  An unsolved challenge in the development of antigen specific immunotherapies
is determining the optimal antigens to target. Comprehension of antigen-MHC
binding is paramount towards achieving this goal. Here, we present CASTELO, a
combined machine learning-molecular dynamics (ML-MD) approach to design novel
antigens of increased MHC binding affinity for a Type 1 diabetes
(T1D)-implicated system. We build upon a small molecule lead optimization
algorithm by training a convolutional variational autoencoder (CVAE) on MD
trajectories of 48 different systems across 4 antigens and 4 HLA serotypes. We
develop several new machine learning metrics including a structure-based anchor
residue classification model as well as cluster comparison scores. ML-MD
predictions agree well with experimental binding results and free energy
perturbation-predicted binding affinities. Moreover, ML-MD metrics are
independent of traditional MD stability metrics such as contact area and RMSF,
which do not reflect binding affinity data. Our work supports the role of
structure-based deep learning techniques in antigen specific immunotherapy
design.
