Antibody Sequence Pattern Identification via VAE Latent Space
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Solution Overview
Problem
Deep sequencing of antibody repertoires in immunology and drug discovery faces challenges in identifying relevant information from large datasets, particularly in determining the extent of convergent selection of antibody sequences in different individuals following antigen exposure.
Innovation Solution
The use of variational autoencoders (VAEs) to provide meaningful representations of immune repertoires, mapping antibody sequences into a lower-dimensional latent space and employing a linear classifier and mixture models to identify convergent sequence patterns predictive of antigen exposure, with the ability to generate novel and functional antibody variants in-silico.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep sequencing of antibody repertoires is performed to identify antigen-specific sequences, then the ability to detect relevant information improves, but the complexity of analyzing large datasets increases
Solution Approach 1:
The patent employs variational autoencoders as an intermediary computational model that transforms high-dimensional antibody sequence data into a lower-dimensional latent space. This intermediary representation simplifies the analysis complexity while preserving the ability to identify antigen-specific sequences through convergent sequence pattern detection
Solution Approach 2:
The method extracts only the most relevant features from the large antibody repertoire datasets by identifying convergent sequence patterns across different individuals. This extraction process isolates the critical information needed for antigen detection while discarding redundant data, thereby reducing analysis complexity
2Measurement precision
If variational autoencoders are used to map antibody repertoires into lower-dimensional latent space, then the identification of convergent sequence patterns improves, but the computational processing requirements increase
Solution Approach 1:
The patent transforms the high-dimensional antibody sequence data into a lower-dimensional latent space using variational autoencoders. This dimensionality reduction enables more efficient computational processing while maintaining the ability to identify convergent sequence patterns, as the essential information is preserved in the reduced dimensional representation
3Measurement precision
If linear classifiers and mixture models are employed to identify predictive patterns, then the accuracy of antigen exposure prediction improves, but the device complexity increases
Solution Approach 1:
The patent segments the complex analysis task into distinct components: variational autoencoders for dimensionality reduction, clustering algorithms for pattern grouping, and linear classifiers for prediction. This segmentation allows each component to be optimized independently, managing overall system complexity while maintaining high prediction accuracy
Data Source
AI summary
The present methods use a variational autoencoder (VAE) and deep generative modelling to learn meaningful representations from the immune repertoires. The system can map input sequences into a lower-dimensional latent space, which reveals a large amount of convergent sequence patterns. The system can identify patterns present in convergent clusters that are highly predictive for antigen exposure and/or antigen specificity. The system can generate, from the latent space, novel functional antibody sequence variants in-silico.