A nonlinear least-squares method for refining a parametric expression describing the estimated errors of reflection intensities in serial crystallographic (SX) data is presented. This approach, which is similar to that used in the rotation method of crystallographic data collection at synchrotrons, propagates error estimates from photon-counting statistics to the merged data. Here, it is demonstrated that the application of this approach to SX data provides better SAD phasing ability, enabling the autobuilding of a protein structure that had previously failed to be built. Estimating the error in the merged reflection intensities requires the understanding and propagation of all of the sources of error arising from the measurements. One type of error, which is well understood, is the counting error introduced when the detector counts X-ray photons. Thus, if other types of random errors (such as readout noise) as well as uncertainties in systematic corrections (such as from X-ray attenuation) are completely understood, they can be propagated along with the counting error, as appropriate. In practice, most software packages propagate as much error as they know how to model and then include error-adjustment terms that scale the error estimates until they explain the variance among the measurements. If this is performed carefully, then during SAD phasing likelihood-based approaches can make optimal use of these error estimates, increasing the chance of a successful structure solution. In serial crystallography, SAD phasing has remained challenging, with the few examples of de novo protein structure solution each requiring many thousands of diffraction patterns. Here, the effects of different methods of treating the error estimates are estimated and it is shown that using a parametric approach that includes terms proportional to the known experimental uncertainty, the reflection intensity and the squared reflection intensity to improve the error estimates can allow SAD phasing even from weak zinc anomalous signal.

中文翻译：

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XFEL 数据的 SAD 定相主要取决于误差模型。

提出了一种非线性最小二乘法，用于细化描述串行晶体学 (SX) 数据中反射强度估计误差的参数表达式。这种方法类似于同步加速器晶体学数据收集的旋转方法中使用的方法，将光子计数统计的误差估计传播到合并的数据。在这里，事实证明，将此方法应用于 SX 数据可提供更好的 SAD 定相能力，从而能够自动构建以前无法构建的蛋白质结构。估计合并反射强度中的误差需要理解和传播测量产生的所有误差源。一种众所周知的误差是探测器对 X 射线光子进行计数时引入的计数误差。因此，如果完全理解其他类型的随机误差（例如读出噪声）以及系统校正中的不确定性（例如来自 X 射线衰减），则它们可以酌情与计数误差一起传播。在实践中，大多数软件包都会传播它们知道如何建模的尽可能多的误差，然后包含误差调整项，这些误差调整项可缩放误差估计，直到它们解释测量之间的方差。如果仔细执行此操作，那么在 SAD 定相期间，基于可能性的方法可以充分利用这些误差估计，从而增加成功结构解决方案的机会。在连续晶体学中，SAD 定相仍然具有挑战性，从头蛋白质结构解决方案的几个例子中，每个都需要数千个衍射图案。这里，估计了处理误差估计的不同方法的效果，并且表明，使用参数方法（包括与已知实验不确定性、反射强度和平方反射强度成比例的项）来改进误差估计可以允许SAD定相即使来自微弱的锌异常信号。