Key protein recognition method based on largest neighbor sub-network

Abstract

The invention discloses a key protein recognition method based on a largest neighbor sub-network. The method comprises the steps of firstly performing sub-network division on an obtained protein interaction network according to sub-cell positioning information, and dividing proteins located in the same sub-cell position in the same sub-network, thereby obtaining a plurality of protein sub-networkswith an interaction relation in the same sub-cell interval; and then, performing topological feature analysis on the largest sub-network corresponding to each protein and where each protein is located, and calculating a critical comprehensive score value based on common neighbors, wherein the higher score value indicates that the protein tends more to be critical, thereby predicting potential critical proteins by means of score sorting. On the basis of simplicity and practicability, the accuracy of critical protein recognition can be well improved, and important reference values and practicalvalues are provided for researchers to perform experimental analysis and deeper research of the practical proteins.

Description

technical field [0001] The invention belongs to the technical field of systems biology, and mainly relates to a method for dividing a protein interaction network into subnets according to subcellular location information and identifying key proteins based on network topology characteristics. Background technique [0002] Protein is an indispensable component of cell and tissue structure, and is a high-molecular organic substance that maintains important life activities, carrying life activities and the execution of physiological functions. There are many types of proteins in organisms, which participate in various life activities such as nutrient transport, physiological function regulation, and biochemical reactions. There are obvious differences in the importance of different kinds of proteins to organisms. Among them, the loss of certain proteins will cause the loss of biological functions of the organism, resulting in pathological changes or cessation of growth and deve...

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Problems solved by technology

[0005] Due to the limitations of existing experimental techniques and other issues, the currently obtained interaction data are not rich and perfect, and there are many false positives (false interactions that do not actually exist) and false negative data (interactions that actually exist but do not exist). have not been detected), so that the existing protein network-based centrality methods cannot efficiently predict key proteins

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