Development and assessment of methods for membrane protein structure prediction

今年，我们在两个重要目标上取得了进展：提高膜蛋白对称性检测的准确性，以及开发膜蛋白集合的定量描述，如下所述。 膜蛋白含有大量的结构对称性，这些对称性可以为该蛋白的功能提供基础。有几种计算方法可用于检测这些关系，但它们对膜蛋白的准确性尚未得到系统的测试。进行这种评估的第一步是制定一个全面的基准，以便对各种方法进行测试。今年，我们完成了MemSTATS系列的开发。我们在这个基准上测试了可用的方法，并确定了大量未检测到对称性的情况（参考文献1）。这项研究确定了发展的机会，改进和自动化的方法来检测膜蛋白的对称性，可以与他们的功能机制和进化起源。该基准将为进一步开发我们的数据库EncoMPASS（结构和对称性分析的膜蛋白百科全书）提供重要基础，该数据库在NINDS内部生物信息学工作人员（Yavaktar和Kumar）的协助下，可通过https://encompass.ninds.nih.gov托管的网络服务器获得。 膜蛋白功能循环通常涉及构象的多重和显著变化，例如，响应于配体的结合。此外，这些个体状态中的每一个实际上都最好地反映在构象的集合中。然而，绘制出这些构象集合是极具挑战性的。原则上，检查溶液中蛋白质的生物物理方法，如电子顺磁共振或氢-氘交换可以提供这种水平的信息，并且可以在结构生物学研究提供的静态结构之上分层。然而，在实践中，从这些生物物理方法中转换数据缺乏严谨性，并且可能具有挑战性。为此，我们开发了一种方法，旨在将来自氢-氘交换数据的数据与从例如，分子动力学模拟。这种方法称为HDXer，是与NHLBI的Faraldo-Gomez实验室共同开发的，基于最大熵理论（参考文献2），使其能够量化结构构象与生物物理数据匹配的程度。我们首先用水溶性周质结合蛋白产生的人工数据说明了该方法的适用性，其次用膜转运蛋白LeuT获得的真实实验数据说明了该方法的适用性。在这两种情况下，HDXer显示出区分在大的构象变化中发现的集合。因此，HDXer可能被证明是以更定量的方式利用过多实验数据的重要工具。

This year we made progress on two important goals: improving the accuracy of detection of symmetry in membrane proteins, and developing quantitative descriptions of membrane proten ensembles, as described below. Membrane proteins contain abundant amounts of structural symmetry and those symmetries can provide the foundation of the function of that protein. Several computational methods are available to detect those relationships, but their accuracy for membrane proteins has not been systematically tested. A first step toward such an assessment is the development of a comprehensive benchmark against which methods can be tested. This year, we completed development of the MemSTATS set of symmetry-containing proteins. We tested available methods on this benchmark, and identified a large number of cases in which symmetries are not detected (Ref. 1). This study identified opportunities for development of improved and automated methodologies for detecting membrane protein symmetries that can be related to their functional mechanisms and evolutionary origins. The benchmark will provide an important foundation for further development of our database called EncoMPASS (Encyclopedia for Membrane Proteins Analyzed by Structure and Symmetry), which, with the assistance of NINDS intramural Bioinformatics staff (Yavaktar and Kumar), is available through a webserver hosted at https://encompass.ninds.nih.gov. Membrane protein functional cycles often involve multiple and dramatic changes in conformation, for example, in response to binding of ligands. Moreover, each of those individual states is actually best reflected by an ensemble of conformations. Mapping out those conformational ensembles is, however, extremely challenging. In principal, biophysical methods that examine proteins in solution, such as electron paramagnetic resonance or hydrogen-deuterium exchange can provide this level of information, and can be layered on top of the static structures provided by structural biology studies. In practice, however, translating the data from these biophysical methods lacks rigor and can be challenging to interpret. To this end, we developed an approach designed to integrate data from hydrogen-deuterium exchange data with structural models obtained from, e.g., molecular dynamics simulations. This method, called HDXer, was developed together with the Faraldo-Gomez lab at NHLBI and is based on maximum entropy theory (Ref. 2), enabling it to quantify the extent to which the conformations of the structure match the biophysical data. We illustrated the applicability of the method first with artificial data generated for a water-soluble periplasmic binding protein, and secondly with real-life experimental data obtained for a membrane transport protein, LeuT. In both cases, HDXer was shown to distinguish ensembles found in a large conformational change. HDXer may therefore prove to be an important tool to leverage a plethora of experimental data in more quantitative ways.