title: Quantifying sequence and structural features of protein-RNA interactions.
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.
authors: Laboratory of Systems Immunology, WPI Immunology Frontier Research Center, Osaka University, Osaka 565-0871, Japan standley@ifrec.osaka-u.ac.jp.
authors: Songling
authors: S
authors: Li
authors: Laboratory of Systems Immunology, WPI Immunology Frontier Research Center, Osaka University, Osaka 565-0871, Japan.
authors: Kazuo
authors: K
authors: Yamashita
authors: Laboratory of Systems Immunology, WPI Immunology Frontier Research Center, Osaka University, Osaka 565-0871, Japan.
authors: Karlou Mar
authors: KM
authors: Amada
authors: Laboratory of Systems Immunology, WPI Immunology Frontier Research Center, Osaka University, Osaka 565-0871, Japan standley@ifrec.osaka-u.ac.jp.
authors: Daron M
authors: DM
authors: Standley
mesh_terms: D000465:Algorithms; D000596:Amino Acids / Q000737:chemistry; D001185:Artificial Intelligence; D001665:Binding Sites; D008958:Models, Molecular; D011485:Protein Binding; D017433:Protein Structure, Secondary; D012313:RNA / Q000737:chemistry / Q000378:metabolism; D016601:RNA-Binding Proteins / Q000737:chemistry* / Q000378:metabolism; D020539:Sequence Analysis, Protein; D012984:Software; D040681:Structural Homology, Protein
chemical_list: D000596:Amino Acids; D016601:RNA-Binding Proteins; D012313:RNA