Advertisement

SPOT-Seq-RNA: Predicting Protein–RNA Complex Structure and RNA-Binding Function by Fold Recognition and Binding Affinity Prediction

  • Yuedong Yang
  • Huiying Zhao
  • Jihua Wang
  • Yaoqi Zhou
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1137)

Abstract

RNA-binding proteins (RBPs) play key roles in RNA metabolism and post-transcriptional regulation. Computational methods have been developed separately for prediction of RBPs and RNA-binding residues by machine-learning techniques and prediction of protein–RNA complex structures by rigid or semiflexible structure-to-structure docking. Here, we describe a template-based technique called SPOT-Seq-RNA that integrates prediction of RBPs, RNA-binding residues, and protein–RNA complex structures into a single package. This integration is achieved by combining template-based structure-prediction software, SPARKS X, with binding affinity prediction software, DRNA. This tool yields reasonable sensitivity (46 %) and high precision (84 %) for an independent test set of 215 RBPs and 5,766 non-RBPs. SPOT-Seq-RNA is computationally efficient for genome-scale prediction of RBPs and protein–RNA complex structures. Its application to human genome study has revealed a similar sensitivity and ability to uncover hundreds of novel RBPs beyond simple homology. The online server and downloadable version of SPOT-Seq-RNA are available at http://sparks-lab.org/server/SPOT-Seq-RNA/.

Keywords

Fold recognition Binding affinity Protein–RNA complex structure Template-based structure prediction Knowledge-based energy function Protein–RNA interactions RNA-binding proteins Torsion-angle prediction Solvent accessible surface area Prediction SPOT-Seq-RNA 

Notes

Acknowledgments

Funding for this work was supported by the National Institutes of Health grants [GM R01 085003 and GM R01 067168 (Co-PI) to Y.Z.] and by the National Natural Science Foundation of China [grant 61271378 to J.W.].

References

  1. 1.
    Bernstein FC, Koetzle TF, Williams GJ, Meyer EF Jr, Brice MD, Rodgers JR, Kennard O, Shimanouchi T, Tasumi M (1977) The Protein Data Bank: a computer-based archival file for macromolecular structures. J Mol Biol 112: 535–542PubMedCrossRefGoogle Scholar
  2. 2.
    Tsvetanova NG, Klass DM, Salzman J, Brown PO (2010) Proteome-wide search reveals unexpected RNA-binding proteins in Saccharomyces cerevisiae. PLoS One 5:e12671Google Scholar
  3. 3.
    Scherrer T, Mittal N, Janga SC, Gerber AP (2010) A screen for RNA-binding proteins in yeast indicates dual functions for many enzymes. PLoS One 5:e15499PubMedCentralPubMedCrossRefGoogle Scholar
  4. 4.
    Castello A, Fischer B, Eichelbaum K, Horos R, Beckmann BM, Strein C, Davey NE, Humphreys DT, Preiss T, Steinmetz LM et al (2012) Insights into RNA biology from an Atlas of mammalian mRNA-binding proteins. Cell 149:1393–1406PubMedCrossRefGoogle Scholar
  5. 5.
    Puton T, Kozlowski L, Tuszynska I, Rother K, Bujnicki JM (2012) Computational methods for prediction of protein-RNA interactions. J Struct Biol 179(3):261–8PubMedCrossRefGoogle Scholar
  6. 6.
    Walia RR, Caragea C, Lewis BA, Towfic FG, Terribilini M, El-Manzalawy Y, Dobbs D, Honavar V (2012) Protein-RNA interface residue prediction using machine learning: an assessment of the state of the art. BMC Bioinformatics 13:89PubMedCentralPubMedCrossRefGoogle Scholar
  7. 7.
    Perez-Cano L, Solernou A, Pons C, Fernandez-Recio J (2010) Structural prediction of protein-RNA interaction by computational docking with propensity-based statistical potentials. Pac Symp Biocomput 15:269–280Google Scholar
  8. 8.
    Zheng S, Robertson TA, Varani G (2007) A knowledge-based potential function predicts the specificity and relative binding energy of RNA-binding proteins. FEBS J 274: 6378–6391PubMedCrossRefGoogle Scholar
  9. 9.
    Tuszynska I, Bujnicki JM (2011) DARS-RNP and QUASI-RNP: new statistical potentials for protein-RNA docking. BMC Bioinformatics 12:348PubMedCentralPubMedCrossRefGoogle Scholar
  10. 10.
    Setny P, Zacharias M (2011) A coarse-grained force field for Protein-RNA docking. Nucleic Acids Res 39:9118–9129PubMedCentralPubMedCrossRefGoogle Scholar
  11. 11.
    Zhao H, Yang Y, Zhou Y (2011) Highly accurate and high-resolution function prediction of RNA binding proteins by fold recognition and binding affinity prediction. RNA Biol 8: 988–996PubMedCentralPubMedCrossRefGoogle Scholar
  12. 12.
    Yang Y, Faraggi E, Zhao H, Zhou Y (2011) Improving protein fold recognition and template-based modeling by employing probabilistic-based matching between predicted one-dimensional structural properties of the query and corresponding native properties of templates. Bioinformatics 27:2076–2082PubMedCentralPubMedCrossRefGoogle Scholar
  13. 13.
    Zhou HY, Zhou Y (2005) SPARKS 2 and SP3 servers in CASP 6. Proteins 61:152–156PubMedCrossRefGoogle Scholar
  14. 14.
    Liu S, Zhang C, Liang SD, Zhou Y (2007) Fold recognition by concurrent use of solvent accessibility and residue depth. Proteins 68: 636–645PubMedCrossRefGoogle Scholar
  15. 15.
    Altschul SF, Madden TL, Schaffer AA, Zhang JH, Zhang Z, Miller W, Lipman DJ (1997) Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res 25:3389–3402PubMedCentralPubMedCrossRefGoogle Scholar
  16. 16.
    Faraggi E, Yang YD, Zhang SS, Zhou Y (2009) Predicting continuous local structure and the effect of its substitution for secondary structure in fragment-free protein structure prediction. Structure 17:1515–1527PubMedCentralPubMedCrossRefGoogle Scholar
  17. 17.
    Faraggi E, Zhang T, Yang Y, Kurgan L, Zhou Y (2011) SPINE X: improving protein secondary structure prediction by multi-step learning coupled with prediction of solvent accessible surface area and backbone torsion angles. J Comput Chem 33:259–263PubMedCentralPubMedCrossRefGoogle Scholar
  18. 18.
    Faraggi E, Xue B, Zhou Y (2009) Improving the prediction accuracy of residue solvent accessibility and real-value backbone torsion angles of proteins by guided-learning through a two-layer neural network. Proteins 74: 847–856PubMedCentralPubMedCrossRefGoogle Scholar
  19. 19.
    Zhao HY, Yang YD, Zhou YQ (2011) Structure-based prediction of RNA-binding domains and RNA-binding sites and application to structural genomics targets. Nucleic Acids Res 39:3017–3025PubMedCentralPubMedCrossRefGoogle Scholar
  20. 20.
    Zhou HY, Zhou Y (2002) Distance-scaled, finite ideal-gas reference state improves structure-derived potentials of mean force for structure selection and stability prediction. Protein Sci 11:2714–2726PubMedCentralPubMedCrossRefGoogle Scholar
  21. 21.
    Zhou Y, Zhou HY, Zhang C, Liu S (2006) What is a desirable statistical energy function for proteins and how can it be obtained? Cell Biochem Biophys 46:165–174PubMedCrossRefGoogle Scholar
  22. 22.
    Zhou YQ, Duan Y, Yang YD, Faraggi E, Lei HX (2011) Trends in template/fragment-free protein structure prediction. Theor Chem Acc 128:3–16PubMedCentralPubMedCrossRefGoogle Scholar
  23. 23.
    Soding J, Biegert A, Lupas AN (2005) The HHpred interactive server for protein homology detection and structure prediction. Nucleic Acids Res 33:W244–W248PubMedCentralPubMedCrossRefGoogle Scholar
  24. 24.
    Zhao H, Yang Y, Zhou Y (2013) Prediction of RNA binding proteins comes of age from low resolution to high resolution. Mol Biosyst 9(10):2417–25PubMedCrossRefGoogle Scholar
  25. 25.
    Zhao H, Yang Y, Janga SC, Kao C, Zhou Y (2013) Prediction and validation of the unexplored RNA-binding protein atlas of the human genome. Proteins, in press (doi: 10.1002/prot.24441)Google Scholar
  26. 26.
    Nowotny M, Gaidamakov SA, Crouch RJ, Yang W (2005) Crystal structures of RNase H bound to an RNA/DNA hybrid: substrate specificity and metal-dependent catalysis. Cell 121:1005–1016PubMedCrossRefGoogle Scholar
  27. 27.
    Dor O, Zhou Y (2007) Achieving 80 % ten-fold cross-validated accuracy for secondary structure prediction by large-scale training. Proteins 66:838–845PubMedCrossRefGoogle Scholar
  28. 28.
    Yang Y, Zhan J, Zhao H, Zhou Y (2012) A new size-independent score for pairwise protein structure alignment and its application to structure classification and nucleic-acid binding prediction. Proteins 80:2080–2088PubMedCentralPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Yuedong Yang
    • 1
    • 2
    • 3
  • Huiying Zhao
    • 1
    • 2
  • Jihua Wang
    • 4
  • Yaoqi Zhou
    • 1
    • 2
    • 3
  1. 1.School of InformaticsIndiana University Purdue UniversityIndianapolisUSA
  2. 2.Center for Computational Biology and BioinformaticsIndiana University School of MedicineIndianapolisUSA
  3. 3.Institute for Glycomics and School of Information and Communication TechnologyGriffith UniversitySouthportAustralia
  4. 4.Shandong Provincial Key Laboratory of Functional Macromolecular Biophysics and Department of PhysicsDezhou UniversityDezhouChina

Personalised recommendations