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- How does AlphaFold2 work?
First, an amino acid sequence with unknown structure is fed into AlphaFold2, which searches the database for similar amino acid sequences and protein structures.
The AI model then aligns all similar amino acid sequences and investigates which parts have been preserved during evolution. And the AlphaFold2 will explore which amino acids could interact with each other in the three-dimensional protein structure.
After that, AlphaFold2 refines the sequence analysis and uses AI analysis.
Ultimately, AlphaFold2 puts together a puzzle of all the amino acids and tests the pathways to produce a hypothetical protein structure. - Why did DB pursue the "computational protein design" interest?
He came across the first edition of the now classic textbook Molecular Biology of the Cell. He began to explore cell biology and eventually he became fascinated by protein structures. - What does it do to create proteins loaded with new functions?
It can lead to new nanomaterials, targeted pharmaceuticals, more rapid development of vaccines, minimal sensors and a greener chemical industry. - How would solving these protein problems change chemistry?
Designing proteins is a very significant tool in the chemical toolbox, and one can use it not only to create new and even life-like things, but also to predict protein structures and design our own proteins.
People can understand why proteins are functioning as they are now by looking at the structure. And they can understand the reason why some cures work.
Researchers can now better understand antibiotic resistance and create images of enzymes that can decompose plastic. - How does a protein find its unique structure?
A protein finds its unique structure through a predetermined folding process that is governed by the sequence of its amino acids. Although there are theoretically many possible configurations, a specific sequence contains all the information needed for a protein to fold efficiently into its correct 3D shape, often within milliseconds. The process is not random, rather, it follows a specific pathway dictated by the amino acid sequence.
John Jumper
John Jumper won the Nobel Prize in Chemistry in 2024 for his work on "for protein structure prediction".
In 2008, Jumper began working with supercomputers on protein simulations, which sparked his interest in using physics to solve medical challenges. After starting his doctoral work in theoretical physics in 2011, he developed innovative methods to simulate protein dynamics. In 2017, after completing his doctorate, he joined Google DeepMind, where his expertise contributed to significant advancements in AlphaFold2. The new version AlphaFold2 was colored by Jumper’s knowledge of proteins.
AlphaFold2
Alphafold2 was one of the projects that won the Nobel Prize in Chemistry in 2024. Its appearance to solve a problem that chemists have wrestled with for over 50 years: predicting the three-dimensional structure of a protein from a sequence of amino acids. With it so that people predict the structure of almost all 200 million known proteins.
The way it works is as follows. First, the data are entered into alphafold2, then the database is searched. After that all the similar amino acid sequences are analyzed, next AI is perfected and finally hypothetical structure.