Prediction algorithm for recognizing tyrosine posttranslational modification sites

Abstract

The invention discloses a prediction algorithm for recognizing tyrosine posttranslational modification sites. The algorithm comprises the steps of data collection, data processing, feature coding, feature optimization and model training and evaluation. The invention furthermore discloses application of the prediction algorithm. According to the algorithm, features of the tyrosine posttranslational modification sites are extracted comprehensively from the perspectives of protein sequence information, evolutional information and physical and chemical properties, Elastic Net is used as an optimization means to automatically select variables to screen multidimensional features, redundant information is removed, a prediction model of tyrosine nitration, sulfuration and phosphorylation sites is constructed in combination with an SVM, the prediction capability of the prediction model is improved, and the prediction quality of the tyrosine posttranslational modification sites is remarkably improved. Through a developed prediction software platform TyrPred, predictive analysis of nitration modification sites, sulfuration modification sites and phosphorylation modification sites of tyrosine on intact protein is realized, and a convenient, economical and rapid research tool and important reference are provided for research of tyrosine posttranslational modification.

Description

technical field [0001] The present invention relates to digital computing or data processing equipment or data processing methods especially for specific applications, and in particular to a prediction algorithm for identifying tyrosine post-translational modification sites. Background technique [0002] Tyrosine post-translational modifications include nitrosation, sulfuration and phosphorylation. Tyrosine nitration is mainly due to the interaction between reactive oxygen species and reactive nitrogen species in tissue cells. A large amount of reactive oxygen species and reactive nitrogen species can directly damage proteins, nucleic acids and lipid macromolecules. Studies have shown that a variety of human diseases such as atherosclerosis, Parkinson's disease, chronic renal failure, etc. are related to tyrosine nitration. Tyrosine sulfuration is mainly tyrosyl protein sulfotransferase catalyzing the sulfuration reaction of tyrosine residues in proteins. This enzyme can ca...

AI Technical Summary

Problems solved by technology

[0003] There are a variety of experimental methods to identify the post-translational modification sites of tyrosine, but these experimental techniques have low identification efficiency, time-consuming and high cost

The methods and tools for predicting tyrosine post-translational modification sites using bioinformatics methods, such as: GPS-NO2 platform for predicting tyrosine nitration sites constructed based on sequence information by Li et al.; Huang et al. , physicochemical properties and autocorrelation coefficient extraction features to identify the predictor of tyrosine sulfuration sites; Xue et al. based on GPS-based tools for hierarchically predicting kinase-specific phosphorylation; and Gao et al. based on amino acid sequence similarity The Musite tool established by features such as disorder scoring and amino acid frequency predicts kinase-specific phosphorylation sites; there are still the following defects: it can only predict tyrosine nitration or sulfuration or phosphorylation, but cannot predict all three at the same time Modification; the training samples collected when building the model are relatively small; the features are not optimized and screened, and the prediction performance is not ideal; except for GPS and Musite, the corresponding prediction software has not been developed for other methods

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