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CitationsVisuals
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| Rank | MPNN Score | RMSD | Mean pLDDT | Max PAE | pTM |
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Our RFdiffusion implementation predicts protein structures and runs them through ProteinMPNN to generate 200 sequences. The best 5 ProteinMPNN sequences (sorted by MPNN Scores), are then passed through AlphaFold2.
Important Metrics
Higher values are correlated with greater accuracy.
Lower ProteinMPNN scores are correlated with greater accuracy.
2D animation of the protein diffusion process over each timestep.
Predicted Aligned Error (PAE)
ProteinMPNN Predicted Sequences
RFdiffusion only predicts the backbone of the protein. Inverse folding models like ProteinMPNN are required to predict sequences that can best fold into the RFdiffusion predicted backbone.
NOTE: Lower ProteinMPNN scores are better.
AI models produce responses and outputs through sophisticated algorithms and learning techniques, which may result in inaccuracies. By engaging with this model, you accept responsibility for any potential harm resulting from its responses or outputs.
Config
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| Configuration Setting | Set Value |
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Files
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Input Files
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Output Files
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Citations
Reference these works when publishing findings derived from this job.
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Watson, Joseph et al. “Broadly applicable and accurate protein design by integrating structure prediction networks and diffusion generative models” bioRxiv.Org, 10 December 2022, https://www.biorxiv.org/content/10.1101/2022.12.09.519842v1. |
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If you specified Cyclic Chains please cite the following as well: Rettie, S.A., Juergens, D., Adebomi, V. et al. Accurate de novo design of high-affinity protein-binding macrocycles using deep learning. Nat Chem Biol (2025). https://doi.org/10.1038/s41589-025-01929-w |
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Neurosnap Inc. (2022). Neurosnap: An online platform for computational biology and chemistry. Available at: https://neurosnap.ai/ |