Proteins are the makers of biology. In the human body, they do most of the work in the cells and are required for the structure, function and regulation of the body’s tissues and organs. Each protein is made up of a precise sequence of amino acids that allows it to fold into a three-dimensional shape that determines its function. In the past, the all-important structure of a protein could only be determined through difficult and time-consuming laboratory analysis. Now comes a dramatic new twist – a window into the basic building blocks of life.
DeepMind, a UK-based company owned by Alphabet, Google’s parent company, has developed an artificial intelligence and machine learning system that can predict the three-dimensional structure of proteins and decode the amino acids that make up each protein. Last year the system had 350,000 entries. On July 28, Demis Hassabis, co-founder and CEO of DeepMind, announced the expansion of the company’s database of folded proteins to more than 200 million – nearly every cataloged protein known to science, including those in humans, plants, bacteria, animals and other organisms – and that the company makes them publicly available and free, accessible with no more effort than a Google search. The database is called AlphaFold, and it’s the biology equivalent of a James Webb Space Telescope, yielding amazing new images of an otherworldly world.
Proteins don’t fold up neatly like dish towels. Many look like a ball of yarn after a cat has played with them. They often have precisely designed moving parts that are linked to chemical events and, most importantly, bind to other molecules. For example, antibodies are proteins produced by the immune system that bind to foreign molecules, such as those on the surface of an invading virus such as E. B. the spikes of the coronavirus. Therefore, scientists have been trying to learn the exact folding of proteins and their functions for decades. Researchers have long used a technique known as X-ray crystallography to better understand the structure of proteins, and the field’s central archive contains about 185,000 experimentally solved structures.
Then came artificial intelligence. AlphaFold algorithms learned how to predict protein folding based on underlying amino acid sequence, leading to an explosion of new information. Another project called RoseTTAFold at the University of Washington’s Institute for Protein Design is pursuing a similar quest. Protein folding predictions need to be verified, in some cases by real experiments. But for drug and vaccine developers who want to know how a protein looks or behaves, the prediction itself – a visual representation – can offer a remarkable head start. Both Science and Nature Methods magazines called the breakthrough the most important of 2021.
With these new methods, the researchers have succeeded in investigating the nuclear pore complex, which acts as a kind of gatekeeper for everything that goes in and out of the cell nucleus. It contains more than 1,000 protein subunits that are woven together, so it’s a difficult puzzle for scientists. Using AlphaFold, the researchers were able to create a model that is almost twice as complete as the old one and covers two-thirds of the complex.
AlphaFold does not reveal all the mysteries of biology, nor is it the only advance needed for drug development or disease control. But the view is really amazing.
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