Protein complex identification method based on core-accessory structure

Abstract

The invention provides a protein complex identification method based on a core-accessory structure. The protein complex identification method based on the core-attachment structure employs the idea of core-attachment, and comprises the steps: constructing a weighted dynamic PPI network, and then finding seed nodes in the PPI network by calculating related attributes of nodes; forming a core of a protein complex through seed nodes and neighbor nodes, then searching a proper attachment for each core, and finally deleting some protein complexes with relatively large overlapping degree to obtain a finally predicted protein complex. The method not only considers the topological characteristics of the protein interaction network, but also considers the biological attributes of the protein, thereby overcoming the negative influence caused by high data noise. By fusing biological attributes and topological characteristics, the accuracy of identifying the protein complex is improved, meanwhile, the prediction result is more accurate, and the prediction efficiency is improved. And the application range and practicability of the technology in the field of biological information are expanded.

Description

technical field [0001] The invention belongs to the technical field of biological information, and provides a protein complex identification method based on the core-appendix structure, mainly through the protein complex identification technology of the core-appendix structure in the protein interaction network, especially in the protein interaction network A method for identifying protein complexes by fusing network topology information and protein biological properties. Background technique [0002] Protein is an indispensable substance in life activities and almost participates in all cycles of life activities, and protein complexes are complexes formed by two or more functionally related polypeptide chains through disulfide bonds or other protein interactions. There are many types of protein complexes, but the properties and functions of many types are still unknown so far. Therefore, identifying protein complexes has become an important research object of proteomics res...

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Problems solved by technology

[0003] Before the present invention was proposed, the field of identification of protein complexes was initially identified by topological features of the network, for example, molecular complex detection algorithm MCODE, Markov clustering algorithm MCL, edge density map clustering algorithm DPClus, polar CMC is a clustering algorithm for identifying protein complexes, but the disadvantages of these methods for identifying key proteins are: (1) only considering the topological characteristics of the network itself, while ignoring the inherent biological characteristics of proteins

(2) There is noise in the PPI network obtained through biological experiments, which makes the protein interaction data have false positives

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