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基于AlphaFold的分子置换法用于解析具有挑战性的晶体结构。

AlphaFold-guided molecular replacement for solving challenging crystal structures.

作者信息

Wang Wei, Gong Zhen, Hendrickson Wayne A

机构信息

Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY 10032, USA.

出版信息

Acta Crystallogr D Struct Biol. 2025 Jan 1;81(Pt 1):4-21. doi: 10.1107/S2059798324011999.

Abstract

Molecular replacement (MR) is highly effective for biomolecular crystal structure determination, increasingly so as the database of known structures has increased. For candidates without recognizable similarity to known structures, however, crystal structure analyses have nearly always required experiments for de novo phase evaluation. Now, with the unprecedented accuracy of AlphaFold predictions of protein structures from amino-acid sequences, an appreciable expansion of the reach of MR for proteins is realized. Here, we sought to automate an AlphaFold-guided MR procedure that tailors predictions to the MR problem at hand. We first optimized the reliability cutoff parameters for residue inclusion as tested in application to a previously MR-intractable problem. We then examined cases where AlphaFold by default predicts a conformation alternative to that of the candidate structure, devising tests for MR solution either from domain-specific predictions or from predictions based on diverse sequence subclusters. We tested subclustering procedures on an enzyme system that entails multiple MR-challenging conformations. The overall process as implemented in Phenix automatically surveys a succession of trials of increasing computational complexity until an MR solution is found or the options are exhausted. Validated MR solutions were found for 92% of one set of 158 challenging problems from the PDB and 93% of those from a second set of 215 challenges. Thus, many crystal structure analyses that previously required experimental phase evaluation can now be solved by AlphaFold-guided MR. In effect, this and related MR approaches are de novo phasing methods.

摘要

分子置换(MR)在生物分子晶体结构测定中非常有效,随着已知结构数据库的增加,其有效性日益凸显。然而,对于与已知结构没有明显相似性的候选物,晶体结构分析几乎总是需要进行从头相位评估实验。如今,凭借AlphaFold从氨基酸序列预测蛋白质结构的前所未有的准确性,实现了MR在蛋白质应用范围的显著扩展。在这里,我们试图自动化一个由AlphaFold引导的MR程序,使其针对手头的MR问题定制预测。我们首先针对先前难以用MR解决的问题,优化了用于残基纳入的可靠性截止参数。然后,我们研究了AlphaFold默认预测的构象与候选结构不同的情况,设计了从特定结构域预测或基于不同序列子簇的预测来求解MR的测试方法。我们在一个具有多个具有MR挑战性构象的酶系统上测试了子聚类程序。在Phenix中实现的整个过程会自动连续地进行一系列计算复杂度不断增加的试验,直到找到MR解决方案或选项用尽。对于来自PDB的一组158个具有挑战性的问题中的92%以及来自第二组215个挑战中的93%,都找到了经过验证的MR解决方案。因此,许多以前需要实验相位评估的晶体结构分析现在可以通过AlphaFold引导的MR来解决。实际上,这种以及相关的MR方法都是从头相位确定方法。

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