By latching on to viruses and bacteria, these antibody proteins are able to detect and tag disease - causing microorganisms for elimination.
For example, antibody proteins utilised by our immune systems are ‘Y-shaped’, and form unique hooks. What any given protein can do depends on its unique 3D structure. Nearly every function that our body performs - contracting muscles, sensing light, or turning food into energy - relies on proteins, and how they move and change. Proteins are large, complex molecules essential to all of life. We’re also excited by the fact that this work has already inspired other, independent implementations, including the model described in this paper, and a community - built, open source implementation, described here.
The AlphaFold code used at CASP13 is available on Github here for anyone interested in learning more or replicating our results. The 3D models of proteins that AlphaFold generates are far more accurate than any that have come before - marking significant progress on one of the core challenges in biology. Our system, AlphaFold – described in peer-reviewed papers now published in Nature and PROTEINS – is the culmination of several years of work, and builds on decades of prior research using large genomic datasets to predict protein structure. We’ve built a dedicated, interdisciplinary team in hopes of using AI to push basic research forward: bringing 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. In our study published in Nature, we demonstrate how artificial intelligence research can drive and accelerate new scientific discoveries. To read about all our work on solving protein folding, go to /AlphaFold.In July of 2021, we made AlphaFold available, for free, to the whole world.In November of 2020, AlphaFold 2 was recognised as a solution to the protein folding problem at CASP14.