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Sequence-Based Prediction of RNA-Binding Residues in Proteins

  • Rasna R. Walia
  • Yasser EL-Manzalawy
  • Vasant G. Honavar
  • Drena Dobbs
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1484)

Abstract

Identifying individual residues in the interfaces of protein–RNA complexes is important for understanding the molecular determinants of protein–RNA recognition and has many potential applications. Recent technical advances have led to several high-throughput experimental methods for identifying partners in protein–RNA complexes, but determining RNA-binding residues in proteins is still expensive and time-consuming. This chapter focuses on available computational methods for identifying which amino acids in an RNA-binding protein participate directly in contacting RNA. Step-by-step protocols for using three different web-based servers to predict RNA-binding residues are described. In addition, currently available web servers and software tools for predicting RNA-binding sites, as well as databases that contain valuable information about known protein–RNA complexes, RNA-binding motifs in proteins, and protein-binding recognition sites in RNA are provided. We emphasize sequence-based methods that can reliably identify interfacial residues without the requirement for structural information regarding either the RNA-binding protein or its RNA partner.

Key words

Protein–RNA interfaces Binding site prediction Machine learning RNA-binding proteins (RBPs) Ribonucleoprotein particles (RNPs) Homology-based prediction RNABindRPlus SNBRFinder PS-PRIP FastRNABindR 

Notes

Acknowledgments

This work was supported in part by NSF DBI0923827 to DD, by NIH GM066387 to VGH and DD, by a Presidential Initiative for Interdisciplinary Research (PIIR) award to DD from Iowa State University, and by the Edward Frymoyer Chair in Information Sciences and Technology held by VGH at Pennsylvania State University. RRW is currently supported by an appointment to the ARS-USDA Research Participation Program administered by the Oak Ridge Institute for Science and Education (ORISE) through an interagency agreement between the US Department of Energy (DOE) and USDA. ORISE is managed by ORAU under DOE contract number DE-AC05-06OR23100. We thank Carla Mann and Usha Muppirala for valuable discussions.

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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • Rasna R. Walia
    • 1
  • Yasser EL-Manzalawy
    • 2
  • Vasant G. Honavar
    • 2
  • Drena Dobbs
    • 3
  1. 1.USDA-ARSAmesUSA
  2. 2.College of Information Sciences and TechnologyPennsylvania State UniversityUniversity ParkUSA
  3. 3.Genetics, Development and Cell Biology DepartmentIowa State UniversityAmesUSA

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