The Protein Revolution: Designing and Securing Custom Proteins with the Power of AI

Wednesday - January 31 2024, 07:57 UTC - 1 year ago

AI has revolutionized custom protein design, unlocking countless new possibilities in medicine and biotechnology. However, with great power comes great responsibility, and the use of barcodes and biosecurity measures may be necessary to prevent misuse or potential risks to public health. Researchers in the field urge for careful consideration of ethical and safety issues as this technology continues to advance.

Two decades ago, engineering designer proteins was a dream. Now, thanks to AI, custom proteins are a dime a dozen. Made-to-order proteins often have specific shapes or components that give them abilities new to nature. From longer-lasting drugs and protein-based vaccines, to greener biofuels and plastic-eating proteins, the field is rapidly becoming a transformative technology.

Custom protein design depends on deep learning techniques. With large language models—the AI behind OpenAI’s blockbuster ChatGPT—dreaming up millions of structures beyond human imagination, the library of bioactive designer proteins is set to rapidly expand.

"It’s hugely empowering," Dr. Neil King at the University of Washington recently told Nature. "Things that were impossible a year and a half ago—now you just do it." .

Yet with great power comes great responsibility. As newly designed proteins increasingly gain traction for use in medicine and bioengineering, scientists are now wondering: What happens if these technologies are used for nefarious purposes? .

A recent essay in Science highlights the need for biosecurity for designer proteins. Similar to ongoing conversations about AI safety, the authors say it’s time to consider biosecurity risks and policies so custom proteins don’t go rogue.

The essay is penned by two experts in the field. One, Dr. David Baker, the director of the Institute for Protein Design at the University of Washington, led the development of RoseTTAFold—an algorithm that cracked the half-decade problem of decoding protein structure from its amino acid sequences alone. The other, Dr. George Church at Harvard Medical School, is a pioneer in genetic engineering and synthetic biology.

They suggest synthetic proteins need barcodes embedded into each new protein’s genetic sequence. If any of the designer proteins becomes a threat—say, potentially triggering a dangerous outbreak—its barcode would make it easy to trace back to its origin.

The system basically provides "an audit trail," the duo write.

Worlds Collide .

Designer proteins are inextricably tied to AI. So are potential biosecurity policies.

Over a decade ago, Baker’s lab used software to design and build a protein dubbed Top7. Proteins are made of building blocks called amino acids, each of which is encoded inside our DNA. Like beads on a string, amino acids are then twirled and wrinkled into specific 3D shapes, which often further mesh into sophisticated architectures that support the protein’s function.

Top7 couldn’t "talk" to natural cell components—it didn’t have any biological effects. But even then, the team concluded that designing new proteins makes it possible to explore "the large regions of the protein universe not yet observed in nature." .

Enter AI. Multiple strategies recently took off to design new proteins at supersonic speeds compared to traditional lab work.

One is structure-based AI similar to image-generating tools like DALL-E. These AI systems are trained on noisy data and learn to remove the noise to find realistic protein structures. Called diffusion models, they gradually learn protein structures that are compatible with biology.

Another involves training neural networks to simulate protein folding, an important process in creating functional proteins. In a recent study, trained networks completed protein folding simulations nearly 50 times faster than conventional methods.