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Ethan H. Li, Laura E. Spaman, Roberto Tejero, Yuanpeng Janet Huang, Theresa A. Ramelot, Keith J. Fraga, James H. Prestegard, Michael A. Kennedy, Gaetano Thomas Montelione
Recent advances in molecular modeling of protein structures are changing the field of structural biology. AlphaFold-2 (AF2), an AI system developed by DeepMind, Inc., utilizes attention-based deep learning to predict models of protein structures with high accuracy relative to structures determined by X-ray crystallography and cryo-electron microscopy (cryoEM). Comparing AF2 models to structures determined using solution NMR data, both high similarities and distinct differences have been observed. Since AF2 was trained on X-ray crystal and cryoEM structures, we assessed how accurately AF2 can model small, monomeric, solution protein NMR structures which (i) were not used in the AF2 training data set, and (ii) did not have homologous structures in the Protein Data Bank at the time of AF2 training. We identified nine open source protein NMR data sets for such “blind” targets, including chemical shift, raw NMR FID data, NOESY peak lists, and (for 1 case) 15N-1H residual dipolar coupling data. For these nine small (70–108 residues) monomeric proteins, we generated AF2 prediction models and assessed how well these models fit to these experimental NMR data, using several well-established NMR structure validation tools. In most of these cases, the AF2 models fit the NMR data nearly as well, or sometimes better than, the corresponding NMR structure models previously deposited in the Protein Data Bank. These results provide benchmark NMR data for assessing new NMR data analysis and protein structure prediction methods. They also document the potential for using AF2 as a guiding tool in protein NMR data analysis, and more generally for hypothesis generation in structural biology research.
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Li, E. H., Spaman, L. E., Tejero, R., Huang, Y. J., Ramelot, T. A., Fraga, K. J., Prestegard, J. H., Kennedy, M. A., & Montelione, G. T. (2023). Blind assessment of monomeric AlphaFold2 protein structure models with experimental NMR data. Journal of Magnetic Resonance, 352, 107481. https://doi.org/10.1016/j.jmr.2023.107481
Li, Ethan H., et al. "Blind assessment of monomeric AlphaFold2 protein structure models with experimental NMR data." Journal of Magnetic Resonance, vol. 352, 2023, pp. 107481, https://doi.org/10.1016/j.jmr.2023.107481.
Li, Ethan H., Laura E. Spaman, Roberto Tejero, Yuanpeng Janet Huang, Theresa A. Ramelot, Keith J. Fraga, James H. Prestegard, Michael A. Kennedy, and Gaetano Thomas Montelione. "Blind assessment of monomeric AlphaFold2 protein structure models with experimental NMR data." Journal of Magnetic Resonance 352 (2023): 107481. https://doi.org/10.1016/j.jmr.2023.107481.
Li E. H., Spaman L. E., Tejero R. [и др.] Blind assessment of monomeric AlphaFold2 protein structure models with experimental NMR data // Journal of Magnetic Resonance. — 2023. — Vol. 352. — P. 107481. — URL: https://doi.org/10.1016/j.jmr.2023.107481
@article{li2023blind,
title = {{Blind assessment of monomeric AlphaFold2 protein structure models with experimental NMR data}},
author = {Ethan H. Li and Laura E. Spaman and Roberto Tejero and Yuanpeng Janet Huang and Theresa A. Ramelot and Keith J. Fraga and James H. Prestegard and Michael A. Kennedy and Gaetano Thomas Montelione},
journal = {Journal of Magnetic Resonance},
year = {2023},
volume = {352},
pages = {107481},
doi = {10.1016/j.jmr.2023.107481},
url = {https://doi.org/10.1016/j.jmr.2023.107481}
}TY - JOUR AU - Li, Ethan H. AU - Spaman, Laura E. AU - Tejero, Roberto AU - Huang, Yuanpeng Janet AU - Ramelot, Theresa A. AU - Fraga, Keith J. AU - Prestegard, James H. AU - Kennedy, Michael A. AU - Montelione, Gaetano Thomas TI - Blind assessment of monomeric AlphaFold2 protein structure models with experimental NMR data JO - Journal of Magnetic Resonance VL - 352 SP - 107481 PY - 2023 DA - 2023/05/19 DO - 10.1016/j.jmr.2023.107481 UR - https://doi.org/10.1016/j.jmr.2023.107481 ER -
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