PDF(Pubmed)
Abstract：
Molecular mass (MM) is one of the key structural parameters obtained by small-angle X-ray scattering (SAXS) of proteins in solution and is used to assess the sample quality, oligomeric composition and to guide subsequent structural modelling. Concentration-dependent assessment of MM relies on a number of extra quantities (partial specific volume, calibrated intensity, accurate solute concentration) and often yields limited accuracy. Concentration-independent methods forgo these requirements being based on the relationship between structural parameters, scattering invariants and particle volume obtained directly from the data. Using a comparative analysis on 165,982 unique scattering profiles calculated from high-resolution protein structures, the performance of multiple concentration-independent MM determination methods was assessed. A Bayesian inference approach was developed affording an accuracy above that of the individual methods, and reports MM estimates together with a credibility interval. This Bayesian approach can be used in combination with concentration-dependent MM methods to further validate the MM of proteins in solution, or as a reliable stand-alone tool in instances where an accurate concentration estimate is not available.
摘要：
分子质量（MM）是通过溶液中蛋白质的小角度X射线散射（SAXS）获得的关键结构参数之一，用于评估样品质量，低聚组成并指导随后的结构建模。MM的浓度依赖性评估依赖于一些额外的数量(部分比容，校准强度,准确的溶质浓度)，并且通常产生有限的准确性。与浓度无关的方法放弃了基于结构参数之间关系的这些要求，散射不变量和粒子体积直接从数据中获得。使用从高分辨率蛋白质结构计算的165,982个独特的散射曲线的比较分析，评估了多种浓度非依赖性MM测定方法的性能。开发了一种贝叶斯推断方法，该方法的准确性高于单个方法的准确性，并报告MM估计以及可信度区间。这种贝叶斯方法可以与浓度依赖性MM方法结合使用，以进一步验证溶液中蛋白质的MM，或作为一个可靠的独立工具的情况下，准确的浓度估计是不可用的。
