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The story so far: The 2024 Nobel Prize for chemistry was jointly awarded to David Baker for his work on computational protein design and to Demis Hassabis and John Jumper for developing technologies to predict the structure of proteins. The Swedish Academy of Royal Sciences announced the winners on October 9.
The chemistry prize concerns two areas in the field of protein research: design and structure.
All life (as we know it) requires proteins and all proteins are made of amino acids. While there are many types of amino acids in nature, only 20 of them in different combinations make up all the proteins in the human body and in most life-forms.
Amino acids are found in tissues — like muscles, skin, and hair — that provide structural support; they’re catalysts in biochemical reactions; move molecules like oxygen across biological membranes; control muscle contraction that lets us move around and have our hearts beat; and help cells communicate with each other to perform their tasks.
A protein has many identities and one of them depends on the arrangement of its amino acids in the three dimensions of space — in other words, its 3D structure. And scientists have spent decades trying to understand how proteins attain these structures.
In 1962, University of Cambridge researchers John Kendrew and Max Perutz won the chemistry Nobel Prize for elucidating the first 3D models of haemoglobin and myoglobin, both proteins, using X-ray crystallography. (This method reveals a crystal’s structure based on how its constituent atoms scatter X-rays. For this the proteins need to be purified and crystallised first.) A year earlier Christian Anfinsen had found that a protein’s 3D structure is governed by the sequence of amino acids in the protein, and won the 1972 chemistry prize.
One notable breakthrough arrived in 1969 when scientists found that a protein doesn’t try to bend into different shapes before settling into its final one. Instead it somehow knows the shape it needs to have and rapidly folds itself to acquire it. The mysterious nature of this ‘knowledge’ of the protein is called the protein-folding problem.
By the late 2010s, scientists had worked out the structures of around 1.7 lakh proteins — a large number yet still small compared to the roughly 200 million proteins in nature. This situation changed drastically around 2018.
Hassabis co-founded DeepMind in 2010 and which Google acquired in 2014. Here, Hassabis and his colleagues unveiled AlphaFold in 2018. AlphaFold is a deep-learning model able to predict the structures of almost all proteins after training on the set of known structures.
DeepMind launched its successor AlphaFold 2 in 2020, when it was able to predict the structure of proteins with an accuracy comparable to that of X-ray crystallography.
How does AlphaFold2 work? | Photo Credit: Johan Jarnestad/The Royal Swedish Academy of Sciences
Jumper led the work on AlphaFold 3, which DeepMind released in May 2024. This model is able to predict the structures of various proteins as well as how two proteins and/or a protein and another molecule might interact.
Given enough computing power, these machine-learning models are capable of deducing the 3D shapes of most proteins in a matter of hours — a task that once occupied human scientists for several months, if not years. However, these machines have not been able to say why a protein prefers a particular structure. Scientists have thus said it can help them test their hypotheses; making sense of them is still the task of humans.
As Derek Lowe, a pharmaceutical researcher and author of a column in Science, put it to The Hindu in June 2024, “If the protein folding problem was set to us by God to teach us how to learn molecular interactions from first principles, we cheated.”
Baker, who received the other half of this year’s chemistry Nobel Prize, developed tools that scientists use to design new proteins with specific shapes and functions. His first notable work was in 2003, when he led a team to create a novel protein and determined its structure using a bespoke computer program they had developed in 1999 called ‘Rosetta’.
The researchers compared Rosetta’s output with that obtained from X-ray crystallography studies and found them to be remarkably similar.
According to the Nobel Committee for Chemistry, “Rosetta was designed to be a general program both for protein structure prediction and design, and it has continuously been developed since its inception, with a large cadre of users and co-developers.”
The ability to design proteins has far-reaching implications. For example, in 2022, Baker’s team developed an antiviral nasal spray to treat COVID-19. At its heart were proteins the team designed using computational methods in the laboratory to stick to vulnerable sites on the viral surface and target the spike protein.
Published - October 09, 2024 09:23 pm IST