AlphaFold: A tour de force in science

Contact Counsellor

Last Updated03/08/2022

DeepMind, a company owned by Google, announced recently that it had predicted the three-dimensional structures of more than 200 million proteins using AlphaFold.

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 uses processes based on “training, learning, retraining and relearning.”
The first step uses the available structures of 1,70,000 proteins in the Protein Data Bank (PDB) to train the computer model.
Then, it uses the results of that training to learn the structural predictions of proteins not in the PDB.
Once that is done, it uses the high-accuracy predictions from the first step to retrain and relearn to gain higher accuracy of the earlier predictions.
By using this method, AlphaFold has now predicted the structures of the entire 214 million unique protein sequences deposited in the Universal Protein Resource (UniProt) database.

Proteins are the business ends of biology, meaning proteins carry out all the functions inside a living cell.
Therefore, knowing protein structure and function is essential to understanding human diseases.
Scientists predict protein structures using x-ray crystallography, nuclear magnetic resonance spectroscopy, or cryogenic electron microscopy.
These techniques are not just time-consuming, they often take years and are based mainly on trial-and-error methods.
The development of AlphaFold is a watershed movement in science and structural biology in particular.