Read the passage carefully and answer the questions that follow. Some words may be highlighted. Pay attention.
“I didn’t think we would get to this point in my lifetime.” That’s how one research leader in structural biology responded to last week’s publication of research in which artificial intelligence (AI) was used to predict the structure of more than 20,000 human proteins, as well as that of nearly all the known proteins produced by 20 model organisms such as Escherichia coli, fruit flies and yeast, but also soya bean and Asian rice. That is a combined total of around 365,000 predictions.
The data, publicly accessible for the first time, were released online on 22 July by researchers at DeepMind, a London-based AI company owned by Alphabet Inc. and situated inside the European Bioinformatics Institute, based at the European Molecular Biology Laboratory (EBI-EMBL) near Cambridge, UK.
The DeepMind team developed a machine-learning tool called AlphaFold. The team trained this program on DNA sequences, including their evolutionary history, and the already-known shapes of tens of the thousands of proteins contained in a public-access database of proteins hosted by the EBI-EMBL researchers. A week earlier, DeepMind also released the source code for AlphaFold and detailed how it was constructed, at the same time that researchers from the University of Washington, Seattle, published details of another protein-structure prediction program — inspired by AlphaFold — called RoseTTAFold3.
Predicting the 3D shape that proteins fold into has been one of biology’s unsolved ‘grand challenges’ since the discovery in 1953 of the structure of DNA itself. Before AI, structure prediction from the sequence was an intensely time-consuming, not to say labour-intensive, process with little guarantee of getting an accurate result. The new data will still need to be validated and experimentally verified. But the AI tools can accurately predict protein structures in minutes to hours which opens up possibilities for applications, for example in the engineering of enzymes to break down environmental pollutants such as micro-plastics.
In the basic science of structural biology, key problems remain unresolved. Although AI in science and technology is good at producing accurate results, it doesn’t (at least for now) explain how, or why, those results happened. The teams at DeepMind, EBI-EMBL, the University of Washington, and elsewhere should be congratulated for crucial breakthroughs. But there is still work to be done to unlock the science — the essential biology, chemistry, and physics — of how and why proteins fold.
In terms of significance, some are comparing the latest advances to the first draft human genome sequence 20 years ago. And it’s true that there are comparisons to be made. Both the Human Genome Project and DeepMind’s catalogue of human protein-structure predictions equip their fields with a tool that is set to markedly accelerate discovery.
Accurately predicting how AI will change biology needs good training data, which we don’t yet have. But in AI, the structural-biology research community — and its collaborators in other fields — have a vast trove of fresh data. In addition to its research and data, AI provides a window into models for research organization and management that universities should study. For today’s researchers and those in future generations, there is much work to follow up on.