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Protein structure prediction

Structural biology at the scale of proteomes

Nature Structural & Molecular Biology volume 30, pages 129–130 (2023)Cite this article

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AlphaFold2 has already changed structural biology, but its true power may lie in how it changes the way we think about cells and organisms. Two studies broadly assess its utility and limitations in providing structural models to shed light in areas such as mutations, protein–protein interactions, and phosphorylation.

The Human Genome Project and its accompanying technological advances paved the way for the genomics revolution, yielding an abundance of sequenced genes and genomes and enabling biologists to study cells, organisms and their evolution using the firm molecular basis of genetic data. Combined with physically inspired mathematical methods such as maximum entropy models1 and, more recently, machine learning, this wealth of sequence data has formed the foundation for the recent revolution in protein structure prediction2, epitomized in the recent release by DeepMind and the European Bioinformatics Institute of 200 million protein structures predicted by AlphaFold23. As one revolution begets another, the widespread availability of structural information posits a new possibility: a structural systems biology in which biological phenomena across the varied scales of life are studied through a structural and mechanistic prism. In this and a previous issue of Nature Structural & Molecular Biology, Akdel et al.4 and Burke et al.5 take some of the first steps toward this goal. Akdel et al.4 assess AlphaFold2 across multiple tasks, including its structural coverage of multiple proteomes, the extent of fold space it models and its ability to predict ligand binding sites, finding that AlphaFold2 systematically outperforms existing state-of-the-art tools. In a complementary paper, using the wealth of AlphaFold2-predicted structures, Burke et al.5 assess the abilities and limitations of AlphaFold2 in structurally modeling the human protein–protein interaction network (less than 5% of which is estimated to have been structurally characterized). They find that AlphaFold2 increases the number of high-accuracy predictions of human protein–protein interactions and use these predictions to provide structural insights into pathogenic mutations and the phosphorylation of protein interaction interfaces.

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Acknowledgements

N.B. is supported by US Defense Advanced Research Projects Agency (DARPA) PANACEA program grant HR0011-19-2-0022 and National Cancer Institute (NCI) grant U54-CA225088.

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Authors and Affiliations

  1. Laboratory of Systems Pharmacology, Program in Therapeutic Science, Harvard Medical School, Boston, MA, USA

    Nazim Bouatta

  2. Department of Systems Biology, Columbia University, New York, NY, USA

    Mohammed AlQuraishi

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  1. Nazim Bouatta
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Correspondence to Nazim Bouatta or Mohammed AlQuraishi.

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Competing interests

M.A. is a member of the Scientific Advisory Boards of Cyrus Biotechnology, Deep Forest Sciences, Nabla Bio, Oracle Therapeutics and FL2021-002, a Foresite Labs company.

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Bouatta, N., AlQuraishi, M. Structural biology at the scale of proteomes. Nat Struct Mol Biol 30, 129–130 (2023). https://doi.org/10.1038/s41594-023-00924-w

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