Dynamics of Substrate-Protease Interactions

该提案的主要目的是实现更好的膜内蛋白酶底物的机械理解，并通过理论预测扩展已知底物的光谱。为此，我们将研究底物跨膜结构域的结构和动力学要求，并使用计算机建模和生物信息学将它们与序列联系起来。因此，我们希望揭示蛋白质性质与可切割性之间的联系。在目标1中，我们将使用分子动力学模拟，以表征已知的γ-分泌酶底物的跨膜结构域的局部和全局动力学。我们将通过灵活性配置文件，允许识别关键的动态图案的特点网站特定的动态。此外，我们将研究是否γ-分泌酶底物共享一个共同的模式，大规模的骨干动力学和是否影响裂解突变干扰这些全球运动。γ-分泌酶底物的骨架动力学将与菱形底物PINK 1和SPPL蛋白酶底物的骨架动力学进行比较。要回答的关键问题将是是否灵活的配置文件之间的酶结合位点，切割位点，铰链和底物跨膜结构域的动力学如何比较的动力学的非底物跨膜结构域被确定由这个consortia.Goal 2是基于序列的预测结构flexibible区域。我们将开发一种机器学习方法，根据两种类型的数据从序列中预测结构灵活性：来自已知跨膜蛋白3D结构的晶体B因子和分子动力学模拟生成的灵活性。在目标3中，膜内蛋白酶的新底物将通过序列分析和机器学习来预测，使用由该联盟确定的底物序列的扩展集以及由其他研究人员确定的越来越多的底物，特别是γ-分泌酶。除了仅仅序列基序，我们将利用广泛的结构特征有关的TM区域，包括灵活性配置文件。此外，我们将利用各种类型的基因组背景，如共表达的底物与其同源蛋白酶以及拓扑结构的分子相互作用网络，以发现额外的候选底物，不一定含有可识别的切割位点基序。

The main objective of the proposal is to achieve a better mechanistic understanding of intramembrane protease substrates and to extend the spectrum of known substrates by theoretical predictions. To this end we will investigate the structural and dynamical requirements of a substrate transmembrane domain and relate them to sequence using in silico modeling and bioinformatics. We thus hope to uncover the code that links protein properties to cleavability. In Goal 1 we will use molecular dynamics simulations in order to characterize the local and global dynamics of the transmembrane domain of known gamma-secretase substrates. We will characterize site-specific dynamics by flexibility profiles which allow the identification of key dynamical motifs. Further, we will investigate whether gamma-secretase substrates share a common pattern of large-scale backbone dynamics and whether or not mutations affecting cleavage interfere with these global motions. The backbone dynamics of gamma-secretase substrates will be compared to that of the rhomboid substrate PINK1 and the substrates of SPPL proteases. The crucial questions to answer will be whether flexibility profiles discriminate between enzyme binding sites, cleavage sites, and hinges and how the structural dynamics of substrate transmembrane domains compares to the dynamics of non-substrate transmembrane domains to be identified by this consortium.Goal 2 is the sequence-based prediction of structurally flexibible regions. We will develop a machine learning approach to predict structural flexibility from sequence based on two types of data: crystallographic B-factors derived from known 3D structures of transmembrane proteins and flexibility profiles generated by molecular dynamics simulations. In Goal 3 novel substrates of intramembrane proteases will be predicted by sequence analysis and machine learning using the expanded set of substrate sequences to be determined by this consortium as well as the growing number of substrates determined by other researchers, in particular for gamma-secretase. Beyond mere sequence motifs we will exploit a broad spectrum of structural features pertaining to TM regions, including the flexibility profiles. Furthermore, we will exploit various types of genomic context, such as co-expression of substrates with their cognate proteases as well as the topology of the molecular interaction network, to uncover additional candidate substrates that do not necessarily contain recognizable cleavage site motifs.