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Nucleic Acids Res. 2014 Sep;42(15):10086-98. doi: 10.1093/nar/gku681. Epub 2014 Jul 25.

Quantifying sequence and structural features of protein-RNA interactions.

Author information

1
Laboratory of Systems Immunology, WPI Immunology Frontier Research Center, Osaka University, Osaka 565-0871, Japan standley@ifrec.osaka-u.ac.jp.
2
Laboratory of Systems Immunology, WPI Immunology Frontier Research Center, Osaka University, Osaka 565-0871, Japan.

Abstract

Increasing awareness of the importance of protein-RNA interactions has motivated many approaches to predict residue-level RNA binding sites in proteins based on sequence or structural characteristics. Sequence-based predictors are usually high in sensitivity but low in specificity; conversely structure-based predictors tend to have high specificity, but lower sensitivity. Here we quantified the contribution of both sequence- and structure-based features as indicators of RNA-binding propensity using a machine-learning approach. In order to capture structural information for proteins without a known structure, we used homology modeling to extract the relevant structural features. Several novel and modified features enhanced the accuracy of residue-level RNA-binding propensity beyond what has been reported previously, including by meta-prediction servers. These features include: hidden Markov model-based evolutionary conservation, surface deformations based on the Laplacian norm formalism, and relative solvent accessibility partitioned into backbone and side chain contributions. We constructed a web server called aaRNA that implements the proposed method and demonstrate its use in identifying putative RNA binding sites.

PMID:
25063293
PMCID:
PMC4150784
DOI:
10.1093/nar/gku681
[Indexed for MEDLINE]
Free PMC Article

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