Protein model refinement for cryo-EM maps using AlphaFold2 and the DAQ score.
Protein model refinement for cryo-EM maps using AlphaFold2 and the DAQ score.
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DOI：
10.1107/s2059798322011676
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发表时间：
2023-01-01
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描述了一种新的协议，DAQ-refine，用于评估从低温电镜图建立的蛋白质模型，并应用局部结构细化。随着越来越多的蛋白质结构模型从低温电子显微镜（cryo-EM）密度图中确定，建立如何评估模型的准确性以及如何在模型包含错误的情况下纠正模型，对于确保存放在公共数据库PDB中的结构模型的质量变得至关重要。本文提出了一种新的方案，用于评估由低温电镜图构建的蛋白质模型，并在模型存在潜在误差的情况下应用局部结构改进。首先，使用最近开发的基于深度学习的模型局部地图评估分数（DAQ）进行模型评估。随后的局部细化由修改后的AlphaFold2过程执行，其中提供修剪过的模板模型和修剪过的多序列比对作为输入，以控制要细化的结构区域，同时保留模型的其他更自信的区域。基准研究表明，该协议，DAQ-refine，持续改善了初始模型的低质量区域。在为初始结构生成的18个精细化模型中，DAQ与模型质量具有较高的相关性，可以为大多数测试用例识别出最准确的模型。平均而言，DAQ-refine获得的改进大于其他现有方法。
A new protocol, DAQ-refine, for evaluating a protein model built from a cryo-EM map and applying local structure refinement is described. As more protein structure models have been determined from cryogenic electron microscopy (cryo-EM) density maps, establishing how to evaluate the model accuracy and how to correct models in cases where they contain errors is becoming crucial to ensure the quality of the structural models deposited in the public database, the PDB. Here, a new protocol is presented for evaluating a protein model built from a cryo-EM map and applying local structure refinement in the case where the model has potential errors. Firstly, model evaluation is performed using a deep-learning-based model–local map assessment score, DAQ, that has recently been developed. The subsequent local refinement is performed by a modified AlphaFold2 procedure, in which a trimmed template model and a trimmed multiple sequence alignment are provided as input to control which structure regions to refine while leaving other more confident regions of the model intact. A benchmark study showed that this protocol, DAQ-refine, consistently improves low-quality regions of the initial models. Among 18 refined models generated for an initial structure, DAQ shows a high correlation with model quality and can identify the best accurate model for most of the tested cases. The improvements obtained by DAQ-refine were on average larger than other existing methods.