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Method for predicting miRNA [micro-RNA (ribonucleic acid)] target proteins of miRNA regulation protein interaction networks

A target protein and protein technology, applied in the field of bioinformatics and molecular biology, can solve the problems of lack of dynamic regulation relationship, expensive experiment interaction, high false positive, etc.

Inactive Publication Date: 2017-03-22
SYSU CMU SHUNDE INT JOINT RES INST +2
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Experimental methods can give the regulatory relationship between miRNA and target mRNA, but the experiment is expensive and can only obtain the interaction between single molecule and single molecule
The calculation method is fast and cheap, and can provide a large number of possible interactions, but the false positive is high and lacks the dynamic regulation relationship between miRNA and target mRNA in specific cells or tissues

Method used

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  • Method for predicting miRNA [micro-RNA (ribonucleic acid)] target proteins of miRNA regulation protein interaction networks
  • Method for predicting miRNA [micro-RNA (ribonucleic acid)] target proteins of miRNA regulation protein interaction networks
  • Method for predicting miRNA [micro-RNA (ribonucleic acid)] target proteins of miRNA regulation protein interaction networks

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0063] Example 1 miRNA target protein prediction method for miRNA-regulated protein interaction network

[0064] A miRNA target protein prediction method for miRNA regulation protein interaction network, comprising the steps of:

[0065] 1. Construct the following three sub-networks respectively:

[0066] (1) Construction of HIPPIE-based human protein-protein interaction network (PPIN)

[0067] Download the entire human protein-protein interaction data from the HIPPIE database, remove self-interactions, repeated interactions, and interactions with an interaction score of 0; obtain protein sequence information (sequence information) from the UniprotKB / Swiss-Prot database according to the protein access number That is, primary structure data), calculate amino acid composition (20 dimensions), dipeptide composition (400 dimensions), autocorrelation descriptors and composition (1221 dimensions), transformation (21 dimensions) and distribution (105 dimensions) in total 1767 dimens...

Embodiment 2

[0097] Example 2 Taking the miRNA network related to cardiovascular and cerebrovascular diseases as an example to verify the miRNA target protein prediction method of the present invention

[0098] 1. Collect data sets and build a node- and edge-weighted miRNA-protein interaction network

[0099] Human protein-protein interaction data were collected from the HIPPIE database, removing self-interactions, repeated interactions, and interactions with an interaction score of 0. According to the protein acquisition number, the protein primary structure data was obtained from the UniprotKB / Swiss-Prot database, and the amino acid composition, dipeptide composition, autocorrelation descriptors and protein primary structure descriptors such as composition, transformation and distribution were calculated. The node weight in the protein network is the primary structure feature of 1767-dimensional protein, and the edge weight is the interaction trust score.

[0100] The data on the intera...

Embodiment 3

[0126] Embodiment 3 compares with other methods

[0127] Currently, four target prediction methods commonly used in the prior art are PITA, miRanda, rna22, and targetspy. These methods are only based on sequence information for prediction, such as matching analysis, secondary structure prediction, and genetic conservation analysis. Data such as gene expression and information on mutual regulation between genes (such as action pathways, protein networks) have not been used reasonably.

[0128] For this purpose, use Mark Menor (Mark Menor, Travers Ching, Xun Zhu, et al. mirMark: asite-level and UTR-level classifier for miRNA target prediction [J]. GenomeBiology, 2014, 15:500) et al. In the data set, 253 positive samples and 362 negative samples are taken as an independent test set.

[0129] The method of the present invention, PITA, miRanda, rna22 and targetspy are analyzed to this data set respectively, and the result is shown in Table 3, and ROC curve and PRC curve are as fol...

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Abstract

The invention discloses a method for predicting miRNA [micro-RNA (ribonucleic acid)] target proteins of miRNA regulation protein interaction networks. The method includes steps of building three sub-networks including a human protein-protein interaction network on the basis of HIPPIE, a miRNA-target protein network on the basis of mirTARbase and a miRNA-miRNA network on the basis of target protein overlapping structures; combining the three sub-networks with one another according to acquisition numbers of proteins and ID (identification) numbers of miRNA molecules in miRbase databases and building fusion miRNA-target protein association relationship networks; representing miRNA-target protein association features on the basis of guilt-by-association principles, building classification prediction models by the aid of random forest and predicting potential interaction association relationships between miRNA and the target proteins. The method has the advantages that research on many-to-many relationships of the miRNA regulation target proteins can be effectively carried out, and accordingly the method has high application value.

Description

technical field [0001] The invention belongs to the technical fields of bioinformatics and molecular biology. More specifically, it relates to a miRNA target protein prediction method for miRNA-regulated protein interaction network. Background technique [0002] microRNA (miRNA) is a non-coding single-stranded small molecule RNA with a length of only 20-24nt, which is highly conserved, time-sequential and tissue-specific. There is a phosphate group at the 5' end of the mature miRNA and a hydroxyl group at the 3' end, which is formed from a single-stranded RNA precursor of about 70-90 nt with a hairpin structure after being processed by Dicer enzyme. Mature miRNA forms RNA-induced gene silencing complex (RNA-induced silencing complex, RISC) to act on target mRNA, and regulate gene expression by cutting target mRNA or inhibiting its translation process. [0003] The gain or loss of miRNA function is closely related to the occurrence and development of various diseases. Prot...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F19/12G06F19/26G16B45/00
CPCG16B5/00G16B45/00
Inventor 邹小勇钟文倩李占潮戴宗
Owner SYSU CMU SHUNDE INT JOINT RES INST
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