Method for efficiently predicting Gram-negative bacteria type III and type IV effector proteins

A Gram-negative bacteria and effector protein technology, applied in proteomics, neural learning methods, biological neural network models, etc., can solve the problems of high cost, time-consuming, low prediction accuracy of effector proteins, etc., and reduce parameters Space, improve generalization ability, improve prediction efficiency and accuracy

Pending Publication Date: 2021-10-22
ZHEJIANG SCI-TECH UNIV
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Problems solved by technology

The premise of in-depth research on T3SEs and T4SEs is to be able to predict them quickly and accurately, although many methods have been used to predict T3SEs and T4SEs, (it is recommended to list the main prediction methods in the prior art and the literature recorded, and list the shortcomings Relative to) but some of these

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  • Method for efficiently predicting Gram-negative bacteria type III and type IV effector proteins
  • Method for efficiently predicting Gram-negative bacteria type III and type IV effector proteins
  • Method for efficiently predicting Gram-negative bacteria type III and type IV effector proteins

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Embodiment Construction

[0044] In order to make the purpose, technical solutions and advantages of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention.

[0045] A method for efficiently predicting Gram-negative bacteria type III and type IV effector proteins, specific steps:

[0046] (1) Feature processing: multiple sequence alignment (MSA) was generated by HHsuite (version 3.0.1), and the database searched by HHsuite was UniRef30 (version 2020_03). Input the T3SEs and T4SEs sequences into the HHsuite program, and then use the HHsuite program to search for homologous sequences in the protein sequence library, and then construct the MSA, and then calculate the one-hot encoding and position-specific score matrix (PSSM) from the MSA , position-specific frequency matrix (PSFM) and precision matrix.

[0047] The model adopts the attenti...

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Abstract

The invention discloses a method for efficiently predicting Gram-negative bacteria type III and type IV effector proteins. The method is a deep neural network framework (CHR) method, and the method comprises the following steps: (1) constructing an integrated deep neural network framework; (2) selecting a data set; (3) using the two-dimensional features and the three-dimensional features as input features of the network; (4) learning a prediction module on the data set by using the built integrated deep neural network framework; (5) setting model parameters; (6) inputting a to-be-detected protein sequence into the model to obtain a protein prediction result. The method not only has a certain reference effect on related research in the future, but also has important significance on knowing related biological functions of gram-negative bacteria.

Description

technical field [0001] The invention relates to a method for predicting effector proteins of Gram-negative bacteria, in particular to a method for efficiently predicting Gram-negative bacteria type III and type IV effector proteins Background technique [0002] With the rapid development of omics technology, microbiology research has also entered a new stage of development. Through microbiology, we can observe the microorganisms and their composition in different natural environments, and clearly understand the role of these microorganisms in many aspects such as human health, environmental restoration, agricultural production, and marine ecology. The macroscopic functions of microorganisms are often the result of the joint action of a complex group of different microorganisms. Therefore, in order to understand the macroscopic functions of microorganisms more clearly, it is necessary to study the physiological activities of microorganisms from the microscopic level. There a...

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Application Information

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IPC IPC(8): G16B20/00G06N3/04G06N3/08
CPCG16B20/00G06N3/08G06N3/045
Inventor 李重周天和李捷
Owner ZHEJIANG SCI-TECH UNIV
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