University of Washington researchers developed LigandMPNN, a deep learning-based protein sequence design method targeting enzymes and small molecule interactions. It explicitly models non-protein atoms and molecules, outperforming existing methods like Rosetta and ProteinMPNN in accuracy, speed, and efficiency. This innovative approach fills a critical gap in protein sequence design, promising improved performance and aiding in protein engineering.
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Researchers from the University of Washington Developed a Deep Learning Method for Protein Sequence Design that Explicitly Models the Full Non-Protein Atomic Context
Introduction
A team of researchers from the University of Washington has collaborated to address the challenges in the protein sequence design method by using a deep learning-based protein sequence design method, LigandMPNN. The model targets enzymes and small molecule binder and sensor designs.
Key Innovations
LigandMPNN explicitly considers non-protein atoms and molecules, crucial for the design of enzymes, protein-DNA/RNA interactions, and protein-small molecule and protein-metal binders. It introduces protein-ligand graphs, leveraging neural networks to model interactions and encode ligand atom geometries.
Performance and Efficiency
The experiment demonstrated LigandMPNN’s superior performance compared to existing models, with higher sequence recovery and efficiency. It is approximately 250 times faster than Rosetta.
Conclusion
LigandMPNN fills a critical gap in existing protein sequence design methods by explicitly including non-protein atoms and molecules. It showcases a noticeable improvement in performance, leading to higher sequence recovery and superior side-chain packing accuracy around small molecules, nucleotides, and metals. LigandMPNN performed exceptionally in designing small molecule and DNA-binding proteins with high affinity and specificity, which would greatly aid protein engineering.
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