The human mediator complex has long been one of the most challenging multi-protein systems for structural biologists to understand.Credit: Yuan He The human genome holds the instructions for more than 20,000 proteins. But only about one-third of those have had their 3D structures determined experimentally. And in many cases, those structures are only partially known. Now, a transformative artificial intelligence (AI) tool called AlphaFold, which has been developed by Google's sister company DeepMind in London, has predicted the structure of nearly the entire human proteome (the full complement of proteins expressed by an organism). In addition, the tool has predicted almost complete proteomes for various other organisms, ranging from mice and maize (corn) to the malaria parasite (see'Folding options').
A computer system that automatically tracks the movements of proteins within a living cell has been developed by a team of biologists and computer vision experts. It could save researchers the hours often spent analysing microscope images by hand, to determine the way a cell works. The system, called CellTracker, automatically analyses a series of still digital images captured through a microscope. Doug Kell at Manchester University in UK, the lead biologist involved with the project, believes the system could dramatically speed up studies of cells' function. "Most people just fix cells [in one place], which kills their metabolism," he told New Scientist.
Novel proteins, created from scratch with no particular design in mind, can sometimes do the work of a natural protein. The discovery may widen the toolkit of synthetic biologists trying to build bespoke organisms. There are more proteins possible than there are atoms in the universe, and yet evolution has tested only a minuscule fraction of them. No one knows whether the vast, untried space of proteins includes some that could have biological uses. Until now, most researchers assembling novel proteins have meticulously selected each amino acid building block so that the resulting protein folds precisely into a pre-planned shape that closely fits the molecules it is intended to interact with.
A protein's function is determined by its 3D shape.Credit: DeepMind An artificial intelligence (AI) network developed by Google AI offshoot DeepMind has made a gargantuan leap in solving one of biology's grandest challenges -- determining a protein's 3D shape from its amino-acid sequence. DeepMind's program, called AlphaFold, outperformed around 100 other teams in a biennial protein-structure prediction challenge called CASP, short for Critical Assessment of Structure Prediction. The results were announced on 30 November, at the start of the conference -- held virtually this year -- that takes stock of the exercise. "This is a big deal," says John Moult, a computational biologist at the University of Maryland in College Park, who co-founded CASP in 1994 to improve computational methods for accurately predicting protein structures. "In some sense the problem is solved."
DeepMind, an AI research lab that was bought by Google and is now an independent part of Google's parent company Alphabet, announced a major breakthrough this week that one evolutionary biologist called "a game changer." "This will change medicine," the biologist, Andrei Lupas, told Nature. The breakthrough: DeepMind says its AI system, AlphaFold, has solved the "protein folding problem" -- a grand challenge of biology that has vexed scientists for 50 years. Proteins are the basic machines that get work done in your cells. They start out as strings of amino acids (imagine the beads on a necklace) but they soon fold up into a unique three-dimensional shape (imagine scrunching up the beaded necklace in your hand).