Molecular Biology Institute, University of California, Los Angeles, Los Angeles, CA 90095, USA.
Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, CA 90095, USA.
Acta Crystallogr D Struct Biol. 2024 Apr 1;80(Pt 4):270-278. doi: 10.1107/S205979832400072X. Epub 2024 Mar 7.
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.
大分子晶体学通常需要从衍射数据中恢复缺失的相位信息，以重建结晶分子的电子密度图。最近的结构都是使用分子置换作为相分析方法来解决的，这需要一个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，分子置换是不可能的。然而，计算机器学习方法的新进展导致了基于序列信息的蛋白质结构预测的重大进展。生成足够准确的预测结构模型的方法为分子置换提供了一种强大的方法。利用这些进展，AlphaFold 的预测结果被应用于基于衍射数据来确定一个未知功能的细菌蛋白（UniProtKB Q63NT7，NCBI 基因座 BPSS0212）的结构，这些衍射数据曾尝试使用 MIR 和异常散射方法进行相位分析但均以失败告终。利用 X 射线和微电子（microED）衍射数据，使用该结构域的预测模型作为起点，有可能解决该蛋白的主要片段的结构。预测结构模型的使用重要地扩展了电子衍射的应用范围，其中结构确定严重依赖于分子置换。
