Dynamics of Substrate-Protease Interactions

底物-蛋白酶相互作用的动力学

• 批准号: 280701529
• 负责人: Professor Dr. Friedrich Simmel, since 2/2020
• 金额: --
• 依托单位: Lehrstuhl Bionanotechnologie und Bioelektronik (aufgelöst)
• 依托单位国家: 德国
• 项目类别: Research Units
• 财政年份: 2015
• 资助国家: 德国
• 起止时间: 2014-12-31 至 2022-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/280701529?language=en
• 关键词: Dynamics; Substrate; Protease; Interactions

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.

该建议的主要目标是通过理论预测更好地了解膜内蛋白酶底物的机理，并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，并将它们与用于电子建模和生物信息学的序列联系起来。因此，我们希望揭开将蛋白质特性与可裂解性联系起来的密码。在目标1中，我们将使用分子动力学模拟来表征已知的伽马分泌酶底物跨膜结构域的局部和全局动力学。我们将通过柔韧性曲线来描述特定地点的动态特征，这允许识别关键的动态主题。此外，我们将调查伽马分泌酶底物是否共享大规模骨架动力学的共同模式，以及影响切割的突变是否干扰这些全局运动。伽马分泌酶底物的骨架动力学将与菱形底物PINK1和SPPL酶底物进行比较。需要回答的关键问题是柔韧性谱是否区分酶结合部位、裂解部位和铰链，以及底物跨膜结构域的结构动力学如何与该联盟要确定的非底物跨膜结构域的结构动力学相比较。目标2是基于序列的结构柔性区域的预测。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柔性：从已知的跨膜蛋白质三维结构中获得的结晶学B因子，以及通过分子动力学模拟生成的柔性轮廓。在目标3中，将使用该联盟将要确定的扩展底物序列集以及其他研究人员确定的越来越多的底物，特别是伽马分泌酶，通过序列分析和机器学习来预测新的膜内蛋白水解酶底物。除了单纯的序列基序外，我们还将利用与TM区域有关的广泛的结构特征，包括灵活性特征。此外，我们将利用各种类型的基因组背景，例如底物与其同源蛋白酶的共表达以及分子相互作用网络的拓扑结构，以发现不一定包含可识别的切割位点基序的其他候选底物。