Tuesday, December 1, 2020

AlphaFold can predict the exact shape of proteins to precisions within the width of an atom.

 





Starting in the 1950s, the complete structures of proteins were determined by tedious laboratory experiments. Experimental techniques that can map out the structure of proteins in the lab, such as cryo-electron microscopy, nuclear magnetic resonance, and x-ray crystallography, can take hundreds of thousands of dollars and years of trial and error for each protein. 
  
A protein is a precise amino acid sequence that automatically folds itself up with many complex twists into a typical shape. The final structure determines how the protein behaves. Proteins' action is the key to the basic mechanisms of life, how we digest food, how hormones regulate behavior, and how our immune system fights disease. Efforts to develop vaccines for covid-19 have focused on the virus’s spike protein, for example. The way the coronavirus snags onto human cells depends on the shape of this protein and the shapes of the proteins on the outside of those cells. Therefore, finding the folded protein structures is an essential and vigorous research area.

The latest version of DeepMind’s deep-learning system, called AlphaFold, can find a protein’s shape in a few days with high accuracy. The new AlphaFold computational methods predicted the structure of dozens of proteins with a margin of error of just 1.6 angstroms—that’s 0.16 nanometers, less than the size of an atom. 

The breakthrough is a game-changer in pharmaceutical research, new drug design, and disease understanding. Many drugs are designed by simulating their 3D molecular structure, which requires knowing the structure of those proteins, which is true for only a quarter of the roughly 20,000 human proteins. AlphaFold will revolutionize this research. In the longer term, predicting protein structure will also help design synthetic proteins, such as enzymes that digest waste or produce biofuels. 
Read the whole article: Nature 588, 203-204 (2020)


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