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Prediction of RNA-binding modules on proteins based on amino acid-nucleotide pairing preference information

A prediction method and amino acid technology, applied in the field of protein-RNA interaction and recognition, can solve the problems of limited, difficult protein acquisition, neglect of amino acid residue interactions and synergistic effects, etc., achieving high efficiency, low workload, and success. high rate effect

Active Publication Date: 2021-12-24
BEIJING UNIV OF TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Compared with sequence-based methods, methods for structure-based prediction of protein-RNA binding sites are very limited, mainly DRNA-3D [10] and RBRDetector [11] , this is mainly due to the fact that the structure of the protein is more difficult to obtain than the sequence
[0005] Most of the current binding site prediction methods are at the residue level, and only consider the properties of the amino acid residues on the surface of the protein, ignoring the interaction and synergistic effects between amino acid residues in the surface adjacent small regions (including internal residues)

Method used

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  • Prediction of RNA-binding modules on proteins based on amino acid-nucleotide pairing preference information
  • Prediction of RNA-binding modules on proteins based on amino acid-nucleotide pairing preference information
  • Prediction of RNA-binding modules on proteins based on amino acid-nucleotide pairing preference information

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0054] The following takes a protein (Catalytic domain of E.coli RNase E) as an example, which interacts with 13-merRNA to form a complex 2COB (PDB ID), to introduce the implementation process of the PPQA method to predict the binding RNA module on the protein. Knowing the structure of the receptor protein in this complex, its RNA-binding interface module was obtained by carrying out this method.

[0055] (1) to (4) are completed under the Linux system.

[0056] (1) Protein structure pretreatment

[0057] First rename the protein receptor to 2c0b_r_b.pdb. When using this method package for the first time, you need to create a storage directory for the result file. Under the working directory of this program package, use the shell to create the directory as follows:

[0058] ...]$ mkdir structures

[0059] ...]$ mkdir data

[0060] ...]$ mkdir data / ReceptorModule

[0061] ...]$ mkdir data / Rsa

[0062] ...]$ mkdir data / Vor

[0063] After creating the above directory, move...

Embodiment 2

[0106] The system comes from the non-redundant non-ribosomal protein-RNA complex structure (Proteins,2012,80(1):14-24), remove the case where the receptor and the ligand are non-single-stranded, and the final research system is 69 complexes Object system (as shown in Table 6). The specific calculation process for each system is the same as in Example 1, and only the results will be described here.

[0107] Table 6 69 non-redundant non-ribosomes, and receptor ligands are single-stranded protein-RNA complexes

[0108]

[0109]

[0110] We compared the results obtained using the method of the present invention (named after the parameter PPQA) with the results obtained by randomly picking interface modules and the PAMA method for protein-protein binding module prediction (see Table 7).

[0111] Table 7 The highest ranking results of the receptor protein interface module in 69 protein-RNA complexes obtained by different methods

[0112]

[0113]

[0114]From the highe...

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Abstract

The invention relates to a method for predicting protein-RNA binding modules based on amino acid-nucleotide pairing preference information, belonging to the technical field of protein-RNA interaction and recognition. In the first step, centering on each amino acid residue in the three-dimensional structure of the protein, divide all residues in contact with it into a module; then remove all internal modules that do not contain any surface residues, and retain at least one The surface module of surface residues; the second step is to define three parameters for the surface module: the interface preference of the module, the internal contact area Q of the module and the accessible surface area A of the module solvent, calculate the product value PPQA of the three, and divide the module according to PPQA Values ​​were sorted from high to low, and the top two modules were identified as possible RNA-binding modules on the protein. This method has a good effect in identifying the binding module on the non-ribosomal RNA binding single-chain protein, and has a high success rate.

Description

technical field [0001] The invention belongs to the technical field of protein-RNA interaction and recognition, and relates to a method for predicting RNA binding modules on non-ribosomal RNA binding single-chain proteins with known structures. Background technique [0002] The interaction between protein and RNA is involved in many important physiological processes of living cells, such as regulation of gene expression, protein synthesis and virus replication. The importance of RNA molecules has been gradually recognized by people. RNA is both an information molecule and a functional molecule. During the occurrence of its many behaviors, such as the transcription, splicing, nuclear export, positioning, translation and degradation of mRNA, RNA must cooperate with A series of proteins bind and are regulated by them. More importantly, the occurrence of some major diseases, such as tumors, cardiovascular diseases, autoimmune diseases, Fragile X Syndrome (Fragile X Syndrome) an...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G16B30/00G16B40/00
Inventor 李春华马梦琳巩卫康刘洋
Owner BEIJING UNIV OF TECH