Predicting affinity using structural and physical modeling

Abstract

Described are methods for predicting affinity of a candidate molecule for a second molecule. The method comprises obtaining a three-dimensional candidate structural representation of the candidate molecule bound to a second molecule; obtaining a plurality of candidate measurements, wherein each candidate measurement is associated with at least one feature of the candidate structural representation; and predicting, with an electronic processor, the affinity of the candidate molecule for the second molecule, wherein the electronic processor is configured to predict the affinity of the candidate molecule for the second molecule based upon the plurality of candidate measurements. The candidate molecule may be a peptide, such as a neoantigen, a viral peptide, a non-mutated self peptide, or a post-translationally modified peptide. The second molecule may be an antigen presenting molecule, such as a class I MHC molecule or a class II MHC molecule.

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