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An unsolved challenge in the development of antigen specific immunotherapiesis determining the optimal antigens to target. Comprehension of antigen-MHCbinding is paramount towards achieving this goal. Here, we present CASTELO, acombined machine learning-molecular dynamics (ML-MD) approach to design novelantigens of increased MHC binding affinity for a Type 1 diabetes(T1D)-implicated system. We build upon a small molecule lead optimizationalgorithm by training a convolutional variational autoencoder (CVAE) on MDtrajectories of 48 different systems across 4 antigens and 4 HLA serotypes. Wedevelop several new machine learning metrics including a structure-based anchorresidue classification model as well as cluster comparison scores. ML-MDpredictions agree well with experimental binding results and free energyperturbation-predicted binding affinities. Moreover, ML-MD metrics areindependent of traditional MD stability metrics such as contact area and RMSF,which do not reflect binding affinity data. Our work supports the role ofstructure-based deep learning techniques in antigen specific immunotherapydesign.

Dynamics-based peptide-MHC binding optimization by a convolutional variational autoencoder: a use-case model for CASTELO