Author(s)

Xi Su ¹, Mingjun Tang ¹, Zipin Zhao ¹, Ning Zhang ¹

Affiliation(s)

¹ Tianjin Key Laboratory of Brain Science and Neuroengineering, Department of Biomedical Engineering, Medical School of Tianjin University, Tianjin, 300072, China

Corresponding Author

Ning Zhang

ABSTRACT

Identifying protein methylation sites experimentally is a challenging and costly task, leading to increased reliance on machine learning-based computational predictors to enhance efficiency. This study aims to improve these predictors through a comprehensive analysis of 553 properties from the AAindex database. We employed support vector machine (SVM) models and utilized 10-fold cross-validation for model evaluation to identify optimal feature combinations for predicting lysine and arginine methylation. The results indicate that the feature set "RACS820104+FUKS010109" yielded the highest performance for lysine methylation, with a Recall (Re) of 71.11%, Precision (Pre) of 75.68%, Accuracy (Acc) of 74.12%, and a Matthews Correlation Coefficient (MCC) of 0.48. For arginine methylation, the feature set "BAEK050101+CHAM810101" achieved a Recall (Re) of 74.60%, Precision (Pre) of 81.08%, Accuracy (Acc) of 78.60%, and an MCC of 0.57. Furthermore, this study explores hydrophobicity as a potentially valuable property for distinguishing methylation from malonylation. This thorough analysis enhances our understanding of the available physicochemical properties, which could lead to the development of more accurate and reliable prediction models.

KEYWORDS

Protein Methylation; Support Vector Machine (SVM); AAindex Database; Feature Selection

CITE THIS PAPER

Xi Su, Mingjun Tang, Zipin Zhao, Ning Zhang, SVM-Based Prediction of Protein Methylation Sites: A Comprehensive Analysis of 553 Properties from the AAindex Database. MEDS Basic Medicine (2024) Vol. 2: 126-135. DOI: http://dx.doi.org/10.23977/medbm.2024.020218.

REFERENCES

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