论文标题
基于威尔逊统计的贝叶斯推论的分子先验分布
A Molecular Prior Distribution for Bayesian Inference Based on Wilson Statistics
论文作者
论文摘要
背景和目标：威尔逊统计数据很好地描述了蛋白质在高频下的功率谱。因此，它发现了在结构生物学中的多个应用，例如，它是在低温电子显微镜（Cryo-EM）中使用的锐化步骤的基础。最近的一篇论文为威尔逊原始论点的形式主义提供了第一个严格的威尔逊统计证据。这种新的分析还导致蛋白质散射潜力的统计估计，这些蛋白质揭示了相邻的傅立叶系数之间的相关性。在这里，我们利用这些估计值来制作新颖的先验，可用于分子结构的贝叶斯推断。方法：我们描述了先验的特性及其超参数的计算。然后，我们在两个合成线性反问题上评估了先前，并与一系列SNR范围的冷冻EM重建中流行的先验进行比较。结果：我们表明，新的先验有效地抑制了噪声，并填充了光谱域中的低SNR区域。此外，它改善了对广泛SNR考虑的问题的估计分辨率，并产生了对掩盖效应不敏感的傅里叶壳相关曲线。结论：我们分析模型中的假设，讨论与其他正则化策略的关系，并假定对冷冻EM中结构确定的潜在影响。
Background and Objective: Wilson statistics describe well the power spectrum of proteins at high frequencies. Therefore, it has found several applications in structural biology, e.g., it is the basis for sharpening steps used in cryogenic electron microscopy (cryo-EM). A recent paper gave the first rigorous proof of Wilson statistics based on a formalism of Wilson's original argument. This new analysis also leads to statistical estimates of the scattering potential of proteins that reveal a correlation between neighboring Fourier coefficients. Here we exploit these estimates to craft a novel prior that can be used for Bayesian inference of molecular structures. Methods: We describe the properties of the prior and the computation of its hyperparameters. We then evaluate the prior on two synthetic linear inverse problems, and compare against a popular prior in cryo-EM reconstruction at a range of SNRs. Results: We show that the new prior effectively suppresses noise and fills-in low SNR regions in the spectral domain. Furthermore, it improves the resolution of estimates on the problems considered for a wide range of SNR and produces Fourier Shell Correlation curves that are insensitive to masking effects. Conclusions: We analyze the assumptions in the model, discuss relations to other regularization strategies, and postulate on potential implications for structure determination in cryo-EM.
