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Prediction of protein structure and AI

Journal of Human Genetics (2024)Cite this article

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Abstract

AlphaFold, an artificial intelligence (AI)-based tool for predicting the 3D structure of proteins, is now widely recognized for its high accuracy and versatility in the folding of human proteins. AlphaFold is useful for understanding structure-function relationships from protein 3D structure models and can serve as a template or a reference for experimental structural analysis including X-ray crystallography, NMR and cryo-EM analysis. Its use is expanding among researchers, not only in structural biology but also in other research fields. Researchers are currently exploring the full potential of AlphaFold-generated protein models. Predicting disease severity caused by missense mutations is one such application. This article provides an overview of the 3D structural modeling of AlphaFold based on deep learning techniques and highlights the challenges in predicting the pathogenicity of missense mutations.

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Fig. 1: AlphaFold model of a G-protein coupled receptor MC1R (melanocyte-stimulating hormone receptor).
Fig. 2: AlphaFold model of a two-domain protein galectin-8 (LGALS8).
Fig. 3: Prediction of missense pathogenicity by AlphaMissense [30].

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Authors and Affiliations

  1. Division of Structural Glycobiology, Institute of Molecular Biomembrane and Glycobiology, Tohoku Medical and Pharmaceutical University, 4-4-1 Komatsushima, Aoba-ku, Sendai, Miyagi, 981-8558, Japan

    Shiho Ohno, Noriyoshi Manabe & Yoshiki Yamaguchi

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  1. Shiho Ohno
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Correspondence to Yoshiki Yamaguchi.

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Ohno, S., Manabe, N. & Yamaguchi, Y. Prediction of protein structure and AI. J Hum Genet (2024). https://doi.org/10.1038/s10038-023-01215-4

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