Impact of Technological Advancements on Protein Studies: Insights from 2024 Chemistry Nobel Laureates
- October 13, 2024
- Posted by: OptimizeIAS Team
- Category: DPN Topics
Impact of Technological Advancements on Protein Studies: Insights from 2024 Chemistry Nobel Laureates
Sub : Sci
Sec: Awareness in IT and Computers
Why in News
The 2024 Nobel Prize in Chemistry was jointly awarded to David Baker, Demis Hassabis, and John Jumper for their significant contributions to protein research, particularly in protein structure prediction and design. Their ground breaking work has redefined our understanding of proteins, crucial to all life forms, and opened new possibilities in the field of biotechnology.
Importance of Proteins: Proteins are the fundamental building blocks of life. They are composed of amino acids and play key roles in various biological processes such as catalysis of biochemical reactions, structural support, molecular transport, muscle contraction, and cell communication.
About Proteins:
Proteins are large, complex molecules made of amino acids that perform vital biological functions.
Proteins are composed of one or more long chains of amino acids linked by peptide bonds.
Proteins have four levels of structure—primary, secondary, tertiary, and quaternary—dictating their shape and function.
Proteins are involved in structural support, catalyzing reactions (enzymes), transport, immune defense, and cellular signaling.
Common types include enzymes, antibodies, structural proteins (e.g., collagen), and transport proteins (e.g., hemoglobin).
Proper folding into a specific 3D shape is crucial for their functionality; misfolding can cause diseases.
Proteins are synthesized in cells by ribosomes through a process called translation, using mRNA as a template.
Proteins are broken down into amino acids via proteolysis, allowing the body to recycle amino acids.
Proteins are involved in every cellular process, from DNA replication to cell structure maintenance.
About Amino Acids:
Amino acids are the basic units that make up proteins. There are 20 standard amino acids used to build proteins in humans and most organisms.
Each amino acid consists of an amino group (-NH2), a carboxyl group (-COOH), and a unique side chain (R-group).
Out of the 20, 9 are essential and must be obtained through diet. The remaining 11 amino acids can be synthesized by the body.
Amino acids are linked together by peptide bonds to form proteins. Amino acids play roles in metabolism, enzyme function, and cell signalling.
Amino acids are encoded by the DNA sequence via codons in the genetic code. Amino acids are crucial for growth, repair, and maintaining body functions.
The Protein-Folding Problem
What is Protein Folding? Protein folding refers to the process by which a protein’s amino acid chain acquires its specific three-dimensional structure, which determines its function. The challenge lies in predicting this structure based on the amino acid sequence alone.
The 1962 Nobel Prize was awarded for the elucidation of the first 3D structures of proteins (hemoglobin and myoglobin) using X-ray crystallography. This set the stage for modern protein research.
Breakthroughs in 1969: Scientists discovered that proteins possess an inherent ability to fold themselves into the correct shape—a phenomenon central to the “protein-folding problem.”
AlphaFold: Revolutionizing Protein Structure Prediction
Co-founded by Demis Hassabis, DeepMind developed AlphaFold, a deep-learning model capable of predicting the 3D structures of proteins with high accuracy. By 2020, its predictions rivalled the precision of traditional X-ray crystallography.
What is AlphaFold?
AlphaFold is a revolutionary tool that predicts the 3D structure of proteins, developed by DeepMind, co-founded by Demis Hassabis in 2010 and acquired by Google in 2014.
AlphaFold 1 (2018): The original model could predict the structure of almost any protein based on known structures.
AlphaFold 2 (2020): Achieved accuracy comparable to X-ray crystallography in predicting protein structures.
AlphaFold 3 (2024): Led by John Jumper, this version expanded its capabilities to predict interactions between proteins and between proteins and other molecules.
AlphaFold is an AI-based protein structure prediction tool. It is based on a computer system called deep neural network. Inspired by the human brain, neural networks use a large amount of input data and provides the desired output exactly like how a human brain would.
The real work is done by the black box between the input and the output layers, called the hidden networks.
AlphaFold is fed with protein sequences as input. When protein sequences enter through one end, the predicted three-dimensional structures come out through the other. It is like a magician pulling a rabbit out of a hat.
How does AlphaFold work?
AlphaFold is an AI-based protein structure prediction tool. It used processes based on “training, learning, retraining and relearning” to predict the structures of the entire 214 million unique protein sequences deposited in the Universal Protein Resource (UniProt) database.
About Rosetta Program: In 2003, David Baker introduced the Rosetta software, used to predict and design protein structures. This tool has been widely adopted in computational biology.
Applications of Protein Design:
COVID-19 Antiviral Spray: In 2022, Baker’s team designed an antiviral protein-based nasal spray that targets the spike protein of the COVID-19 virus.
Commercial Reactions: Baker’s work also led to the design of new enzymes for industrially valuable chemical reactions, including those used to manufacture atorvastatin (a cholesterol-lowering drug) and vitamin B6.