Intrinsically disordered proteins and flexible regions in multi-domain proteins display substantial conformational heterogeneity. Characterizing the conformational ensembles of these proteins in solution typically requires combining one or more biophysical techniques with computational modelling or simulations. Experimental data can either be used to assess the accuracy of a computational model or to refine the computational model to get a better agreement with the experimental data. In both cases, one generally needs a so-called forward model, i.e. an algorithm to calculate experimental observables from individual conformations or ensembles. In many cases, this involve one or more parameters that need to be set, and it is not always trivial to determine the optimal values or to understand the impact on the choice of parameters. For example, in the case of small-angle X-ray scattering (SAXS) experiments, many forward models include parameters that describe the contribution of the hydration layer and displaced solvent to the background-subtracted experimental data. Often, one also needs to fit a scale factor and a constant background for the SAXS data, but across the entire ensemble. Here, we present a protocol to dissect the effect of free-parameters on the calculated SAXS intensities, and to identify a reliable set of values. We have implemented this procedure in our Bayesian/Maximum Entropy framework for ensemble refinement, and demonstrate the results on four intrinsically disordered proteins and a three-domain protein connected by flexible linkers. Our results show that the resulting ensembles can depend on the parameters used for solvent effects, and suggests that these should be chosen carefully. We also find a set of parameters that work robustly across all proteins.

中文翻译：

---

根据小角度 X 射线散射数据细化柔性蛋白质的构象集合

多域蛋白质中的固有无序蛋白质和柔性区域显示出大量的构象异质性。表征这些蛋白质在溶液中的构象集合通常需要将一种或多种生物物理技术与计算建模或模拟相结合。实验数据既可用于评估计算模型的准确性，也可用于改进计算模型以获得与实验数据更好的一致性。在这两种情况下，人们通常都需要一个所谓的前向模型，即一种从单个构象或集合计算实验观测值的算法。在许多情况下，这涉及需要设置的一个或多个参数，确定最佳值或了解对参数选择的影响并不总是微不足道的。例如，在小角度 X 射线散射 (SAXS) 实验的情况下，许多前向模型包括描述水化层和置换溶剂对背景扣除实验数据的贡献的参数。通常，还需要为 SAXS 数据拟合一个比例因子和一个恒定的背景，但要跨越整个集合。在这里，我们提出了一个协议来剖析自由参数对计算的 SAXS 强度的影响，并确定一组可靠的值。我们已经在我们的贝叶斯/最大熵框架中实现了这个程序以进行整体优化，并展示了四个本质上无序的蛋白质和一个由灵活链接器连接的三域蛋白质的结果。我们的结果表明，所产生的合奏可能取决于用于溶剂效应的参数，并建议应谨慎选择这些。我们还发现了一组对所有蛋白质都有效的参数。