Immunoglobulin Library Design via Somatic Hypermutation Hotspots
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Solution Overview
Problem
Existing methods for optimizing antibodies generate a large number of variants, increasing time and resources needed to select antibodies with desired characteristics, and introduce uncertainty when optimizing both heavy and light chains of fully human antibodies.
Innovation Solution
A method for designing an immunoglobulin library by identifying related immunoglobulins through somatic hypermutation, comparing amino acid sequences, and selecting sites for modification to generate a library with optimized biological properties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional methods generate a large library of antibody variants by replacing each amino acid residue with many potential substitutions, then the comprehensiveness of the library increases, but the time and resources required to generate and screen the library increase significantly
Solution Approach 1:
The patent applies local quality by focusing mutations only at somatic hypermutation hotspots rather than uniformly across all amino acid residues. This concentrates the diversity-generating capability at specific locations (hotspots) that naturally exhibit high mutation rates in vivo, thereby maintaining library comprehensiveness at functionally relevant positions while dramatically reducing the total number of variants that need to be generated and screened.
Solution Approach 2:
The patent employs preliminary action by pre-identifying somatic hypermutation hotspots based on sequence alignment of related immunoglobulins before library construction. This preliminary identification of promising mutation sites allows the library design to focus resources on positions most likely to yield improved antibodies, avoiding the time-consuming process of generating and screening all possible variants at all positions.
2Manufacturing precision
If both heavy and light chains of fully human antibodies are optimized simultaneously, then the thoroughness of optimization increases, but the workload increases and uncertainty about antibody properties increases
Solution Approach 1:
The patent applies segmentation by separating the optimization process into independent stages: first identifying somatichypermutation hotspots in the lead antibody, then designing the library based on these identified sites. This segmentation allows the optimization to proceed systematically through defined steps rather than attempting simultaneous optimization of all parameters, reducing workload while maintaining thoroughness.
Solution Approach 2:
The patent employs feedback by using sequence comparison of related immunoglobulins to identify conserved somatichypermutation hotspots, which then inform the library design. This feedback loop ensures that the optimization focuses on positions that have naturally been targeted by the immune system, reducing uncertainty about which modifications are most likely to succeed while maintaining comprehensive optimization coverage.
Data Source
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AI summary
The invention relates to a method of designing an immunoglobulin library for optimisation of a biological property of a first lead immunoglobulin and libraries of optimised immunoglobulins produced by such methods.