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B IOINFORMATICS AND C OMPUTATIONAL B IOLOGY A Computational Method to Identify RNA Binding Sites in Proteins Jeff Sander Iowa State University Rocky 2006.

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Presentation on theme: "B IOINFORMATICS AND C OMPUTATIONAL B IOLOGY A Computational Method to Identify RNA Binding Sites in Proteins Jeff Sander Iowa State University Rocky 2006."— Presentation transcript:

1 B IOINFORMATICS AND C OMPUTATIONAL B IOLOGY A Computational Method to Identify RNA Binding Sites in Proteins Jeff Sander Iowa State University Rocky 2006 Presented by

2 B IOINFORMATICS AND C OMPUTATIONAL B IOLOGY Biological Motivation Protein / RNA complexes –mRNA, tRNA, rRNA –miRNA, siRNA, RNAi –RNA viruses Which amino acids are directly responsible for binding RNA? AMINOACYL TRANSFER RNA SYNTHETASE Terribilini et al (2006) RNA Wang & Brown (2006) NAR Jeong & Miyano (2006) Tras Sys Biol

3 B IOINFORMATICS AND C OMPUTATIONAL B IOLOGY Sequence Based Predictions Results: CC: 0.33 Acc: 0.86Sp+: 0.46Sen+: 0.36 Server: RNABindR http://bindr.gdcb.iastate.edu/RNABindR/ ARVHNTRQQGATLAFLTLRQQASLIQ Terribilini et al (2006) RNA Dataset –Protein RNA complexes from the Protein Data Bank –Less than 30% identity and 3.5A or better resolution –147 Protein RNA complexes –14% interacting residues / 86% non-interacting Classifier - Naïve Bayes

4 B IOINFORMATICS AND C OMPUTATIONAL B IOLOGY Using Additional Information PSSM PSI-Blast Str Neighbor Combination Actual ARVHNTRQQGATLAF

5 B IOINFORMATICS AND C OMPUTATIONAL B IOLOGY Results MethodCCAccSP+SN+ Sequence0.330.860.460.36 PSSM0.350.860.480.38 PSI-Blast0.350.870.500.36 Str Neighbors0.350.860.480.38 PSSM & PSI-Blast0.370.870.520.38 PSSM & Str Neigh0.390.870.530.39 Str & PSI-Blast0.380.870.550.36 PSSM & Str & PSI0.390.880.560.37 MethodCCAccSP+SN+ Sequence0.330.860.460.36 PSSM0.350.860.480.38 PSI-Blast0.350.870.500.36 Str Neighbors0.350.860.480.38 PSSM & PSI-Blast0.370.870.520.38 PSSM & Str Neigh0.390.870.530.39 Str & PSI-Blast0.380.870.550.36 PSSM & Str & PSI0.390.880.560.37

6 B IOINFORMATICS AND C OMPUTATIONAL B IOLOGY Conclusions Drena Dobbs Vasant Honavar Robert Jernigan Michael Terribilini Changhui Yan Jae-Hyung Lee Funding: USDA MGET, NIH, CIAG, ISU Acknowledgements Evolutionary and structural information can enhance prediction over basic sequence Combining classifiers can provide enhanced predictions over individual classifiers

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