ArticleBioinformatics (Oxford, England)2019
Capsule network for protein post-translational modification site prediction.
Article in Bioinformatics (Oxford, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 66 papers.
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Who cites it
66 citing papers in PubMed.
- Artificial intelligence in plant salt stress research: from predictive models to multi-omics integration.Journal of experimental botany · 2026Review
- DeepPTMPred: a multi-modal deep learning framework for accurate prediction of protein post-translational modification sites.Briefings in bioinformatics · 2026Article
- KSDiffusion: conditional diffusion for kinase-specific phosphorylation site prediction under data-limited and imbalanced regimes.Briefings in bioinformatics · 2026Article
- DrugPTM-Bench: A Large-Scale Dataset for Predictive Modeling of Drug-Induced Cell Type-Specific Protein Post-Translational Modifications.bioRxiv : the preprint server for biology · 2026Article
- Protein Post-Translational Modifications in the Regulation of Ferroptosis: New Opportunities and Challenges for Cancer Immunotherapy.International journal of biological sciences · 2026Review
- A bioinformatics pipeline for identifying clinically relevant immune and adhesion biomarkers in thyroid risk stratification.Frontiers in molecular biosciences · 2026Article
- BeitAI-pHLA: Multiallele Peptide-HLA Class I Binding Prediction Using Protein Language Model and Multi-Instance Learning.Computational and structural biotechnology journal · 2026Article
- DCPPS: Prediction of Kinase-Specific Phosphorylation Sites Using Dynamic Embedding and Cross-Representation Interaction.Interdisciplinary sciences, computational life sciences · 2025Article
- E2 Tyrosine 102 Regulates MmuPV1 Pathogenesis In Vivo.Pathogens (Basel, Switzerland) · 2025Article
- Article
- Ser/Thr phosphorylation ofmBio · 2025Article
- MTPrompt-PTM: A Multi-Task Method for Post-Translational Modification Prediction Using Prompt Tuning on a Structure-Aware Protein Language Model.Biomolecules · 2025Article
- DCHS1 Modulates Forebrain Proportions in Modern Humans via a Glycosylation Change.bioRxiv : the preprint server for biology · 2025Article
- Review
- MDDeep-Ace: species-specific acetylation site prediction based on multi-domain adaptation.PeerJ · 2025Article
- Sitetack: a deep learning model that improves PTM prediction by using known PTMs.Bioinformatics (Oxford, England) · 2024Article
- GBMPhos: A Gating Mechanism and Bi-GRU-Based Method for Identifying Phosphorylation Sites of SARS-CoV-2 Infection.Biology · 2024Article
- Comprehensive Review and Assessment of Computational Methods for Prediction of N6-Methyladenosine Sites.Biology · 2024Review
- Post-translational modification prediction via prompt-based fine-tuning of a GPT-2 model.Nature communications · 2024Article
- CapsEnhancer: An Effective Computational Framework for Identifying Enhancers Based on Chaos Game Representation and Capsule Network.Journal of chemical information and modeling · 2024Article
6 more citing papers are in PubMed but not listed here.
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3 authors.
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Abstract
motivationComputational methods for protein post-translational modification (PTM) site prediction provide a useful approach for studying protein functions. The prediction accuracy of the existing methods has significant room for improvement. A recent deep-learning architecture, Capsule Network (CapsNet), which can characterize the internal hierarchical representation of input data, presents a great opportunity to solve this problem, especially using small training data.
resultsWe proposed a CapsNet for predicting protein PTM sites, including phosphorylation, N-linked glycosylation, N6-acetyllysine, methyl-arginine, S-palmitoyl-cysteine, pyrrolidone-carboxylic-acid and SUMOylation sites. The CapsNet outperformed the baseline convolutional neural network architecture MusiteDeep and other well-known tools in most cases and provided promising results for practical use, especially in learning from small training data. The capsule length also gives an accurate estimate for the confidence of the PTM prediction. We further demonstrated that the internal capsule features could be trained as a motif detector of phosphorylation sites when no kinase-specific phosphorylation labels were provided. In addition, CapsNet generates robust representations that have strong discriminant power in distinguishing kinase substrates from different kinase families. Our study sheds some light on the recognition mechanism of PTMs and applications of CapsNet on other bioinformatic problems. AVAILABILITY AND IMPLEMENTATION: The codes are free to download from https://github.com/duolinwang/CapsNet_PTM. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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