Hydrogen-deuterium exchange combined with mass spectrometry (HDX-MS) is a widely applied biophysical technique that probes the structure and dynamics of biomolecules without the need for site-directed modifications or bio-orthogonal labels. The mechanistic interpretation of HDX data, however, is often qualitative and subjective, owing to a lack of quantitative methods to rigorously translate observed deuteration levels into atomistic structural information. To help address this problem, we have developed a methodology to generate structural ensembles that faithfully reproduce HDX-MS measurements. In this approach, an ensemble of protein conformations is first generated, typically using molecular dynamics simulations. A maximum-entropy bias is then applied post hoc to the resulting ensemble such that averaged peptide-deuteration levels, as predicted by an empirical model, agree with target values within a given level of uncertainty. We evaluate this approach, referred to as HDX ensemble reweighting (HDXer), for artificial target data reflecting the two major conformational states of a binding protein. We demonstrate that the information provided by HDX-MS experiments and by the model of exchange are sufficient to recover correctly weighted structural ensembles from simulations, even when the relevant conformations are rarely observed. Degrading the information content of the target data—e.g., by reducing sequence coverage, by averaging exchange levels over longer peptide segments, or by incorporating different sources of uncertainty—reduces the structural accuracy of the reweighted ensemble but still allows for useful insights into the distinctive structural features reflected by the target data. Finally, we describe a quantitative metric to rank candidate structural ensembles according to their correspondence with target data and illustrate the use of HDXer to describe changes in the conformational ensemble of the membrane protein LeuT. In summary, HDXer is designed to facilitate objective structural interpretations of HDX-MS data and to inform experimental approaches and further developments of theoretical exchange models.

中文翻译：

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通过模拟结构集合的最大熵重加权解释氢-氘交换数据

氢-氘交换结合质谱 (HDX-MS) 是一种广泛应用的生物物理技术，无需定点修饰或生物正交标记即可探测生物分子的结构和动力学。然而，由于缺乏将观察到的氘化水平严格转化为原子结构信息的定量方法，对 HDX 数据的机械解释通常是定性和主观的。为了帮助解决这个问题，我们开发了一种方法来生成忠实再现 HDX-MS 测量结果的结构集合。在这种方法中，首先生成蛋白质构象的集合，通常使用分子动力学模拟。然后将最大熵偏差事后应用于所得的集合，使得平均肽氘化水平，正如经验模型所预测的那样，在给定的不确定性水平内与目标值一致。我们评估了这种称为 HDX 整体重新加权 (HDXer) 的方法，用于反映结合蛋白的两种主要构象状态的人工目标数据。我们证明 HDX-MS 实验和交换模型提供的信息足以从模拟中恢复正确加权的结构集合，即使很少观察到相关构象。降低目标数据的信息含量——例如，通过降低序列覆盖率，通过对较长肽段的交换水平进行平均，或者通过合并不同的不确定性来源——降低重新加权集合的结构准确性，但仍然允许对目标数据所反映的独特结构特征进行有用的洞察。最后，我们描述了根据候选结构集合与目标数据的对应关系对它们进行排序的定量度量，并说明了使用 HDXer 来描述膜蛋白 LeuT 构象集合的变化。总之，HDXer 旨在促进 HDX-MS 数据的客观结构解释，并为实验方法和理论交换模型的进一步发展提供信息。我们描述了一种定量度量，根据候选结构集合与目标数据的对应关系对其进行排序，并说明使用 HDXer 来描述膜蛋白 LeuT 构象集合的变化。总之，HDXer 旨在促进 HDX-MS 数据的客观结构解释，并为实验方法和理论交换模型的进一步发展提供信息。我们描述了一种定量度量，根据候选结构集合与目标数据的对应关系对其进行排序，并说明使用 HDXer 来描述膜蛋白 LeuT 构象集合的变化。总之，HDXer 旨在促进 HDX-MS 数据的客观结构解释，并为实验方法和理论交换模型的进一步发展提供信息。