Fig. 2: Incorporation of structure prediction metrics increases design success rate on new targets.
From: Improving de novo protein binder design with deep learning
a Results of Prospective Campaigns. For each target the SC50 from YSD is shown for all designs which showed binding by YSD (like Kd’s, lower values are better). The number of designs included in each library for each target is indicated by the bars in the top panel. The AF2-predicted structure of the top scoring on-target design is shown as a cartoon. No binders were identified to Site 2 of IL2 receptor-ɑ so this campaign is not included here or in panel C. b The experimental success rate for libraries filtered by DL-based filtering versus Physically based filtering for the four prospective targets. c The computational efficiency (the number of designs with pAE_interaction <10 per CPU-s) for the ProteinMPNN sequence design plus Rosetta relax protocol outperforms that of the original Rosetta sequence design protocol. Source data are provided as a Source Data file.
