Tejero Roberto, Huang Yuanpeng Janet, Ramelot Theresa A, Montelione Gaetano T
Departamento de Química Física, Universidad de Valencia, Valencia, Spain.
Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY, United States.
Front Mol Biosci. 2022 Jun 13;9:877000. doi: 10.3389/fmolb.2022.877000. eCollection 2022.
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 accuracies 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 N-H 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.
利用深度学习的分子建模方面的最新进展有可能彻底改变结构生物学领域。特别是，人们观察到AlphaFold能够提供蛋白质结构模型，其准确性可与中等分辨率的X射线晶体结构相媲美，并且与实验性蛋白质核磁共振（NMR）和冷冻电子显微镜结构的原子坐标匹配度极高。在此，我们评估这样一个假设：相对刚性的小蛋白质的AlphaFold模型具有与实验性溶液NMR结构相似的准确性（基于与实验数据的比较）。我们选择了六种通过NMR和X射线晶体学确定结构的代表性小蛋白质，并使用AlphaFold对它们进行建模。然后，我们使用集成在蛋白质结构验证软件套件（PSVS）中的几种结构验证工具，评估这些模型与实验NMR数据的拟合程度，包括NOESY峰列表（RPF-DP分数）、预测刚性与化学位移数据之间的比较（ANSURR分数），以及由PSVS套件中集成的软件工具分析的N-H剩余偶极耦合数据（RDC Q因子）。值得注意的是，用AlphaFold预测的蛋白质结构模型与NMR数据的拟合通常与相应的实验NMR或X射线晶体结构相似，甚至更好。在比较AlphaFold2对蛋白质结构预测关键评估（CASP）中的三个目标的预测和NMR结构时也得出了类似的结论。这些结果与广泛存在的误解相矛盾，即AlphaFold无法准确模拟溶液NMR结构。它们还证明了PSVS在蛋白质NMR结构的模型与数据评估中的价值，以及使用AlphaFold模型指导实验NMR数据分析以及更广泛地在结构生物学中的应用潜力。
