The recipe for proteins — large molecules consisting of amino acids that are the fundamental building blocks of tissues, muscles, hair, enzymes, antibodies, and other essential parts of living organisms — are encoded in DNA. It’s these genetic definitions that circumscribe their three-dimensional structures, which in turn determines their capabilities. But protein “folding,” as it’s called, is notoriously difficult to figure out from a corresponding genetic sequence alone. DNA contains only information about chains of amino acid residues and not those chains’ final form.
In December 2018, DeepMind attempted to tackle the challenge of protein folding with a machine learning system called AlphaFold. The product of two years of work, the Alphabet subsidiary said at the time that AlphaFold could predict structures more precisely than prior solutions. Lending credence to this claim, the system beat 98 competitors in the Critical Assessment of Structure Prediction (CASP) protein-folding competition in Cancun, where it successfully predicted the structure of 25 out of 43 proteins.
DeepMind now asserts that AlphaFold has outgunned competing protein-folding-predicting methods for a second time. In the results from the 14th CASP assessment, a newer version of AlphaFold — AlphaFold 2 — has average error comparable to the width of an atom (or 0.1 of a nanometer), competitive with the results from experimental methods.
“We have been stuck on this one problem — how do proteins fold up — for nearly 50 years,” University of Maryland professor John Moult, cofounder and chair of CASP, told reporters during a briefing last week. “To see DeepMind produce a solution for this, having worked personally on this problem for so long and after so many stops and starts, wondering if we’d ever get there, is a very special moment.”
Protein folding
Solutions to many of the world’s challenges, like developing treatments for diseases, can ultimately be traced back to proteins. Antibody proteins are shaped like a “Y,” for example, enabling them to latch onto viruses and bacteria, and collagen proteins are shaped like cords, which transmit tension between cartilage, bones, skin, and ligaments. In SARS-CoV-2, the novel coronavirus, a spike-like protein changes shape to interact with another protein on the surface of human cells, allowing it to force entry.
It was biochemist Christian Anfinsen who hypothesized in 1972 that a protein’s amino acid sequence could determine its structure. This laid the groundwork for attempts to predict a protein’s structure based on its amino acid sequence as an alternative to expensive, time-consuming experimental methods like nuclear magnetic resonance, X-ray crystallography, and cryo-electron microscopy. Complicating matters, however, is the raw complexity of protein folding. Scientists estimate that because of the incalculable number of interactions between the amino acids, it would take longer than 13.8 billion years to figure out all the possible configurations of a typical protein before identifying the right structure.
Above: AlphaFold’s architecture in schematic form.
DeepMind says its approach with AlphaFold draws inspiration from the fields of biology, physics, machine leaning, and the work of scientists over the past half-century. Taking advantage of the fact that a folded protein can be thought of as a “spatial graph,” where amino acid residues (amino acids contained within a peptide or protein) are nodes and edges connect the residues in close proximity, AlphaFold leverages an AI algorithm that attempts to interpret the structure of this graph while reasoning over the implicit graph that it’s building using evolutionarily related sequences, multiple sequence alignment, and a representation of amino acid residue pairs.
By iterating through this process, AlphaFold can learn to predict the underlying structure of a protein and determine its shape within days, according to DeepMind. Moreover, the system can self-assess which parts of each protein structure are reliable using an internal confidence measure.
DeepMind says that the newest release of AlphaFold, which will be detailed in a forthcoming paper, was trained on roughly 170,000 protein structures from the Protein Data Bank, an open source database for structural data of large biological molecules. The company tapped 128 of Google’s third-generation tensor processing units (TPUs), special-purpose AI accelerator chips available through Google Cloud, for compute resources roughly equivalent to 100 to 200 graphics cards. Training took a few weeks. For the sake of comparison, it took DeepMind 44 days to train a single agent within its StarCraft 2-playing AlphaStar system using 32 third-gen TPUs.
DeepMind declined to reveal the cost of training AlphaFold. But Google charges Google Cloud customers $ 32 per hour per third-generation TPU, which works out to about $ 688,128 per week.
Measuring progress
In 1994, Moult and University of California, Davis professor Krzysztof Fidelis founded CASP as a biennial blind assessment to catalyze research, monitor progress, and establish the state of the art in protein structure prediction. It’s considered the gold standard for benchmarking predictive techniques, because CASP chooses structures that have only recently been experimentally selected as targets for teams to test their prediction methods against. Some were still awaiting validation at the time of AlphaFold’s assessment.
Because the target structures aren’t published in advance, CASP participants must blindly predict the structure of each of the proteins. These predictions are then compared to the ground-truth experimental data when this data become available.
The primary metric used by CASP to measure the accuracy of predictions is the global distance test, which ranges from 0 to 100. It’s essentially the percentage of amino acid residues within a certain threshold distance from the correct position. A score of around 90 is informally considered to be competitive with results obtained from experimental methods; AlphaFold achieved a median score of 92.4 overall and a median score of 87 for proteins in the free-modeling category (i.e., those without templates).
Above: The results of the CASP14 competition.
“What we saw in CASP14 was a group delivering atomic accuracy off the bat,” Moult said. “This [progress] gives you such excitement about the way science works — about how you can never see exactly, or even approximately, what’s going to happen next. There are always these surprises. And that really as a scientist is what keeps you going. What’s going to be the next surprise?”
Real-world applications
DeepMind makes the case that AlphaFold, if further refined, could be applied to previously intractable problems in the field of protein folding, including those related to epidemiological efforts. Earlier this year, the company predicted several protein structures of SARS-CoV-2, including ORF3a, whose makeup was formerly a mystery. At CASP14, DeepMind predicted the structure of another coronavirus protein, ORF8, which has since been confirmed by experimentalists.
Beyond pandemic response, DeepMind expects that AlphaFold will be used to explore the hundreds of millions of proteins for which science currently lacks models. Since DNA specifies the amino acid sequences that comprise protein structures, advances in genomics have made it possible to read protein sequences from the natural world, with 180 million protein sequences and counting in the publicly available Universal Protein database. In contrast, given the experimental work needed to translate from sequence to structure, only around 170,000 protein structures are in the Protein Data Bank.
DeepMind says it’s committed to making AlphaFold available “at scale” and collaborating with partners to explore new frontiers, like how multiple proteins form complexes and interact with DNA, RNA, and small molecules. Improving the scientific community’s understanding of protein folding could lead to more effective diagnoses and treatment of diseases such as Parkinson’s and Alzheimer’s, as these are believed to be caused by misfolded proteins. And it could aid in protein design, leading to protein-secreting bacteria that make wastewater biodegradable, for instance, and enzymes that can help manage pollutants such as plastic and oil.
Above: A ground-truth folded protein compared with AlphaFold 2’s prediction.
In any case, it’s a milestone for DeepMind, whose work has principally focused on the games domain. Its AlphaStar system bested professional players at StarCraft 2, following wins by AlphaZero at Go, chess, and shogi. While some of DeepMind’s work has found real-world application, chiefly in datacenters, Waymo’s self-driving cars, and the Google Play Store’s recommendation algorithms, DeepMind has yet to achieve a significant AI breakthrough in a scientific area such as protein folding or glass dynamics modeling. These new results might mark a shift in the company’s fortunes.
“AlphaFold represents a huge leap forward that I hope will really accelerate drug discovery and help us to better understand disease. It’s pretty mind blowing,” DeepMind CEO Demis Hassabis said during the briefing last week. “We advanced the state of the art in the field, so that’s fantastic, but there’s still a long way to go before we’ve solved it.”
