Synthetic DNA can control the production of proteins in cells

With the help of AI, researchers at Chalmers University of Technology, Sweden, have been able to design synthetic DNA that controls the production of proteins in cells. The technology could contribute to the development and production of vaccines, drugs for serious diseases, as well as alternative dietary proteins much faster and at significantly lower costs than today.

How our genes are expressed is a process that is fundamental to the functionality of cells in all living organisms. Simply put, the genetic code in DNA is transcribed into the messenger RNA (mRNA) molecule, which tells the cell’s factory which protein to produce and in what quantities.

Researchers have put a lot of effort into trying to control gene expression because it can, among other things, contribute to the development of protein-based drugs. A recent example is the Covid-19 mRNA vaccine, which instructs the body’s cells to produce the same protein found on the surface of the coronavirus. The body’s immune system can then learn to make antibodies against the virus. Similarly, it is possible to teach the body’s immune system to defeat cancer cells or other complex diseases if one understands the genetic code behind the production of specific proteins.

Most of today’s new drugs are protein-based, but the techniques to make them are expensive and slow because it is difficult to control how DNA is expressed. Last year, a research group at Chalmers led by Alexey Zheleznyak, associate professor of systems biology, took an important step in understanding and controlling how much protein is made from a particular DNA sequence.

“First it was about being able to fully ‘read’ the instructions of the DNA molecule. Now we have been able to design our own DNA, which contains the exact instructions to control the amount of a specific protein,” says Alexey Zheleznyak about the breakthrough research group’s latest important study.

DNA molecules to order

The principle behind the new method is similar to when AI generates faces that look like real people. By learning what a large selection of faces look like, AI can create completely new but natural-looking faces. Then it’s easy to change a face, such as saying it should look older or have a different hairstyle. On the other hand, programming a believable face from scratch, without the use of AI, would be much more difficult and time-consuming. Likewise, the researchers’ artificial intelligence was taught the structure and regulatory code of DNA. The AI ​​then designs synthetic DNA where it is easy to modify its regulatory information in the desired direction of gene expression. Simply put, the AI ​​is told what part of the gene is desired and then “imprints” the appropriate DNA sequence.

“DNA is an incredibly long and complex molecule. It is therefore experimentally extremely challenging to make changes to it by iteratively reading and changing, then reading and changing again. Thus, it takes years of research to find something that works. Instead, it is much more efficient to let the AI ​​learn the principles of navigation in DNA. What would otherwise take years is now shortened to weeks or days,” says first author Jan Zrimec, a research fellow at the National Institute of Biology in Slovenia and a former postdoc in the group of Aleksey Zhelezniak.

The researchers developed their method in the yeast Saccharomyces cerevisiae, whose cells resemble mammalian cells. The next step is to use human cells. The researchers hope that their progress will have an impact on the development of new as well as existing drugs.

“Protein-based drugs for complex diseases or alternative resistant dietary proteins can take many years and can be extremely expensive to develop. Some are so expensive that it is impossible to get a return on investment, making them economically unviable. With our technology, it is possible to develop and produce proteins much more efficiently so that they can be brought to market,” says Alexey Zheleznyak.

Reference: Zrimec J, Fu X, Muhammad AS, et al. Controlling gene expression with deep generative design of regulatory DNA. Nat Common. 2022;13(1):5099. do: 10.1038/s41467-022-32818-8

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