DeepDISE: DNA Binding Site Prediction Using a Deep Learning Method
| العنوان: | DeepDISE: DNA Binding Site Prediction Using a Deep Learning Method |
|---|---|
| المؤلفون: | Zeezoo Ryu, Zhong-Ru Xie, Kuan Y. Chang, Samuel Godfrey Hendrix |
| المصدر: | International Journal of Molecular Sciences Volume 22 Issue 11 International Journal of Molecular Sciences, Vol 22, Iss 5510, p 5510 (2021) |
| بيانات النشر: | MDPI, 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | 0301 basic medicine, Proteome, QH301-705.5, Computer science, drug design, Protein Conformation, Systems biology, convolutional neural network, Computational biology, Catalysis, Article, Inorganic Chemistry, 03 medical and health sciences, Protein structure, Animals, Humans, Protein–DNA interaction, binding site prediction, Biology (General), Physical and Theoretical Chemistry, Binding site, QD1-999, Molecular Biology, Spectroscopy, Binding Sites, 030102 biochemistry & molecular biology, Drug discovery, business.industry, Deep learning, Organic Chemistry, deep learning, Computational Biology, systems biology, General Medicine, DNA, Computer Science Applications, DNA binding site, DNA-Binding Proteins, Chemistry, 030104 developmental biology, protein–DNA interaction, Artificial intelligence, business, Software |
| الوصف: | It is essential for future research to develop a new, reliable prediction method of DNA binding sites because DNA binding sites on DNA-binding proteins provide critical clues about protein function and drug discovery. However, the current prediction methods of DNA binding sites have relatively poor accuracy. Using 3D coordinates and the atom-type of surface protein atom as the input, we trained and tested a deep learning model to predict how likely a voxel on the protein surface is to be a DNA-binding site. Based on three different evaluation datasets, the results show that our model not only outperforms several previous methods on two commonly used datasets, but also demonstrates its robust performance to be consistent among the three datasets. The visualized prediction outcomes show that the binding sites are also mostly located in correct regions. We successfully built a deep learning model to predict the DNA binding sites on target proteins. It demonstrates that 3D protein structures plus atom-type information on protein surfaces can be used to predict the potential binding sites on a protein. This approach should be further extended to develop the binding sites of other important biological molecules. |
| وصف الملف: | application/pdf |
| اللغة: | English |
| تدمد: | 1422-0067 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5a98dae51e480ad60dedd6c34d0fb478 http://europepmc.org/articles/PMC8197219 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....5a98dae51e480ad60dedd6c34d0fb478 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 14220067 |
|---|