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Machine learning-based apparatus for engineering mesoscale peptides and methods and systems thereof

A machine learning model and engineering technology, applied in peptide preparation methods, machine learning, chemical instruments and methods, etc., can solve problems such as inability to fully explore large topological spaces, and large computational load on modeling platforms

Pending Publication Date: 2022-04-26
伊比奥公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Modeling platforms (such as Rosetta) are too computationally intensive to adequately explore large topological spaces, such as those that reproduce a given protein structure

Method used

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  • Machine learning-based apparatus for engineering mesoscale peptides and methods and systems thereof
  • Machine learning-based apparatus for engineering mesoscale peptides and methods and systems thereof
  • Machine learning-based apparatus for engineering mesoscale peptides and methods and systems thereof

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Experimental program
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Effect test

Embodiment approach I-1

[0094] Embodiment I-1. A method comprising:

[0095] Train a machine learning model based on a first plurality of blueprint records, or representations thereof, and a first plurality of scores, from each blueprint record in the first plurality of blueprint records and from each score in the first plurality of Scores are associated; and

[0096] executing said machine learning model after said training to generate a second plurality of blueprint records having at least one desired score,

[0097] The second plurality of blueprint records is configured to be received as input in computational protein modeling to generate an engineered polypeptide based on the second plurality of blueprint records.

Embodiment approach I-2

[0098] Embodiment I-2. The method of embodiment I-1, comprising:

[0099] receiving a representation of the reference object structure of the reference object; and

[0100] The first plurality of blueprint records is generated from a predetermined portion of the reference target structure, each blueprint record from the first plurality of blueprint records comprising a target residue position and a scaffold residue position, each target residue A position corresponds to a target residue from among a plurality of target residues.

Embodiment approach I-3

[0101] Embodiment I-3. The method of Embodiment I-1 or I-2, wherein in at least one blueprint record, the target residue positions are discontinuous.

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Abstract

Provided herein are methods of designing engineered polypeptides that reproduce molecular structure characteristics of a predetermined portion of a reference protein structure, such as antibody epitopes or protein binding sites. A machine learning (ML) model is trained by labeling a blueprint record generated from a reference target structure with a score calculated based on computational protein modeling of a polypeptide structure generated by the blueprint record. The method may include training an ML model based on a first set of blueprint records or a representation thereof and a first set of scores, each blueprint record from the first set of blueprint records being associated with each score from the first set of scores. After the training, the machine learning model may be executed to generate a second set of blueprint records. An engineered set of polypeptides is then generated based on the second set of blueprint records.

Description

[0001] Cross References to Related Applications [0002] This application claims priority and benefit to U.S. Patent Application No. 62 / 855,767, filed May 31, 2019, entitled "Meso-Scale Engineered Peptides and Methods of Selecting," which is incorporated herein by reference in its entirety. technical field [0003] The present disclosure relates generally to the field of artificial intelligence / machine learning, and more particularly to methods and apparatus for training and using machine learning models for engineering peptides. Background technique [0004] Computational design can be used to design novel therapeutic proteins that mimic natural proteins, or to design vaccines that display one or more desired epitopes from pathogenic antigens. Computationally designed proteins can also be used to generate or select binding agents. For example, antibody libraries (eg, phage display libraries) can be panned against a designed protein bait to select clones that bind the bait,...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): A61K38/16C07K14/00G06N5/02
CPCC07K14/00G01N33/6845G06N20/20G06N20/10C07K1/10G06N5/01G06N3/044G06N3/045Y02A90/10G16B15/20G06N20/00G16B5/30G16B40/20G06N5/04G16B5/00C07K14/001
Inventor M·P·格雷文A·T·田口K·E·豪瑟
Owner 伊比奥公司
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