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Putting AlphaFold models to work with phenix.process_predicted_model and ISOLDE

机译:将 AlphaFold 模型与 phenix.process_predicted_model 和 ISOLDE 配合使用

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AlphaFold has recently become an important tool in providing models for experimental structure determination by X-ray crystallography and cryo-EM. Large parts of the predicted models typically approach the accuracy of experimentally determined structures, although there are frequently local errors and errors in the relative orientations of domains. Importantly, residues in the model of a protein predicted by AlphaFold are tagged with a predicted local distance difference test score, informing users about which regions of the structure are predicted with less confidence. AlphaFold also produces a predicted aligned error matrix indicating its confidence in the relative positions of each pair of residues in the predicted model. The phenix.process_predicted_ model tool downweights or removes low-confidence residues and can break a model into confidently predicted domains in preparation for molecular replacement or cryo-EM docking. These confidence metrics are further used in ISOLDE to weight torsion and atom-atom distance restraints, allowing the complete AlphaFold model to be interactively rearranged to match the docked fragments and reducing the need for the rebuilding of connecting regions.
机译:AlphaFold最近已成为一个重要的工具为实验提供模型结构通过x射线晶体学和决心低温电子显微镜。通常的方法实验的准确性结构决定的,虽然有当地经常错误和错误的域的相对方向。在模型中残留的蛋白质预测的预测当地AlphaFold标记距离差异测试成绩,通知用户哪些区域的结构预计用更少的信心。产生一个预测对齐误差矩阵表明其相对的信心每一对残留的位置预测模型。模型工具downweights或删除低残留,可以一个模型分解成自信预测领域为分子做准备更换或者低温电子显微镜对接。指标进一步用于伊索德的重量扭力和atom-atom距离限制,允许完成AlphaFold模型交互式地重新安排来匹配对接碎片和减少的需要连接区域的重建。

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