Robust deep learning–based protein sequence design using ProteinMPNN

🤖 Plain-English Summary

Although deep learning has revolutionized protein structure prediction, almost all experimentally characterized de novo protein designs have been generated using physically based approaches such as Rosetta. The amino acid sequence at different positions can be coupled between single or multiple chains, enabling application to a wide range of current protein design challenges.

🔑 Key Findings

• Here, we describe a deep learning-based protein sequence design method, ProteinMPNN, that has outstanding performance in both in silico and experimental tests.
• On native protein backbones, ProteinMPNN has a sequence recovery of 52.4% compared with 32.9% for Rosetta.
• The amino acid sequence at different positions can be coupled between single or multiple chains, enabling application to a wide range of current protein design challenges.

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