Variational Autoencoder for Influenza Antigen Design
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Solution Overview
Problem
Current seasonal influenza vaccines, particularly those using H3N2 strains recommended by public health authorities, have not provided broad protection for the general population over the past five years, due to immunological relatedness splitting into divergent clades.
Innovation Solution
A machine learning algorithm is implemented to generate influenza antigens for use as vaccines by reducing wildtype hemagglutinin sequences into a lower-dimensional space using a variational autoencoder, predicting immune responses, and selecting top candidate vaccine components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional public health authority strain selection methods are used for influenza vaccines, then the vaccine development process is simple and fast, but the vaccine does not provide broad protection against divergent clades
Solution Approach 1:
The patent performs preliminary actions by pre-training machine learning models on extensive influenza hemagglutinin sequence data before vaccine strain selection. The variational autoencoder and immune response prediction models are trained in advance to capture clade-specific patterns, enabling the system to evaluate and select strains with broad protective potential before actual vaccine development begins.
Solution Approach 2:
The patent introduces machine learning models as intermediaries between traditional strain selection and vaccine efficacy evaluation. The variational autoencoder serves as an intermediary to generate and evaluate hypothetical strain variants, while the immune response prediction model acts as an intermediary to predict cross-clade protection without requiring extensive animal testing for each candidate.
2Reliability
If machine learning algorithms are used to generate influenza antigens, then broad protection against divergent clades can be achieved, but the computational complexity and time required increase significantly
Solution Approach 1:
The patent performs preliminary actions by pre-training machine learning models on extensive influenza hemagglutinin sequence data before vaccine strain selection. The variational autoencoder and immune response prediction models are trained in advance to capture clade-specific patterns, enabling the system to evaluate and select strains with broad protective potential before actual vaccine development begins.
Solution Approach 2:
The patent implements feedback mechanisms where the immune response prediction model evaluates generated antigen candidates and provides feedback to guide further optimization. The system uses predicted immune responses against multiple clades as feedback signals to iteratively improve candidate selection, allowing the algorithm to learn from prediction outcomes and refine strain recommendations.
3Reliability
If machine learning algorithms are used to generate influenza antigens, then broad protection against divergent clades can be achieved, but computational resources and algorithm complexity increase
Solution Approach 1:
The patent segments the complex vaccine design problem into distinct computational modules: a variational autoencoder for generating and transforming hemagglutinin sequences, an immune response prediction model for evaluating candidate efficacy, and a selection algorithm for identifying optimal strains. Each module handles a specific aspect of the problem, making the overall system more manageable and interpretable despite the complexity.
Data Source
AI summary
One or more data objects are received defining a plurality of wild-type amino acid sequences. From the one or more data objects, a plurality of reduced-dimension sequences are generated in a reduced-dimension space. A plurality of candidate sequences are generated in the reduced-dimension space using the plurality of reduced-dimension sequences. One or more data objects defining a viral amino acid sequence are received. Viral sequences in the reduced-dimension space are received. As input to a titer-predictor, each of the candidate sequences and at least one of the reduced-dimension viral sequences are provided. As output from the titer-predictor, a candidate-score for each of the candidate sequences is received. At least one candidate sequence from among the candidate sequences are selected. At least one new amino acid sequence is generated. Each of the generated amino acid sequences is suitable for manufacturing a respective vaccine.