Optimizing Proteins Using Model Based Optimizations

A method of generating a sequence of a protein predicted to have altered immunogenicity compared to a sampling of protein sequences, the protein having a desired function, comprising:

• evaluating the immunogenicity of a sampling of protein sequences;
• weighting the sampling of protein sequences according to an estimated probability of a particular generated protein sequence having a deviation in immunogenicity of a particular percentile of immunogenicity of the sampling of protein sequences, resulting in a first weighted sampling of protein sequences;
• evaluating the immunogenicity of a sampling of protein sequences using first sampling of protein sequences;
• reweighting the sampling of protein sequences from the first weighted sampling of protein sequences evaluated for immunogenicity according to their evaluated immunogenicity, resulting in a second weighted sampling of protein sequences;
• generating a protein sequence from the second weighted sampling of protein sequences, the protein sequence representing a protein predicted to have an altered immunogenicity;
• and
• providing a generative model that preserves the desired function, the generative model comprising an encoder neural network and a decoder neural network.

A method of generating a sequence of a protein predicted to have altered immunogenicity compared to a sampling of protein sequences, the protein having a desired function, comprising:

• providing a generative model that preserves the desired function, the generative model comprising an encoder neural network and a decoder neural network;
• evaluating the immunogenicity of a sampling of protein sequences from a generative model, wherein the generative model preserves the desired function, the generative model further comprising an encoder neural network and a decoder neural network;
• providing a first updated generative model by retraining the generative model by weighting the sampling of protein sequences from the generative model according to an estimated probability of a particular generated protein sequence having a deviation in immunogenicity of a particular percentile of immunogenicity of the sampling of protein sequences;
• evaluating the immunogenicity of a sampling of protein sequences using the first updated generative model;
• providing a second updated generative model by retraining the first updated generative model by weighting the sampling of protein sequences from the first updated generative model evaluated for immunogenicity according to their evaluated immunogenicity in the retraining;
• and
• generating a protein sequence from the further updated generative model, the protein sequence representing a protein predicted to have an altered immunogenicity.

An isolated protein sequence comprising an amino acid sequence that differs from a sample of amino acid sequences by at least one amino acid addition, deletion, or substitution relative to the sample generated by:

• evaluating the immunogenicity of a sampling of protein sequences;
• selecting a set of protein sequences from the sampling by weighting the sampling of protein sequences according to an estimated probability of a particular generated protein sequence having a deviation in immunogenicity of a particular percentile of immunogenicity of the sampling of protein sequences, wherein the sampling of protein sequences is from a generative model and the generative model is configured to preserve the desired function;
• and
• generating a protein sequence based on the selected set of protein sequences, the protein sequence representing a protein predicted to have an altered immunogenicity.