So it goes, over and over again. But somewhat often, amidst this repeated copy-pasting, things get mixed up. Mutations arise in the copies. Sometimes the mutation does nothing at all, because different sequences that encode the same proteins make up for the error.
But every once in a while, mutations go perfectly right. Scientists are always on the lookout for signs of potential escape. So far, things are looking okay. But the genetic code offers too many possibilities to test every evolutionary branch the virus might take over time. Last winter, Brian Hie, a computational biologist at MIT and a fan of the lyric poetry of John Donne, was thinking about this problem when he alighted upon an analogy: What if we thought of viral sequences the way we think of written language?
Every viral sequence has a sort of grammar, he reasoned—a set of rules it needs to follow in order to be that particular virus. When mutations violate that grammar, the virus reaches an evolutionary dead end.
The analogy had a simple, almost too simple, elegance. But to Hie, it was also practical. Our bodies have B cells and T cells tailored for nearly every pathogen the world can throw at us, but it takes time for the right immune cells to find the invader and multiply. In the meantime, we get sick. The good news is that while this war is raging, the immune system also produces memory B and T cells, which make a record of the battles. If we get exposed to the same pathogen again, the immune system has an arsenal at the ready and responds much more rapidly.
We may experience mild symptoms, or none at all. The goal of a vaccine, then, is to expose the body to a pathogen without making us sick so that the immune system is primed to fight it on any subsequent exposure. This can be done by exposing the body to specific pieces of a virus or weakened versions of it.
Crucially, the vaccine must include key parts of the virus, called antigens, that are immunogenic, meaning that they're recognizable to B cells and T cells and will therefore trigger the desired adaptive immune response. When faced with a new pathogen, the first question for vaccine designers is: Which parts of it are the most immunogenic?
The outer membrane is often studded with so-called spike proteins , which enable the virus to bind to receptors on a host cell and inject its payload of genetic material. For this reason, spike proteins are a typical target for vaccines. If the immune system creates antibodies that disable the spike protein, the virus cannot break into cells.
However, for any given virus there are tens of thousands of different subcomponents of the outer proteins that the immune system can recognize, and therefore tens of thousands of different possibilities for vaccine targeting. This is a prime opportunity for AI. Machine-learning tools can predict, based on training data sets from known pathogens, which pieces of the virus the immune system is most likely to recognize. Armed with this information, immunologists can design vaccines around a more manageable number of potential targets.
The targets are then integrated into vaccine candidates and tested in animals to see if they provoke a good immune response. Using the neural-network algorithms NetMHCpan These targets, or epitopes , are components of the virus that B cells and T cells will likely recognize. As expected, many of the system's top recommended targets were located on the virus's spike protein. Chen's team recommended, in a paper on the preprint server bioRxiv, that these epitopes be included in the design of COVID vaccines.
Chen's machine-learning tools are among several dozen that have been built over the years to aid immunology work. Having identified a target on the virus's surface, researchers can then develop a vaccine.
If the plan is to use an inactivated virus as a vaccine, for example, researchers will grow the live virus in the lab and kill it using heat, radiation, or a chemical method so that it can't replicate when injected into the body.
Then researchers must make sure that the key immunogenic components weren't damaged when the virus was killed, as those parts must be intact in order to provoke an immune response. The next steps are to test the vaccine in the lab, then in small animals, and finally in humans.
To train software to sift through target sites on a virus, it's important to first understand the three-dimensional structure of viral proteins. Viral proteins are made of linear chains of chemicals called amino acids, which spontaneously fold into compact, ribbonlike structures.
Vaccine developers must choose targets on the virus's outer layer that face outward, so that they're physically accessible to immune-system weaponry. When the pandemic hit, researchers at the University of Basel, in Switzerland, used a protein-modeling tool called Swiss-Model to predict the structures of the proteins on the outer surface of the SARS-CoV-2 virus.
Their predictions were later shown to be consistent with the virus's actual protein structures. Algorithms that predict such targets are nice to have, he says, but probably not necessary in the case of COVID But AI can do much more than zero in on the immunogenic sites on a virus.
Many vaccine developers are already using computational tools to design and synthesize the genetic components of DNA-based vaccines. Inovio's DNA vaccines work by mimicking a part of the genetic sequence of the pathogen. These so-called nucleic-acid vaccines contain segments of genetic instructions, in the form of DNA or RNA, that code for a key immunogenic component of the virus.
When the nucleic acid is inserted into human cells, the cells produce the antigen, which triggers an immune response. Inovio researchers knew, based on previous research on other coronaviruses, that the spike protein of SARS-CoV-2 would likely elicit an immune response.
So that region of the virus's genome became their starting point for a vaccine. There are many different ways to write out a DNA sequence that codes for the production of the same protein.
To find the one that will work best as a vaccine, that bit of code has to be enhanced with other genetic and molecular elements. Inovio's proprietary gene-optimization algorithm showed researchers how to do this in such a way that the vaccine would provoke the large-scale production of an immunogenic spike protein. The vaccine performed well in animals—as shown by a study that she and her colleagues published in May in Nature Communications.
The FDA also conducts periodic evaluations of the manufacturing processes involved in producing the vaccines. Besides, what would be the purpose? If there is real concern about what is going into any products in general, it may be best to strengthen the FDA. Unless, of course, they are trying to get you to use dietary supplements instead of getting vaccinated.
This is a BETA experience. You may opt-out by clicking here. More From Forbes. Jan 13, , pm EST. Stanford Medicine spokesperson said in a statement to The Verge.
Our intent was to develop an ethical and equitable process for distribution of the vaccine. We apologise to our entire community, including our residents, fellows, and other frontline care providers, who have performed heroically during our pandemic response. We are immediately revising our plan to better sequence the distribution of the vaccine.
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