ESM-IF1
Meta AI (FAIR)
Structure-conditioned inverse folding model: given a protein backbone, predicts sequences likely to fold into it. General-purpose (not antibody-specific).
Best For
General protein scaffold redesign; starting point for antibody engineering
License
Open Source (MIT)
Strengths
- +MIT license
- +General-purpose
- +Can guide protein complex engineering
Limitations
- −Less accurate than antibody-specific tools on CDRs
- −Single-chain training
R&D Pipeline Coverage
Related Tools
AntiFold
OPIG (Oxford)
Antibody-specific inverse folding model fine-tuned from ESM-IF1. Designs sequences predicted to maintain structural fold given an antibody backbone.
ProteinMPNN
Baker Lab / IPD (University of Washington)
Inverse folding model: generates amino acid sequences predicted to fold into a target 3D backbone structure. Standard component of all modern protein design pipelines.
More in Antibody Design
DiffAb
Luo et al. (NeurIPS 2022)
Diffusion-based generative model that jointly designs antibody CDR sequences and 3D structures conditioned on antigen structure.
RFantibody
Baker Lab / IPD (University of Washington)
RFdiffusion fine-tuned for de novo antibody design. Generates VHHs, scFvs, and full antibodies targeting user-specified epitopes. Experimentally validated with cryo-EM.
AntiFold
OPIG (Oxford)
Antibody-specific inverse folding model fine-tuned from ESM-IF1. Designs sequences predicted to maintain structural fold given an antibody backbone.
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