MGF6mARice: prediction of DNA N6-methyladenine sites in rice by exploiting molecular graph feature and residual block

Abstract

DNA N6-methyladenine (6mA) is produced by the N6 position of the adenine being methylated, which occurs at the molecular level, and is involved in numerous vital biological processes in the rice genome. Given the shortcomings of biological experiments, researchers have developed many computational methods to predict 6mA sites and achieved good performance. However, the existing methods do not consider the occurrence mechanism of 6mA to extract features from the molecular structure. In this paper, a novel deep learning method is proposed by devising DNA molecular graph feature and residual block structure for 6mA sites prediction in rice, named MGF6mARice. Firstly, the DNA sequence is changed into a simplified molecular input line entry system (SMILES) format, which reflects chemical molecular structure. Secondly, for the molecular structure data, we construct the DNA molecular graph feature based on the principle of graph convolutional network. Then, the residual block is designed to extract higher level, distinguishable features from molecular graph features. Finally, the prediction module is used to obtain the result of whether it is a 6mA site. By means of 10-fold cross-validation, MGF6mARice outperforms the state-of-the-art approaches. Multiple experiments have shown that the molecular graph feature and residual block can promote the performance of MGF6mARice in 6mA prediction. To the best of our knowledge, it is the first time to derive a feature of DNA sequence by considering the chemical molecular structure. We hope that MGF6mARice will be helpful for researchers to analyze 6mA sites in rice.

StructuralDPPIV: a novel deep learning model based on atom structure for predicting dipeptidyl peptidase-IV inhibitory peptides

“…interaction and binding affinity prediction of drug–target ( Karimi et al 2019 , Yang et al 2021 , Zeng et al 2021 ), peptide toxicity prediction ( Wei et al 2021 ). Recent studies show that SMILES was used to encode DNA for the prediction of rice 6 mA sites by deep learning methods and achieved good performance ( Liu et al 2022 ).…”

StructuralDPPIV: A novel deep learning model based on atom-structure for predicting dipeptidyl peptidase-IV inhibitory peptides

“…It had been widely used in many fields of bioinformatics, e.g., interaction and binding affinity prediction of drug-target (Karimi, et al, 2019;Yang, et al, 2021;Zeng, et al, 2021), peptide toxicity prediction (Wei, et al, 2021). Recent studies show that SMILES was used to encode DNA for the prediction of rice 6mA sites by deep learning methods and achieved good performance (Liu, et al, 2022).…”

MuLan-Methyl - Multiple Transformer-based Language Models for Accurate DNA Methylation Prediction

“…There is a large number of papers that address the problem of identifying methylation sites, however, most of them focus on specific form of modification (10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29), and only a few methods address all three types of methylation mentioned above (30)(31)(32)(33)(34), including iDNA-MS, iDNA-ABT, and iDNA-ABF. Note that the database presented in (31) is now widely used as a benchmark dataset for assessing model performance (21,23,(32)(33)(34).…”

“…While different deep-learning based methods all address the same goal, they differ in the details of the features employed and the model structure. Input features include an encoding of the sequence, of course, but may also include biochemical properties (10, 12), or a DNA molecular graph representation (22), say. Utilized model structures include Convolutional Neural Networks (CNN), Graph Convolutional Neural Networks (GCN), Bidirectional Encoder Representation from Transformers (BERT) (35), as well as machine learning algorithms.…”