AlphaFold Models of Small Proteins Rival the Accuracy of Solution NMR Structures

By R. Tejero, Yuanpeng Janet Huang, T. Ramelot, Gaetano T. Montelione. 2022

37 citations recorded by the source corpus.

Recent advances in molecular modeling using deep learning have the potential to revolutionize the field of structural biology. In particular, AlphaFold has been observed to provide models of protein structures with accuracy rivaling medium-resolution X-ray crystal structures, and with excellent atomic coordinate matches to experimental protein NMR and cryo-electron microscopy structures. Here we assess the hypothesis that AlphaFold models of small, relatively rigid proteins have accuracies (based on comparison against experimental data) similar to experimental solution NMR structures. We selected six representative small proteins with structures determined by both NMR and X-ray crystallography, and modeled each of them using AlphaFold. Using several structure validation tools integrated under the Protein Structure Validation Software suite (PSVS), we then assessed how well these models fit to experimental NMR data, including NOESY peak lists (RPF-DP scores), comparisons between predicted rigidity and chemical shift data (ANSURR scores), and 15N-1H residual dipolar coupling data (RDC Q factors) analyzed by software tools integrated in the PSVS suite. Remarkably, the fits to NMR data for the protein structure models predicted with AlphaFold are generally similar, or better, than for the corresponding experimental NMR or X-ray crystal structures. Similar conclusions were reached in comparing AlphaFold2 predictions and NMR structures for three targets from the Critical Assessment of Protein Structure Prediction (CASP). These results contradict the widely held misperception that AlphaFold cannot accurately model solution NMR structures. They also document the value of PSVS for model vs. data assessment of protein NMR structures, and the potential for using AlphaFold models for guiding analysis of experimental NMR data and more generally in structural biology.

Subfields: Materials Chemistry, Molecular Biology

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In Materials Chemistry

1. Yi Zhao
2. Zhenyu Yang
3. Ron O. Dror
4. Runsheng Chen
5. Xiaoxi Zeng

In Molecular Biology

1. Yi Zhao
2. Zhenyu Yang
3. Ron O. Dror
4. Runsheng Chen
5. Xiaoxi Zeng
6. A. Bax
7. Yang Shen

Citations in the corpus

• AlphaFold2 and its applications in the fields of biology and medicine (2023, background): “68,69 Typically, the de novo structure determination of domains or proteins using NMR, which is time-consuming, may be replaced by the AF2 structures.”
• How accurately can one predict drug binding modes using AlphaFold models? (2023, background): “Indeed, in the most recent round of the Community Assessment of Structure Prediction (CASP)—a blind protein structure prediction competition— AlphaFold 2 (AF2) demonstrated an unprecedented ability to predict protein structures with atomic accuracy, sometimes rivaling the accuracy of certain experim”
• Predicting protein flexibility with AlphaFold (2023, methodology): “44 A comparison of the accuracy of a large pool of NMR structures with their corresponding AF2 structures was based on the Accuracy of NMR Structures Using RCI and Rigidity (ANSURR) method, 45 which provides an independent assessment of model accuracy by comparing protein flexibility predicted from…”