USC researchers create an AI model that forecasts the precision of protein-DNA binding, recently published in Nature Methods, that accurately predicts how various proteins may bind to DNA. This technological breakthrough, called Deep Predictor of Binding Specificity (DeepPBS), has the potential to significantly reduce the time needed for developing new drugs and medical treatments.
| Date | August 9, 2024 |
| Source | University of Southern California |
| Summary | A new artificial intelligence model can predict how different proteins may bind to DNA. |
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How researchers create an AI model that forecasts the precision of protein-DNA binding:
- DeepPBS is a geometric deep learning model designed to predict the binding specificity of protein-DNA interactions based on the structures of protein-DNA complexes. By inputting the structure of a protein-DNA complex into an online computational tool, researchers can determine how a protein might bind to any DNA sequence or region of the genome, bypassing the need for high-throughput sequencing or structural biology experiments.
- “Structures of protein-DNA complexes usually involve proteins bound to a single DNA sequence,” explained Remo Rohs, professor and founding chair of the Department of Quantitative and Computational Biology at USC Dornsife College of Letters, Arts and Sciences. “DeepPBS provides a much-needed AI tool to reveal protein-DNA binding specificity.”
- DeepPBS uses a geometric deep learning approach, analyzing data through geometric structures to predict binding specificity. The AI tool generates spatial graphs that depict protein structure and the relationship between protein and DNA representations, offering predictions for binding specificity across different protein families, something many current methods can’t do.
- “Having a universal method for all proteins, not just those from well-studied families, is crucial for researchers. This approach also opens the door to designing new proteins,” said Rohs.
- The field of protein-structure prediction has seen rapid advancements with tools like DeepMind’s AlphaFold, which predicts protein structure from sequences. DeepPBS complements these methods by predicting specificity for proteins lacking experimental structures.
- Rohs highlighted that DeepPBS has numerous potential applications. It could accelerate the design of new drugs and treatments targeting specific mutations in cancer cells and contribute to breakthroughs in synthetic biology and RNA research.
FAQ on Researchers create an AI model that forecasts the precision of protein-DNA binding:
1. What is protein-DNA binding?
Protein-DNA binding refers to the interaction between a protein and a specific DNA sequence. This binding is crucial for various biological processes, such as gene regulation, DNA replication, and repair. The specific binding of proteins to DNA sequences helps control when and how genes are expressed in a cell.
2. Why is protein-DNA binding important?
Protein-DNA binding is essential for maintaining the proper functioning of cells. It regulates gene expression, allowing cells to respond to environmental changes, develop properly, and maintain homeostasis. Disruptions in protein-DNA binding can lead to diseases, including cancer and genetic disorders.
3. How do proteins recognize specific DNA sequences to bind with it?
Proteins recognize specific DNA sequences through a combination of chemical and structural interactions. The shape of the DNA helix and the specific sequence of bases allow proteins to bind selectively. Proteins typically have domains that fit into the grooves of the DNA helix, interacting with the bases to ensure precise binding.