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Method for predicting self-interaction effect of protein

A prediction method, protein technology, applied in the field of machine learning and bioinformatics

Active Publication Date: 2018-01-19
XINJIANG TECHN INST OF PHYSICS & CHEM CHINESE ACAD OF SCI
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

Solved the problem of predicting whether proteins self-interact

Method used

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  • Method for predicting self-interaction effect of protein
  • Method for predicting self-interaction effect of protein
  • Method for predicting self-interaction effect of protein

Examples

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Embodiment Construction

[0051] Below in conjunction with accompanying drawing, preferred embodiment of the present invention is described further:

[0052] Such as figure 1 As shown, a method for predicting protein self-interactions, including the following steps:

[0053] Step 101: selection and establishment of data sets, using the two gold standard data sets of human and yeast in the UniProt database to construct a data set for predicting protein self-interaction;

[0054] Step 102: Generation of PSSM matrix, the position of each protein sequence can be expressed as a matrix of M × 20, wherein M represents the number of residues in a protein, and the columns of the matrix represent 20 amino acids. By using BLAST Position-specific (PSI-BLAST) converts each protein into a PSSM matrix;

[0055] Step 103: Fourier descriptor extracts eigenvalues. First, multiply the PSSM transposition matrix of each protein transformation with the original PSSM matrix, so that each protein sequence is converted into ...

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Abstract

The invention discloses a method for predicting self-interaction effect of protein. The method comprises the steps of selection and establishment of a data set, generation of a PSSM matrix, extractionof a feature value by a Fourier descriptor, construction of a training set and a testing set and construction of a classifier model. According to the method, the feature value of a sample set is extracted by the Fourier descriptor, the number of times of multiplication required for discrete Fourier transform of a computing data set of a computer is greatly reduced, and the computing quantity is reduced. A model can be constructed by a random projection method, the prediction precision is greatly improved, and a good prediction effect can be achieved. The method is low in computing cost and small in power consumption; the self-interaction effect of the protein can be predicted effectively; and the prediction effect can reach 93% or above.

Description

technical field [0001] The invention relates to the fields of machine learning and bioinformatics, in particular to a method for predicting protein self-interaction. Background technique [0002] The invention relates to the fields of machine learning and bioinformatics, in particular to a method for predicting protein self-interaction. Whether proteins can interact with themselves is a challenging task. In recent years, many studies have shown that homo-oligomerization plays an important role in biological processes, such as regulation of gene expression, signal transduction, enzyme activation, and immune response. In summary, protein self-interaction is a very important factor for the regulation of cell function. In addition, protein self-interactions are beneficial to improve protein stability and prevent protein denaturation by reducing its surface area. So far, most computational methods for predicting protein interactions have certain limitations for protein self-in...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F19/24G06F19/18
Inventor 陈沾衡尤著宏李晓蒋同海王延斌方昱斌陈沾兴
Owner XINJIANG TECHN INST OF PHYSICS & CHEM CHINESE ACAD OF SCI
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