Damietta protein design toolkit


If you used one of the Damietta tools, please cite:

Grin et al., The Damietta Server: a comprehensive protein design toolkit, 2024, Nucleic Acids Research (doi:10.1093/nar/gkae297).

Maksymenko et al., The design of functional proteins using tensorized energy calculations, 2023, Cell Reports Methods (doi:10.1016/j.crmeth.2023.100560).

If you used OpenMM, please cite:

Eastman et al., OpenMM 7: Rapid development of high performance algorithms for molecular dynamics, 2017, PloS Computational Biology (doi:10.1371/journal.pcbi.1005659).

If you relied on the ProteinMPNN-provided suggestions, please cite:

Dauparas et al., Robust deep learning–based protein sequence design using ProteinMPNN, 2022, Science (doi:10.1126/science.add2187).

The structure viewer is based on the WebGL protein viewer pv.
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