Computational Directed Evolution for Antibody Affinity Maturation
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Solution Overview
Problem
Current methods for antibody maturation, such as directed evolution, are inefficient in improving antibody affinity and specificity, particularly when targeting multiple properties simultaneously, and require extensive experimental testing.
Innovation Solution
A machine learning approach called computational directed evolution (CDE) is employed to computationally mature antibody sequences by training and fine-tuning models on antibody sequences labeled with specific properties, allowing for the optimization of multiple objectives like affinity and expression, and generating improved antibody sequences through a weighted combination of model outputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If directed evolution methods are used for antibody maturation, then affinity improvement can be achieved through multiple rounds of mutation and selection, but the process requires extensive experimental testing and is inefficient when targeting multiple properties simultaneously
Solution Approach 1:
The patent applies preliminary action by training machine learning models on existing antibody sequence data before actual antibody maturation is needed. The models are pre-trained to predict affinity and other properties, allowing rapid evaluation of mutated sequences without immediate experimental testing. This preliminary computational preparation significantly reduces the time required for subsequent maturation experiments.
Solution Approach 2:
The patent replaces the mechanical/experimental system of directed evolution with a computational machine learning system. Instead of relying on physical experimental cycles of mutation and selection, the invention uses trained ML models to predict the outcomes of mutations, substituting computational analysis for extensive wet-lab experimentation. This substitution maintains reliability while dramatically reducing time requirements.
2Adaptability or versatility
If directed evolution is used to target multiple properties simultaneously, then multi-property optimization is possible, but the efficiency decreases and requires more extensive testing
Solution Approach 1:
The patent merges multiple property prediction capabilities into a single integrated machine learning framework. The system trains models that can simultaneously predict multiple antibody properties (affinity, specificity, stability, etc.) and combines these predictions to evaluate mutated sequences across all desired properties at once. This merging allows multi-property optimization without the efficiency loss associated with separate experimental testing for each property.
Solution Approach 2:
The machine learning models serve multiple functions simultaneously - they can predict affinity, evaluate stability, assess solubility, and optimize for multiple other properties all within a single computational framework. This multi-functionality allows the system to handle diverse optimization goals without requiring separate experimental protocols for each property, thereby maintaining high productivity while achieving versatile multi-property optimization.
3Manufacturing precision
If traditional directed evolution methods are employed, then antibody sequences can be improved through iterative mutation and selection, but numerous experimental constructs are required
Solution Approach 1:
The patent uses copying by creating computational models that replicate the complex relationships between antibody sequences and their properties. Instead of physically creating and testing numerous experimental constructs, the system creates virtual copies through ML model predictions, allowing evaluation of many potential mutations in silico before selecting a small number of promising candidates for actual experimental validation. This copying approach maintains manufacturing precision while dramatically reducing the quantity of physical constructs needed.
Solution Approach 2:
The machine learning models serve as intermediaries between the antibody sequence space and experimental validation. Rather than directly testing numerous experimental constructs, the ML models act as a filtering intermediary that predicts which sequences are most likely to succeed. This intermediary layer allows for precise sequence optimization by evaluating many candidates computationally and selecting only the most promising ones for physical experimentation, thereby reducing the total number of constructs required.
Data Source
AI summary
Controlling antibody affinity and expression are key to clinical applications. High affinity antibodies correlate with higher specificity and can be used at lower doses. Presently, antibody maturation is tackled with directed evolution methods. In this case, an initial library of mutated binders is seeded into a process and affinity is improved through multiple rounds of mutation and selection. However, the present disclosure employs a machine learning approach to computationally mature antibody sequences using a process having parallels to directed evolution. These antibody sequences can be manufactured into physical antibodies after their computation and verification. Additionally, the present method has the potential to outperform directed evolution when targeting a specific affinity, and is applicable to general protein-protein interactions.