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A round-robin approach provides a detailed assessment of biomolecular small-angle scattering data reproducibility and yields consensus curves for benchmarking

机译:循环方法可对生物分子小角散射数据的可重复性进行详细评估,并生成用于基准测试的一致性曲线

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摘要

Through an expansive international effort that involved data collection on 12 small-angle X-ray scattering (SAXS) and four small-angle neutron scattering (SANS) instruments, 171 SAXS and 76 SANS measurements for five proteins (ribonuclease A, lysozyme, xylanase, urate oxidase and xylose isomerase) were acquired. From these data, the solvent-subtracted protein scattering profiles were shown to be reproducible, with the caveat that an additive constant adjustment was required to account for small errors in solvent subtraction. Further, the major features of the obtained consensus SAXS data over the q measurement range 0-1 A degrees(-1) are consistent with theoretical prediction. The inherently lower statistical precision for SANS limited the reliably measured q-range to 2.2 A degrees(-1) showed good mutual agreement, affirming that this region has interpretable features for structural modelling. SAS measurements with inline size-exclusion chromatography (SEC) proved to be generally superior for eliminating sample heterogeneity, but with unavoidable sample dilution during column elution, while batch SAS data collected at higher concentrations and for longer times provided superior statistical precision. Careful merging of data measured using inline SEC and batch modes, or low- and high-concentration data from batch measurements, was successful in eliminating small amounts of aggregate or interparticle interference from the scattering while providing improved statistical precision overall for the benchmarking data set.
机译:通过一个广泛的国际努力参与数据收集12日小角度x射线(粉煤灰)和四个小角中子散射散射(SANS)仪器,171粉煤灰和76年无测量5蛋白(核糖核酸酶溶菌酶,木聚糖酶、尿酸氧化酶和木糖异构酶)。solvent-subtracted蛋白质散射配置文件被证明是可再生的,警告一个积分常数调整是必需的在溶剂占小错误减法。获得共识一枝数据问(1)测量范围0 - 1度与理论预测相符。本质上降低SANS的统计精度有限的可靠测量q-range 2.2度(1)显示良好的共同协议,确认这个地区解释功能结构模型。测量与内联凝胶排阻色谱法(SEC)证明是普遍的上级来消除样本异质性,但期间不可避免的样本稀释柱洗脱,而批SAS数据收集更高的浓度和时间更长时间提供优越的统计精度。使用内联SEC和合并数据的测量批处理模式,或低收入和高浓度的数据从批量测量,是成功的消除少量聚合或颗粒间的散射的干扰同时提供改进的统计精度整体的基准数据集。

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