دورية أكاديمية
ProSTAGE: Predicting Effects of Mutations on Protein Stability by Using Protein Embeddings and Graph Convolutional Networks
| العنوان: | ProSTAGE: Predicting Effects of Mutations on Protein Stability by Using Protein Embeddings and Graph Convolutional Networks |
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| المؤلفون: | Gen Li, Sijie Yao, Long Fan |
| سنة النشر: | 2024 |
| مصطلحات موضوعية: | Biophysics, Biochemistry, Genetics, Molecular Biology, Cancer, Infectious Diseases, Plant Biology, Virology, Biological Sciences not elsewhere classified, Mathematical Sciences not elsewhere classified, Information Systems not elsewhere classified, model combines graph, larger data set, friendly web server, secondary structure prediction, relationships among structure, modeling sequence information, using protein embeddings, deep learning method, predict stability changes, protein language models, language models, protein stability, fuses structure, sequence embedding, protein embedding, protein design, novel method, accurate method, subcellular localization |
| الوصف: | 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. |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | unknown |
| العلاقة: | https://figshare.com/articles/journal_contribution/ProSTAGE_Predicting_Effects_of_Mutations_on_Protein_Stability_by_Using_Protein_Embeddings_and_Graph_Convolutional_Networks/24932964Test |
| DOI: | 10.1021/acs.jcim.3c01697.s001 |
| الإتاحة: | https://doi.org/10.1021/acs.jcim.3c01697.s001Test https://figshare.com/articles/journal_contribution/ProSTAGE_Predicting_Effects_of_Mutations_on_Protein_Stability_by_Using_Protein_Embeddings_and_Graph_Convolutional_Networks/24932964Test |
| حقوق: | CC BY-NC 4.0 |
| رقم الانضمام: | edsbas.E8CA3E77 |
| قاعدة البيانات: | BASE |
| DOI: | 10.1021/acs.jcim.3c01697.s001 |
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