Macromolecular crystallography generally requires the recovery of missing phase information from diffraction data to reconstruct an electron-density map of the crystallized molecule. Most recent structures have been solved using molecular replacement as a phasing method, requiring an a priori structure that is closely related to the target protein to serve as a search model; when no such search model exists, molecular replacement is not possible. New advances in computational machine-learning methods, however, have resulted in major advances in protein structure predictions from sequence information. Methods that generate predicted structural models of sufficient accuracy provide a powerful approach to molecular replacement. Taking advantage of these advances, AlphaFold predictions were applied to enable structure determination of a bacterial protein of unknown function (UniProtKB Q63NT7, NCBI locus BPSS0212) based on diffraction data that had evaded phasing attempts using MIR and anomalous scattering methods. Using both X-ray and micro-electron (microED) diffraction data, it was possible to solve the structure of the main fragment of the protein using a predicted model of that domain as a starting point. The use of predicted structural models importantly expands the promise of electron diffraction, where structure determination relies critically on molecular replacement.

中文翻译：

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使用 X 射线和电子晶体学 AlphaFold 辅助确定功能未知的细菌蛋白的结构

高分子晶体学通常需要从衍射数据中恢复丢失的相位信息，以重建结晶分子的电子密度图。大多数最新的结构都是使用分子置换作为定相方法来解决的，需要与目标蛋白密切相关的先验结构作为搜索模型；当不存在这样的搜索模型时，分子替换是不可能的。然而，计算机器学习方法的新进展使得根据序列信息预测蛋白质结构取得了重大进展。生成足够准确的预测结构模型的方法为分子替换提供了强大的方法。利用这些进步，AlphaFold预测被应用于基于衍射数据来确定功能未知的细菌蛋白（UniProtKB Q63NT7，NCBI 位点 BPSS0212）的结构，这些数据避开了使用 MIR 和反常散射方法进行定相尝试。使用 X 射线和微电子 (microED) 衍射数据，可以使用该结构域的预测模型作为起点来解析蛋白质主要片段的结构。预测结构模型的使用重要地扩展了电子衍射的前景，其中结构确定关键依赖于分子替换。