Dynamics of Substrate-Protease Interactions

• Applicants: Professor Dr. Friedrich Simmel, since 2/2020; Professor Dr. Martin Zacharias
• Subject Area: Biophysics
• Term: from 2015 to 2023
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 263531414

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.

• DFG Programme: Research Units
• Subproject of: FOR 2290: Understanding Intramembrane Proteolysis
• Ehemalige Antragstellerin: Dr. Christina Scharnagl, until 2/2020