Peter, Christine
0000-0002-1471-5440 Berufsbeschreibung
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Conformational and functional characterization of artificially conjugated non-canonical ubiquitin dimers
Ubiquitylation is an eminent posttranslational modification referring to the covalent attachment of single ubiquitin molecules or polyubiquitin chains to a target protein dictating the fate of such labeled polypeptide chains. Here, we have biochemically produced artificially Lys11-, and Lys27-, and Lys63-linked ubiquitin dimers based on click-chemistry generating milligram quantities in high purity. We show that the artificial linkage used for the conjugation of two ubiquitin moieties represents a fully reliable surrogate of the natural isopeptide bond by acquiring highly resolved nuclear magnetic resonance (NMR) spectroscopic data including ligand binding studies. Extensive coarse grained and atomistic molecular dynamics (MD) simulations allow to extract structures representing the ensemble of domain-domain conformations used to verify the experimental data. Advantageously, this methodology does not require individual isotopic labeling of both ubiquitin moieties as NMR data have been acquired on the isotopically labeled proximal moiety and complementary MD simulations have been used to fully interpret the experimental data in terms of domain-domain conformation. This combined approach intertwining NMR spectroscopy with MD simulations makes it possible to describe the conformational space non-canonically Lys11-, and Lys27-linked ubiquitin dimers occupy in a solution averaged ensemble by taking atomically resolved information representing all residues in ubiquitin dimers into account.
EncoderMap : Dimensionality Reduction and Generation of Molecule Conformations
Molecular simulation is one example where large amounts of high-dimensional (high-d) data are generated. To extract useful information e.g. about relevant states and important conformational transitions, a form of dimensionality reduction is required. Dimensionality reduction algorithms differ in their ability to efficiently project large amounts of data to an informative low-dimensional (low-d) representation and the way the low and high-d representations are linked. We propose a dimensionality reduction algorithm called encoder-map which is based on a neural network autoencoder in combination with a non-linear distance metric. A key advantage of this method is that it establishes a functional link from the high-d to the low-d representation and vice versa. This allows not only to efficiently project data points to the low-d representation but also to generate high-d representatives for any point in the low-d map. The potential of the algorithm is demonstrated for molecular simulation data of a small, highly-flexible peptide as well as for folding simulations of the 20-residue Trp-cage protein. We demonstrate that the algorithm is able to efficiently project the ensemble of high-d structures to a low-d map where major states can be identified and important conformational transitions are revealed. We also show that molecular conformations can be generated for any point or any connecting line between points on the low-d map. This ability of inverse mapping from the low-d to the high-d representation is particularly relevant for the use in algorithms that enhance the exploration of conformational space or the sampling of transitions between conformational states.
Snapshots of a modified nucleotide moving through the confines of a DNA polymerase
DNA polymerases have evolved to process the four canonical nucleotides accurately. Nevertheless, these enzymes are also known to process modified nucleotides, which is the key to numerous core biotechnology applications. Processing of modified nucleotides includes incorporation of the modified nucleotide and postincorporation elongation to proceed with the synthesis of the nascent DNA strand. The structural basis for postincorporation elongation is currently unknown. We addressed this issue and successfully crystallized KlenTaq DNA polymerase in six closed ternary complexes containing the enzyme, the modified DNA substrate, and the incoming nucleotide. Each structure shows a high-resolution snapshot of the elongation of a modified primer, where the modification "moves" from the 3'-primer terminus upstream to the sixth nucleotide in the primer strand. Combining these data with quantum mechanics/molecular mechanics calculations and biochemical studies elucidates how the enzyme and the modified substrate mutually modulate their conformations without compromising the enzyme's activity significantly. The study highlights the plasticity of the system as origin of the broad substrate properties of DNA polymerases and facilitates the design of improved systems.
Back-mapping based sampling : Coarse grained free energy landscapes as a guideline for atomistic exploration
One ongoing topic of research in MD simulations is how to enable sampling to chemically and biologically relevant time scales. We address this question by introducing a back-mapping based sampling (BMBS) that combines multiple aspects of different sampling techniques. BMBS uses coarse grained (CG) free energy surfaces (FESs) and dimensionality reduction to initiate new atomistic simulations. These new simulations are started from atomistic conformations that were back-mapped from CG points all over the FES in order to sample the entire accessible phase space as fast as possible. In the context of BMBS, we address relevant back-mapping related questions like where to start the back-mapping from and how to judge the atomistic ensemble that results from the BMBS. The latter is done with the use of the earth mover’s distance, which allows us to quantitatively compare distributions of CG and atomistic ensembles. By using this metric, we can also show that the BMBS is able to correct inaccuracies of the CG model. In this paper, BMBS is applied to a just recently introduced neural network (NN) based approach for a radical coarse graining to predict free energy surfaces for oligopeptides. The BMBS scheme back-maps these FESs to the atomistic scale, justifying and complementing the proposed NN based CG approach. The efficiency benefit of the algorithm scales with the length of the oligomer. Already for the heptamers, the algorithm is about one order of magnitude faster in sampling compared to a standard MD simulation.
Relative Resolution : A Multipole Approximation at Appropriate Distances
Recently, we introduced Relative Resolution as a hybrid formalism for fluid mixtures [1]. The essence of this approach is that it switches molecular resolution in terms or relative separation: While nearest neighbors are characterized by a detailed fine-grained model, other neighbors are characterized by a simplified coarse-grained model. Once the two models are analytically connected with each other via energy conservation, Relative Resolution can capture the structural and thermal behavior of (nonpolar) multi-component and multi-phase systems across state space. The current work is a natural continuation of our original communication [1]. Most importantly, we present the comprehensive mathematics of Relative Resolution, basically casting it as a multipole approximation at appropriate distances; the current set of equations importantly applies for all systems (e.g, polar molecules). Besides, we continue examining the capability of our multiscale approach in molecular simulations, importantly showing that we can successfully retrieve not just the statics but also the dynamics of liquid systems. We finally conclude by discussing how Relative Resolution is the inherent variant of the famous "cell-multipole" approach for molecular simulations.
The structure of the ubiquitin-like modifier FAT10 reveals an alternative targeting mechanism for proteasomal degradation
FAT10 is a ubiquitin-like modifier that directly targets proteins for proteasomal degradation. Here, we report the high-resolution structures of the two individual ubiquitin-like domains (UBD) of FAT10 that are joined by a flexible linker. While the UBDs of FAT10 show the typical ubiquitin-fold, their surfaces are entirely different from each other and from ubiquitin explaining their unique binding specificities. Deletion of the linker abrogates FAT10-conjugation while its mutation blocks auto-FAT10ylation of the FAT10-conjugating enzyme USE1 but not bulk conjugate formation. FAT10- but not ubiquitin-mediated degradation is independent of the segregase VCP/p97 in the presence but not the absence of FAT10's unstructured N-terminal heptapeptide. Stabilization of the FAT10 UBDs strongly decelerates degradation suggesting that the intrinsic instability of FAT10 together with its disordered N-terminus enables the rapid, joint degradation of FAT10 and its substrates without the need for FAT10 de-conjugation and partial substrate unfolding.
Simulating and analysing configurational landscapes of protein-protein contact formation
Interacting proteins can form aggregates and protein-protein interfaces with multiple patterns and different stabilities. Using molecular simulation one would like to understand the formation of these aggregates and which of the observed states are relevant for protein function and recognition. To characterize the complex configurational ensemble of protein aggregates, one needs a quantitative measure for the similarity of structures. We present well-suited descriptors that capture the essential features of non-covalent protein contact formation and domain motion. This set of collective variables is used with a nonlinear multi-dimensional scaling-based dimensionality reduction technique to obtain a low-dimensional representation of the configurational landscape of two ubiquitin proteins from coarse-grained simulations. We show that this two-dimensional representation is a powerful basis to identify meaningful states in the ensemble of aggregated structures and to calculate distributions and free energy landscapes for different sets of simulations. By using a measure to quantitatively compare free energy landscapes we can show how the introduction of a covalent bond between two ubiquitin proteins at different positions alters the configurational states of these dimers.
Towards a molecular basis of ubiquitin signaling : a dual-scale simulation study of ubiquitin dimers
Covalent modification of proteins by ubiquitin or ubiquitin chains is one of the most prevalent post-translational modifications in eukaryotes. Different types of ubiquitin chains are assumed to selectively signal respectively modified proteins for different fates. In support of this hypothesis, structural studies have shown that the eight possible ubiquitin dimers adopt different conformations. However, at least in some cases, these structures cannot sufficiently explain the molecular basis of the selective signaling mechanisms. This indicates that the available structures represent only a few distinct conformations within the entire conformational space adopted by a ubiquitin dimer. Here, molecular simulations on different levels of resolution can complement the structural information. We have combined exhaustive coarse grained and atomistic simulations of all eight possible ubiquitin dimers with a suitable dimensionality reduction technique and a new method to characterize protein-protein interfaces and the conformational landscape of protein conjugates. We found that ubiquitin dimers exhibit characteristic linkage type-dependent properties in solution, such as interface stability and the character of contacts between the subunits, which can be directly correlated with experimentally observed linkage-specific properties.
Author summary
Post-translational modification of proteins by covalent attachment of ubiquitin is a key cellular process, regulating for example the fate and recycling of proteins. We present a new method to combine multiscale simulation with advanced analysis methods to characterize the states of ubiquitin-ubiquitin conjugates. We found that the linkage position affects the conformational space of ubiquitin dimers, determining the number and stability of relevant states, the character of subunit contacts and the nature of the surface exposed to possible binding partners.
Single molecule translocation in smectics illustrates the challenge for time-mapping in simulations on multiple scales
Understanding the connections between the characteristic dynamical time scales associated with a coarse-grained (CG) and a detailed representation is central to the applicability of the coarse-graining methods to understand molecular processes. The process of coarse graining leads to an accelerated dynamics, owing to the smoothening of the underlying free-energy landscapes. Often a single time-mapping factor is used to relate the time scales associated with the two representations. We critically examine this idea using a model system ideally suited for this purpose. Single molecular transport properties are studied via molecular dynamics simulations of the CG and atomistic representations of a liquid crystalline, azobenzene containing mesogen, simulated in the smectic and the isotropic phases. The out-of-plane dynamics in the smectic phase occurs via molecular hops from one smectic layer to the next. Hopping can occur via two mechanisms, with and without significant reorientation. The out-of-plane transport can be understood as a superposition of two (one associated with each mode of transport) independent continuous time random walks for which a single time-mapping factor would be rather inadequate. A comparison of the free-energy surfaces, relevant to the out-of-plane transport, qualitatively supports the above observations. Thus, this work underlines the need for building CG models that exhibit both structural and dynamical consistency to the underlying atomistic model.