A watershed moment for protein structure prediction

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Two threads of research in the quest for methods that predict the 3D structures of proteins from their amino-acid sequences have become fully intertwined. The result is a leap forward in the accuracy of predictions.

By

  1. Mohammed AlQuraishi

    1. Mohammed AlQuraishi is in the Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, Massachusetts 02115, USA.

Proteins perform or catalyse nearly all chemical and mechanical processes in cells. Synthesized as linear chains of amino-acid residues, most proteins spontaneously fold into one or a small number of favoured three-dimensional structures. The sequence of amino acids specifies a protein’s structure and range of motion, which in turn determine its function. Over decades, structural biologists have experimentally determined thousands of protein structures, but the difficulty of these studies has made the promise of a computational approach for predicting protein structure from sequence alluring. Writing in Nature, Senior et al.1 describe an algorithm, AlphaFold, that takes a leap forward in solving this classic problem by bringing to bear modern machine-learning techniques.

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Nature 577, 627-628 (2020)

doi: https://doi.org/10.1038/d41586-019-03951-0

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