When Pietro Fontana joined the Wu Lab at Harvard Medical School and Boston Children’s Hospital in May 2019, he had before him what has been called one of the world’s hardest, giant jigsaw puzzles. It was the task of piecing together a model of the nuclear pore complex, one of the largest molecular machines in human cells.
“It was very challenging from the start,” he explains. The complex has been called a behemoth for good reason: it is made up of more than 30 different protein subunits, termed nucleoporins, and in total contains more than 1,000 of them, intricately weaved together.
So when he sat down to use AlphaFold in his work for the first time two years later – together with Alexander Tong of University of California, Berkeley, who was more familiar with the AI system – he was uncertain if it would help. But what followed in the summer of 2021 was a somewhat unexpected breakthrough moment. AlphaFold predicted the structures of nucleoporins that had not been previously determined, unveiling more of the nuclear pore complex in the process. Thanks to the AI they could generate a near-complete model of the complex’s cytoplasmic ring.
“Many components were already well known, but with AlphaFold, we also built the ones that were structurally unknown,” he says. “I started to realise how it’s actually a big and useful tool for us. I think AlphaFold has completely changed the idea of structural biology.”
Molecular scientists like Fontana have dedicated themselves to deciphering the nuclear pore complex for decades. It is important because it is a gatekeeper for everything that goes in and out of the nucleus and is thought to hold answers to a growing number of serious human diseases, including amyotrophic lateral sclerosis (ALS) and other neurodegenerative illnesses. Knowing how the complex is assembled could open the door to other groundbreaking, even life-saving, discoveries.
The sheer size complex alone is challenging enough, but its many varied parts represent an added complication. “That’s one major difficulty in achieving a resolution [clear enough] that we can interpret the sequence and structure of the complex,” says Hao Wu, the lab’s principal investigator. Even with a lot of data, the team had previously only managed structural images of medium resolution.
Missing pieces of the puzzle also stymied progress. Without the full set, it is hard to tell how the jigsaw fits, says Wu. “To figure out how the different protein subunits come together, you really need to have some assistance on their individual structures,” Wu explains.
This is where AlphaFold changed the game for the Wu Lab which also included Ying Dong and Xiong Pi. Running it on proteins found in the eggs of the African clawed frog (Xenopus laevis) – used as a model system – the team, managed to map all the different subunit structures, which were unknown up until then. “When we started trying, we didn’t really know if the predictions would fit the map nicely,” Wu recalls. “But that’s what happened. That was pretty remarkable.”
Of course, science is a collaborative effort. When it comes to solving a riddle as intricate as the nuclear pore complex, it’s not just teamwork, but the culmination of the diligence and tenacity of many teams aroundthe world. Across the Atlantic, scientists from Max Planck Institute of Biophysics (MPIBP) and European Molecular Biology Laboratory (EMBL) in Germany have used AlphaFold in combination with cryo-electron tomography to model the human NPC. What they have achieved so far is a new model twice as complete as the old one. Now covering two-thirds of the NPC, a huge part of the puzzle has been solved, and a bigstep has been taken towards understanding how it controls what goes in and out of the cell nucleus.
There is still a way to go – the final third remains. And while AlphaFold will make the remaining puzzle easier to solve, scientists are also aware of its limitations. According to Wu, the AI system worked well in the case of the nuclear pore complex because its subunits contained repeated helical structures, which tend to be easier to predict. But it might not be as straightforward for other proteins.
It is important not to treat AlphaFold – nor any other AI tool for that matter – as a be-all and end-all. Tong. “In fact, AlphaFold can give you some very strange results,” says Wu. “But if you understand how it predicts, you can take that into account [in the analysis].”
Still, it’s clear AlphaFold has not only expanded the limits of science, but also done it in a timeframe previously thought impossible. “I’m glad AlphaFold came out at the right moment because it sped everything up significantly,” says Fontana.
Fontana P., Dong Y., Pi X., Tong A.B., Hecksel C.W., Wang L., Fu TM., Bustamante C., Wu H. Structure of cytoplasmic ring of nuclear pore complex by integrative cryo-EM and AlphaFold. Science 376, 6598, (2022). DOI:10.1126/science.abm9326.