Principles for computational design of binding antibodies

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Assessing and enhancing foldability in designed proteins (2021, background): “Indeed, the significance of misfolding has served as the primary motivation for studies of the design principles of protein folds [2,26,27] and for developing general methods to improve designed proteins’ foldability and stability [11,13,27,28].”
Improving de novo protein binder design with deep learning (2022, unknown): “…provide much faster routes to af ﬁ nity reagents having desired bio-physical properties that target speci ﬁ c surface patches, and there has been considerable progress in computational design of protein binding proteins based on extension of binding motifs observed in protein structures 8 – 12 .”
AbPredict 2: A server for accurate and unstrained structure prediction of antibody variable domains (2018, methodology): “Instead of selecting the sequence-identical but conformationally distant HCDR3 backbone, AbPredict selected as template an HCDR3 which exhibited 1 Å rmsd despite having ≤10% sequence identity to 4KMT(Norn et al., 2017).”