By CAPosts 19 January, 2021 - 05:35am 174 views
Natural language processing (NLP) algorithms can now generate protein sequences and predict virus mutations, such as changes that could help the coronavirus evade the immune system.
In a study published recently in the journal Science , a group of researchers, including experts from MIT, show how they use these algorithms to identify mutations in advance.
The basic idea is that the interpretation of a virus by an immune system is analogous to the interpretation that a human makes of a sentence.
It is a new method to identify and predict those mutations that can cause a virus to escape from the immune system or not respond adequately to vaccines and treatments already developed for the treatment of that pathogen.
To carry forward In this task the researchers used the linguistic concepts of grammar and semantics. The genetic aptitude of a virus, that is, its characteristics to infect a person, can be interpreted in grammatical terms; whereas mutations or variations of a virus can be interpreted in terms of semantics.
Thus, for example, changes in its surface proteins that make it undetectable by certain antibodies can be read, within this perspective, as a “ alteration of its meaning ”. In other words, a virus with different mutations of this type can have different meanings and, therefore, may require different antibodies capable of fighting it.
For this, the researchers trained the NLP model on thousands of genetic sequences taken from three different viruses: 45,000 unique sequences for a strain of influenza; 60,000 for an HIV strain and between 3,000 and 4,000 for a Sars-Cov-2 strain, as explained in the MIT Technology Review journal.
Know in advance which mutations, capable of eluding the responses of the immune system, could lead to giving itself would help to take more appropriate sanitary measures.
The researchers were applying this analysis model in the new variants of the coronavirus, among them the mutation of the United Kingdom, that of Denmark, as well as the variants of South Africa, Singapore and Malaysia. They soon found that all of them have a high potential for immune escape, although this was not tested in the laboratory but arises from predictive analysis based on neural networks. But the model missed another change in the South African variant that has raised concern because it may allow it to not respond to vaccines.
The use of these algorithms helps predict potential mutations immediately, which would help accelerate processes in the design of health strategies, study of new treatments and development of vaccines. Although it is incipient, it shines a light on a path that will surely have to continue to be explored.