Antibody Library Preparation Using ML-Optimized CDR-H3 Sequences
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Solution Overview
Problem
Conventional synthetic antibody libraries face challenges in achieving high functional diversity and amplification efficiency due to errors in DNA synthesis and amplification processes, leading to low-quality antibodies with reduced binding affinity and specificity.
Innovation Solution
A method involving the design and synthesis of complementarity determining region (CDR) sequences using a machine learning model to optimize CDR-H3 sequences, enhancing the amplification efficiency and functional diversity of the antibody library, and constructing a phage-display antibody library using scFv fragments with optimized CDR sequences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If conventional DNA synthesis and amplification methods are used to construct synthetic antibody libraries, then the library construction process is simple and scalable, but errors are introduced during synthesis and amplification leading to low functional diversity
Solution Approach 1:
The patent applies preliminary action by using error correction codes during the DNA synthesis stage to prevent errors before they affect library quality. The synthesis system incorporates real-time monitoring and correction mechanisms that detect and fix errors during oligonucleotide synthesis, ensuring high fidelity templates are produced before amplification begins.
Solution Approach 2:
The patent implements feedback mechanisms through multiple rounds of PCR amplification with nested primers that selectively amplify only correct sequences. The system monitors amplification efficiency and sequence accuracy at each stage, using this feedback to adjust synthesis parameters and select only high-quality clones for the final library.
2Quantity of substance
If the library size is increased to improve the likelihood of finding high-affinity antibodies by chance, then the probability of obtaining desired antibodies increases, but the functional diversity decreases due to accumulated errors
Solution Approach 1:
The patent segments the antibody library into multiple sub-libraries, each containing a manageable number of high-fidelity sequences. This segmentation allows the system to maintain high functional diversity within each sub-library while collectively providing the large library size needed for comprehensive antibody screening. Each sub-library is constructed and validated independently to ensure sequence accuracy.
3Adaptability or versatility
If artificial diversity is introduced into existing antibody genes to create synthetic libraries, then the library can be constructed with controlled sequences, but the generated diversity may not be compatible with antibody frameworks reducing quality
Solution Approach 1:
The patent applies local quality by introducing artificial diversity only in specific regions (CDRs) while maintaining the framework regions with high sequence identity to natural antibodies. The synthesis system uses position-specific error correction and validation to ensure that only appropriate diversity elements are introduced at permissible locations, maintaining framework integrity and clone quality.
Data Source
AI summary
The present disclosure relates to a novel method of preparing an antibody library and the prepared library therefrom. The antibody library prepared by the preparation method according to an embodiment of the present disclosure contains antibodies having excellent physical properties to a large number of antigens, and thus can be favorably used as an antibody library that has functional diversity, contains a variety of unique sequences, and also has improved amplification efficiency after panning.