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

Kim OT, Yura K, Go N - Nucleic Acids Res. (2006)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Quantum Bioinformatics Team, Center for Computational Science and Engineering, Japan Atomic Energy Agency, Kizu-cho, Souraku-gun, Kyoto 619-0215, Japan.

ABSTRACT
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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Related in: MedlinePlus

A graphical matrix of the surface residue doublet coefficients Cij (A), the interface residue doublet coefficients Dij (B) and the residue doublet interface propensities Pij (C) color-coded in a logarithm (log2) scale. Cys–Cys pair in A and Cys–Cys and Cys–Trp pairs in B are off the scale. In (C), a value with a cross mark indicates that the data are not statistically sufficient to warrant the result.
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fig2: A graphical matrix of the surface residue doublet coefficients Cij (A), the interface residue doublet coefficients Dij (B) and the residue doublet interface propensities Pij (C) color-coded in a logarithm (log2) scale. Cys–Cys pair in A and Cys–Cys and Cys–Trp pairs in B are off the scale. In (C), a value with a cross mark indicates that the data are not statistically sufficient to warrant the result.

Mentions: The surface residue doublet coefficients Cij, the interface residue doublet coefficients Dij and the residue doublet interface propensities Pij in Equation 7 are shown in Figure 2. All values are shown in a log2 scale and are color-coded. As seen in Figure 2A, the logarithm of the surface residue doublet coefficients Cij of hydrophobic residues were weakly positive. The logarithm of the coefficients of pairs, each with positively and negatively charged residues, was also positive and those of positively charged residue pairs and those of negatively charged residue pairs were negative. These data suggest that hydrophobic residues are paired and charged residues form salt bridges on the protein surface. The interface residue doublet coefficients Dij were, as a whole, much more variable than the surface doublet coefficients (Figure 2B). The calculated coefficients for hydrophobic residue pairs were much greater than those seen for the surface residues, but the coefficients of positively and negatively charged residue pairs were negative and this was the opposite of that observed for the surface residue doublet coefficients, suggesting that salt bridge formation was not favored at RNA interfaces. Negatively charged residues were less common in the RNA interfaces and positively charged residues can mediate electrostatic interactions with RNA.


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

Kim OT, Yura K, Go N - Nucleic Acids Res. (2006)

A graphical matrix of the surface residue doublet coefficients Cij (A), the interface residue doublet coefficients Dij (B) and the residue doublet interface propensities Pij (C) color-coded in a logarithm (log2) scale. Cys–Cys pair in A and Cys–Cys and Cys–Trp pairs in B are off the scale. In (C), a value with a cross mark indicates that the data are not statistically sufficient to warrant the result.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC1761430&req=5

fig2: A graphical matrix of the surface residue doublet coefficients Cij (A), the interface residue doublet coefficients Dij (B) and the residue doublet interface propensities Pij (C) color-coded in a logarithm (log2) scale. Cys–Cys pair in A and Cys–Cys and Cys–Trp pairs in B are off the scale. In (C), a value with a cross mark indicates that the data are not statistically sufficient to warrant the result.
Mentions: The surface residue doublet coefficients Cij, the interface residue doublet coefficients Dij and the residue doublet interface propensities Pij in Equation 7 are shown in Figure 2. All values are shown in a log2 scale and are color-coded. As seen in Figure 2A, the logarithm of the surface residue doublet coefficients Cij of hydrophobic residues were weakly positive. The logarithm of the coefficients of pairs, each with positively and negatively charged residues, was also positive and those of positively charged residue pairs and those of negatively charged residue pairs were negative. These data suggest that hydrophobic residues are paired and charged residues form salt bridges on the protein surface. The interface residue doublet coefficients Dij were, as a whole, much more variable than the surface doublet coefficients (Figure 2B). The calculated coefficients for hydrophobic residue pairs were much greater than those seen for the surface residues, but the coefficients of positively and negatively charged residue pairs were negative and this was the opposite of that observed for the surface residue doublet coefficients, suggesting that salt bridge formation was not favored at RNA interfaces. Negatively charged residues were less common in the RNA interfaces and positively charged residues can mediate electrostatic interactions with RNA.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Quantum Bioinformatics Team, Center for Computational Science and Engineering, Japan Atomic Energy Agency, Kizu-cho, Souraku-gun, Kyoto 619-0215, Japan.

ABSTRACT
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.

Show MeSH
Related in: MedlinePlus