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The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins

Abstract

Artificial intelligence-driven advances in protein structure prediction in recent years have raised the question: has the protein structure-prediction problem been solved? Here, with a focus on nonglobular proteins, we highlight the many strengths and potential weaknesses of DeepMind’s AlphaFold2 in the context of its biological and therapeutic applications. We summarize the subtleties associated with evaluation of AlphaFold2 model quality and reliability using the predicted local distance difference test (pLDDT) and predicted aligned error (PAE) values. We highlight various classes of proteins that AlphaFold2 can be applied to and the caveats involved. Concrete examples of how AlphaFold2 models can be integrated with experimental data in the form of small-angle X-ray scattering (SAXS), solution NMR, cryo-electron microscopy (cryo-EM) and X-ray diffraction are discussed. Finally, we highlight the need to move beyond structure prediction of rigid, static structural snapshots toward conformational ensembles and alternate biologically relevant states. The overarching theme is that careful consideration is due when using AlphaFold2-generated models to generate testable hypotheses and structural models, rather than treating predicted models as de facto ground truth structures.

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Fig. 1: Overview of AF2.
Fig. 2: Example applications of AF2 predictions that deviate from the experimental structure.
Fig. 3: Integration of AlphaFold models with experimental data.

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Data availability

PyMOL sessions containing comparisons of AlphaFold models (extracted from the literature or the AlphaFold database) compared with experimental structures together with python script used to color code structures based on pLDDT values are freely available at https://github.com/mcshanlab/AlphaFold_Models_Agarwal_McShan.

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Acknowledgements

A.C.M. acknowledges start-up funds from the Georgia Institute of Technology. V.A. acknowledges support from the National Science Foundation (CHE-2238650) and the National Institutes of Health (R35GM142882).

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A.C.M. and V.A. conceived, wrote and edited the manuscript. A.C.M. generated figures and analyzed models.

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Agarwal, V., McShan, A.C. The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins. Nat Chem Biol (2024). https://doi.org/10.1038/s41589-024-01638-w

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