AI works on artificial DNA for future drug development

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With the help of artificial intelligence (AI), researchers at Chalmers University of Technology in Sweden have succeeded in designing synthetic DNA that controls the cells’ protein production. 

The technology can contribute to the development and production of vaccines, drugs for severe diseases, as well as alternative food proteins much faster and at significantly lower costs than today.

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

Researchers have worked on trying to control gene expression because 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 the body’s cells 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. 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.

Researchers at Chalmers University of Technology took an important step

Most of today’s new drugs are protein-based, but the techniques for producing them are expensive and slow, because it is difficult to control how the DNA is expressed. Last year, a research group at Chalmers University of Technology, 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 DNA sequence.

“First it was about being able to fully ‘read’ the DNA molecule’s instructions. Now we have succeeded in designing our own DNA that contains the exact instructions to control the quantity of a specific protein,” Zelezniak said about the group’s breakthrough.

DNA molecules made-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 looks like, AI can then create completely new but natural-looking faces. It is then easy to modify a face by, for example, saying 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. 

Similarly, the researchers’ AI has been taught the structure and regulatory code of DNA. The AI then designs synthetic DNA, where it is easier to modify its regulatory information in the desired direction of gene expression. The AI is told how much of a gene is desired and then ‘prints’ the appropriate DNA sequence.

“DNA is an incredibly long and complex molecule. It is thus experimentally extremely challenging to make changes to it by iteratively reading and changing it, then reading and changing it again. This way it takes years of research to find something that works. Instead, it is much more effective to let an AI learn the principles of navigating DNA. What otherwise takes years is now shortened to weeks or days,” said first author Jan Zrimec, a research associate at the National Institute of Biology in Slovenia and past postdoc in Zelezniak’s group.

Yeast cells

The researchers have developed their method in the yeast Saccharomyces cerevisiae, whose 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 as well as existing drugs.

“Protein-based drugs for complex diseases or alternative sustainable food proteins can take many years and can be extremely expensive to develop. Some are so expensive that it is impossible to obtain a return on investment, making them economically non viable. With our technology, it is possible to develop and manufacture proteins much more efficiently so that they can be marketed,” Zelezniak said.

The authors of the study, Controlling gene expression with deep generative design of regulatory DNA, 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 Nielsenand Aleksej Zelezniak.

The researchers are at Chalmers University of Technology, Sweden; National Institute of Biology, Slovenia; Biomatter Designs, Lithuania; Institute of Biotechnology, Lithuania; BioInnovation Institute, Denmark; King’s College London, U.K.

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