Summary

Inverse folding of CDRs benefits from masking contiguous stretches of residues during training (1). This is in contrast to inverse folding of framework residues, which like generic proteins benefit from random masking (AKA “shotgun masking”; (2)).

Figures

Exp/PredLayer DecayOAS Gaussian NoiseTest MaskingFR Avg.CDR1HCDR2HCDR3HCDR1LCDR2LCDR3L
Exp--None0.8980.7310.7120.5690.7230.7360.718
Exp-None0.8980.7350.6980.5660.7160.7020.713
Exp-None0.8950.7410.7000.5840.7160.7410.725
ExpNone0.8940.7270.7020.5730.7200.7280.727
Exp--CDRs0.8940.6800.6370.4320.6770.6890.661
Exp-CDRs0.8940.6960.6510.4340.6920.6800.659
Exp-CDRs0.8900.6750.6570.4310.6660.6890.658
ExpCDRs0.8910.6810.6530.4300.6660.6980.655
Pred--None0.9090.7530.7160.5610.7380.7310.722
Pred-None0.9050.7490.7040.5580.7290.7250.722
Pred-None0.9070.7500.7300.5720.7460.7370.730
PredNone0.9030.7440.7130.5540.7440.7330.718
Pred--CDRs0.9040.7060.6500.4450.6910.6870.665
Pred-CDRs0.9010.7090.6570.4350.7010.6900.658
Pred-CDRs0.9030.6950.6540.4350.6750.6750.654
PredCDRs0.8980.6990.6470.4330.6820.6820.658
Table from (1)
1.
Hoie M, Hummer A, Olsen T, Nielsen M, Deane C. AntiFold: Improved antibody structure design using inverse folding. In: GenBio@NeurIPS2023. 2023. Available from: https://openreview.net/forum?id=bxZMKHtlL6
2.
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