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AlphaFold 2: Why It Works and Its Implications for Understanding the Relationships of Protein Sequence, Structure, and Function

  • Jeffrey Skolnick*
    Jeffrey Skolnick
    Center for the Study of Systems Biology, School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
    *Email: [email protected]
    More by Jeffrey Skolnick
    Orcidhttps://orcid.org/0000-0002-1877-4958
  • , 
  • Mu Gao
    Mu Gao
    Center for the Study of Systems Biology, School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
    More by Mu Gao
  • , 
  • Hongyi Zhou
    Hongyi Zhou
    Center for the Study of Systems Biology, School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
    More by Hongyi Zhou
  • , and 
  • Suresh Singh
    Suresh Singh
    Twilight Design, 4 Adams Road, Kendall Park, New Jersey 08824, United States
    More by Suresh Singh
Cite this: J. Chem. Inf. Model. 2021, 61, 10, 4827–4831
Publication Date (Web):September 29, 2021
https://doi.org/10.1021/acs.jcim.1c01114
Copyright © 2021 American Chemical Society
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    Abstract

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    AlphaFold 2 (AF2) was the star of CASP14, the last biannual structure prediction experiment. Using novel deep learning, AF2 predicted the structures of many difficult protein targets at or near experimental resolution. Here, we present our perspective of why AF2 works and show that it is a very sophisticated fold recognition algorithm that exploits the completeness of the library of single domain PDB structures. It has also learned local side chain packing rearrangements that enable it to refine proteins to high resolution. The benefits and limitations of its ability to predict the structures of many more proteins at or close to atomic detail are discussed.

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