AlphaFold 3: Artificial Intelligence Model

Google Deepmind has unveiled the third major version of its “AlphaFold” – AlphaFold 3, artificial intelligence model, designed to help scientists design drugs and target disease more effectively.

Structure of Proteins

Proteins are the polymers of α-amino acids and they are connected to each other by peptide bond or peptide linkage.
There are twenty amino acids found in the body, which assist in producing thousands of distinct proteins in the body.
Proteins may have one or more polypeptide chains.
- Each polypeptide in a protein has amino acids linked with each other in a specific sequence and it is this sequence of amino acids that is said to be the primary structure of that protein.
Any change in this primary structure i.e., the sequence of amino acids creates a different protein.

Functions of Proteins

Digestion – Digestive enzymes
Messenger function
Movement
Structure and Support – Keratin, a structural protein
Cellular communication

What is AlphaFold?

An AI tool developed by Google’s DeepMind in 2018 to predict how proteins fold.
- This artificial intelligence technology that helps scientists understand the behavior of the microscopic mechanisms that drive the cells in the human body.
Solution to Protein Folding Problem: An early version of AlphaFold, released in 2020, successfully solved the “the protein folding problem.”
- Each protein is made up of a string of smaller building blocks called amino acids, which contain all the information to transform proteins — from a single sequence to a folded, functional 3D structure.

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AlphaFold 3

It’s built on the foundations of AlphaFold 2 & had made a fundamental breakthrough in protein structure prediction in 2020, by predicting the 3D structure of a protein from its amino acid structure.
It expands beyond proteins to provide accurate predictions for protein interactions with other biomolecules in living cells – such as DNA, RNA, and small molecules.
Significance: It can predict the behaviour of other microscopic biological mechanisms, including DNA, where the body stores genetic information, and RNA, which transfers information from DNA to proteins.
It will potentially help to streamline the creation of new drugs and vaccines.

From Noise To Signal

Original AlphaFold: It was trained on the thousands of sequences and protein structures present in the protein data bank, a giant protein repository where scientists submit experimentally determined protein structures.
Unlike its predecessors: AlphaFold 3 uses a diffusion model, which is what image-generating software also uses.
- The model works by first training on protein structures, adding noise to the data, and then trying to de-noise it.
- This way, the model becomes able to work its way back from a noisy structure to a real protein structure.
- This architecture also helps AlphaFold 3 handle a much larger input dataset.

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