Current Affair 1:

Try to understand the basics, why AlphaFold?

Background:

There are over 200 million known proteins, with many more found yearly. Each one has a unique 3D shape determining how it works and what it does.

But figuring out the exact structure of a protein can sometimes take years and hundreds of thousands of dollars, meaning scientists could only study a tiny fraction of them. This slowed down research to tackle disease and find new medicines.

The protein-folding problem

If you could unravel a protein, you would see that it’s like a string of beads made of a sequence of different chemicals known as amino acids. These sequences are assembled according to the genetic instructions of an organism's DNA.

Experimental methods to determine the structure of proteins include nuclear magnetic resonance and X-ray crystallography. These rely on extensive trial and error, years of painstaking work, and multi-million-dollar specialized equipment.

So, for decades, scientists tried to find a method to reliably determine a protein’s structure from its sequence of amino acids alone. This grand scientific challenge is known as the protein-folding problem.

THE SOLUTION:

Things changed when Google DeepMind’s protein-structure prediction software AlphaFold burst into the scene in 2020. They changed more drastically in 2021 with the highly improved AlphaFold 2.

AlphaFold uses machine learning and artificial intelligence (AI) to accurately predict protein structures from an amino acid sequence, seemingly solving the protein-folding problem without learning any of the deeper physical principles that drive this biological process.

In 2024, AlphaFold 3 was introduced, which predicts the structure and interactions of all of life’s molecules.

AlphaFold 3 goes beyond proteins to a broad spectrum of biomolecules including DNA, RNA, and even small molecules, also known as ligands, which encompass many drugs. This leap could unlock more transformative science, from developing biorenewable materials and more resilient crops, to accelerating drug design and genomics research.

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