AI adapts artificial DNA for future drug development

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

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

Researchers have put a lot of effort into trying to control gene expression, as it can, among other things, contribute to the development of protein-based drugs. A recent example is the mRNA vaccine against Covid-19, which instructed cells in the body to produce the same protein found on the surface of the coronavirus. The body’s immune system could then learn to form 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 production techniques are both expensive and slow because it is difficult to control DNA expression. Last year, a research group at Chalmers, led by Aleksej Zelezniak, associate professor of systems biology, took an important step in understanding and controlling how much of a protein is made from a certain sequence. of DNA.

“The first thing was to be able to completely ‘read’ the instructions of the DNA molecule. Now we have succeeded in designing our own DNA that contains the exact instructions to control the amount of a specific protein,” says Aleksej Zelezniak about the latest important study from the research group. breakthrough.

Tailor-made DNA molecules

The principle of the new method is similar to the case where an AI generates faces that look like real people. By learning what a wide selection of faces look like, the AI ​​can then create completely new but natural-looking faces. It is then easy to modify a face by saying for example that 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 have been much more difficult and time-consuming. Likewise, the researchers’ AI learned 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 how much of a gene it wants and then “prints” the appropriate DNA sequence.

“DNA is an incredibly long and complex molecule. It is therefore experimentally extremely difficult to make changes to it by reading and modifying it iteratively, then reading and modifying it again. That way, it takes years of research to find something that works. Instead, it’s much more efficient to let an AI learn the principles of DNA navigation. What would otherwise take years is now reduced to weeks or days,” says first author Jan Zrimec, research associate at the National Biological Institute of Slovenia and former postdoctoral fellow in Aleksej Zelezniak’s group.

The researchers developed their method in the yeast Saccharomyces cerevisiaewhose cells resemble mammalian cells. The next step is to use human cells. The researchers hope their progress will have an impact on the development of new and existing drugs.

“Protein-based drugs for complex diseases or sustainable dietary protein alternatives 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, which makes them economically unviable. With our technology, it is possible to develop and manufacture proteins much more efficiently so that they can be commercialized,” says Aleksej Zelezniak.

The authors of the study are Jan Zrimec, Xiaozhi Fu, Azam Sheikh Muhammad, Christos Skrekas, Vykintas Jauniskis, Nora K. Speicher, Christoph S. Börlin, Vilhelm Verendel, Morteza Haghir Chehreghani, Devdatt Dubhashi, Verena Siewers, Florian David, Jens Nielsen and Aleksej Zelezniak.

The researcher is active at Chalmers University of Technology, Sverige; National Institute of Biology, Slovenia; Biomatter Designs, Lithuania; Institute of Biotechnology, Lithuania; BioInnovation Institute, Denmark; King’s College London, UK.

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