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arXiv · 2012.00672

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

David Bell·Giacomo Domeniconi·Chih-Chieh Yang·Ruhong Zhou·Leili Zhang·Guojing Cong
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Abstract

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.

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David Bell, Giacomo Domeniconi, Chih-Chieh Yang, Ruhong Zhou, Leili Zhang, Guojing Cong. 2020-11-29. Dynamics-based peptide-MHC binding optimization by a convolutional variational autoencoder: a use-case model for CASTELO. https://arxiv.org/abs/2012.00672

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