# US 12,762,409 B1 · Predicting protein structural stability using intermediate outputs of a protein structure prediction neural network > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,762,409 B1 - Document ID: US12762409B1 - Status: Granted - Kind: B1 - Application number: 17/400,976 - Filed: 2021-08-12 - Granted: 2026-10-13 - Earliest claimed: 2020-09-23 - Assignees: GDM Holding LLC - Applicants: GDM Holding LLC - Inventors: David Reiman; John Jumper; Raphael John Lamarre Townshend; Simon Kohl; Andrew W. Senior - Web page: https://patentgrep.com/patent/US12762409B1 ## CPC - G06N 3/084: Backpropagation, e.g. using gradient descent - G06N 3/045: Combinations of networks - G16B 30/20: Sequence assembly - G16B 40/00: ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding - G06F 18/217: {Validation; Performance evaluation; Active pattern learning techniques} - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G16B: BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) ## Abstract Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for predicting structural stability of a protein specified by an amino acid sequence. In one aspect, a method comprises: processing an input comprising a representation of the amino acid sequence of the protein using a structure prediction neural network to generate an intermediate output of the structure prediction neural network, wherein the structure prediction neural network is configured 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; and processing the intermediate output of the structure prediction neural network using a stability prediction neural network to generate a predicted structural stability score characterizing the predicted structural stability of the protein specified by the amino acid sequence. ## Claims ### Claim 1 A method performed by one or more computers, the method 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. ### Claim 2 (depends on 1) The method of claim 1, further comprising: determining gradients of the predicted structural stability score with respect to the representation of the amino acid sequence; and using the gradients of the predicted structure stability score with respect to the representation of the amino acid sequence to update the amino acid sequence. ### Claim 3 (depends on 2) The method of claim 2, wherein determining gradients of the predicted structural stability score with respect to the representation of the amino acid sequence comprises: backpropagating gradients of the predicted structural stability score through both the stability prediction neural network and the structure prediction neural network. ### Claim 4 (depends on 2) The method of claim 2, wherein using the gradients of the predicted structural stability score with respect to the representation of the amino acid sequence to update the amino acid sequence comprises: updating the amino acid sequence to encourage an increase in a structural stability of the protein specified by the amino acid sequence. ### Claim 5 (depends on 2) The method of claim 2, wherein using the gradients of the predicted structural stability score with respect to the representation of the amino acid sequence to update the amino acid sequence comprises: updating the amino acid sequence to encourage a decrease in a structural stability of the protein specified by the amino acid sequence. ### Claim 6 (depends on 2) The method of claim 2, wherein using the gradients of the predicted structural stability score with respect to the representation of the amino acid sequence to update the amino acid sequence comprises: updating the amino acid sequence to encourage a structural stability of the protein specified by the amino acid sequence to match a target structural stability. ### Claim 7 (depends on 2) The method of claim 2, further comprising making the protein specified by the amino acid sequence. ### Claim 8 (depends on 1) The method of claim 1, further comprising: determining, based at least in part on the predicted structural stability score, that the protein specified by the amino acid sequence satisfies a design criterion; and providing an output that specifies the amino acid sequence. ### Claim 9 (depends on 1) The method of claim 1, further comprising determining the amino acid sequence of a physical protein molecule, and predicting the structural stability of the physical protein molecule specified by the determined amino acid sequence by generating the predicted structural stability score for the physical protein molecule specified by the determined amino acid sequence. ### Claim 10 (depends on 1) The method of claim 1, wherein the intermediate output of the structure prediction neural network comprises features characterizing the amino acid sequence of the protein. ### Claim 11 (depends on 1) The method of claim 1, wherein the input comprising the representation of the amino acid sequence of the protein comprises a respective embedding of an amino acid in each position of the amino acid sequence. ### Claim 12 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. ### Claim 13 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. ### Claim 14 (depends on 13) The non-transitory computer storage media of claim 13, wherein the operations further comprise: determining gradients of the predicted structural stability score with respect to the representation of the amino acid sequence; and using the gradients of the predicted structure stability score with respect to the representation of the amino acid sequence to update the amino acid sequence. ### Claim 15 (depends on 14) The non-transitory computer storage media of claim 14, wherein determining gradients of the predicted structural stability score with respect to the representation of the amino acid sequence comprises: backpropagating gradients of the predicted structural stability score through both the stability prediction neural network and the structure prediction neural network. ### Claim 16 (depends on 14) The non-transitory computer storage media of claim 14, wherein using the gradients of the predicted structural stability score with respect to the representation of the amino acid sequence to update the amino acid sequence comprises: updating the amino acid sequence to encourage an increase in a structural stability of the protein specified by the amino acid sequence. ### Claim 17 (depends on 14) The non-transitory computer storage media of claim 14, wherein using the gradients of the predicted structural stability score with respect to the representation of the amino acid sequence to update the amino acid sequence comprises: updating the amino acid sequence to encourage a decrease in a structural stability of the protein specified by the amino acid sequence. ### Claim 18 (depends on 14) The non-transitory computer storage media of claim 14, wherein using the gradients of the predicted structural stability score with respect to the representation of the amino acid sequence to update the amino acid sequence comprises: updating the amino acid sequence to encourage a structural stability of the protein specified by the amino acid sequence to match a target structural stability. ### Claim 19 (depends on 13) The non-transitory computer storage media of claim 13, wherein the operations further comprise: determining, based at least in part on the predicted structural stability score, that the protein specified by the amino acid sequence satisfies a design criterion; and providing an output that specifies the amino acid sequence. ### Claim 20 (depends on 13) The non-transitory computer storage media of claim 13, wherein the operations further comprise determining the amino acid sequence of a physical protein molecule, and predicting the structural stability of the physical protein molecule specified by the determined amino acid sequence by generating the predicted structural stability score for the physical protein molecule specified by the determined amino acid sequence.