The A.I. Blog

Predicting 3D shapes of Proteins with Google's DeepMind

Google's DeepMind AI defeated 18-time world champion Lee Sedol in traditional game "Go"

AlphaGo is the first computer program to defeat a professional human Go player, the first program to defeat a Go world champion, and arguably the strongest Go player in history.

AlphaGo's 4-1 victory in Seoul, South Korea, in March 2016 was watched by over 200 million people worldwide. It was a landmark achievement that experts agreed was a decade ahead of its time, and earned AlphaGo a 9 dan professional ranking (the highest certification) - the first time a computer Go player had ever received the accolade.

Demis Hassabis,CEO of DeepMind, said “For us, this is a really key moment. This is a lighthouse project, our first major investment in terms of people and resources into a fundamental, very important, real-world scientific problem.”

The human body can make vast numbers of different proteins ranging from tens of thousands to billions. Each one is a chain of amino acids. And there are 20 different types of amino acids. A protein can twist and bend between each amino acid, so that a protein with hundreds of amino acids has the potential to take on a staggering number of different structures around a googol cubed, or 1 followed by 300 zeroes.

Source=>Google DeepMind

The 3D form a protein adopts depends on the number and types of amino acids it contains. The shape also determines its role in the body. Heart cells, for example, are dotted with proteins folded in such a way that any adrenaline in the bloodstream sticks to them and ramps up the heart rate. Meanwhile, antibodies in the immune system are proteins that fold into specific shapes which latch onto invading bugs. Nearly every function in the body, from tensing muscles and sensing light to turning food into energy, can be traced back to the shape and movement of proteins.

DeepMind entered AlphaFold into the Critical Assessment of Structure Prediction (CASP) competition, a biannual protein-folding olympics that attracts research groups from around the world. The aim of the competition is to predict the structures of proteins from lists of their amino acids which are sent to teams every few days over several months. The structures of these proteins have recently been cracked by laborious and costly traditional methods, but not made public. The team that submits the most accurate predictions wins.

To build AlphaFold, DeepMind trained a neural network on thousands of known proteins until it could predict 3D structures from amino acids alone. Given a new protein to work on, AlphaFold uses the neural network to predict the distances between pairs of amino acids, and the angles between the chemical bonds that connect them. In a second step, AlphaFold tweaks the draft structure to find the most energy-efficient arrangement. The program took a fortnight to predict its first protein structures, but now rattles them out in a couple of hours.

Hassabis agrees there is far more to do. “We’ve not solved the protein folding problem, this is just a first step,” he said. “It’s a hugely challenging problem, but we have a good system and we have a tonne of ideas we haven’t implemented yet.”