Amino acid residue doublet propensity in the protein?RNA interface and its application to RNA interface prediction - RNA

Amino acid residue doublet propensity in the protein?RNA interface and its application to RNA interface prediction
November 27, 2006
Oanh T. P. Kim1, Kei Yura1,2,3,* and Nobuhiro Go2
1 Quantum Bioinformatics Team, Center for Computational Science and Engineering, Japan Atomic Energy Agency Kizu-cho, Souraku-gun, Kyoto 619-0215, Japan 2 Research Unit for Quantum Beam Life Science Initiative, Quantum Beam Science Directorate, Japan Atomic Energy Agency Kizu-cho, Souraku-gun, Kyoto 619-0215, Japan 3 CREST, JST, Japan Atomic Energy Agency Kizu-cho, Souraku-gun, Kyoto 619-0215, Japan 4 Computational Biology Group, Quantum Beam Science Directorate, Japan Atomic Energy Agency Kizu-cho, Souraku-gun, Kyoto 619-0215, Japan 5 Bioinformatics Unit, Nara Institute of Science and Technology Takayama-cho, Ikoma-shi, Nara 630-0196, Japan
Nucleic Acids Research
Protein?RNA interactions play essential roles in a number of regulatory mechanisms for gene expression such as RNA splicing, transport, translation and post-transcriptional control. As the number of available protein?RNA complex 3D structures has increased, it is now possible to statistically examine protein?RNA interactions based on 3D structures. We performed computational analyses of 86 representative protein?RNA complexes retrieved from the Protein Data Bank. Interface residue propensity, a measure of the relative importance of different amino acid residues in the RNA interface, was calculated for each amino acid residue type (residue singlet interface propensity). In addition to the residue singlet propensity, we introduce a new residue-based propensity, which gives a measure of residue pairing preferences in the RNA interface of a protein (residue doublet interface propensity). The residue doublet interface propensity contains much more information than the sum of two singlet propensities alone. The prediction of the RNA interface using the two types of propensities plus a position-specific multiple sequence profile can achieve a specificity of about 80%. The prediction method was then applied to the 3D structure of two mRNA export factors, TAP (Mex67) and UAP56 (Sub2). The prediction enables us to point out candidate RNA interfaces, part of which are consistent with previous experimental studies and may contribute to elucidation of atomic mechanisms of mRNA export.
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