ArticleCell reports methods2023
MIND-S is a deep-learning prediction model for elucidating protein post-translational modifications in human diseases.
Article in Cell reports methods, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Current computational tools for protein lysine acylation site prediction.Briefings in bioinformatics · 2024Pooled it
- CLASPP: A unified model for predicting post-translational modifications.PLoS computational biology · 2026Article
- Threonine Phosphorylation Is a Bioreversible Surrogate of Proline Hydroxylation in a Collagen Triple Helix.Journal of the American Chemical Society · 2026Article
- CLASPP: A unified model for predicting post-translational modifications.bioRxiv : the preprint server for biology · 2026Article
- NphosNet: Predicting Protein N-Phosphorylation Sites via xLSTM and Enhanced PLM Features with a Weighted Three-Channel Cross-Attention Mechanism.Interdisciplinary sciences, computational life sciences · 2026Article
- Learning the PTM code through a coarse-to-fine mechanism-aware framework.Nature communications · 2026Article
- Uncovering Enzyme-Specific Post-Translational Modifications: An Overview of Current Methods.Proteomes · 2025Review
- Substrate prediction for RiPP biosynthetic enzymesDigital discovery · 2025Article
- Immunopeptidomics for autoimmunity: unlocking the chamber of immune secrets.NPJ systems biology and applications · 2025Review
- Large Language Model (LLM)-Based Advances in Prediction of Post-translational Modification Sites in Proteins.Methods in molecular biology (Clifton, N.J.) · 2025Review
- Advances in Prediction of Posttranslational Modification Sites Known to Localize in Protein Supersecondary Structures.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Post-translational modifications of proteins in cardiovascular diseases examined by proteomic approaches.The FEBS journal · 2025Review
- Artificial Intelligence Transforming Post-Translational Modification Research.Bioengineering (Basel, Switzerland) · 2024Review
- Sitetack: a deep learning model that improves PTM prediction by using known PTMs.Bioinformatics (Oxford, England) · 2024Article
- MFPSP: Identification of fungal species-specific phosphorylation site using offspring competition-based genetic algorithm.PLoS computational biology · 2024Article
- Article
- Informatic challenges and advances in illuminating the druggable proteome.Drug discovery today · 2024Review
- Substrate Prediction for RiPP Biosynthetic Enzymes via Masked Language Modeling and Transfer Learning.ArXiv · 2024Article
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Authors and funding
9 authors.
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Abstract
We present a deep-learning-based platform, MIND-S, for protein post-translational modification (PTM) predictions. MIND-S employs a multi-head attention and graph neural network and assembles a 15-fold ensemble model in a multi-label strategy to enable simultaneous prediction of multiple PTMs with high performance and computation efficiency. MIND-S also features an interpretation module, which provides the relevance of each amino acid for making the predictions and is validated with known motifs. The interpretation module also captures PTM patterns without any supervision. Furthermore, MIND-S enables examination of mutation effects on PTMs. We document a workflow, its applications to 26 types of PTMs of two datasets consisting of ∼50,000 proteins, and an example of MIND-S identifying a PTM-interrupting SNP with validation from biological data. We also include use case analyses of targeted proteins. Taken together, we have demonstrated that MIND-S is accurate, interpretable, and efficient to elucidate PTM-relevant biological processes in health and diseases.
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Registered trials
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