ArticleJournal of chemical information and modeling2024
ProSTAGE: Predicting Effects of Mutations on Protein Stability by Using Protein Embeddings and Graph Convolutional Networks.
Article in Journal of chemical information and modeling, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
21 citing papers in PubMed.
- The limits of bio-molecular modeling with large language models: a cross-scale evaluation.Bioinformatics (Oxford, England) · 2026Article
- Applications and limitations of AI tools in enzyme design.Protein science : a publication of the Protein Society · 2026Review
- Bridging Algorithms and Biocatalysis: Perspectives on AI-Supported Enzyme Engineering.Molecules (Basel, Switzerland) · 2026Review
- Proteoform medicine: characterizing and targeting protein forms in human disease.Nature reviews. Genetics · 2026Review
- Protein Language Models: Applications and Perspectives.Journal of proteome research · 2026Review
- JanusDDG: a physics-informed neural network for sequence-based protein stability via two-fronts attention.Communications biology · 2026Article
- Generalizable and scalable protein stability prediction with rewired protein generative models.Nature communications · 2025Article
- Accurate predictions of protein mutational effects accelerated with a hybrid-topology free energy protocol.Communications chemistry · 2025Article
- Graph neural network integrated with pretrained protein language model for predicting human-virus protein-protein interactions.Briefings in bioinformatics · 2025Article
- Beyond digital twins: the role of foundation models in enhancing the interpretability of multiomics modalities in precision medicine.FEBS open bio · 2025Review
- Predicting protein stability changes upon mutations with dual-view ensemble learning from single sequence.Briefings in bioinformatics · 2025Article
- ProCeSa: Contrast-Enhanced Structure-Aware Network for Thermostability Prediction with Protein Language Models.Journal of chemical information and modeling · 2025Article
- Shared-weight graph framework for comprehensive protein stability prediction across diverse mutation types.Briefings in bioinformatics · 2025Article
- Decoding the effects of mutation on protein interactions using machine learning.Biophysics reviews · 2025Review
- ProG-SOL: Predicting Protein Solubility Using Protein Embeddings and Dual-Graph Convolutional Networks.ACS omega · 2025Article
- SSEmb: A joint embedding of protein sequence and structure enables robust variant effect predictions.Nature communications · 2024Article
- Variant Impact Predictor database (VIPdb), version 2: trends from three decades of genetic variant impact predictors.Human genomics · 2024Article
- Deciphering GB1's Single Mutational Landscape: Insights from MuMi Analysis.The journal of physical chemistry. B · 2024Article
- EnzyACT: A Novel Deep Learning Method to Predict the Impacts of Single and Multiple Mutations on Enzyme Activity.Journal of chemical information and modeling · 2024Article
- Variant Impact Predictor database (VIPdb), version 2: Trends from 25 years of genetic variant impact predictors.bioRxiv : the preprint server for biology · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Protein thermodynamic stability is essential to clarify the relationships among structure, function, and interaction. Therefore, developing a faster and more accurate method to predict the impact of the mutations on protein stability is helpful for protein design and understanding the phenotypic variation. Recent studies have shown that protein embedding will be particularly powerful at modeling sequence information with context dependence, such as subcellular localization, variant effect, and secondary structure prediction. Herein, we introduce a novel method, ProSTAGE, which is a deep learning method that fuses structure and sequence embedding to predict protein stability changes upon single point mutations. Our model combines graph-based techniques and language models to predict stability changes. Moreover, ProSTAGE is trained on a larger data set, which is almost twice as large as the most used S2648 data set. It consistently outperforms all existing state-of-the-art methods on mutation-affected problems as benchmarked on several independent data sets. The protein embedding as the prediction input achieves better results than the previous results, which shows the potential of protein language models in predicting the effect of mutations on proteins. ProSTAGE is implemented as a user-friendly web server.
Indexed as
Identifiers
What Socratic holds
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.