Disentangled Wasserstein Autoencoder for TCR Binding
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Solution Overview
Problem
Current methods for protein engineering, particularly in T-cell receptor design for immunotherapy, face challenges in efficiently separating functional and structural embeddings in protein sequences, requiring significant domain knowledge and being computationally complex, with limited research on leveraging machine learning for TCR engineering.
Innovation Solution
The use of a disentangled Wasserstein autoencoder framework that separates T-cell receptor sequences into functional and structural embeddings, allowing for the introduction of minimal mutations to enhance binding affinity while preserving the structural backbone, utilizing an auxiliary classifier to predict binding probabilities and generate new sequences with improved immunotherapy targeting capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional protein engineering methods are used to separate functional and structural residues in TCR sequences, then domain knowledge and expertise can be applied to identify important residues, but the process becomes computationally complex and time-consuming
Solution Approach 1:
The patent segments the TCR sequence embedding space into two distinct subspaces: functional embeddings (capturing peptide-binding related information) and structural embeddings (capturing fold and stability related information). This segmentation is achieved through a disentangled autoencoder architecture with separate encoder branches that independently learn these two types of representations, allowing precise identification of functional residues without overwhelming computational complexity
Solution Approach 2:
The patent introduces an intermediary auxiliary classifier that operates on the functional embeddings to predict peptide-binding outcomes. This classifier acts as a mediator between the complex sequence data and the binding prediction task, simplifying the overall computational process while maintaining high prediction accuracy by focusing only on the functional subspace
2Strength
If comprehensive mutations are introduced to TCR sequences to enhance binding affinity, then binding strength can be improved, but the structural integrity of the TCR may be compromised
Solution Approach 1:
By separating the embedding space into functional and structural components, the patent enables independent manipulation of these aspects. When enhancing binding affinity, only the functional embeddings are modified while the structural embeddings remain unchanged, ensuring that structural integrity is preserved while achieving improved binding strength
Solution Approach 2:
The patent applies local quality changes by introducing mutations only in the functional subspace of the TCR sequence. The disentangled representation allows identifying specific residues that contribute to binding affinity and modifying only those local regions, while the overall structural framework remains intact through preservation of structural embeddings
3Manufacturing precision
If extensive computational resources are used for TCR sequence design and analysis, then more thorough protein engineering can be performed, but the processing time and computational cost increase significantly
Solution Approach 1:
The patent segments the computational task into independent functional and structural analysis streams. The disentangled autoencoder processes TCR sequences by learning separate representations for binding affinity and structural properties, allowing parallel processing and reducing the overall computational time while maintaining engineering precision
Solution Approach 2:
The auxiliary classifier serves as an intermediary that provides fast predictions on functional embeddings without requiring full sequence analysis. This intermediary model enables rapid screening and evaluation of TCR variants, significantly reducing computational processing time while maintaining precise protein engineering capabilities
Data Source
AI summary
A computer-implemented method for learning disentangled representations for T-cell receptors to improve immunotherapy is provided. The method includes optionally introducing a minimal number of mutations to a T-cell receptor (TCR) sequence to enable the TCR sequence to bind to a peptide, using a disentangled Wasserstein autoencoder to separate an embedding space of the TCR sequence into functional embeddings and structural embeddings, feeding the functional embeddings and the structural embeddings to a long short-term memory (LSTM) or transformer decoder, using an auxiliary classifier to predict a probability of a positive binding label from the functional embeddings and the peptide, and generating new TCR sequences with enhanced binding affinity for immunotherapy to target a particular virus or tumor.