Fast Prediction of Protein Methylation Sites Using a Sequence-Based Feature Selection Technique

Abstract

Protein methylation, an important post-translational modification, plays crucial roles in many cellular processes. The accurate prediction of protein methylation sites is fundamentally important for revealing the molecular mechanisms undergoing methylation. In recent years, computational prediction based on machine learning algorithms has emerged as a powerful and robust approach for identifying methylation sites, and much progress has been made in predictive performance improvement. However, the predictive performance of existing methods is not satisfactory in terms of overall accuracy. Motivated by this, we propose a novel random-forest-based predictor called MePred-RF, integrating several discriminative sequence-based feature descriptors and improving feature representation capability using a powerful feature selection technique. Importantly, unlike other methods based on multiple, complex information inputs, our proposed MePred-RF is based on sequence information alone. Comparative studies on benchmark datasets via vigorous jackknife tests indicate that our proposed MePred-RF method remarkably outperforms other state-of-the-art predictors, leading by a 4.5% average in terms of overall accuracy. A user-friendly webserver that implements the proposed method has been established for researchers' convenience, and is now freely available for public use through http://server.malab.cn/MePred---‑RF. We anticipate our research tool to be useful for the largescale prediction and analysis of protein methylation sites.

Gene2vec: gene subsequence embedding for prediction of mammalian N6-methyladenosine sites from mRNA

“…In the above context, computational tools were developed for detecting different modification sites, including protein methylation (Wei et al 2018d), protein phosphorylation ) and dephosphorylation (Jia et al 2017), protein O-GlcNAcylation , histone crotonylation (Qiu et al 2017), DNA N 4 -methylcytosine (Chen et al 2017c;Wei et al 2018b), RNA pseudouridine (Chen et al 2016b), and various RNA adenosine modifications (Chen et al 2018). However, it has been proven that sequence alignment (e.g., PSI-BLAST) cannot accurately identify the modification sites.…”

An integrated method for the identification of novel genes related to oral cancer

“…Thus, these two methods were trained on the validated OC-related gene set S oc , through which the optimal parameters can be determined. We used the jackknife test [46, 47], which is one of the classic cross-validation methods [48, 49], to evaluate the performance of these two methods, i . e ., each OC-related gene in S oc was singled out sequentially, and the remaining genes in S oc were used to generate predictions under various combinations of parameters.…”

Multi-Site Protein Subcellular Localization Based on Deep Convolutional Neural Network

“…An important characteristic of the deep convolutional neural network (DCNN) is that complex nonlinear transformations can be used to select different categories of features without specifying features [20][21][22][23]. This characteristic differs from the shallow machine learning algorithm which requires specific requirements for the extracted features.…”