AI's protein-folding revolution [Better than AGI. Why? Because it's real.]

Ewen Callaway, What's next for AlphaFold and the AI protein-folding revolution, Nature 604, 13 April 2022, 234-238, doi: https://doi.org/10.1038/d41586-022-00997-5

In the past half-year, AlphaFold mania has gripped the life sciences. “Every meeting I’m in, people are saying ‘why not use AlphaFold?’,” says Christine Orengo, a computational biologist at University College London.

In some cases, the AI has saved scientists time; in others it has made possible research that was previously inconceivable or wildly impractical. It has limitations, and some scientists are finding its predictions to be too unreliable for their work. But the pace of experimentation is frenetic.

Even those who developed the software are struggling to keep up with its use in areas ranging from drug discovery and protein design to the origins of complex life. “I wake up and type AlphaFold into Twitter,” says John Jumper, who leads the AlphaFold team at DeepMind. “It’s quite the experience to see everything.”

Pay attention:

This year, DeepMind plans to release a total of more than 100 million structure predictions. That is nearly half of all known proteins — and hundreds of times more than the number of experimentally determined proteins in the Protein Data Bank (PDB) structure repository.

AlphaFold deploys deep-learning neural networks: computational architectures inspired by the brain’s neural wiring to discern patterns in data. It has been trained on hundreds of thousands of experimentally determined protein structures and sequences in the PDB and other databases. Faced with a new sequence, it first looks for related sequences in databases, which can identify amino acids that have tended to evolve together, suggesting they’re close in 3D space. The structure of existing related proteins provides another way to estimate distances between amino-acid pairs in the new sequence.

AlphaFold iterates clues from these parallel tracks back and forth as it tries to model the 3D positions of amino acids, continually updating its estimate. Specialists say the software’s application of new ideas in machine learning research seems to be what makes AlphaFold so good — in particular, its use of an AI mechanism termed ‘attention’ to determine which amino-acid connections are most salient for its task at any moment.

A running start:

For scientists who want to determine the detailed structure of a specific protein, an AlphaFold prediction isn’t necessarily an immediate solution. Rather, it provides an initial approximation that can be validated or refined by experiment — and which itself helps to make sense of experimental data. Raw data from X-ray crystallography, for instance, appear as patterns of diffracted X-rays. Typically, scientists need a starting guess at a protein’s structure to interpret these patterns. Previously, they’d often cobble together information from related proteins in the PDB or use experimental approaches, says Randy Read, a structural biologist at the University of Cambridge, UK, whose lab specialized in some of these methods. Now, AlphaFold’s predictions have rendered such approaches unnecessary for most X-ray patterns, Read says, and his lab is working to make better use of AlphaFold in experimental models. “We’ve totally refocused our research.”

Drug discovery:

Researchers at pharmaceutical companies and biotechnology firms are excited about AlphaFold’s potential to help with drug discovery, says Shoichet. “Critical optimism is how I’d describe it.” In November 2021, DeepMind launched its own spin-off, IsoMorphic Labs, which aims to apply AlphaFold and other AI tools to drug discovery. But the company has said little else about its plans.

New proteins:

AI tools are not just changing how scientists determine what proteins look like. Some researchers are using them to make entirely new proteins. “Deep learning is completely transforming the way that protein design is being done in my group,” says David Baker, a biochemist at the University of Washington in Seattle and a leader in the field of designing proteins, as well as predicting their structures. His team, with computational chemist Minkyung Baek, led the work to develop RoseTTAFold.

The technology has its limitations, but it has people dreaming big:

The AlphaFold revolution has inspired Kosinski to dream big. He imagines that AlphaFold-inspired tools could be used to model not just individual proteins and complexes, but entire organelles or even cells down to the level of individual protein molecules. “This is the dream we will follow for the next decades.”