AI adapts artificial DNA for future drug development

image: AI adapts artificial DNA
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Credit: Yen Strandqvist

With the help of an artificial intelligence, researchers at Chalmers University of Technology in Sweden have succeeded in engineering the synthetic DNA that controls the production of proteins in cells. The technology can help develop and produce vaccines, drugs for serious diseases and alternative dietary proteins much faster and at significantly lower cost than today.

How our genes are expressed is a fundamental process for cell function in all living organisms. Simply put, the genetic code in the DNA is transcribed into the messenger RNA (mRNA) molecule, which tells the cell’s factory which protein to make and how much.

Researchers have worked hard to try to control gene expression because it can, among other things, help in the development of protein-based medicines. A recent example is the mRNA vaccine against Covid-19, which has 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. Similarly, the body’s immune system can be taught to defeat cancer cells or other complex diseases if you understand the genetic code behind the production of specific proteins.

Most of today’s new drugs are protein-based, but the techniques to produce them are expensive and slow because it is difficult to control how DNA is expressed. Last year, a research team at Chalmers, led by Aleksej Zelezniak, associate professor of systems biology, took an important step in understanding and controlling the amount of a protein produced from a given DNA sequence.

“Before it was about being able to completely ‘read’ the instructions of the DNA molecule. Now we have managed to design our DNA that contains the exact instructions to control the amount of a specific protein,” says Aleksej Zelezniak about the last important work of the research group carried out.

DNA molecules made to order

The principle behind the new method is similar to when an AI generates faces that look like real people. By learning what a large selection of faces look like, the AI ​​can then create all-new yet natural-looking faces. It is therefore 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 artificial intelligence, would have been much more difficult and time consuming. Similarly, the researchers’ AI was taught the structure and regulatory code of DNA. AI then designs synthetic DNA, where it is easy to change its regulatory information in the desired direction of gene expression. Simply put, 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 therefore experimentally extremely challenging to make changes to it by reading and iteratively changing it, then reading and changing it again. This way it takes years of research to find something that works. Instead, it’s far more effective 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, a research associate at the National Institute of Biology in Slovenia and a postdoc in Aleksej Zelezniak’s group.

The researchers developed their method in Sacc yeastharomyces cerevisiae, whose cells resemble mammalian cells. The next step is to use human cells. The researchers hope their advances will impact the development of new and existing drugs.

“Protein-based drugs for complex diseases or alternative sustainable 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 commercialised,” says Aleksej Zelezniak.

The study authors 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 Alexey Zelezniak.

The researchers are 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.

For more information, please contact: Alexey ZelezniakAssociate Professor, Department of Biology and Biological Engineering, Chalmers University of Technology, +46 31 772 81 71, aleksej.zelezniak@chalmers.se​

Read the full study: Controlling Gene Expression with Deep Generative Design of Regulatory DNA

Caption: Aleksej Zelezniak, Associate Professor, Department of Biology and Biological Engineering, Chalmers University of Technology

Illustration credit: Yen Strandqvist, Chalmers


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