Disulfide bond link mode detection method based on PCA dimensionality reduction technology

A connection mode and detection method technology, applied in character and pattern recognition, biological testing, material inspection products, etc., can solve the problems of resource consumption, information redundancy, consumption of running time, etc., to increase the relative effective data volume, reduce The effect of data redundancy

Inactive Publication Date: 2009-07-08
SHANGHAI JIAO TONG UNIV
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Problems solved by technology

These vectors contain some information redundancy, resulting in resource consumption
At the same time, there is too much useless information in the too high vector, which can not help improve the detection accuracy, but consumes the running time

Method used

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  • Disulfide bond link mode detection method based on PCA dimensionality reduction technology
  • Disulfide bond link mode detection method based on PCA dimensionality reduction technology
  • Disulfide bond link mode detection method based on PCA dimensionality reduction technology

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Embodiment Construction

[0016] The embodiments of the present invention are described in detail below in conjunction with the accompanying drawings: this embodiment is implemented on the premise of the technical solution of the present invention, and detailed implementation methods and specific operating procedures are provided, but the protection scope of the present invention is not limited to the following the described embodiment.

[0017] Such as figure 1 As shown, the method for detecting disulfide bond connection patterns based on PCA dimensionality reduction technology involved in this embodiment has a process as follows: First, extract the protein multiple sequence feature vector and the secondary structure feature vector detected by PSIPRED from the protein database; secondly , using PCA dimensionality reduction technology to reduce the 623-dimensional vector to 300; finally, input the 300-dimensional vector into the SVR classifier to obtain the detection result.

[0018] The 623-dimension...

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Abstract

The invention relates to a detection method of disulfide bond connection mode based on PCA dimension reduction technology in the technical field of biological information. The detection method comprises the following steps: firstly, extracting multiple sequence eigenvector from a protein database and secondary structure eigenvector detected by PSIPRED to obtain a 623-dimensional vector; secondly, adopting dimension reduction technology of a principle component analytical method to reduce the 623-dimensional eigenvector to 300 dimensions; and finally, inputting the 300-dimensional vector into an SVR classifier to carry out detection and obtain detection result. The detection method increases the relatively effective characteristic quantity of protein eigenvector input in the SVR, and can be used to increase the detection precision of disulfide bond connection mode.

Description

technical field [0001] The invention relates to a detection method in the field of biotechnology, in particular to a detection method of a disulfide bond connection pattern based on PCA dimensionality reduction technology. Background technique [0002] Cysteine ​​is unique among the 20 amino acids in that they can pair with each other to form disulfide bonds. Disulfide bonds can exist within the same protein polypeptide bond or between different polypeptide bonds. For many proteins, disulfide bonds are a permanent feature of their final folded products and play an important role in protein structure maintenance and activity retention. Disulfide bonds can be critical steps in protein translation and folding pathways, or simply enhance protein steric stability. Recent findings also suggest that even if the final protein product does not contain disulfide bonds, disulfide bonds can still play an important role in facilitating folding. The existence of disulfide bonds is of g...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01N33/48G06K9/62
Inventor 杨杰裔东亮
Owner SHANGHAI JIAO TONG UNIV
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