Context: The Nobel Prize in Chemistry for 2024 has been jointly awarded to David Baker for his work on computational protein design (building new proteins) and Demis Hassabis and John M. Jumper for predicting proteins’ complex structures using an artificial intelligence (AI) model called AlphaFold.
Proteins: Structure & function
- Proteins are the building blocks of life. Proteins are biomolecules or polymers formed from the sequences of amino acids (monomers).
- 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.
Types of proteins:
1. Based on Protein Folding:
- Primary: linear sequence of amino acids in a polypeptide chain.
- Secondary: localised folding patterns within the polypeptide chain, primarily stabilised by hydrogen bonds.
- Alpha Helices: Coiled structures that resemble a spring.
- Beta Sheets: Flat, sheet-like structures formed by hydrogen bonds between different segments of the polypeptide chain.
- Tertiary: 3-dimensional folding in a single polypeptide chain by bonding between different side chains. Tertiary structure determines the protein's function and interactions with other molecules.
- Quaternary: bonding between more than two polypeptide chains. E.g., Haemoglobin, the protein that carries oxygen in blood, is a protein with a quaternary structure (made of four subunits working together).
2. Based on Structure:
- Globular proteins: These are spherical or globular in shape. They are often involved in biological processes like enzymes (which speed up chemical reactions) and transport proteins (which carry molecules around the body). E.g., Haemoglobin (carries oxygen in the blood) and Insulin (regulates blood sugar).
- Fibrous proteins: These are long, thin, and fibrous in shape. They are often structural proteins, providing strength and support to tissues. E.g., Keratin (found in hair, nails, and skin), collagen (found in bones, tendons, and ligaments).
Functions of Proteins:
- Proteomics is the large-scale study of proteins, their structures, functions, and interactions within a biological system.
- They provide structural support, are catalysts in biochemical reactions, move molecules like oxygen across biological membranes, control muscle contraction and help cells communicate with each other to perform their tasks among other functions.
Protein-folding problem:
- Protein does not 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.
- Determining structure is the first and most important step in determining protein function.
- Proteins are long ribbons in which the 20 different amino acid building blocks can be sequentially arranged to form innumerable combinations.
- Even if researchers know the sequence of amino acids in a ribbon, the ribbon can twist and fold in an astronomical number of possible shapes for each sequence, thereby making protein structure determination extremely challenging.
- For instance, if a protein consists of only 100 amino acids, the protein can assume at least 1047 different 3D structures.
- The structures of proteins can be determined through techniques such as- X-ray crystallography, NMR spectroscopy, and electron microscopy.
- By the late 2010s, scientists had determined 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 (after the launch of AlphaFold).
Contribution of the Nobel Winners:
1. Hassabis and Jumper (AlphaFold AI Model):
- Hassabis co-founded DeepMind in 2010. Here, Hassabis and his colleagues unveiled AlphaFold in 2018. AlphaFold is a deep-learning model able to predict the 3D 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.
- 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.
- These machine-learning models are capable of deducing the 3D shapes of most proteins in a matter of hours — a task that once required several months/years.
- AlphaFold has now predicted the structure of almost all 200 million proteins from nearly a million species. The code for the AlphaFold model has been publicly available since 2021, and the AI tool has been used by more than two million people from 190 countries.
2. Baker (computer software Rosetta):
- Baker developed computerised methods to create proteins that did not previously exist and which, in many cases, have entirely new functions.
- Baker used his computer software Rosetta to generate new proteins that never existed naturally.
- Instead of predicting the protein structure based on amino acid sequences, he created new protein structures and used Rosetta to determine the amino acid sequence. It was done by searching a database of all known protein structures and looking for short fragments of proteins that had similarities with the desired structure.
- Rosetta then optimised these fragments and proposed an amino acid sequence.
- Baker too made the code for Rosetta freely available so that researchers can develop the software and find new areas of application.
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