Work

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

Year: 2021

Type: editorial

Abstract: 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.

Source: Journal of Chemical Information and Modeling

DOI: 10.1021/acs.jcim.1c01114

Authors: Jeffrey Skolnick, Mu Gao, Hongyi Zhou, Suresh Singh

Institution: Georgia Institute of Technology

Language: English

Topic: Protein Structure and Dynamics

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: green

Funders: National Institutes of Health, National Institute of General Medical Sciences

Award: Interplay of inherent promiscuity and specificity in protein biochemical function with applications to drug discovery and exome analysis

OpenAlex ID: W3201869122

Cites: 62

Cited by: 256

Related to: 10