There's an age-old adage in biology: structure determines function. In order to understand the function of the myriad proteins that perform vital jobs in a healthy body--or malfunction in a diseased one--scientists have to first determine these proteins' molecular structure. But this is no easy feat: protein molecules consist of long, twisty chains of up to thousands of amino acids, chemical compounds that can interact with one another in many ways to take on an enormous number of possible three-dimensional shapes. Figuring out a single protein's structure, or solving the "protein-folding problem, can take years of finicky experiments. But earlier this year an artificial intelligence program called AlphaFold, developed by the Google-owned company DeepMind, predicted the 3-D structures of almost every known protein--about 200 million in all. DeepMind CEO Demis Hassabis and senior staff research scientist John Jumper were jointly awarded this year's $3-million Breakthrough Prize in Life ...

DeepMind solves a 50-year old problem in Protein Folding Prediction. AlphaFold 2 improves over DeepMind's 2018 AlphaFold system with a new architecture and massively outperforms all competition. In this Video, we take a look at how AlphaFold 1 works and what we can gather about AlphaFold 2 from the little information that's out there. CASP14 Result Bar Chart: https://www.predictioncenter.org/casp14/zscores_final.cgi Paper Title: High Accuracy Protein Structure Prediction Using Deep Learning Abstract: Proteins are essential to life, supporting practically all its functions. They are large complex molecules, made up of chains of amino acids, and what a protein does largely depends on its unique 3D structure.