Methods and systems for prediction of peptide presentation by major histocompatibility complex molecules

A computer-implemented method for predicting an amino acid-immunoprotein complex (IPC) interaction comprising:

• accessing a set of amino acid sequences, each of the amino acid sequences of the set having been identified from at least one protein;
• accessing an immunoprotein complex (IPC) sequence identified for an IPC of a subject;
• processing, using one or more first processing blocks in a processing subsystem of a machine-learning model, a set of amino acid sequence representations to generate a set of transformed amino acid sequence representations based on a set of element-focused scores representing binding cores of the set of amino acid sequence representations, wherein each of the amino acid sequence representations was generated based on one of the amino acid sequences appended with a beginning-of-sequence (BOS) token;
• processing, using a second processing block in the processing subsystem, the IPC sequence to generate an IPC sequence embedding;
• generating composite representations by aggregating each of the transformed BOS token representations of the set of transformed amino acid sequence representations with the IPC sequence embedding;
• and
• determining one or more predicted amino acid-IPC interactions based on the composite representations.

A computer-implemented method for predicting an amino acid-immunoprotein complex (IPC) interaction comprising:

• accessing a set of amino acid sequences, each of the amino acid sequences of the set having been identified from at least one protein;
• accessing an immunoprotein complex (IPC) sequence identified for an IPC of a subject;
• generating an IPC sequence embedding based on the IPC sequence;
• processing, using one or more processing blocks in a processing subsystem of a machine-learning model, the set of amino acid sequences to generate a set of transformed amino acid sequence representations based on a set of element-focused scores representing binding cores of the set of amino acid sequence representations;
• generating, using a cross-attention machine-learning module in the processing subsystem, a set of composite representations based on the set of transformed amino acid sequence representations and the IPC sequence embedding;
• and
• determining one or more predicted amino acid-IPC interactions based on the composite representations.