Methods and systems for improved major histocompatibility complex (mhc)-peptide binding prediction of neoepitopes using a recurrent neural network encoder and attention weighting

A computer program product embedded in a non-transitory computer-readable medium comprising instructions executable by a computer processor for predicting major histocompatibility complex (MHC)-peptide binding affinity, which, when executed by a processor, cause the processor to perform one or more steps comprising:

• obtaining a plurality of training peptide sequences;
• configuring a neural network model to be trained to predict major histocompatibility complex (MHC)-peptide binding affinity using the plurality of training peptide sequences, wherein configuring the neural network model comprises configuring an encoder of the neural network model comprising a recurrent neural network (RNN) to process an input training peptide sequence to generate a fixed-dimension encoding output by applying a final hidden state of the RNN at intermediate state outputs of the RNN to generate attention weighted outputs, and linearly combining the attention weighted outputs;
• training the neural network model using the plurality of training peptide sequences;
• and
• configuring a computing device to use the trained neural network model to predict MHC-peptide binding affinity for a test peptide sequence.

A computing system for predicting major histocompatibility complex (MHC)-peptide binding affinity, comprising:

• a processor;
• a main memory device;
• a persistent storage device;
• a training engine executable on the processor according to software instructions stored in one of the main memory device and the persistent storage device and configured to:
• obtain a plurality of training peptide sequences;
• configure a neural network model to be trained to predict major histocompatibility complex (MHC)-peptide binding affinity using the plurality of training peptide sequences, wherein configuring the neural network model comprises configuring an encoder of the neural network model comprising a recurrent neural network (RNN) to process an input training peptide sequence to generate a fixed-dimension encoding output by applying a final hidden state of the RNN at intermediate state outputs of the RNN to generate attention weighted outputs, and linearly combining the attention weighted outputs;
• train the neural network model using the plurality of training peptide sequences;
• and
• a prediction engine in communication with the training engine and configured to:
• obtain a test peptide sequence;
• input the test peptide sequence into the trained neural network model;
• and
• generate an MHC-peptide binding affinity prediction using the trained neural network model.

A computing device comprising:

• a processor;
• a main memory device;
• a persistent storage device;
• a prediction engine executable on the processor according to software instructions stored in one of the main memory device and the persistent storage device and configured to:
• obtain a test peptide sequence;
• access a trained neural network model, wherein the neural network model is trained using a plurality of training peptide sequences by processing each training peptide sequence using an encoder of the neural network model comprising a recurrent neural network (RNN) to generate a fixed-dimension encoding output by applying a final hidden state of the RNN at intermediate state outputs of the RNN to generate attention weighted outputs, and linearly combining the attention weighted outputs;
• input the test peptide sequence into the trained neural network model;
• and
• generate an MHC-peptide binding affinity prediction using the trained neural network model.