Artificial intelligence used to predict 3D structure of proteins

A deep learning system can predict the structure of a protein using its genetic sequence more accurately than any previous modelling system, according to a study by researchers at DeepMind and UCL. Nearly every function our body performs relies on proteins. Predicting the intricate 3D structure of a protein is important because its structure largely determines its function and, once the structure is known, scientists can develop drugs that target this unique shape. Protein structure can be determined experimentally, using techniques such as cryo-electron microscopy, nuclear magnetic resonance and X-ray crystallography, but these are extremely costly and time consuming. The new study found that a deep learning system called AlphaFold could model protein structure from scratch - i.e., based only on genetic sequence - better than any previous modelling system and with a similar accuracy to systems drawing on templates of previously solved proteins. Professor David Jones (UCL Computer Science), Head of the UCL Bioinformatics Group and study co-author, said: "The 3D structure of a protein is probably the single most useful piece of information scientists can obtain to help understand what the protein does and how it works in cells. "Experimental techniques to determine protein structures are time consuming and expensive, so there's a huge demand for better computer algorithms to calculate the structures of proteins directly from the gene sequences which encode them, and DeepMind's work on applying AI to this long-standing problem in molecular biology is a definite advance.