Predicting protein structural stability using intermediate outputs of a protein structure prediction neural network

A system comprising:

• one or more computers;
• and
• one or more storage devices communicatively coupled to the one or more computers, wherein the one or more storage devices store instructions that, when executed by the one or more computers, cause the one or more computers to perform operations comprising:
• processing an input comprising a representation of an amino acid sequence of a protein using a structure prediction neural network, in accordance with current values of structure prediction neural network parameters, to generate an intermediate output of the structure prediction neural network that defines an embedding of the amino acid sequence of the protein,
• wherein the structure prediction neural network is configured through training by a machine learning training technique to process a representation of an input amino acid sequence to generate an output characterizing a predicted structure of a protein specified by the input amino acid sequence,
• wherein the intermediate output of the structure prediction neural network comprises an output generated by one or more hidden layers of the structure prediction neural network;
• processing the embedding of the amino acid sequence of the protein that is defined by the intermediate output of the structure prediction neural network using a stability prediction neural network, in accordance with current values of stability prediction neural network parameters, to generate a predicted structural stability score characterizing the predicted structural stability of the protein specified by the amino acid sequence;
• and
• training the stability prediction neural network to reduce an error in the predicted structural stability score, comprising:
• determining gradients of the error with respect to the current values of the stability prediction neural network parameters and the structure prediction neural network parameters, comprising backpropagating gradients of the error through the stability prediction neural network and into the structure prediction neural network;
• and
• updating the current values of the stability prediction neural network parameters and the structure prediction neural network parameters using the gradients.

One or more non-transitory computer storage media storing instructions that when executed by one or more computers cause the one or more computers to perform operations comprising:

• processing an input comprising a representation of an amino acid sequence of a protein using a structure prediction neural network, in accordance with current values of structure prediction neural network parameters, to generate an intermediate output of the structure prediction neural network that defines an embedding of the amino acid sequence of the protein,
• wherein the structure prediction neural network is configured through training by a machine learning training technique to process a representation of an input amino acid sequence to generate an output characterizing a predicted structure of a protein specified by the input amino acid sequence,
• wherein the intermediate output of the structure prediction neural network comprises an output generated by one or more hidden layers of the structure prediction neural network;
• processing the embedding of the amino acid sequence of the protein that is defined by the intermediate output of the structure prediction neural network using a stability prediction neural network, in accordance with current values of stability prediction neural network parameters, to generate a predicted structural stability score characterizing the predicted structural stability of the protein specified by the amino acid sequence;
• and
• training the stability prediction neural network to reduce an error in the predicted structural stability score, comprising:
• determining gradients of the error with respect to the current values of the stability prediction neural network parameters and the structure prediction neural network parameters, comprising backpropagating gradients of the error through the stability prediction neural network and into the structure prediction neural network;
• and
• updating the current values of the stability prediction neural network parameters and the structure prediction neural network parameters using the gradients.