ArticleBiomolecules2021
MDCAN-Lys: A Model for Predicting Succinylation Sites Based on Multilane Dense Convolutional Attention Network.
Article in Biomolecules, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Low-input deep learning platform for citrullinated peptide identification, autoantigen discovery and rheumatoid arthritis treatment stratification.Nature biomedical engineering · 2026Article
- Identifying protein succinylation sites using generative transformer and a two-dimensional representation with a deep capsule network.iScience · 2025Article
- RLSuccSite: succinylation sites prediction based on reinforcement learning dynamic with balanced reward mechanism and three-peaks enhanced method for physicochemical property scores.Journal of cheminformatics · 2025Article
- DeepPD: A Deep Learning Method for Predicting Peptide Detectability Based on Multi-feature Representation and Information Bottleneck.Interdisciplinary sciences, computational life sciences · 2025Article
- Impact of Lysine Succinylation on the Biology of Fungi.Current issues in molecular biology · 2024Review
- EMDL_m6Am: identifying N6,2'-O-dimethyladenosine sites based on stacking ensemble deep learning.BMC bioinformatics · 2023Article
- Protein post-translational modification by lysine succinylation: Biochemistry, biological implications, and therapeutic opportunities.Genes & diseases · 2023Review
- EMDL-ac4C: identifying N4-acetylcytidine based on ensemble two-branch residual connection DenseNet and attention.Frontiers in genetics · 2023Article
- PD-BertEDL: An Ensemble Deep Learning Method Using BERT and Multivariate Representation to Predict Peptide Detectability.International journal of molecular sciences · 2022Article
- Improving protein succinylation sites prediction using embeddings from protein language model.Scientific reports · 2022Article
- Deep_KsuccSite: A novel deep learning method for the identification of lysine succinylation sites.Frontiers in genetics · 2022Article
- Mini-review: Recent advances in post-translational modification site prediction based on deep learning.Computational and structural biotechnology journal · 2022Review
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Authors and funding
5 authors.
Funding
Abstract
Lysine succinylation is an important post-translational modification, whose abnormalities are closely related to the occurrence and development of many diseases. Therefore, exploring effective methods to identify succinylation sites is helpful for disease treatment and research of related drugs. However, most existing computational methods for the prediction of succinylation sites are still based on machine learning. With the increasing volume of data and complexity of feature representations, it is necessary to explore effective deep learning methods to recognize succinylation sites. In this paper, we propose a multilane dense convolutional attention network, MDCAN-Lys. MDCAN-Lys extracts sequence information, physicochemical properties of amino acids, and structural properties of proteins using a three-way network, and it constructs feature space. For each sub-network, MDCAN-Lys uses the cascading model of dense convolutional block and convolutional block attention module to capture feature information at different levels and improve the abstraction ability of the network. The experimental results of 10-fold cross-validation and independent testing show that MDCAN-Lys can recognize more succinylation sites, which is consistent with the conclusion of the case study. Thus, it is worthwhile to explore deep learning-based methods for the recognition of succinylation sites.
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