RBPBind: Quantitative Prediction of Protein-RNA Interactions |
| |
| Institution: | 1. Institute for Genomic Medicine, Nationwide Children''s Hospital, 700 Children''s Dr, Columbus, OH 43205, USA;2. Department of Physics, The Ohio State University, 191 West Woodruff Av, Columbus, OH 43210, USA;3. Department of Physics, Department of Chemistry & Biochemistry, Division of Internal Medicine, Center for RNA Biology, The Ohio State University, 191 West Woodruff Av, Columbus, OH 43210, USA;1. Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA;2. Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA;3. Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ 08901, USA;4. Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA;5. Department of Bioengineering, University of California at Berkeley, Berkeley, CA 94720, USA;6. Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK;7. Global Phasing Ltd, Sheraton House, Castle Park, Cambridge CB3 0AK, UK;8. University of Konstanz, 78457 Konstanz, Germany;9. Department of Biochemistry, Netherlands Cancer Institute, Amsterdam, the Netherlands;10. Oncode Institute, 3521 AL Utrecht, the Netherlands;11. UKRI-STFC Rutherford Appleton Laboratory, Didcot OX11 0FA, UK;12. CCP4, Research Complex at Harwell, Rutherford Appleton Laboratory, Didcot OX11 0FA, UK;13. Protein Data Bank Japan, Institute for Protein Research, Osaka University, Suita, Osaka 565-0871, Japan;14. Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA;15. The Bridge Institute, Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA, USA;16. Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California, La Jolla, CA 92093, USA;1. Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA;2. Department of Biological Chemistry, University of Michigan, Ann Arbor, MI 48109, USA;1. Institute of Structural and Molecular Biology, Birkbeck, University of London, Malet Street, London WC1E 7HX, UK;2. School of Biological and Behavioural Sciences, Queen Mary University of London, London E1 4NS, UK;1. Computational Biology Program, The University of Kansas, Lawrence, KS 66047, USA;2. Centre for Integrative Systems Biology and Bioinformatics, Department of Life Sciences, Imperial College London, South Kensington, London SW7 2AZ, UK;3. Department of Molecular Biosciences, The University of Kansas, Lawrence, KS 66045, USA |
| |
| Abstract: | There are hundreds of RNA binding proteins in the human genome alone and their interactions with messenger and other RNAs in a cell regulate every step in an RNA's life cycle. To understand this interplay of proteins and RNA it is important to be able to know which protein binds which RNA how strongly and where. Here, we introduce RBPBind, a web-based tool for the quantitative prediction of the interaction of single-stranded RNA binding proteins with target RNAs that fully takes into account the effect of RNA secondary structure on binding affinity. Given a user-specified RNA and a protein selected from a set of several RNA-binding proteins, RBPBind computes their binding curve and effective binding constant. The server also computes the probability that, at a given protein concentration, a protein molecule will bind to any particular nucleotide along the RNA. The sequence specificity of the protein-RNA interaction is parameterized from public RNAcompete experiments and integrated into the recursions of the Vienna RNA package to simultaneously take into account protein binding and RNA secondary structure. We validate our approach by comparison to experimentally determined binding affinities of the HuR protein for several RNAs of different sequence contexts from the literature, showing that integration of raw sequence affinities into RNA secondary structure prediction significantly improves the agreement between computationally predicted and experimentally measured binding affinities. Our resource thus provides a quick and easy way to obtain reliable predicted binding affinities and locations for single-stranded RNA binding proteins based on RNA sequence alone. |
| |
| Keywords: | protein-RNA interactions RNA secondary structure binding affinity web server |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
