K_net: Lysine Malonylation Sites Identification With Neural Network

Abstract

Lysine Malonylation (Kmal) is a newly discovered protein post-translational modifications (PTMs) type, which plays an important role in many biological processes. Therefore, identifying and understanding Kmal sites is very critical in the studies of biology and diseases. The typical methods are time-wasting and expensive. Nowadays, many researchers have proposed machine learning (ML) methods to deal with PTMs's identification issue. Especially, some deep learning (DL) methods are also utilized in this field. In this work, we proposed K_net, which employed Convolutional Neural Network to identify the potential sites. Meanwhile, we proposed a new verification method Split to Equal Validation (SEV), which can well solve the impact of sample imbalance on prediction results. More Specifically, Acc, Sn, Sp, MCC and AUC values were adopted to evaluate the prediction performance of predictors. In total, CNN_Kmal achieved the better performance than other methods. INDEX TERMS Lysine malonylation, deep learning, convolutional neural network, split to equal validation.

SubRF_Seq: Identification of Sub-Golgi Protein Types with Random Forest with Partial Sequence Information

“…It will cause the prediction category towards the category with many samples. erefore, for the evaluation method, we chose an SE verification method proposed by Sun et al [45]. e advantage of this verification method is that data processing and cross-validation can be implemented at the same time.…”

Mini-review: Recent advances in post-translational modification site prediction based on deep learning

“…In addition to those discussed, deep learning can also be applied for other PTMs’ predictions, including methylation [110] , S-nitrosylation [111] , succinylation [112] , [113] , malonylation [114] , [115] , S-sulphenylation [116] , [117] , crotonylation [118] , [119] , [120] , [121] , 2- hydroxyisobutyrylation [122] , glutarylation [123] , N-palmitoylation [124] carbonylation [125] , and SUMOylation [126] . In particular, crotonylation prediction has demonstrated highly accurate results based on deep-learning methods.…”

Deep Learning in Proteomics

“…Deep learning has been used in the prediction of PTM sites for phosphorylation, [96][97][98][99][100] ubiquitination, [101,102] acetylation, [103][104][105][106] glycosylation, [107] malonylation, [108,109] succinylation, [110,111] glycation, [112] nitration/nitrosylation, [113] crotonylation [114] and other modifications [115][116][117]224] as shown in Table 3. MusiteDeep, the first deep learning-based PTM prediction tool, provides both general phosphosite prediction and kinase-specific phosphosite prediction for five kinase families, each with more than 100 known substrates.…”