KYG: RNA interface residue prediction from protein 3D structure

Three steps for predicting RNA interface residues
(The simplest way: Leave 1 as default, input PDB in 2, and touch the button in 3. Trouble? Visit FAQ, help and/or sample.)
  1. Select a method, (quality of prediction : i(mediocre) < ii < iii < ... < viii < ix(best)) help
      • PDB file, multiple sequence alignment and ID are required in the next step [ Build an alignment? Click here ]
      • Only PDB file is required in the next step
      • PDB file, multiple sequence alignment and ID are required in the next step [ Build an alignment? Click here ]
      • Only PDB file is required in the next step
      • PDB file, multiple sequence alignment and ID are required in the next step [ Build an alignment? Click here ]
      • Only PDB file is required in the next step
      • PDB file, multiple sequence alignment and ID are required in the next step [ Build an alignment? Click here ]
    1. [The Easiest-to-Use Method]
      • Only PDB file is required in the next step
      • PDB file, multiple sequence alignment and ID are required in the next step [ Build an alignment? Click here ]
  2. Input Data,
    For all methods: PDB file (single chain entry only): help
    For method i, iii, v, vii and ix: Multiple sequence alignment (ClustalW format): help
    For method i, iii, v, vii and ix: ID in the alignment with 3D structure:
    help
  3. And .
  4. If something goes wrong, consult FAQ.

Citation
Refer the following paper, when this prediction is used;
Kim, O.T.P., Yura, K., Go, N. (2006) Amino acid residue doublet propensity in the protein-RNA interface and its application to RNA interface prediction. Nuc. Acids. Res. 34 (22), 6450-6460.

Summary of the Method

Protein-RNA interactions play essential roles in a number of regulatory mechanisms of gene expression such as RNA splicing, transport, translation and post transcriptional control. As the number of available protein-RNA complex three-dimensional (3D) structures has increased, it is now possible to statistically examine protein-RNA interactions based on 3D structures.

We carry out computational analyses of 86 representative protein-RNA complexes retrieved from Protein Data Bank. Interface residue propensity, which gives a measure for the relative importance of different amino acid residues in the RNA interface, is 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 RNA interface of a protein (residue doublet interface propensity). The residue doublet interface propensity is found to have a significant amount of information as compared to the sum of singlet propensity alone of the residues in RNA interface.

Prediction of RNA interface with two types of propensities plus a position-specific multiple sequence profile reaches specificity of about 80%.

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