The second method optimised scores through gradient descent—a mathematical technique commonly used in machine learning for making small, incremental improvements—which resulted in highly accurate structures. This technique was applied to entire protein chains rather than to pieces that must be folded separately before being assembled into a larger structure, to simplify the prediction process.
The code is available on Github for anyone interested in learning more, or replicating our protein folding results.
What happens next?
While we’re thrilled by the success of our protein folding model, there’s still much to be done in the realm of protein biology, and we’re excited to continue our efforts in this field. We’re committed to establishing ways that AI can contribute to basic scientific discovery, with the hope of making real-world impact. This approach might serve to ultimately improve our understanding of the body and how it works, enabling scientists to target and design new, effective cures for diseases more efficiently. Scientists have only mapped structures for about half of all the proteins made by human cells. Some rare diseases involve mutations in a single gene, resulting in a malformed protein which can have profound effects on the health of an entire organism. A tool like AlphaFold might help rare disease researchers predict the shape of a protein of interest rapidly and economically. As scientists acquire more knowledge about the shapes of proteins and how they operate through simulations and models, this method may eventually help us contribute to efficient drug discovery, while also reducing the costs associated with experimentation. Our hope is that AI will be useful for disease research, and ultimately improve the quality of life for millions of patients around the world.
But potential benefits aren’t restricted to health alone–understanding protein folding will assist in protein design, which could unlock a tremendous number of benefits. For example, advances in biodegradable enzymes—which can be enabled by protein design—could help manage pollutants like plastic and oil, helping us break down waste in ways that are more friendly to our environment. In fact, researchers have already begun engineering bacteria to secrete proteins that will make waste biodegradable, and easier to process.
The success of our first foray into protein folding is indicative of how machine learning systems can integrate diverse sources of information to help scientists come up with creative solutions to complex problems at speed. Just as we’ve seen how AI can help people master complex games through systems like AlphaGo and AlphaZero, we similarly hope that one day, AI breakthroughs will help serve as a platform to advance our understanding of fundamental scientific problems, too.
It’s exciting to see these early signs of progress in protein folding, demonstrating the utility of AI for scientific discovery. Even though there’s a lot more work to do before we’re able to have a quantifiable impact on treating diseases, managing waste, and more, we know the potential is enormous. With a dedicated team focused on delving into how machine learning can advance the world of science, we’re looking forward to seeing the many ways our technology can make a difference.
Listen to our podcast featuring the researchers behind this work.
This blog post is based on the following work:
AlphaFold: Improved protein structure prediction using potentials from deep learning (Nature)
Protein structure prediction using multiple deep neural networks in CASP13 (PROTEINS)
Play with the model yourself: see our Github repo
This work was done in collaboration with Andrew Senior, Richard Evans, John Jumper, James Kirkpatrick, Laurent Sifre, Tim Green, Chongli Qin, Augustin Žídek, Sandy Nelson, Alex Bridgland, Hugo Penedones, Stig Petersen, Karen Simonyan, Steve Crossan, Pushmeet Kohli, David Jones, David Silver, Koray Kavukcuoglu and Demis Hassabis
Source: https://deepmind.com/blog/article/AlphaFold-Using-AI-for-scientific-discovery
