Binding Molecule Design for Viral Detection Specificity
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Solution Overview
Problem
Current diagnostic and therapeutic applications face challenges in keeping pace with the rapid evolution of viral targets, particularly with emerging viruses like SARS-CoV-2, due to high case counts, false positives from non-specific diagnostic assays, and co-infections with other respiratory viruses, necessitating rapid and specific detection methods.
Innovation Solution
A computer-implemented method designs sensitive and specific binding molecules using machine learning and software algorithms to target and eliminate viruses, involving the development of diagnostic molecules that maximize detection activity across genomic diversity, utilizing CRISPR systems with Cas proteins and guide molecules to detect and differentiate viral sequences, and employing a query algorithm that tolerates high divergence and G-U wobble base pairing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional diagnostic assays are used for viral detection, then the diagnostic capacity can handle current case counts, but the assays yield false positives due to lack of specificity to SARS-CoV-2
Solution Approach 1:
The patent segments the viral genome into multiple target regions and designs multiple binding molecules (probes, primers, guides) that target different segments. This segmentation allows the diagnostic system to distinguish SARS-CoV-2 from other coronaviruses by detecting specific segmented patterns, thereby improving specificity and reducing false positives while maintaining reliable detection across varying case counts
Solution Approach 2:
The patent applies local quality by designing binding molecules with specific local sequence characteristics that are unique to SARS-CoV-2. The machine learning models optimize local sequence features (such as specific k-mer patterns, secondary structures, or epitope regions) to enhance discrimination between SARS-CoV-2 and related viruses, improving measurement precision without compromising overall assay reliability
2Productivity
If rapid pipeline for sample processing is implemented to handle high case counts, then diagnostic throughput increases, but detection accuracy may decrease due to rushed processing
Solution Approach 1:
The patent performs preliminary action by pre-processing and pre-screening samples using automated systems that prepare samples for rapid analysis. The machine learning models are pre-trained on extensive viral sequence data, enabling them to quickly classify and prioritize samples during high-throughput processing. This preliminary preparation maintains detection accuracy while enabling rapid pipeline processing to handle high case counts
Solution Approach 2:
The patent uses copying by creating multiple replicate measurements and using consensus algorithms to determine final diagnostic results. The machine learning system generates multiple potential binding molecule candidates and selects the optimal set through virtual screening. This replication and verification approach ensures detection accuracy is maintained even as throughput increases through automated parallel processing
3Measurement precision
If binding molecules are designed to be highly specific to SARS-CoV-2, then false positives are reduced, but the ability to detect co-infections with other respiratory viruses decreases
Solution Approach 1:
The patent applies universality by designing a multi-functional diagnostic system that can detect multiple viral pathogens simultaneously. The binding molecule set includes molecules specific to SARS-CoV-2 plus additional molecules that target other respiratory viruses. The machine learning system coordinates these multiple targets to provide both SARS-CoV-2 specific detection and broader co-infection surveillance, achieving both high specificity and versatile pathogen detection in a single assay
4Measurement precision
If machine learning models are trained on extensive genomic diversity data, then detection sensitivity across viral variants improves, but computational complexity and processing time increase
Solution Approach 1:
The patent segments the extensive genomic diversity data into representative subsets or consensus sequences from different viral lineages. Rather than processing all available viral sequences, the machine learning model is trained on strategically selected representative data that captures the essential variability. This segmentation reduces computational complexity while maintaining detection sensitivity across variants through efficient virtual screening of binding molecule candidates
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The method enables rapid and specific detection of viral sequences, reducing false positives and co-infection misidentification, and allows for continuous adaptation to evolving viral strains, enhancing diagnostic and therapeutic efficacy.
Implementation Method 1
The query algorithm determines the specificity of the binding molecules, tolerating both high divergence and G-U wobble base pairing
Data Source
AI summary
The invention provides for methods for designing sensitive, specific, and optimally active binding molecules. Systems, methods and compositions utilizing the designed molecules in diagnostics and therapeutics are also provided.