Meiqi Wu, Pengchao Lu, Yingxi Yang, Liwen Liu, Hui Wang, Yan Xu and Jixun Chu* Pages 362 - 370 ( 9 )
Background: Lysine lipoylation which is a rare and highly conserved post-translational modification of proteins has been considered as one of the most important processes in the biological field. To obtain a comprehensive understanding of regulatory mechanism of lysine lipoylation, the key is to identify lysine lipoylated sites. The experimental methods are expensive and laborious. Due to the high cost and complexity of experimental methods, it is urgent to develop computational ways to predict lipoylation sites.
Methodology: In this work, a predictor named LipoSVM is developed to accurately predict lipoylation sites. To overcome the problem of an unbalanced sample, synthetic minority over-sampling technique (SMOTE) is utilized to balance negative and positive samples. Furthermore, different ratios of positive and negative samples are chosen as training sets.
Results: By comparing five different encoding schemes and five classification algorithms, LipoSVM is constructed finally by using a training set with positive and negative sample ratio of 1:1, combining with position-specific scoring matrix and support vector machine. The best performance achieves an accuracy of 99.98% and AUC 0.9996 in 10-fold cross-validation. The AUC of independent test set reaches 0.9997, which demonstrates the robustness of LipoSVM. The analysis between lysine lipoylation and non-lipoylation fragments shows significant statistical differences.
Conclusion: A good predictor for lysine lipoylation is built based on position-specific scoring matrix and support vector machine. Meanwhile, an online webserver LipoSVM can be freely downloaded from https://github.com/stars20180811/LipoSVM.
Lysine lipoylation, prediction, amino acids, support vector machine, post-translational modifications, scoring matrix.
Department of Applied Mathematics, University of Science and Technology Beijing, Beijing 100083, Equipment Leasing Company of China Petroleum Pipeline Engineering Co., Ltd. 065000 Langfang City, Hebei Province, Department of Chemical and Biological Engineering, Hong Kong University of Science and Technology, Hong Kong, Department of Applied Mathematics, University of Science and Technology Beijing, Beijing 100083, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100080, Department of Applied Mathematics, University of Science and Technology Beijing, Beijing 100083, Department of Applied Mathematics, University of Science and Technology Beijing, Beijing 100083