Back to results
US 12,603,153 B2Granted
De novo generation of high diversity proteins in silico with selective affinity and cross-reactivity minimization
AssigneeThaumachron LLC
InventorAndrew Simpson
Life of this patent
Estimating the term…
Abstract
This disclosure relates to a platform for de novo in silico generation of high diversity protein complexes, namely antibodies and TCRs, with selective affinity and cross-reactivity minimization given the antigen complex's sequences and additional conditional information.
Claims17
A system for de novo generation of protein complexes in silico, the system comprising:
- a system processor connected to an input/output interface and a memory via a communications network,
- wherein said processor is programmed with a generative model,
- wherein said generative model is programmed to generate a plurality of high diversity protein complex outputs de novo using a first set of input information, the first set of input information comprising one or more antigen complex conditional information input value,
- wherein said protein complex outputs each have selective affinity and cross-reactivity minimization based on said first set of input information,
- wherein said generative model comprises one or more generative adversarial network (GAN), and
- wherein said generative model is further trained with one or more neural network module with one of the group consisting of:
- protein data, immune epitope data, antibody affinity data, TCR affinity data, off-target binding data, and combinations thereof;
- a protein folding module configured to assist said generative model to produce three-dimensional (3D) structural data for said protein complex outputs;
- and
- a discriminator module configured to compare said protein complex outputs with real protein complex data for continuous training of said generative model.
7 more elements
A method of generating high diversity protein complexes in silico, the method comprising:
- providing a system processor connected to an input/output interface and a memory via a communications network,
- wherein said processor is programmed with a generative model, and
- wherein said generative model is trained using one or more neural network module with protein data, immune epitope data, antibody affinity data, TCR affinity data, and off-target binding data and programmed for generation of high diversity protein complexes de novo to target an antigen of using a first set of input information, wherein the first set input information comprises one or more antigen complex conditional information input value;
- providing a protein folding module configured to assist said generative model to produce three-dimensional (3D) structural data for said protein complex outputs;
- providing a discriminator module configured to compare said protein complex outputs with real protein complex data for continuous training of said generative model;
- inputting the first set of input information into said system processor;
- and
- generating a plurality of high diversity protein complex outputs de novo with selective affinity and cross-reactivity minimization based on said first set of input information.
5 more elements
A system for de novo generation of protein complexes in silico, the system comprising:
- a system processor connected to an input/output interface and a memory via a communications network:
- wherein said processor is programmed with a generative model:
- wherein said generative model is programmed to generate a plurality of high diversity protein complex outputs de novo using a first set of input information, the first set of input information comprising one or more antigen complex conditional information input value;
- wherein said input information is selected from the group consisting of:
- an antigen complex sequence, species information, isotype information, subtype information, allotype information, affinity information, cross-reactivity conditions, and combinations thereof;
- wherein said generative model comprises one or more generative adversarial network (GAN);
- wherein said generative model is further trained with one or more neural network module with one of the group consisting of:
- protein data, immune epitope data, antibody affinity data, TCR affinity data, off-target binding data, and combinations thereof;
- a protein folding module configured to assist said generative model to produce three-dimensional (3D) structural data for said protein complex outputs;
- an affinity module configured to predict affinity between said protein complex outputs and an antigen to be targeted to update generated affinity output to match expected affinity and minimize cross-reactivity of said protein complex outputs;
- wherein said protein complex outputs are selected from the group consisting of:
- antibody outputs, T-cell receptor (TCR) outputs, and combinations thereof;
- wherein said protein complex outputs each have selective affinity and cross-reactivity minimization based on said first set of input information;
- and
- wherein said protein complex outputs comprise protein complex affinity information, epitope information, and paratope information.
12 more elements
Dependencies are inferred from the claim text.