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

Abstract

Techniques are provided for predicting MHC-peptide binding affinity. A plurality of training peptide sequences is obtained, and a neural network model is trained to predict MHC-peptide binding affinity using the training peptide sequences. An encoder of the neural network model comprising an RNN is configured 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. A fully connected layer following the encoder is configured to process the fixed-dimension encoding output to generate an MHC-peptide binding affinity prediction output. A computing device is configured to use the trained neural network to predict MHC-peptide binding affinity for a test peptide sequence.

We claim: 
     
         1 . A computing system-implemented method of predicting major histocompatibility complex (MHC)-peptide binding affinity, the method 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.   
     
     
         2 . The method of  claim 1 , wherein applying the final hidden state at an intermediate state output of the RNN to generate an attention weighted output comprises taking a dot product, a weighted product, or other function, of the final hidden state and the intermediate state output. 
     
     
         3 . The method of  claim 1 , further comprising applying weights learned through the training of the neural network to the final hidden state prior to applying the final hidden state at intermediate state outputs of the RNN to generate attention weighted outputs. 
     
     
         4 . The method of  claim 1 , further comprising concatenating the final hidden state with a final hidden state of an encoder of a second neural network model prior to applying the final hidden state at intermediate state outputs of the RNN to generate attention weighted outputs. 
     
     
         5 . The method of  claim 4 , wherein the second neural network model is configured to predict MHC-peptide binding affinity for an MHC allele input. 
     
     
         5 . The method of  claim 1 , wherein each one of the attention weighted outputs corresponds to an amino acid position of the input training peptide sequence. 
     
     
         6 . The method of  claim 1 , wherein each one of the attention weighted outputs is a single value. 
     
     
         7 . The method of  claim 1 , wherein the RNN comprises one of a Long Short Term Memory (LSTM) RNN and Gated Recurrent Unit (GRU) RNN or variant thereof. 
     
     
         8 . The method of  claim 1 , wherein the RNN comprises a bidirectional RNN. 
     
     
         9 . The method of  claim 8 , wherein the fixed-dimension encoding output is determined by concatenating outputs of the bidirectional RNN. 
     
     
         10 . The method of  claim 1 , wherein the training peptide sequences comprise a plurality of sequence lengths. 
     
     
         11 . The method of  claim 1 , wherein the training peptide sequences are one of one-hot, BLOSUM, PAM, or learned embedding encoded. 
     
     
         12 . The method of  claim 1 , wherein each training peptide sequence is between 6-20 amino acids in length. 
     
     
         13 . The method of  claim 1 , wherein each training peptide sequence is between 10-30 amino acids in length. 
     
     
         14 . The method of  claim 1 , wherein each training peptide sequence is a positive MHC-peptide binding example. 
     
     
         15 . The method of  claim 1 , wherein the test peptide sequence is between 6-20 amino acids in length. 
     
     
         16 . The method of  claim 1 , wherein the test peptide sequence is between 10-30 amino acids in length. 
     
     
         17 . The method of  claim 1 , wherein the test peptide sequence has a sequence length different from a sequence length of at least one of the training peptide sequences. 
     
     
         18 . The method of  claim 1 , wherein the test peptide sequence is one of one-hot, BLOSUM, PAM, or learned embedding encoded. 
     
     
         19 . The method of  claim 1 , wherein the MHC-peptide binding affinity prediction is a single prediction value. 
     
     
         20 . The method of  claim 1 , wherein the MHC-peptide binding affinity prediction for the test peptide sequence relates to increased likelihood of activating a T-cell response to a tumor. 
     
     
         21 . The method of  claim 1 , wherein configuring the neural network model further comprises configuring at least one fully connected layer of the neural network model following the encoder to process the fixed-dimension encoding output to generate an MHC-peptide binding affinity prediction output. 
     
     
         22 . The method of  claim 21 , wherein the at least one fully connected layer comprises two fully connected layers. 
     
     
         23 . The method of  claim 21 , wherein the at least one fully connected layer comprises one of a deep convolutional neural network, a residual neural network, a densely connected convolutional neural network, a fully convolutional neural network, or an RNN. 
     
     
         24 . The method of  claim 21 , wherein predicting MHC-peptide binding affinity for the test peptide sequence comprises:
 processing the test training peptide sequence using the encoder of the trained neural network model 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, and   processing the fixed-dimension encoding output using the at least one fully connected layer of the trained neural network model to generate an MHC-peptide binding affinity prediction output.   
     
     
         25 . 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.   
     
     
         26 . 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. 
   
     
     
         26 . 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.