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Abstract
Predicting protein–DNA binding specificity is a challenging yet essential task for understanding gene regulation. Protein–DNA complexes usually exhibit binding to a selected DNA target site, whereas a protein binds, with varying degrees of binding specificity, to a wide range of DNA sequences. This information is not directly accessible in a single structure. Here, to access this information, we present Deep Predictor of Binding Specificity (DeepPBS), a geometric deep-learning model designed to predict binding specificity from protein–DNA structure. DeepPBS can be applied to experimental or predicted structures. Interpretable protein heavy atom importance scores for interface residues can be extracted. When aggregated at the protein residue level, these scores are validated through mutagenesis experiments. Applied to designed proteins targeting specific DNA sequences, DeepPBS was demonstrated to predict experimentally measured binding specificity. DeepPBS offers a foundation for machine-aided studies that advance our understanding of molecular interactions and guide experimental designs and synthetic biology.
Academic Publication Geometric deep learning of protein–DNA binding specificity
Research Abstract & Technology Focus
Correlated Market Trend: Adaptive Learning
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Geometric deep learning of protein–DNA binding specificity
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Frequently Asked Questions (FAQ)
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What is the core focus of the research titled 'Geometric deep learning of protein–DNA binding specificity'?
This literature focuses on: Abstract Predicting protein–DNA binding specificity is a challenging yet essential task for understanding gene regulation. Protein–DNA complexes usually exhibit binding to a selected DNA target site, whereas a protein binds, with...
Are there open-source GitHub repositories related to Geometric deep learning of protein–DNA binding specificity?
Yes, open-source projects like THU-MAIC/OpenMAIC (Open Multi-Agent Interactive Classroom — Get an immersive, multi-agent learning experience in just one click) are actively building upon these concepts.
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What other academic literature is closely related to 'Geometric deep learning of protein–DNA binding specificity'?
Yes, highly correlated activity was mapped. An entry titled 'Geometric deep learning of protein–DNA binding specificity' discusses this: Abstract Predicting protein–DNA binding specificity is a challenging yet essential task for understanding gene regulation. Protei...
Are there commercial applications of 'Geometric deep learning of protein–DNA binding specificity' in market news publications?
Yes, highly correlated activity was mapped. An entry titled 'Zero-shot design of drug-binding proteins via neural iterative selection−expansion' discusses this: By pairing two neural networks in an iterative optimization algorithm, small-molecule binding proteins can be designed from scratch with high accu...
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Commercial Realization
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GitHubTHU-MAIC/OpenMAIC
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GitHubWenyuChiou/awesome-agentic-ai-zh
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Product HuntPadel Chess
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Product HuntScholé