Computational Antibody Papers
Filter by tags
antibody design
Filter by published year
2024
TitleKey points
- They introduce a diffusion model for antibodies and successfully test the designs experimentally.
- They separate backbone first and sequence-structure co-design. When one performs backbone first, and then puts the sequence that could fit it, one might run into the issue of 1) no available sequence fitting the backbone well 2) missing the sequence-backbone dependencies that could be otherwise learned in end-to-end fashion.
- They employ fixed length representation (2x149 residues) using the Aho scheme - that’s quite important and can be done because antibodies have a fairly conserved frame of reference.
- To impose physical constraints they define idealized backbone reference they project onto as well as coarse-grained side chain representation that follows a similar principle.
- Since they use fixed-length representation they can use priors on positional frequencies.
- They train the network to reproduce the frequency of amino acids, from the paired sequences in OAS.
- For structure generation, they compare against IgFold, noting that they receive very similar performance.
- They train the network to reproduce the distribution of trastuzumab binding sequences (dataset from Mason et al. 2021). They train the generator model on the binders and a classifier on binders/nonbinders. They demonstrate that the AbDiffiser generator designs have higher probability than other methods (MEAN, RefineGNN) to produce binders, according to their classifier.
- They selected 16 designs for experimental validation. In vitro validation resulted in 37% of constructs binding her2 successfully, with one slightly improved over trastuzumab. The improved one was 4 substitutions away from trastuzumab, so not an obvious distance away.