Limits...
Quantifying sequence and structural features of protein-RNA interactions.

Li S, Yamashita K, Amada KM, Standley DM - Nucleic Acids Res. (2014)

Bottom Line: 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.

View Article: PubMed Central - PubMed

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

Show MeSH
Comparison of prediction results of aaRNA, BindN+ and SRCPred. The figure shows the Csy4-crRNA complex (PDB entry 4AL5). (A) actual contact pattern of the complex. Red colored residues are in RNA contact under a 3.5 Å cutoff. (B) mapping of aaRNA binary binding propensities onto residues, with high (low) colored red (blue). (C) residues in red are positively predicted by the aaRNA under a stringency of 85% expected specificity. (D–E) show the raw and the threshold-calibrated (85% expected specificity), respectively, for BindN+ colored in the same way. (F) prediction results for SRCPred for any di-nucleotide under a 0.05 expected precision.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4150784&req=5

Figure 5: Comparison of prediction results of aaRNA, BindN+ and SRCPred. The figure shows the Csy4-crRNA complex (PDB entry 4AL5). (A) actual contact pattern of the complex. Red colored residues are in RNA contact under a 3.5 Å cutoff. (B) mapping of aaRNA binary binding propensities onto residues, with high (low) colored red (blue). (C) residues in red are positively predicted by the aaRNA under a stringency of 85% expected specificity. (D–E) show the raw and the threshold-calibrated (85% expected specificity), respectively, for BindN+ colored in the same way. (F) prediction results for SRCPred for any di-nucleotide under a 0.05 expected precision.

Mentions: In addition to the benchmark tests presented above, we provide an illustrative example in Figure 5, the Csy4-crRNA complex. In general, sequence-based predictors were more likely to predict charged, polar or aromatic residues on the surface as positive binding sites regardless of their local structural environment. In contrast, due to spatial features introduced here, aaRNA gave more priority to such residues when localized in characteristic RNA binding sites, as learned from the training set. Hence, the aaRNA method could effectively decrease the number of FP predictions, as compared to Figure 5C, E, and F. Importantly, these structural features could facilitate identification of RNA binding sites that are invisible from the point of view of the linear amino acid sequence. As a result, more residues in actual RNA contact could be predicted by aaRNA, and the resulting binding patch appeared more native-like, as illustrated in Figure 5B and C.


Quantifying sequence and structural features of protein-RNA interactions.

Li S, Yamashita K, Amada KM, Standley DM - Nucleic Acids Res. (2014)

Comparison of prediction results of aaRNA, BindN+ and SRCPred. The figure shows the Csy4-crRNA complex (PDB entry 4AL5). (A) actual contact pattern of the complex. Red colored residues are in RNA contact under a 3.5 Å cutoff. (B) mapping of aaRNA binary binding propensities onto residues, with high (low) colored red (blue). (C) residues in red are positively predicted by the aaRNA under a stringency of 85% expected specificity. (D–E) show the raw and the threshold-calibrated (85% expected specificity), respectively, for BindN+ colored in the same way. (F) prediction results for SRCPred for any di-nucleotide under a 0.05 expected precision.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4150784&req=5

Figure 5: Comparison of prediction results of aaRNA, BindN+ and SRCPred. The figure shows the Csy4-crRNA complex (PDB entry 4AL5). (A) actual contact pattern of the complex. Red colored residues are in RNA contact under a 3.5 Å cutoff. (B) mapping of aaRNA binary binding propensities onto residues, with high (low) colored red (blue). (C) residues in red are positively predicted by the aaRNA under a stringency of 85% expected specificity. (D–E) show the raw and the threshold-calibrated (85% expected specificity), respectively, for BindN+ colored in the same way. (F) prediction results for SRCPred for any di-nucleotide under a 0.05 expected precision.
Mentions: In addition to the benchmark tests presented above, we provide an illustrative example in Figure 5, the Csy4-crRNA complex. In general, sequence-based predictors were more likely to predict charged, polar or aromatic residues on the surface as positive binding sites regardless of their local structural environment. In contrast, due to spatial features introduced here, aaRNA gave more priority to such residues when localized in characteristic RNA binding sites, as learned from the training set. Hence, the aaRNA method could effectively decrease the number of FP predictions, as compared to Figure 5C, E, and F. Importantly, these structural features could facilitate identification of RNA binding sites that are invisible from the point of view of the linear amino acid sequence. As a result, more residues in actual RNA contact could be predicted by aaRNA, and the resulting binding patch appeared more native-like, as illustrated in Figure 5B and C.

Bottom Line: 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.

View Article: PubMed Central - PubMed

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

Show MeSH