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

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Tytuł:: AlphaFold 2: Why It Works and Its Implications for Understanding the Relationships of Protein Sequence, Structure, and Function.
Autorzy:: Skolnick J; Center for the Study of Systems Biology, School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia 30332, United States.
Gao M; Center for the Study of Systems Biology, School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia 30332, United States.
Zhou H; Center for the Study of Systems Biology, School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia 30332, United States.
Singh S; Twilight Design, 4 Adams Road, Kendall Park, New Jersey 08824, United States.
Źródło:: Journal of chemical information and modeling [J Chem Inf Model] 2021 Oct 25; Vol. 61 (10), pp. 4827-4831. Date of Electronic Publication: 2021 Sep 29.
Typ publikacji:: Journal Article
Język:: English
Imprint Name(s):: Original Publication: Washington, D.C. : American Chemical Society, c2005-
MeSH Terms:: Protein Folding*
Proteins*
Algorithms ; Amino Acid Sequence
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Grant Information:: R35 GM118039 United States GM NIGMS NIH HHS
Substance Nomenclature:: 0 (Proteins)
Entry Date(s):: Date Created: 20210929 Date Completed: 20211122 Latest Revision: 20211122
Update Code:: 20240105
PubMed Central ID:: PMC8592092
DOI:: 10.1021/acs.jcim.1c01114
PMID:: 34586808
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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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