Computational Vaccine Design via CRISPR Fragment Removal
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Solution Overview
Problem
Existing methods for developing COVID-19 vaccines are inefficient in rapidly identifying and removing problematic virus fragments associated with high transmissibility and mortality, requiring extensive laboratory testing and time-consuming processes.
Innovation Solution
A computationally derived reductive vaccine approach using CRISPR to remove frequently occurring fragments from a 'Super Organism' containing Civet Sars, Bat Sars, BetaCov RtRs, and BetaCov RtRI-related sequences, identified through statistical analysis of NIH GenBank databases, to create a 'neutered' vaccine candidate.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional methods are used to remove each fragment or structure via Crispr one by one and test the resulting organism, then the vaccine development is thorough and reliable, but the time required for development is excessive
Solution Approach 1:
The patent performs preliminary computational analysis to identify and remove all potential problematic function fragments via various fragment length groups or via matches to related organisms before actual vaccine candidate creation. This preliminary computational screening of the genetic database allows the invention to narrow down hundreds of potential candidates to one or two vaccine candidates, avoiding the need for sequential Crispr removal and testing of each fragment individually.
2Productivity
If all potential problematic function fragments are removed via computational methods and fragment length groups, then the number of vaccine candidates is reduced to one or two, but the complexity of computational analysis increases
Solution Approach 1:
The patent segments the genetic database analysis into various fragment length groups and categorizes fragments by their potential problematic functions. By dividing the complex genetic sequence into manageable fragment segments and analyzing them systematically through computational methods, the invention can efficiently screen thousands or millions of sequence records to identify common problematic structures across COVID-19 variants.
Solution Approach 2:
The patent uses computational algorithms and bioinformatics tools as intermediaries to bridge the gap between raw genetic database data and vaccine candidate identification. These computational intermediaries automatically analyze sequence records, identify problematic fragments, and filter candidates, replacing the need for manual sequential Crispr removal and testing while maintaining thoroughness.
3Productivity
If fragments are removed based on frequency and consistency across genetic database, then the vaccine candidate is computationally reduced, but the risk of removing essential immune-recognizable structures increases
Solution Approach 1:
The patent applies different selection criteria to different regions of the virus genome based on their functional importance. Rather than uniformly removing all frequent fragments, the computational analysis identifies and preserves fragments that are essential for immune recognition while removing only those frequent fragments that represent problematic functions. This localized quality control ensures that immunologically important regions are maintained even if they appear frequently across variants.
Solution Approach 2:
The patent adjusts the frequency threshold and consistency parameters used for fragment removal based on the specific vaccine candidate being developed. By dynamically changing these parameters, the computational method can balance between removing enough problematic fragments to reduce vaccine candidates to one or two, while preserving sufficient immune-recognizable structures to maintain vaccine effectiveness.
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
This method enables the rapid identification and removal of 92 high-frequency fragments, potentially reducing vaccine development time by focusing on fragments common across the COVID-19 database, thereby creating a vaccine candidate that can provoke a useful immune response and shield against the virus.
Implementation Method 1
A computationally derived reductive vaccine approach using CRISPR to remove frequently occurring fragments from a 'Super Organism' containing Civet Sars, Bat Sars, BetaCov RtRs, and BetaCov RtRI-related sequences
Data Source
AI summary
A vaccine candidate is herein described comprised by statistically significant DNA fragments related to Civet SARS, Bat Sars, and BtRs BetaCov, BtRI BetaCov, and Neoromicia resulting in three types of compositions: 1) a composition of statistically significant DNA fragments, 2) a composition of RNA transcripts corresponding to the statistically significant DNA fragments, and 3) a computational reduction composition wherein the DNA fragments are fully or partially subtracted from a base organism, resulting in a synthetic organism which has a high statistical likelihood of problematic functions being partially or fully removed.