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A Computational Method to Identify RNA Binding Sites in Proteins

A Computational Method to Identify RNA Binding Sites in Proteins. Jeff Sander Iowa State University Rocky 2006. Presented by. Biological Motivation. Protein / RNA complexes mRNA, tRNA, rRNA miRNA, siRNA, RNAi RNA viruses. Which amino acids are directly responsible for binding RNA?.

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A Computational Method to Identify RNA Binding Sites in Proteins

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  1. A Computational Method to Identify RNA Binding Sites in Proteins Jeff Sander Iowa State University Rocky 2006 Presented by

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

  3. Sequence Based Predictions • 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 • Results: CC: 0.33 Acc: 0.86 Sp+: 0.46 Sen+: 0.36 • Server: RNABindR http://bindr.gdcb.iastate.edu/RNABindR/ ARVHNTRQQGATLAFLTLRQQASLIQ Terribilini et al (2006) RNA

  4. Using Additional Information • PSSM • PSI-Blast • Str Neighbor • Combination • Actual ARVHNTRQQGATLAF ARVHNTRQQGATLAF ARVHNTRQQGATLAF ARVHNTRQQGATLAF ARVHNTRQQGATLAF

  5. Results

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

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