Summary
Substitution matrices from Inverse folding closely match the (BLOSUM62) matrix, except proline (1–2). (4) found that predicted proline residue embeddings distinguish from others earlier in the network than other residues.
Figures
Ref (1)
Figure 1B from (2)
Figure 1H from (4)
1.
Hsu C, Verkuil R, Liu J, Lin Z, Hie B, Sercu T, et al. Learning inverse folding from millions of predicted structures. openRxiv; 2022. Available from: https://doi.org/10.1101/2022.04.10.487779
2.
Castorina LV, Petrenas R, Subr K, Wood CW. PDBench: evaluating computational methods for protein-sequence design. Bioinformatics. 2023;39(1). Available from: https://doi.org/10.1093/bioinformatics/btad027
3.
Zhou B, Zheng L, Wu B, Tan Y, Lv O, Yi K, et al. Protein Engineering with Lightweight Graph Denoising Neural Networks. Journal of Chemical Information and Modeling. 2024;64(9):3650–61. Available from: https://doi.org/10.1021/acs.jcim.4c00036
4.
Akpinaroglu D, Seki K, Guo A, Zhu E, Kelly MJS, Kortemme T. Structure-conditioned masked language models for protein sequence design generalize beyond the native sequence space. openRxiv; 2023. Available from: https://doi.org/10.1101/2023.12.15.571823