CarSite-II: an integrated classification algorithm for identifying carbonylated sites based on K-means similarity-based undersampling and synthetic minority oversampling techniques

• Published in: BMC bioinformatics Vol. 22; no. 1; pp. 216 - 17
• Main Authors: Zuo, Yun, Lin, Jianyuan, Zeng, Xiangxiang, Zou, Quan, Liu, Xiangrong
• Format: Journal Article
• Language: English
• Published: London BioMed Central 26.04.2021; BioMed Central Ltd; Springer Nature B.V; BMC
• Subjects: Aging; Algorithms; Alzheimer's disease; Amino acids; Amyotrophic lateral sclerosis; Analysis; Bioinformatics; Biomedical and Life Sciences; Carbonyl compounds; Carbonylation; Carbonyls; Classifiers; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Correlation coefficient; Correlation coefficients; Datasets; Diabetes; Diabetes mellitus; Etiology; Huntingtons disease; Identification and classification; K-means similarity-based undersampling; Life Sciences; Methodology; Methodology Article; Microarrays; Multiple sclerosis; Neurodegenerative diseases; Oversampling; Oxidative stress; Post-translation; Post-translational modification; Protein Carbonylation; Protein post-translational modification; Protein Processing, Post-Translational; Proteins; Proteins - metabolism; Reactive oxygen species; Resampling; Residues; Rotation forest; Sequence analysis; Similarity; Support Vector Machine; Support vector machines; The integrated classifier; Translation; China; Rotation forest; Carbonylation; Protein post-translational modification; The integrated classifier; K-means similarity-based undersampling
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-021-04134-3

Background Carbonylation is a non-enzymatic irreversible protein post-translational modification, and refers to the side chain of amino acid residues being attacked by reactive oxygen species and finally converted into carbonyl products. Studies have shown that protein carbonylation caused by reactive oxygen species is involved in the etiology and pathophysiological processes of aging, neurodegenerative diseases, inflammation, diabetes, amyotrophic lateral sclerosis, Huntington’s disease, and tumor. Current experimental approaches used to predict carbonylation sites are expensive, time-consuming, and limited in protein processing abilities. Computational prediction of the carbonylation residue location in protein post-translational modifications enhances the functional characterization of proteins. Results In this study, an integrated classifier algorithm, CarSite-II, was developed to identify K, P, R, and T carbonylated sites. The resampling method K-means similarity-based undersampling and the synthetic minority oversampling technique (SMOTE-KSU) were incorporated to balance the proportions of K, P, R, and T carbonylated training samples. Next, the integrated classifier system Rotation Forest uses “support vector machine” subclassifications to divide three types of feature spaces into several subsets. CarSite-II gained Matthew’s correlation coefficient (MCC) values of 0.2287/0.3125/0.2787/0.2814, False Positive rate values of 0.2628/0.1084/0.1383/0.1313, False Negative rate values of 0.2252/0.0205/0.0976/0.0608 for K/P/R/T carbonylation sites by tenfold cross-validation, respectively. On our independent test dataset, CarSite-II yield MCC values of 0.6358/0.2910/0.4629/0.3685, False Positive rate values of 0.0165/0.0203/0.0188/0.0094, False Negative rate values of 0.1026/0.1875/0.2037/0.3333 for K/P/R/T carbonylation sites. The results show that CarSite-II achieves remarkably better performance than all currently available prediction tools. Conclusion The related results revealed that CarSite-II achieved better performance than the currently available five programs, and revealed the usefulness of the SMOTE-KSU resampling approach and integration algorithm. For the convenience of experimental scientists, the web tool of CarSite-II is available in http://47.100.136.41:8081/