Thanks to deep learning, the central mysteries of structural biology are falling like dominos.
Just last year, DeepMind shocked the biomedical field with AlphaFold, an algorithm that predicts protein structures with jaw-dropping accuracy. The University of Washington (UW) soon unveiled RoseTTAFold, an AI that rivaled AlphaFold in predictive ability. A few weeks later, DeepMind released a near complete catalog of all protein structures in the human body.
Together, the teams essentially solved a 50-year-old grand challenge in biology, and because proteins are at the heart of most medications, they may also have seeded a new era of drug development. For the first time, we have unprecedented insight into the protein engines of our cells, many of which had remained impervious to traditional lab techniques.
Yet one glaring detail was missing. Proteins don’t operate alone. They often associate into complexes—small groups that interact to carry out critical tasks in our cells and bodies.
This month, the UW team upped their game.
Tapping into both AlphaFold and RoseTTAFold, they tweaked the programs to predict which proteins are likely to tag-team and sketched up the resulting complexes into a 3D models.
Using AI, the team predicted hundreds of complexes—many of which are entirely new—that regulate DNA repair, govern the cell’s digestive system, and perform other critical biological functions. These under-the-hood insights could impact the next generation of DNA editors and spur new treatments for neurodegenerative disorders or anti-aging therapies.
“It’s a really cool result,” said Dr. Michael Snyder at Stanford University, who was not involved in the study, to Science.
Like a compass, the results can guide experimental scientists as they test the predictions and search for new insights into how our cells grow, age, die, malfunction, and reproduce. Several predictions further highlighted how our cells absorb external molecules—a powerful piece of information that could help us coerce normally reluctant cells to gulp up medications.
“It.gives you a lot of potential new drug targets,” said study author Dr. Qian Cong at the University of Texas Southwestern Medical Center.
The Cell’s Lego Blocks
Our bodies are governed by proteins, each of which intricately folds into 3D shapes. Like unique Lego bricks, these shapes allow the proteins to combine into larger structures, which in turn conduct the biological processes that propel life.
Too abstract? An example: when cells live out their usual lifespan, they go through a process called apoptosis—in Greek, the falling of the leaves—in which the cell gently falls apart without disturbing its neighbors by leaking toxic chemicals. The entire process is a cascade of protein-protein interactions. One protein grabs onto another protein to activate it. The now-activated protein is subsequently released to stir up the next protein in the chain, and so on, eventually causing the aging or diseased cell to sacrifice itself.
Another example: in neurons during learning, synapses (the hubs that connect brain cells) call upon a myriad of proteins that form a complex together. This complex, in turn, spurs the neuron’s DNA to make proteins that etch the new memory into the brain.
“Everything in biology works in complexes. So, knowing who works with who is critical,” said Snyder.
For decades, scientists have relied on painfully slow processes to parse out those interactions. One approach is computational: map out a protein’s structure down to the atomic level and predict “hot spots” that might interact with another protein. Another is experimental: using both biological lab prowess and physics ingenuity, scientists can isolate protein complexes from cells—like sugar precipitating from lemonade when there’s too much of it—and use specialized equipment to analyze the proteins. It’s tiresome, expensive, and often plagued with errors.
Here Comes the Sun
Deep learning is now shining light on the whole enterprise....