Wenzheng Bao*, De- shuang Huang and Yuehui Chen
Recently, experiments demonstrated the lysine malonylation modification is a significant process in several organisms and cells. Meanwhile, malonylation plays an important role in the regulation of protein subcellular localization, stability, translocation to lipid rafts and many other protein functions. Therefore, identification malonylation will be contributed to understanding the molecular mechanism in the field of biology. Nevertheless, several existing experimental approaches, which can hardly meet the need of the high speed data generation, are expensive and time-consuming. Moreover, some machine learning methods can hardly meet the high-accuracy need in this issue. In this study, we proposed a method, which is named MSIT, utilized the amino acid residues and profile information to identify the lysine malonylation sites with the tree structural neural network in the peptides sequence level. The MSIT achieves F1 value of 0.8699 in E.coli. Meanwhile, the value of TPRs can reach 89.34%. MSIT outperformed existing malonylation site identification methods and features on different species datasets. Based on these measures it can be demonstrated that MSIT will be helpful in identifying candidate malonylation sites.
Post Translational Modification, Modification Sites Identification, Flexible Neural Tree
School of Information and Electrical Engineering, Xuzhou University of Technology, Xuzhou 221018, Institute of Machine Learning and Systems Biology, School of Electronics and Information Engineering, Tongji University, Shanghai201804, School of Information, University of Jinan, Jinan 250022