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A handful of teams around the world have started using AlphaFold in work on antibiotic resistance, cancer, covid, and more. Roland Dunbrack at the Fox Chase Cancer Center in Philadelphia is one early adopter. He leads a team that has been using computers to predict protein structures for years. Other teams at the lab then use these structures to guide their experiments.

AlphaFold has introduced an unprecedented level of accuracy to Dunbracks work. They are accurate enough to make biological judgments from, to interpret mutations in a cancer gene, he says of its predictions. We always tried to do that with computer-generated models before, but we were often wrong.

When colleagues ask him to model proteins, Dunbrack says, he can now be more confident in what he gives them. Otherwise, he says, I get really nervous, worried that theyll come back to me and say, We wasted all this money and your model was terribleit didnt work.

AlphaFold can still make mistakes, but when it works well it can be hard to tell the difference between its predictions and a structure produced in the lab, says Dunbrack. He runs AlphaFold predictions on a computer platform called ColabFold, hosted by Harvard University and running on Google GPUs. Every night I set one up before I go to sleep, and they take a few hours to run, he says.

Its a super useful tool that everybody in my lab is using, says Kliment Verba, a structural biologist at the University of California, San Francisco. Verba mostly works on cancer, but in the early weeks of the covid-19 pandemic, he joined a loose consortium of researchers studying the SARS-CoV-2 virus. In particular, he wanted to figure out how its proteins hijacked host proteins.

Verba and his colleagues had produced part of the structure for the viral protein they were interested in, but they were missing a piece. Many proteins have multiple domainsdensely folded sections, a few hundred amino acids long, that can each have a separate function. One domain might bind to DNA, another might bind to another protein, and so on. Theyre multiheaded beasts, says Dunbrack.

Structurally, domains are like knots in a rope, connected by loose, looping strands that flop around. In the protein he was studying, Verbas team had figured out the rough shape of the rope but not the detailed structure of all the knots. Without that detail, there was little they could say about how it worked.

They realized, though, that this protein was one of those DeepMind had already run through AlphaFold and shared online. AlphaFolds prediction wasnt perfect; the looping strands werent quite right. But it had the shape of the proteins four domains. The researchers took AlphaFolds predictions for the domains and lined them up with the rough shape they had. It was remarkably close.

I remember that moment when I saw it fit, says Verba. It was amazing. We were now the only ones in the world with the full structure. They published their findings soon after.

Verba thinks AlphaFolds strength lies in finding structures for proteins that have not yet been fully studied. Many of the proteins we care about have been studied for decades, he says. People have spent careers chipping away at them, so we have a fairly good idea what they look like. But that still leaves a lot of uncharted territory.

Verba is interested in kinases, for example. Kinases are enzymes that play a crucial role in regulating the normal function of cells. If they stop working properly, they can cause cancer. Only around half of the 500 or so kinases in the human body are well understood; the remainder is known as the dark kinome.

Researchers like Verba and Dunbrack are interested in developing cancer drugs that target the kinome. But this is where AlphaFolds limitations kick in.

Because working out the structure of a protein in the lab is costly, it is typically done only once the protein has been picked as a promising candidatewhich might be months into the drug discovery process. The hope, Deane says, is that AlphaFold could reverse that sequence, making the pipeline move faster. Now I can start with the structureI can identify where it has pockets on the surface, places where I can bind drug molecules, she says.

A lot of the time these small transformations are the crux of biological function.

Yetas Deane acknowledgesyou need more than a static structure to fully understand how a drug and a protein might interact. Proteins do not stay still; their structures can cycle through subtle reconfigurations. A lot of the time these small transformations are the crux of biological function, says Verba.

Whats more, a protein may be open to receiving a drug in one state but not others. And judging from what researchers are seeing so far, AlphaFold appears to predict the most common state of these structures, which may not be the state that is important for drug development.

Proteins can also change shape when drugs bind to them, which can affect how the drug works. In the worst-case scenario, a drug binding to a protein can have unpredictable knock-on effects on adjoining proteins, potentially even reversing what the drug was designed to dofor example, activating rather than inhibiting some function.

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