TRAINING PROTEIN STRUCTURE PREDICTION NEURAL NETWORKS USING REDUCED MULTIPLE SEQUENCE ALIGNMENTS

• Main Authors: Reiman, David, Jumper, John, Evans, Richard Andrew, Green, Timothy Frederick Goldie
• Format: Patent
• Language: English
• Published: 09.11.2023
• Subjects: CALCULATING; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS; COMPUTING; COUNTING; INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTEDFOR SPECIFIC APPLICATION FIELDS; PHYSICS

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training neural networks to predict the structure of a protein. In one aspect, a method comprises: obtaining, for each of a plurality of proteins, a full multiple sequence alignment for the protein; generating, for each of the plurality of proteins, target structure parameters characterizing a structure of the protein from the full multiple sequence alignment for the protein, comprising processing a representation of the full multiple sequence alignment for the protein using the structure prediction neural network to generate output structure parameters characterizing a structure of the protein, and determining the target structure parameters for the protein based on the output structure parameters for the protein; determining, for each of the plurality of proteins, a reduced multiple sequence alignment for the protein, comprising removing or masking data from the full multiple sequence alignment for the protein.