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/.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
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: 535–542
Tsvetanova NG, Klass DM, Salzman J, Brown PO (2010) Proteome-wide search reveals unexpected RNA-binding proteins in Saccharomyces cerevisiae. PLoS One 5:e12671
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:e15499
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 149:1393–1406
Puton T, Kozlowski L, Tuszynska I, Rother K, Bujnicki JM (2012) Computational methods for prediction of protein-RNA interactions. J Struct Biol 179(3):261–8
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 13:89
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 15:269–280
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: 6378–6391
Tuszynska I, Bujnicki JM (2011) DARS-RNP and QUASI-RNP: new statistical potentials for protein-RNA docking. BMC Bioinformatics 12:348
Setny P, Zacharias M (2011) A coarse-grained force field for Protein-RNA docking. Nucleic Acids Res 39:9118–9129
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: 988–996
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 27:2076–2082
Zhou HY, Zhou Y (2005) SPARKS 2 and SP3 servers in CASP 6. Proteins 61:152–156
Liu S, Zhang C, Liang SD, Zhou Y (2007) Fold recognition by concurrent use of solvent accessibility and residue depth. Proteins 68: 636–645
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 25:3389–3402
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 17:1515–1527
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 33:259–263
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: 847–856
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 39:3017–3025
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 11:2714–2726
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 46:165–174
Zhou YQ, Duan Y, Yang YD, Faraggi E, Lei HX (2011) Trends in template/fragment-free protein structure prediction. Theor Chem Acc 128:3–16
Soding J, Biegert A, Lupas AN (2005) The HHpred interactive server for protein homology detection and structure prediction. Nucleic Acids Res 33:W244–W248
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):2417–25
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)
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 121:1005–1016
Dor O, Zhou Y (2007) Achieving 80 % ten-fold cross-validated accuracy for secondary structure prediction by large-scale training. Proteins 66:838–845
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 80:2080–2088
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.].
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer Science+Business Media New York
About this protocol
Cite this protocol
Yang, Y., Zhao, H., Wang, J., Zhou, Y. (2014). SPOT-Seq-RNA: Predicting Protein–RNA Complex Structure and RNA-Binding Function by Fold Recognition and Binding Affinity Prediction. In: Kihara, D. (eds) Protein Structure Prediction. Methods in Molecular Biology, vol 1137. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-0366-5_9
Download citation
DOI: https://doi.org/10.1007/978-1-4939-0366-5_9
Published:
Publisher Name: Humana Press, New York, NY
Print ISBN: 978-1-4939-0365-8
Online ISBN: 978-1-4939-0366-5
eBook Packages: Springer Protocols