AI Unfolds the Science Behind a 50-Year-Old Protein-Folding Problem
By: Madison Hunter
Tags: Machine Learning, Biotechnology, Artificial Intelligence, Future, Science

DeepMind’s AlphaFold.

Three decades after CASP issued its challenge to the scientific community, a most promising solution has surfaced using AI to predict the shapes of protein structures with accuracy that has yet to be seen. DeepMind, a company devoted to developing artificial intelligence systems to solve intelligence and advance scientific discovery, partnered with CASP to solve biology’s grand challenge. AlphaFold is DeepMind’s deep-learning system that has been proven to “accurately predict the structure of proteins to within the width of an atom”.

AlphaFold was trained to analyze the structure of proteins using a databank of roughly 170,000 protein structures. During testing, AlphaFold achieved an average score of 92.4 GDT (Global Distance Test) in its predictions. The competitive threshold score: 90 GDT. This means that AlphaFold’s abilities are easily competitive with the results obtained using experimental methods, such as cryo-electron microscopy, nuclear magnetic resonance, and x-ray crystallography. These experimental methods can take years to determine the shape of a protein. In contrast, AlphaFold can manage it in a few days. Achieving a score of over 90 reveals that any difference in the predicted and actual structure could be from experimental error in the experimental method — not the software. Alternatively, an error could predict an alternative protein configuration that could occur due to natural variation. The global distance test the results are based on is a scale of 0 to 100 which dictates how close the AI predicted structure is to the actual shape of the protein as determined from experimental methods.

AlphaFold was able to predict the structure of proteins with a margin of error of 1.6 angstroms (0.16 nanometers). This is equivalent to roughly the width of an atom. AlphaFold was able to accomplish the unthinkable. While scientists can determine the sequence of amino acids that make up a protein, there are thousands (an understatement) of ways that a protein can fold to achieve a three-dimensional shape. Back in 1972, Christian Anfinsen won the Nobel Prize in chemistry for proving that protein shape is determined by the sequence of amino acids. However, for the next 50 or so years, the scientific community had been grappling with trying to understand the shapes that proteins could fold into.

According to DeepMind, their latest version of AlphaFold used in the recent CASP experiment was created using an attention-based neural network system. The system was trained end-to-end to be able to interpret the structure of a spacial graph which can be used to predict the shape of a folded protein. The system then goes on to reason over the implicit graph that it is building. AlphaFold “uses evolutionarily related sequences, multiple sequence alignment (MSA), and a representation of amino acid residue pairs to refine this graph.” This iterative process allowed AlphaFold to develop solid predictions about the shape of a protein in as little as a few days. Finally, an internal confidence method in AlphaFold is used to determine which parts of the predicted protein shape are reliable.

AlphaFold’s ability to predict the shape of proteins has given new life to the study of diseases, something that has been thrust into the forefront of biology in the last year with a new urgency. DeepMind’s technology could help virologists identify proteins that have become impaired to help understand how they interact in this new malfunctioning state. Success in this field could lead to more precise drug development, further insight into existing experimental methods, and hopefully the realization of more efficient treatments for a variety of diseases and viruses.

DeepMind has even had the chance to use their AI in a real-world situation, having aided in early studies on the protein structures of the SARS-CoV-2 virus. AlphaFold was able to identify several related protein structures that had previously been unknown, helping scientists to better understand the nature of the virus.

DeepMind is currently in the process of preparing a paper concerning their system that will be submitted to a peer-reviewed journal.