Tatarstan Academy of Sciences

Machine-learning systems from the company and from a rival academic group are now open source and freely accessible.

It’s protein-structure prediction for the people. Software that accurately determines the 3D shape of proteins is set to become widely available to scientists.

On 15 July, the London-based company DeepMind released an open-source version of its deep-learning neural network AlphaFold 2 and described its approach in a paper in Nature1. The network dominated a protein-structure prediction competition last year.

Meanwhile, an academic team has developed its own protein-prediction tool inspired by AlphaFold 2, which is already gaining popularity with scientists. That system, called RoseTTaFold, performs nearly as well as AlphaFold 2, and is described in a Science paper also published on 15 July2.

The open-source nature of the tools means that the scientific community should be able to build on the advances to create even more powerful and useful software, says Jinbo Xu, a computational biologist at the University of Chicago in Illinois, who was not involved in either effort.

Structure to function

Proteins are made of strings of amino acids that, when folded into 3D shapes, determine the function of those proteins in cells. For decades, researchers have used experimental techniques such as X-ray crystallography and cryo-electron microscopy to determine protein structures. But such methods can be time-consuming and costly, and some proteins are not amenable to such analysis.

DeepMind sent shock waves through the scientific world last year, when it showed that its software could accurately predict the structure of many proteins using the sequence of the proteins alone (which is determined by DNA). Researchers had been working on this challenge for decades, and AlphaFold 2 performed so well in a biennial protein-prediction exercise called CASP that the competition’s co-founder declared that “in some sense the problem is solved”.

DeepMind — which has a reputation for being cagey about its work — described AlphaFold 2 in a brief presentation at CASP on 1 December. It promised to publish a paper outlining the network in more detail and to make the software available to researchers, but said little else. 

Read more on https://www.nature.com/