AI-Assisted Antibody Affinity Modification via Deep Learning
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Solution Overview
Problem
Traditional affinity maturation techniques for antibody and macromolecular drugs are limited by their dependence on antigen/target structural information, making it difficult to design drugs for unknown structures or epitopes, and they struggle to efficiently search the vast mutation spaces for optimal sequences, resulting in low screening hit rates and high experimental costs.
Innovation Solution
An AI-assisted affinity modification system that generates mutation libraries through deep learning-based sequence prediction, allowing for the optimization of amino acid sequences to enhance affinity without relying on structural information, enabling the screening of one billion-level mutation spaces and reducing downstream experiment costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional affinity maturation techniques (point mutation, CDR recombination, chain replacement) are used, then affinity can be improved for known targets, but the method is highly dependent on antigen/target structure and cannot design targets with unknown structure or epitope
Solution Approach 1:
The patent replaces traditional structure-based mechanical design methods with an AI-based sequence prediction system. The deep learning model directly predicts optimal amino acid sequences for affinity enhancement without requiring antigen structural information, substituting the traditional structure-dependent workflow with an information-independent computational approach.
Solution Approach 2:
The system changes the fundamental parameter from structure-based design to sequence-based design. By using AI to directly optimize amino acid sequences without structural input, the method transforms the design paradigm from requiring structural parameters to working solely with sequence parameters, enabling universal applicability.
2Reliability
If traditional mutation methods are used to search mutation spaces, then some affinity improvements can be found, but the screening hit rate is low and downstream experiments are costly
Solution Approach 1:
The AI model performs preliminary action by predicting and ranking optimal mutation sequences before any experimental screening occurs. The system pre-calculates affinity scores for numerous candidate sequences and presents only the highest-scoring candidates for experimental validation, thereby improving the hit rate of downstream experiments.
Solution Approach 2:
The system creates a computational copy of the mutation space exploration process through AI prediction, allowing virtual screening of sequences that would be prohibitively expensive to test experimentally. This computational copying enables exhaustive search of mutation spaces without proportional experimental costs.
3Reliability
If traditional modification methods are used, then affinity for a specific target can be optimized, but it is difficult to design drugs targeting multiple targets and design methods cannot be reused
Solution Approach 1:
The AI-based sequence prediction system achieves universality by being applicable to any antibody-drug target combination without requiring target-specific structural input. The same computational model can be reused across multiple targets and antibody types, providing a universal platform for affinity optimization that transcends specific target characteristics.
4Quantity of substance
If exhaustive mutation library construction is attempted with traditional methods, then complete sequence space can be explored, but the display ability limitations make it challenging and resource-intensive
Solution Approach 1:
The patent substitutes physical library construction and display methods with computational sequence generation and prediction. Instead of physically constructing and displaying large mutation libraries with limited capacity, the system computationally generates and evaluates sequences, removing the physical constraints of library display ability.
Data Source
AI summary
The present invention provides an affinity modification system of antibody/macromolecular drug, wherein the affinity modification system comprises: an interaction module, set to: input template sequence information of antibody/macromolecular drugs, modification requirements of single/multi-targets of antibody/macromolecular drugs and optional user-defined screening requirements to generate interaction antibody/macromolecular drug sequence information; affinity modification module, set to: according to the interaction antibody/macromolecular drug sequence information, perform corresponding partial or exhaustive numeration of possible sequence in a part of the full variable range to obtain a mutation library, and perform sequence-based affinity prediction on the mutation library based on a deep learning model, so as to obtain the sequence information of the modified antibody/macromolecular drug; an output module, designed to: according to the sequence information of the modified antibody/macromolecular drug, output the sequence information of the candidate antibody/macromolecular drug. The invention also provides a corresponding affinity modification method.