Patent Application
20210403880
The present invention focuses on a computational reduction vaccine for Covid-19 with reduction fragments larger than 100 base pairs.
A computational reduction vaccine may be defined herein as a vaccine candidate which is arrived at by removing various non-repetitive fragments in a virus or bacteria first computationally, then via Crispr in an a “Super-Organism” (an organism which contains all, or the majority, of those fragments), and then utilizing the remaining organism as a traditional “live” or “dead” vaccine, which even though marginally computationally reduced, is still recognizable by the human immune system as an invader and therefore provokes a useful immune response. That immune response then shields the recipient from the actual virus going forward.
It is now possible via Python modules such as Numpy (numerical Python) and Biopython (a module specifically designed for computationally manipulating DNA sequences), to analyze in great detail and with great speed thousands, or even millions of sequence records available through the NIH GenBank databases.
Those computational methods are not herein described, but the statistical analysis below will illustrate the efficacy of the method in determining the frequency of various structures, as well as the ability to track those structures though time. It is along those two lines—frequency of appearance, and consistency of appearance, across an entire genetic database that one can derive vaccine candidates computationally.
The traditional way to do this would be to remove each fragment or structure via Crispr one by one and test the resulting organism for problematic function. Once problematic function was discovered, use the resulting live or dead virus in a vaccine. However, in the case of Covid-19, where solutions are demanded in shorter time frames, it is more efficient to simply remove all potential problematic function fragments via various fragment length groups in order to create one or two potential vaccine candidates instead of hundreds. This is the first of two such vaccines.
There are several types of vaccines. This invention introduces a new type of vaccine which is a computationally derived reductive vaccine. A computationally derived reductive vaccine utilizes statistical computation to arrive at a list of fragments which can then be removed from live viruses or bacteria via Crispr to arrive at “neutered” versions which can then form the basis for the vaccine.
Computational reduction in this case may be defined as non-laboratory computational reduction of organisms into fragments, which then can be assessed on the basis of frequency across an entire range of similar organisms as well as computationally tested to confirm that those structures are unique to the virus or bacteria in question. The particulars of the method of discovery for these fragments is proprietary.
What is not proprietary is the statistical analysis of the fragments which are outlined in
The result of this patent is relatively simple. When a “Super Organism” or Covid-19 sample which contains all, or most, of the fragments outlined below is found, that Super Organism can then be genetically modified in a laboratory using Crispr to remove those fragments. Once all those fragments are removed from the organism, it can then be tested to see if problematic function remains.
“Problematic function” in the case of Covid-19 is two-fold: functions of the virus which cause high transmissibility rates, and functions of the virus which cause high mortality rates. It may take us years to figure out exactly what those functions are and where they appear exactly on the genetic assay. This patent provides a shortcut by simply removing all of the most likely candidates for those problematic functions by identifying fragments which appear often enough not to be considered mutations (i.e. fragments only appearing in one or two samples).
The scan of the entire database of Covid-19 provides 18 fragments longer than 100 base pairs which appear more than 32% of the time across the entire database. These fragments are unique to Covid-19 and cannot be found in any other virus in the NIH GenBank databases. Those 18 fragments are listed below. Each fragment table lists the NIH organism Record ID, number of appearances of that identical fragment in the Covid-19 reference database, appearance percentage expressed as a decimal, and the fragment.
Fragment 1:
Fragment 2:
Fragment 3:
Fragment 4:
Fragment 5:
Fragment 6:
Fragment 7:
Fragment 8:
Fragment 9:
Fragment 10:
Fragment 11:
Fragment 12:
Fragment 13:
Fragment 14:
Fragment 15:
Fragment 16:
Fragment 17:
Fragment 18:
Additionally, it is possible to state that the most likely vaccine candidate fragments in more targeted versions of this patent are Fragments 14-16, as they appear in approximately 70% of the database, but not in nearly all samples which would include early (non-transmissible, low-fatality) Covid-19 organisms.
In creation of the vaccine candidate we can also view that vaccine not only as a reductive entity which can be manufactured from a variety of possible starting organisms, but also as a complete organism which has potentially been “neutered” of its destructive features.
In order to arrive at that possibility, we must first find a Covid-19 sample which contains all of these structures. Of the 3,898 complete Covid-19 sequences in the Jun. 16, 2020 Covid-19 database, there are 2,462 which contain the above sequences, and 1,525 which contain the maximum of 17 of the 18 of those sequences. However, since there is some overlap between fragments, only 8 need be removed.
So to create a reductive vaccine, computationally those fragments are removed to create the vaccine candidate as shown in this patent's sequence file. The original reference sequence is also included and can also be downloaded from NIH via the reference MT370902.1. As previously stated, there are also 2,467 other reference candidates which could be used as Super Organisms or Base Organisms for the next generation of vaccines. That list is available upon request.
This application also seeks to cover the RNA transcript of each of the fragments. It may well be that RNA transcript vaccines based on these fragments would be of equal or greater efficacy in triggering a useful immune response.
It should also be noted that these fragments are 100 base pairs or greater, which means a fragment has only a 1 in 1.60 novemdecillion (4100) chance of occurring—in the entire history of the planet. In other words, even at a 32% recurrence rate across the entire Covid-19 genome, these fragments represent viable mathematical targets for vaccines.
This application identifies 18 such fragments.
Claims moved to separate file
Abstract moved to separate file.
| Number | Date | Country | |
|---|---|---|---|
| 63045101 | Jun 2020 | US |
