title: Accurate in silico identification of protein succinylation sites using an iterative semi-supervised learning technique.
abstract: As a widespread type of protein post-translational modifications (PTMs), succinylation plays an important role in regulating protein conformation, function and physicochemical properties. Compared with the labor-intensive and time-consuming experimental approaches, computational predictions of succinylation sites are much desirable due to their convenient and fast speed. Currently, numerous computational models have been developed to identify PTMs sites through various types of two-class machine learning algorithms. These methods require both positive and negative samples for training. However, designation of the negative samples of PTMs was difficult and if it is not properly done can affect the performance of computational models dramatically. So that in this work, we implemented the first application of positive samples only learning (PSoL) algorithm to succinylation sites prediction problem, which was a special class of semi-supervised machine learning that used positive samples and unlabeled samples to train the model. Meanwhile, we proposed a novel succinylation sites computational predictor called SucPred (succinylation site predictor) by using multiple feature encoding schemes. Promising results were obtained by the SucPred predictor with an accuracy of 88.65% using 5-fold cross validation on the training dataset and an accuracy of 84.40% on the independent testing dataset, which demonstrated that the positive samples only learning algorithm presented here was particularly useful for identification of protein succinylation sites. Besides, the positive samples only learning algorithm can be applied to build predictors for other types of PTMs sites with ease. A web server for predicting succinylation sites was developed and was freely accessible at http://59.73.198.144:8088/SucPred/.
authors.affiliation: School of Computer Science and Information Technology, Northeast Normal University, Changchun, 130117, China. Electronic address: zhaoxw303@nenu.edu.cn.
authors.forename: Xiaowei
authors.initials: X
authors.lastname: Zhao
authors.affiliation: School of Computer Science and Information Technology, Northeast Normal University, Changchun, 130117, China.
authors.forename: Qiao
authors.initials: Q
authors.lastname: Ning
authors.affiliation: School of Computer Science and Information Technology, Northeast Normal University, Changchun, 130117, China.
authors.forename: Haiting
authors.initials: H
authors.lastname: Chai
authors.affiliation: Key Laboratory of Intelligent Information Processing of Jilin Universities, Northeast Normal University, Changchun 130117, China. Electronic address: zhiqiang.ma967@gmail.com.
authors.forename: Zhiqiang
authors.initials: Z
authors.lastname: Ma
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D000596:Amino Acids / Q000737:chemistry; D001185:Artificial Intelligence; D001665:Binding Sites; D019295:Computational Biology; D003198:Computer Simulation; D020407:Internet; D008239:Lysine / Q000737:chemistry; D008969:Molecular Sequence Data; D011499:Protein Processing, Post-Translational; D011506:Proteins / Q000737:chemistry*; D015203:Reproducibility of Results; D013386:Succinates / Q000737:chemistry*; D000069553:Supervised Machine Learning; D060388:Support Vector Machine
chemical_list: D000596:Amino Acids; D011506:Proteins; D013386:Succinates; D008239:Lysine
keywords: Multiple features; Positive samples only learning; Succinylated proteins