ArticleNature communications2024
Post-translational modification prediction via prompt-based fine-tuning of a GPT-2 model.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
33 citing papers in PubMed.
- EmmaEmb: A quantitative framework for analyzing embedding spaces in molecular biology.Patterns (New York, N.Y.) · 2026Article
- Beyond the Capsid: How Can Post-Translational Modifications Modulate the Multifunctionality of the Orthoflavivirus Capsid Protein?Molecules (Basel, Switzerland) · 2026Review
- CLASPP: A unified model for predicting post-translational modifications.PLoS computational biology · 2026Article
- Artificial intelligence in plant salt stress research: from predictive models to multi-omics integration.Journal of experimental botany · 2026Review
- CLASPP: A unified model for predicting post-translational modifications.bioRxiv : the preprint server for biology · 2026Article
- RNF213 Is an Interferon-Stimulated Gene That Targets Influenza A Virus NP and Activates MDA5 to Restrict Infection.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Deep learning-driven decoding of ubiquitination: from regulatory mechanisms to targeted protein degradation.Biology direct · 2026Review
- Learning the PTM code through a coarse-to-fine mechanism-aware framework.Nature communications · 2026Article
- ANYI: The ANnotated Yeast Interactome.bioRxiv : the preprint server for biology · 2026Article
- VUStruct: A compute pipeline for high throughput and personalized structural biology.PLoS computational biology · 2026Article
- Post-translational modifications of silk proteins.RSC chemical biology · 2026Review
- Large language models for bioinformatics.Quantitative biology (Beijing, China) · 2026Review
- Protein Language Models: Applications and Perspectives.Journal of proteome research · 2026Review
- Keeping it centered: decoding the activities that regulate yeast histone H3 variant Cse4 and confine it to centromeres.Frontiers in cell and developmental biology · 2026Review
- Venom gland transcriptomics and bioactivity profiling suggest bifunctional hyaluronidase activity in the venom ofFrontiers in molecular biosciences · 2026Article
- Precision Profiling of the Cardiovascular Post-Translationally Modified Proteome.Journal of cardiovascular development and disease · 2026Review
- Drug-tolerant persister cells in cancer: bridging the gaps between bench and bedside.Nature communications · 2025Review
- Review
- Charting the Proteins of Oropouche Virus.Viruses · 2025Review
- AI4Protein: transforming the future of protein design.Science China. Life sciences · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Post-translational modifications (PTMs) are pivotal in modulating protein functions and influencing cellular processes like signaling, localization, and degradation. The complexity of these biological interactions necessitates efficient predictive methodologies. In this work, we introduce PTMGPT2, an interpretable protein language model that utilizes prompt-based fine-tuning to improve its accuracy in precisely predicting PTMs. Drawing inspiration from recent advancements in GPT-based architectures, PTMGPT2 adopts unsupervised learning to identify PTMs. It utilizes a custom prompt to guide the model through the subtle linguistic patterns encoded in amino acid sequences, generating tokens indicative of PTM sites. To provide interpretability, we visualize attention profiles from the model's final decoder layer to elucidate sequence motifs essential for molecular recognition and analyze the effects of mutations at or near PTM sites to offer deeper insights into protein functionality. Comparative assessments reveal that PTMGPT2 outperforms existing methods across 19 PTM types, underscoring its potential in identifying disease associations and drug targets.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.