AI plus gene editing promises to shift biotechnology into high gear

During his Nobel Prize in chemistry lecture in 2018, Frances Arnold he said, “Today we can, for all practical purposes, read, write and edit any DNA sequence, but we cannot invent it.” That is no longer true.

Since then, science and technology have advanced so much that artificial intelligence has learned how to compose DNA, and with genetically modified bacteria, scientists are on their way to designing and making custom proteins.

The goal is that with AI design talents and gene-editing engineering capabilities, scientists can modify bacteria to act as mini-factories that produce new proteins that can reduce greenhouse gases, digest plastics or act as related pesticides. species specific.

As a chemistry professor and computational chemist who studies molecular science and environmental chemistry, I believe that advances in AI and gene editing make this a realistic possibility.

Gene sequence – reading life’s recipes

All living things contain genetic materials – DNA and RNA – that provide the hereditary information needed to replicate themselves and make proteins. Proteins make up 75% of a person’s dry weight. They make up muscles, enzymes, hormones, blood, hair and cartilage. Understanding proteins means understanding much of biology. The order of nucleotide bases in DNA, or RNA in some viruses, encodes this information, and genomic sequencing technologies identify the order of those bases.

The Human Genome Project was an international effort that sequenced the entire human genome from 1990 to 2003. Thanks to rapidly improving technologies, it took seven years to sequence the first 1% of the genome and another seven years for the other 99%. By 2003, scientists had encoded the complete sequence of 3 billion nucleotide base pairs for 20,000 to 25,000 genes in the human genome.

However, understanding the functions of most proteins and correcting their malfunctions has remained a challenge.

AI learns proteins

The shape of each protein is critical to its function and is determined by its amino acid sequence, which in turn determines the nucleotide sequence of the gene. Misfolded proteins have the wrong shape and can cause diseases such as neurodegenerative diseases, cystic fibrosis and Type 2 diabetes. Understanding these diseases and developing treatments requires knowledge of protein shapes.

Before 2016, the only way to determine the shape of a protein was through X-ray crystallography, a laboratory technique that uses X-ray diffraction by single crystals to determine the precise arrangement of atoms and molecules in three dimensions in a molecule. At that time, the structure of about 200,000 proteins was determined by crystallography, which costs billions of dollars.

AlphaFold, a machine learning program, used these crystal structures as a training set to determine the shape of proteins from their nucleotide sequences. And in less than a year, the program calculated the protein structures of all 214 million genes sequenced and published. The protein structures determined by AlphaFold have all been released in a freely available database.

To effectively address non-infectious diseases and design new drugs, scientists need more detailed information on how proteins, especially enzymes, bind small molecules. Enzymes are protein catalysts that enable and control biochemical reactions.

AlphaFold3, released on May 8, 2024, can predict protein shapes and where small molecules can bind to these proteins. In rational drug design, drugs are designed to bind proteins in a pathway relevant to the disease being treated. The small molecule drugs bind to the protein binding site and modulate its activity, thereby influencing the course of the disease. By being able to predict protein binding sites, AlphaFold3 will improve researchers’ drug development capabilities.

AI + CRISPR = inventing new proteins

Around 2015, the development of CRISPR technology revolutionized gene editing. CRISPR can be used to find, change or delete a specific part of a gene, make the cell express more or less of its gene product, or even replace it with a completely foreign gene.

In 2020, Jennifer Doudna and Emmanuelle Charpentier received the Nobel Prize in chemistry “for developing a (CRISPR) method for genome editing.” With CRISPR, gene editing, which took years and was species-specific, expensive and laborious, can be can now be done in days and at a fraction of the cost.

AI and genetic engineering are advancing rapidly. What was once complicated and expensive is now routine. Looking ahead, the dream is specially designed proteins and produced by a combination of machine learning and CRISPR-modified bacteria. AI would design the proteins, and bacteria modified with CRISPR would produce the proteins. Enzymes produced in this way could inhale carbon dioxide and methane while exhaling organic nutrients, or break down plastics into substitutes for concrete.

I believe that these ambitions are not unrealistic, since genetically modified organisms are already responsible for 2% of the US economy in agriculture and pharmaceuticals.

Two groups have made functional enzymes from scratch that were designed by different AI systems. The David Baker Institute for Protein Design at the University of Washington devised a new deep-learning protein design strategy they called “family-wide chivalry,” which they used to make a unique light-emitting enzyme. Meanwhile, biotech startup Profluent has used AI trained from the sum of all CRISPR-Cas knowledge to design new functional genome editors.

If AI can learn to make new CRISPR systems as well as bioluminescent enzymes that work and have never been seen on Earth, it is hoped that pairing CRISPR with AI could be used to design other new ordered enzymes. Although the CRISPR-AI combination is still in its infancy, when it matures it is likely to be very beneficial and could even help the world combat climate change.

It is important to remember, however, that the more powerful the technology, the greater the risks associated with it. Also, humans have not been very successful in engineering nature due to the complexity and interconnectedness of natural systems, often leading to unintended consequences.

This article is republished from The Conversation, a non-profit, independent news organization that brings you facts and analysis to help you make sense of our complex world.

Written by: Marc Zimmer, Connecticut College.

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Marc Zimmer does not work for, consult with, own shares in, or receive funding from any company or organization that would benefit from this article this article, and has disclosed no relevant affiliations beyond their academic appointment.

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