Antibody Modeling Pipeline for Sub-Angstrom Accuracy
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Solution Overview
Problem
Current methods for generating monoclonal antibodies, such as hybridoma technology and phage display, are limited by random chance and host immune responses, and struggle with predicting the structure of complementarity determining regions (CDRs) like CDR-H3, which are crucial for epitope binding, due to their diversity and lack of simple sequence-structure relationships.
Innovation Solution
The development of computer-implemented systems and methods that utilize a sequence database of CDRs and backbone dihedral angles based on known antibody structures, combined with simulated annealing and Point Specific Scoring Matrices (PSSM), to generate antibody models targeted to specific epitopes, predict antibody-antigen complexes, and optimize structural conformations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If computational methods are used to predict antibody structures, then prediction accuracy can reach sub-angstrom levels, but computational intensity increases significantly
Solution Approach 1:
The antibody structure prediction is divided into separate modules: CDR loop prediction, framework region modeling, and epitope binding interface prediction. Each module can be independently optimized and executed, reducing the overall computational burden while maintaining sub-angstrom accuracy through specialized algorithms for each segment
Solution Approach 2:
The system performs preliminary actions by pre-calculating and storing canonical CDR loop conformations and framework region structures in databases. During actual prediction, these pre-computed structures are retrieved and assembled, significantly reducing computational intensity compared to de novo structure calculation while maintaining high prediction accuracy
2Adaptability or versatility
If CDR-H3 loops are modeled using general loop prediction methods, then structure diversity is captured, but computational complexity increases and accuracy decreases for loops longer than 12 amino acids
Solution Approach 1:
The system applies local quality by treating CDR-H3 loops differently from other CDR regions. While most CDR loops follow canonical structures, CDR-H3 is modeled with specialized algorithms that account for its unique diversity and length variations, allowing accurate prediction of diverse conformations without excessive computational complexity
Solution Approach 2:
The system changes parameters by adjusting the modeling approach based on CDR-H3 loop length. For shorter loops, canonical structure methods are used; for longer loops exceeding 12 amino acids, specialized sampling and scoring methods are applied, optimizing both accuracy and computational efficiency for each case
3Productivity
If hybridoma technology is used to generate monoclonal antibodies, then antibodies can be produced, but host rejection occurs and therapeutic potential is limited
Solution Approach 1:
The system creates humanized copies of non-human antibody CDR regions by mapping them onto human framework structures. This copying approach maintains the antigen-binding specificity of original antibodies while replacing immunogenic non-human framework sequences with human equivalents, eliminating host rejection while preserving therapeutic potential
Solution Approach 2:
The system changes the structural parameters of antibody frameworks by systematically replacing non-human framework residues with human counterparts while maintaining CDR loop configurations. This parameter change transforms murine or non-human antibodies into humanized versions that evade host immune response while retaining productivity and therapeutic efficacy
4Quantity of substance
If phage display libraries are generated with random sequencing, then large numbers of antibody variants can be produced, but the process relies on random chance rather than rational design
Solution Approach 1:
The system performs preliminary rational design by pre-selecting CDR sequences and framework regions with predicted high affinity and specificity before library construction. This preliminary filtering based on computational models reduces reliance on random chance while maintaining diverse antibody variant generation, improving the efficiency of identifying functional antibodies
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables the generation of antibody libraries and structural models with improved accuracy and efficiency, reducing computational intensity and overcoming the limitations of existing methods by leveraging known antibody structures and energy-based refinements.
Implementation Method 1
evaluating one or more structural models from said databases using a simulated annealing process
Data Source
AI summary
This disclosure presents methods for antibody structure prediction and design. We utilize the growing number of antibody structures and sequences are used with powerful protein modeling methods to design and predict antibody structural models up to sub-angstrom accuracy. The invention also relates to systems and methods for generating an antibody library. Specifically, the invention relates to computer-implemented systems and methods for generating an antibody library for a predetermined epitope. The invention further relates to determining structural models of the interface between an antibody and its antigen. The invention also relates to determining structural models of an unbound complementarity determining region of an antibody.