ArticlePLoS computational biology2024
Combining machine learning with structure-based protein design to predict and engineer post-translational modifications of proteins.
Article in PLoS computational biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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Who cites it
22 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
- Accurate prediction of asparagine deamidation in biologics using advanced machine learning models.Briefings in bioinformatics · 2026Article
- CLASPP: A unified model for predicting post-translational modifications.bioRxiv : the preprint server for biology · 2026Article
- UniPTMs: a unified multi-type PTM site prediction model via master-slave architecture-based multi-stage fusion strategy and hierarchical contrastive loss.BMC bioinformatics · 2026Article
- Protein Post-Translational Modifications in the Regulation of Ferroptosis: New Opportunities and Challenges for Cancer Immunotherapy.International journal of biological sciences · 2026Review
- Protein Architecture and Composition in Mycobacterium tuberculosis.Advances in experimental medicine and biology · 2026Review
- Precision Profiling of the Cardiovascular Post-Translationally Modified Proteome.Journal of cardiovascular development and disease · 2026Review
- The open-source Masala software suite: Facilitating rapid methods development for synthetic heteropolymer design.bioRxiv : the preprint server for biology · 2025Article
- Mechanism-Driven Features Enable Asn Deamidation Reactivity Prediction via Machine Learning Methods.Journal of chemical information and modeling · 2025Article
- Computational design of an mRNA vaccine targeting antifungal-resistant Lomentospora prolificans.Scientific reports · 2025Article
- MTPrompt-PTM: A Multi-Task Method for Post-Translational Modification Prediction Using Prompt Tuning on a Structure-Aware Protein Language Model.Biomolecules · 2025Article
- In silico design of foldable lasso peptides.Biophysical journal · 2025Article
- Self-supervised machine learning methods for protein design improve sampling but not the identification of high-fitness variants.Science advances · 2025Article
- Glycan masking in immunogen design: computational and experimental methods.Frontiers in immunology · 2025Review
- Fungi-Kcr: a language model for predicting lysine crotonylation in pathogenic fungal proteins.Frontiers in cellular and infection microbiology · 2025Article
- Article
- Large Language Model (LLM)-Based Advances in Prediction of Post-translational Modification Sites in Proteins.Methods in molecular biology (Clifton, N.J.) · 2025Review
- Artificial Intelligence Transforming Post-Translational Modification Research.Bioengineering (Basel, Switzerland) · 2024Review
- Integrative Multi-PTM Proteomics Reveals Dynamic Global, Redox, Phosphorylation, and Acetylation Regulation in Cytokine-Treated Pancreatic Beta Cells.Molecular & cellular proteomics : MCP · 2024Article
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Authors and funding
8 authors.
Funding
Abstract
Post-translational modifications (PTMs) of proteins play a vital role in their function and stability. These modifications influence protein folding, signaling, protein-protein interactions, enzyme activity, binding affinity, aggregation, degradation, and much more. To date, over 400 types of PTMs have been described, representing chemical diversity well beyond the genetically encoded amino acids. Such modifications pose a challenge to the successful design of proteins, but also represent a major opportunity to diversify the protein engineering toolbox. To this end, we first trained artificial neural networks (ANNs) to predict eighteen of the most abundant PTMs, including protein glycosylation, phosphorylation, methylation, and deamidation. In a second step, these models were implemented inside the computational protein modeling suite Rosetta, which allows flexible combination with existing protocols to model the modified sites and understand their impact on protein stability as well as function. Lastly, we developed a new design protocol that either maximizes or minimizes the predicted probability of a particular site being modified. We find that this combination of ANN prediction and structure-based design can enable the modification of existing, as well as the introduction of novel, PTMs. The potential applications of our work include, but are not limited to, glycan masking of epitopes, strengthening protein-protein interactions through phosphorylation, as well as protecting proteins from deamidation liabilities. These applications are especially important for the design of new protein therapeutics where PTMs can drastically change the therapeutic properties of a protein. Our work adds novel tools to Rosetta's protein engineering toolbox that allow for the rational design of PTMs.
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