Published December 15, 2023 | Version 0.0.1
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Structure-conditioned masked language models for protein sequence design generalize beyond the native sequence space

Creators

  • 1. ROR icon University of California, San Francisco
  • 2. ROR icon University of California, Berkeley

Description

Frame2seq models as described in: "Structure-conditioned masked language models for protein sequence design generalize beyond the native sequence space"

Files

Files (93.0 MB)

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md5:ec095f7ba4d92c942edece1a70744e53
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Additional details

Identifiers

DOI
10.1101/2023.12.15.571823

Related works

Describes
Preprint: 10.1101/2023.12.15.571823 (DOI)

Dates

Created
2023-12-15

References

  • Structure-conditioned masked language models for protein sequence design generalize beyond the native sequence space Deniz Akpinaroglu, Kosuke Seki, Amy Guo, Eleanor Zhu, Mark J. S. Kelly, Tanja Kortemme bioRxiv 2023.12.15.571823; doi: https://doi.org/10.1101/2023.12.15.571823