ArticleInternational journal of molecular sciences2021
DeepDISE: DNA Binding Site Prediction Using a Deep Learning Method.
Article in International journal of molecular sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Predicting protein-nucleic acid interactions via protein language models with biophysical and evolutionary priors.iScience · 2026Article
- Contemporary opportunities and potential of Auger electron-emitting theranostics.Theranostics · 2026Review
- DRBpred: A sequence-based machine learning method to effectively predict DNA- and RNA-binding residues.Computers in biology and medicine · 2024Article
- Protein-DNA binding sites prediction based on pre-trained protein language model and contrastive learning.Briefings in bioinformatics · 2023Article
- Ion-pumping microbial rhodopsin protein classification by machine learning approach.BMC bioinformatics · 2023Article
- Mass Spectrometry for Assessing Protein-Nucleic Acid Interactions.Analytical chemistry · 2023Review
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
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