Evolution at Warp Speed: Building Proteins with AI

Category Artificial Intelligence

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Building proteins with AI has two main strategies - the IKEA approach and starting with a vision and design tailored to individual needs. The University of Washington used a top-down approach grounded in AI reinforced learning to design structures, such as a 20-sided shell that can be used in medicine and gene therapies, that outperform the latest vaccine candidates in clinical trials.

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Building proteins with AI is like furnishing a house.

There are two main strategies. One is the IKEA approach: you buy pre-made pieces that easily snap together, but can only hope the furniture somewhat fits your space. While relatively simple, you have no control over the dimensions or functions of the final product.

The other way starts with a vision and design perfectly tailored to your needs. But the hard part is finding—or building—individual pieces for the custom design.

The AI approach used to create protein complexes is a top-down approach rather than the more common bottom-up approach.

The same two methods apply to engineering protein complexes using AI. Similar to a cabinet, protein complexes are made of multiple sub-units that intricately bind together. These mega structures—with shapes ranging from a twenty-sided die to tunnels that open and close—form the foundation of our metabolism, immune defenses, and brain functions.

Previous attempts at shaping protein architectures mostly used the IKEA approach. It’s revolutionary: AI-based designs have already generated COVID vaccines at lightning speed. While powerful, the approach is limited by available protein "building blocks".

Along with its application in medicine, the approach also has potential applications in gene therapies, drug delivery and even other areas of AI research.

This month, a team led by Dr. David Baker from the University of Washington took protein design to a new custom level. Starting with specific dimensions, shapes, and other properties, the team tapped into a machine learning algorithm to build protein complexes tailored to specific biological responses.

In other words, rather than the usual bottom-up method, they went top-down.

One design, for example, is a 20-sided shell that mimics the outer protective layer of viruses. When dotted with immune-stimulating proteins from the flu virus, the AI-designed protein shell sparked an immune response in mice that outperformed the latest vaccine candidates in clinical trials.

Reinforcement learning is a type of algorithm that is used to teach artificial intelligence agents to accomplish tasks through trial and error.

The AI isn’t just for vaccines. The same strategy could build more compact and efficient carriers for gene therapies or carry antibodies and other drugs that need extra protection from being immediately broken down in the body.

But more broadly, the study shows that it’s possible to design massively complex protein architectures starting from an overall vision, rather than working with the biological equivalent of two-by-four boards.

The Monte Carlo Tree Search (MCTS) is one type of reinforcement learning algorithm used by AI agents.

"It’s astounding that the team could do this," said Dr. Martin Noble at Newcastle University, who was not involved in the work. "It takes evolution billions of years to design single proteins that fold just right, but this is another level of complexity, to fold proteins to fit so well together and make closed structures." .

--- Evolution at Warp Speed --- .

At the heart of the new work is reinforcement learning. You’ve probably heard of it. Loosely based on how the brain learns through trial and error, reinforcement learning powers multiple AI agents that have taken the world by storm. Perhaps the best known is AlphaGo, the DeepMind brainchild that triumphed over the human world champion in the board game Go. More recently, reinforcement learning has been speeding progress in self-driving cars and even developing better algorithms by streamlining fundamental computations.

The study of AI architecture used to create protein complexes was conducted under Dr. David Baker at the University of Washington.

In the new study, the team tapped into a type of reinforcement learning algorithm called the Monte Carlo tree search (MCTS). While sounding like a casino move, it’s a popular reinforcement learning strategy that searches for optimized decisons in any given problem.

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