Today’s news drop is good news for tech lovers. The three hard-working scientists have earned the Nobel Prize 2024 in Chemistry for their work on proteins. They provide people the opportunity to discover the wonders of the world from a unique perspective. Read more about the AI chemistry innovations, scientists' point-of-views, and their usage in educational or scientific institutions.
The 2024 Nobel Prize in Chemistry
The Nobel Committee for Chemistry has chosen David Baker, John Jumper, and Demis Hassabis for the Nobel Prize 2024. The trio have combined artificial intelligence and biology. They come up with advanced AI software that will predict protein structures, and patterns, and discover entirely new proteins.
Point to be Noted: the three chemists will receive the award and a cash price of 11 million Swedish kroner ($1 million) in a ceremony that Stockholm will host.
Advances in Protein Synthesis
Many of you already know proteins are considered the living body’s fundamental building blocks and are present in every cell. Amino acids make up proteins, which are responsible for several important biological functions.
Defining protein structure is important and Hassabis and Jumper’s AI model has effectively solved the problem. It accurately determines the structure of any protein virtually. It can develop new proteins in response to previous structures and patterns.
The innovation which formerly seemed impossible has now opened ways for many other chemists and biologists to determine human body structures at molecular levels. Additionally, this encourages the field of structural biology to enable discoveries of new proteins and speed up the process of drugs and developments.
David Baker’s AI Model
David Baker currently works as a research member at the University of Washington in Seattle. He comes from an unorthodox educational background, such as beginning with philosophy and turned his head into social science. Finally, he transitioned into cell biology and the study of protein structures.
In the early 2000s, Baker came up with his computational AI model to predict protein patterns, but it was not a huge success. However, he has developed the AI software “Rosetta by Baker,” by which scientists can determine protein structures based on their patterns.
Hassabis & Jumper's AI Model
Hassabis and Jumper work at DeepMind at Google in London. Both chemists come from different educational fields. Hassabis started with physics and mathematics, then he got his head into proteins and their dynamics. John Jumper began with mathematics and physics and then started studying theoretical physics and protein simulation.
Two chemists debuted their AI software model in 2018, but it did not achieve success. In recent years, the duo has developed the AI tool “AlphaFold2 by Hassabis and Jumper”. It was created to determine nearly all organism’s protein structures, including human beings.
RoseTTAFold: Baker’s Incorporation of Two Models
David Baker’s Rosetta model was specific to protein patterns. It raised the need for some further advancements. For this reason, the scientists combined AlphaFold2 into his existing software, and the newer RoseTTAFold came into existence and helped improve predictions for protein structures.
The incorporated model is now used in pharmaceuticals, nanotechnology, and vaccines. Additionally, it opens ways to predict protein structures more accurately, increasing the precision and possible uses of protein design.
Baker’s work allows many biologists and other scientists to understand proteins that occur naturally. It also helps in designing new proteins for uses, such as in healthcare departments and technological industries.
Applications in Real Life and Their Restrictions
Applications
- Both AI models provide an understanding of antibiotic resistance
- Both ensure faster protein discovery and drug development
- Baker’s or Hassabis and Jumper’s model enables enzyme creation that breaks down plastic.
Limitations
- Whether it is Baker’s model or Hassabis and Jumper’s model, you will notice certain limitations:
- Both models do not help to predict the structure of deformed proteins, such as diseases like cancer.
- Models do not determine protein in the absence of a fixed structure.
- RoseTTAFold and AlphaFold2 do not eliminate the use of conventional techniques like X-ray crystallography and electron microscopy for AI protein predictions.
Combining AI and Biology: The Future of Science
If comparing RoseTTAFold and AlphaFold2 models, the latter marked its importance as the “Digital Age of Biology”. It helps researchers, chemists, and other scientists to advance studies of almost 200 million proteins, predict their structures, discover new ones, and speed up medicine and biological research.