In Silico Viral Variant Detection Using Protein Transformers
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Solution Overview
Problem
Current methods for assessing the transmissibility and immune escape potential of viral variants, such as SARS-CoV-2, are resource-intensive and time-consuming, making it difficult to scale up and address the rapid emergence of variants with increased infectivity and immune evasion capabilities.
Innovation Solution
An in silico approach combining structural modeling of viral proteins and protein transformer language models to rank variants based on transmissibility and immune escape potential, using a log-likelihood metric and semantic change scores to identify high-risk variants and inform vaccine development.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If experimental techniques are used to profile viral variants for transmissibility and immune escape potential, then measurement precision is improved, but productivity deteriorates due to resource intensity and time consumption
Solution Approach 1:
The patent replaces wet-lab experimental techniques with in silico computational methods. Specifically, it uses structural modeling to predict viral protein behavior and transformer language models to analyze sequence data, substituting physical experiments with computer-based simulations and machine learning predictions.
Solution Approach 2:
The patent creates computational copies of viral proteins and sequences to perform virtual experiments. By modeling three-dimensional structures and using language models to process sequence data, it generates predictions without needing physical viral samples, enabling rapid assessment of multiple variants simultaneously.
2Reliability
If experimental techniques are used to assess viral variants, then reliability is improved through direct measurement, but loss of time increases due to the inability to scale across multiple variants
Solution Approach 1:
The patent performs preliminary computational assessments of viral variants before experimental validation. By using structural modeling and language models to predict transmissibility and immune escape potential, it identifies high-risk variants that warrant further experimental study, reducing the overall time needed to characterize multiple variants.
Solution Approach 2:
The patent substitutes time-consuming wet-lab experiments with rapid in silico computational methods, including structural modeling and transformer-based sequence analysis, enabling simultaneous assessment of numerous variants without the time constraints of physical experimentation.
3Measurement precision
If comprehensive experimental profiling is performed on all single-residue mutations, then measurement precision is improved, but device complexity and resource requirements increase substantially
Solution Approach 1:
The patent replaces complex experimental profiling systems with computational models. Structural modeling predicts how amino acid changes affect protein structure and function, while transformer language models analyze sequence patterns, eliminating the need for elaborate wet-lab infrastructure.
Solution Approach 2:
The patent develops universal computational tools that can assess any viral variant with a given protein sequence. The structural modeling approach and language model framework are broadly applicable to different viruses and protein types, providing a versatile platform without requiring variant-specific experimental setups.
Data Source
AI summary
The present disclosure provides technologies for identifying, characterizing, and/or monitoring sequences of a variant of a reference infectious agent (e.g., but not limited to viral variants, for example in some embodiments SARS-CoV-2 variants) for transmissibility factors and/or immune escape potential, and/or for detecting and/or monitoring variants in environmental or biological samples, and/or for designing, preparing, and/or administering vaccines for such variants.