Residual artificial neural network to generate protein sequences

A system comprising:

• one or more hardware processing units;
• and
• one or more non-transitory memory devices storing computer-readable instructions that, when executed by the one or more hardware processing units, cause the system to perform operations comprising:
• obtaining training data including a plurality of amino acid sequences, individual amino acid sequences of the plurality of amino acid sequences corresponding to an antibody fragment of a plurality of antibody fragments;
• obtaining an additional amino acid sequence of an additional antibody component that is not included in the plurality of antibody components;
• identifying a position of the additional amino sequence, the position corresponding to an initial amino acid included in the additional amino acid sequence;
• analyzing the additional amino acid sequence, using the training data, with respect to a plurality of candidate amino acids to determine respective probabilities for individual candidate amino acids of the plurality of candidate amino acids being located at the position;
• and
• determining, based on the respective probabilities, an amount of loss of the initial amino acid being located at the position.

A method comprising:

• obtaining, by a computing system including one or more computing devices having one or more processors and memory, training data including a plurality of amino acid sequences, individual amino acid sequences of the plurality of amino acid sequences corresponding to an individual protein of a plurality of proteins;
• obtaining, by the computing system, a base amino acid sequence of at least a portion of a first additional protein that is not included in the plurality of proteins;
• obtaining, by the computing system, a grafting sequence of at least a portion of a second additional protein that is not included in the plurality of proteins;
• determining, by the computing system and based on position modification data, a plurality of first positions of the base sequence that include first amino acids that are to be modified;
• determining, by the computing system and based on the position modification data, a plurality of second positions of the grafting sequence that include second amino acids that are to replace the first amino acids;
• generating, by the computing system, a combined amino acid sequence that includes a modified version of the base sequence with the first amino acids being replaced by the second amino acids;
• analyzing the combined amino acid sequence, by the computing system and using the training data, with respect to a plurality of candidate amino acids to determine respective probabilities for individual candidate amino acids of the plurality of candidate amino acids being located at a position of the combined sequence;
• determining, by the computing system and based on the respective probabilities, an amount of loss of an initial amino acid being located at the position.
• determining, by the computing system and based on the amount of loss, a candidate amino acid from among the plurality of candidate amino acids to replace the initial amino acid at the position;
• and
• generating, by the computing system, a modified version of the additional amino acid sequence having the candidate amino acid located at the position.