Fig. 4: Case study on two peptides in HBV vaccine research.
From: Geometric deep learning improves generalizability of MHC-bound peptide predictions
A HBV case study experiment. Sequences isolated from HBV have been used to synthesize SLPs. The SLPs were then added to DCs cultures from 6 healthy donors. Samples were then analyzed by liquid chromatography tandem mass spectrometry (LC-MS/MS), leading to the identification of peptides presented on the surface of DCs. The peptides were then fed into NetMHCpan4.1b, MHCflurry2.0 BA and our StrB GNN and EGNN. Only our StrB methods successfully identified both peptides as binders. Figure created in the Mind the Graph platform www.mindthegraph.com. B Outputs and cutoffs from predictors tested. The GNN and EGNN here are trained on the whole ~100 K binding affinity data. “SB” stands for Strong Binder, for which the generally used IC50 cutoff is 500 nM and “WB” stands for Weak Binder, for which the generally used IC50 cutoff is 5000 nM. C, D Structural models for HLYSHPIILG (green) and HLYSHPIIL (cyan). Expected polar interactions and hydrogen bonds are shown as yellow dashed lines. Structural models are generated by PANDORA37. Visualizations are done in PYMOL77.
