Republic of China

Personalised recommendations