AlphaFold: Using AI for scientific discovery Posted on : Dec 03 - 2018

Today we’re excited to share DeepMind’s first significant milestone in demonstrating how artificial intelligence research can drive and accelerate new scientific discoveries. With a strongly interdisciplinary approach to our work, DeepMind has brought together experts from the fields of structural biology, physics, and machine learning to apply cutting-edge techniques to predict the 3D structure of a protein based solely on its genetic sequence.

Our system, AlphaFold, which we have been working on for the past two years, builds on years of prior research in using vast genomic data to predict protein structure. The 3D models of proteins that AlphaFold generates are far more accurate than any that have come before—making significant progress on one of the core challenges in biology.

What is the protein folding problem?

Proteins are large, complex molecules essential in sustaining life. Nearly every function our body performs—contracting muscles, sensing light, or turning food into energy—can be traced back to one or more proteins and how they move and change. The recipes for those proteins—called genes—are encoded in our DNA.

What any given protein can do depends on its unique 3D structure. For example, antibody proteins that make up our immune systems are ‘Y-shaped’, and are akin to unique hooks. By latching on to viruses and bacteria, antibody proteins are able to detect and tag disease-causing microorganisms for extermination. Similarly, collagen proteins are shaped like cords, which transmit tension between cartilage, ligaments, bones, and skin. Other types of proteins include CRISPR and Cas9, which act like scissors and cut and paste DNA; antifreeze proteins, whose 3D structure allows them to bind to ice crystals and prevent organisms from freezing; and ribosomes that act like a programmed assembly line, which help build proteins themselves.

But figuring out the 3D shape of a protein purely from its genetic sequence is a complex task that scientists have found challenging for decades. The challenge is that DNA only contains information about the sequence of a protein’s building blocks called amino acid residues, which form long chains. Predicting how those chains will fold into the intricate 3D structure of a protein is what’s known as the “protein folding problem”.

The bigger the protein, the more complicated and difficult it is to model because there are more interactions between amino acids to take into account. As noted in Levinthal’s paradox, it would take longer than the age of the universe to enumerate all the possible configurations of a typical protein before reaching the right 3D structure. View More