Proteins are one of the most crucial yet most unpredictable building blocks of life–until recently, that is, when three scientists were awarded the Nobel Prize in Chemistry for their monumental advancements in creating and predicting 3D structures of proteins.
Proteins are responsible for various functions in living systems that are crucial for survival. They protect us from diseases in the form of antibodies, transport molecules and cellular information within cells, provide structure to our cells, facilitate and speed up vital chemical reactions in the form of enzymes, and carry out countless other functions. Proteins are made up of chains of amino acids, and all amino acids contain a carboxyl group, an amino group, and a side chain. Each side chain has different defining characteristics, and when numerous amino acids are linked together the side chains determine how the protein will fold to form a 3D structure. The unique 3D structure of a protein is the defining factor for that protein’s function, so we must understand the shape of a protein in order to understand its role in the body.
The first recipient of this Nobel Prize, David Baker and his team, created Rosetta, a system which can generate the 3D shape for a protein given its amino acid chain. However, Baker’s most groundbreaking discovery with his software came from reversing this process. He found that when he and his team gave Rosetta a made-up protein structure, it created an amino acid chain that would fold into that 3D shape. To confirm Rosetta’s prediction, they synthesized the suggested amino acid chain in their lab, and a structure exactly like Rosetta’s was produced! This system has since been used to speed up the process of understanding protein functions when a new protein is discovered and to create various proteins that had never existed in nature before, including one that can detect fentanyl and another that can block COVID-19.
The second recipients, Demis Hassabis and John Jumper, were recognized for their software called AlphaFold (as well as its successor, AlphaFold2), which can predict protein structures given amino acid chains with unparalleled accuracy. AlphaFold could predict protein structure with up to 60% accuracy, and AlphaFold2 can predict protein shapes almost as well as X-ray crystallography, the standard laboratory technique for discovering protein structure and function. The AI model even color codes areas of the predicted protein shape based on its confidence in its accuracy. AlphaFold’s system has correctly predicted the structure of almost every single protein known to scientists at this point!
These protein prediction softwares and their recognition through the honor of the Nobel Prize directly connects to what we have learned about proteins thus far in AP Biology. Proteins are made up of different combinations of the 20 amino acids known to scientists, and their individual shapes are determined by the interactions between the side chains contained within each different amino acid. Due to the varying characteristics of the side chains, we understand that they will each react differently with their surrounding amino acids as well as with the water that surrounds the protein as a whole. These differing interactions cause the amino acids to connect to and react with each other in different ways, thus causing the protein to fold, bend, twist, and invert itself in unique ways which cause the function-determining shape of the protein. The softwares developed by these award-winning scientists help us to predict the shape (and therefore the function) of any given protein by analyzing the side chains’ characteristics and interactions with one another. Their discoveries represent not only a groundbreaking advancement in protein research, but also a monumentally productive use of AI as a resource to scientists attempting to gain a better understanding of the things that make life possible. Do you think that their discoveries will contribute to the revolution of AI in a positive and helpful light, or do you think that the use of AI at a highly developed level will add to the fear associated with technological advancements?
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