Prediction method for protein post-translational modification methylation loci

Abstract

The invention discloses a prediction method for protein post-translational modification methylation loci, and belongs to the field of bioinformatics. Protein methylation modification participates in cell functions and many life activities of cell processes, and recognition of protein methylation modification loci has very important significance in understanding of the life activities of cells. The prediction method combines with sequence information, evolutionary information and physical and chemical properties to conduct feature coding on a protein methylation sequence, an information gain optimization feature method is adopted and combines with a support vector machine to construct a prediction model, and it is shown through independent testing results that the prediction method has a good prediction property on the protein methylation loci; meanwhile, a network prediction platform is developed and used for conducting online prediction on the protein methylation loci.

Description

technical field [0001] The invention belongs to the field of bioinformatics, and in particular relates to a method for predicting methylation sites of protein post-translational modifications. Background technique [0002] Protein post-translational modifications (PTMs) play an important role in the regulatory mechanism of cells and affect various properties of proteins, including protein folding, activity and their biological functions. Therefore, in-depth study of PTMs plays an important role in understanding the pathogenesis of human diseases. Protein methylation is one of the most common post-translational modifications of proteins. Under the catalysis of methyltransferase, the methyl group is converted from N - Transfer of adenosylmethionine to the corresponding protein. Protein methylation not only plays an important role in the genetic modification of eukaryotic chromatin, but also plays a very important role in cell differentiation, development, gene expression, ge...

AI Technical Summary

Problems solved by technology

For example, the number of training samples collected when building the model is relatively small, the features used in feature encoding are relatively single, and the model built is too simple without detailed classification, etc.

With the rapid development of modern technology, more and more methylation sites have been identified, and existing models and methods cannot meet the multi-type and high-precision prediction requirements

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