Viral Escape Profiling via Language-Based Models
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Solution Overview
Problem
The ability of viruses to mutate and evade the human immune system poses a significant obstacle to the development of antiviral therapies and vaccines, as current high-throughput experimental techniques are inefficient in profiling viral escape mutations across multiple strains.
Innovation Solution
A machine learning approach using language-based models, specifically a Bi-directional Long Short-Term Memory (BiLSTM) architecture, is employed to model viral escape by combining 'semantic change' and 'grammaticality' in viral protein sequences, enabling the identification of mutations that preserve infectivity while altering antigenic recognition, thus facilitating rapid antiviral and vaccine development.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-throughput experimental techniques are used to profile viral escape mutations, then measurement precision is improved, but productivity deteriorates due to substantial effort required even for a single viral strain
Solution Approach 1:
The patent replaces mechanical experimental techniques with a computational language-based model. The model uses natural language processing algorithms to predict viral escape mutations by analyzing protein sequence semantics, substituting wet-lab experimental workflows with in-silico computational analysis that provides both high precision and scalability across multiple viral strains
Solution Approach 2:
The patent creates a computational copy of the viral protein language corpus and uses this digital representation to simulate and predict escape mutations. By working with copied sequence data rather than physical viral samples, the system achieves rapid profiling without the time-consuming experimental procedures required by traditional methods
2Reliability
If comprehensive testing of combinatorial mutations across many viral strains is performed, then reliability is improved, but loss of time increases making the process infeasible
Solution Approach 1:
The patent performs preliminary action by pre-training the language-based model on a comprehensive corpus of viral protein sequences from multiple host species before actual escape prediction. This pre-training phase establishes the model's understanding of viral sequence semantics and grammar, enabling it to rapidly predict escape mutations without requiring time-consuming experimental validation of each potential mutation
Solution Approach 2:
The patent creates a universal language-based model that can process and predict escape mutations across multiple viral strains and protein types simultaneously. The model's language understanding capabilities transfer across different viral contexts, allowing reliable escape prediction for various strains without requiring separate testing campaigns for each
Data Source
AI summary
A method of viral escape profiling is used in association with antiviral or vaccine development. The method begins by training a language-based model against training data comprising a corpus of viral protein sequences of a given viral protein to model a viral escape profile. The viral escape profile represents, for one or more regions of the given viral protein, a relative viral escape potential of a mutation, the relative viral escape potential being derived as a function that combines both “semantic change,” representing a degree to which the mutation is recognized by the human immune system (i.e., antigenic change), and “grammaticality,” representing a degree to which the mutation affects viral infectivity (i.e. viral fitness). Using the model, a region of the given viral protein having an escape potential of interest is identified. Information regarding the region is then output to a vaccine or anti-viral therapeutic design and development workflow.