FFF：基于片段引导的柔性拟合用于构建完整蛋白质结构
FFF: Fragments-Guided Flexible Fitting for Building Complete Protein Structures
AI总结：
本文提出 FFF 方法，通过多级识别网络提取 cryo-EM 图特征，结合片段生成与柔性拟合，将结构预测与识别相融合，以解决低信噪比下无法构建完整蛋白质结构的问题，并在基准测试中优于基线方法。
摘要：
冷冻电子显微镜（cryo-EM）是一种用于重建生物分子（尤其是大型蛋白质复合物和分子组装体）三维（3D）结构的技术。随着分辨率提高到近原子尺度，从 cryo-EM 密度图中从头构建蛋白质结构成为可能。近年来，基于识别的从头构建方法已显示出简化这一流程的潜力。然而，由于低信噪比（SNR）问题，它无法构建完整结构。与此同时，AlphaFold 在蛋白质结构预测方面取得了重大突破。这启发我们将片段识别与结构预测方法相结合来构建完整结构。在本文中，我们提出了一种名为 FFF 的新方法，该方法通过柔性拟合将蛋白质结构预测与蛋白质结构识别连接起来。首先，使用多级识别网络从输入的三维 cryo-EM 密度图中捕获各种结构特征。接着，利用伪肽向量和基于这些提取特征的蛋白质序列比对方法生成蛋白质结构片段。最后，通过柔性拟合使用预测的蛋白质片段构建完整结构模型。根据我们的基准测试，FFF 在构建完整蛋白质结构方面优于基线方法。
英文摘要：
Cryo-electron microscopy (cryo-EM) is a technique for reconstructing the 3-dimensional (3D) structure of biomolecules (especially large protein complexes and molecular assemblies). As the resolution increases to the near-atomic scale, building protein structures de novo from cryo-EM maps becomes possible. Recently, recognition-based de novo building methods have shown the potential to streamline this process. However, it cannot build a complete structure due to the low signal-to-noise ratio (SNR) problem. At the same time, AlphaFold has led to a great breakthrough in predicting protein structures. This has inspired us to combine fragment recognition and structure prediction methods to build a complete structure. In this paper, we propose a new method named FFF that bridges protein structure prediction and protein structure recognition with flexible fitting. First, a multi-level recognition network is used to capture various structural features from the input 3D cryo-EM map. Next, protein structural fragments are generated using pseudo peptide vectors and a protein sequence alignment method based on these extracted features. Finally, a complete structural model is constructed using the predicted protein fragments via flexible fitting. Based on our benchmark tests, FFF outperforms the baseline methods for building complete protein structures.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
