Peter, Christine
0000-0002-1471-5440 Berufsbeschreibung
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Specifying conformational heterogeneity of multi-domain proteins at atomic resolution
The conformational landscape of multi-domain proteins is inherently linked to their specific functions. This also holds for polyubiquitin chains that are assembled by two or more ubiquitin domains connected by a flexible linker thus showing a large interdomain mobility. However, molecular recognition and signal transduction are associated with particular conformational substates that are populated in solution. Here, we apply high-resolution NMR spectroscopy in combination with dual-scale MD simulations to explore the conformational space of K6-, K29-, and K33-linked diubiquitin molecules. The conformational ensembles are evaluated utilizing a paramagnetic cosolute reporting on solvent exposure plus a set of complementary NMR parameters. This approach unravels a conformational heterogeneity of diubiquitins and explains the diversity of structural models that have been determined for K6-, K29-, and K33-linked diubiquitins in free and ligand-bound states so far. We propose a general application of the approach developed here to demystify multi-domain proteins occurring in nature.
Generating a conformational landscape of ubiquitin chains at atomistic resolution by back-mapping based sampling
Ubiquitin chains are flexible multidomain proteins that have important biological functions in cellular signalling. Computational studies with all-atom molecular dynamics simulations of the conformational spaces of polyubiquitins can be challenging due to the system size and a multitude of long-lived meta-stable states. Coarse graining is an efficient approach to overcome this problem—at the cost of losing high-resolution details. Recently, we proposed the back-mapping based sampling (BMBS) approach that reintroduces atomistic information into a given coarse grained (CG) sampling based on a two-dimensional (2D) projection of the conformational landscape, produces an atomistic ensemble and allows to systematically compare the ensembles at the two levels of resolution. Here, we apply BMBS to K48-linked tri-ubiquitin, showing its applicability to larger systems than those it was originally introduced on and demonstrating that the algorithm scales very well with system size. In an extension of the original BMBS we test three different seeding strategies, i.e. different approaches from where in the CG landscape atomistic trajectories are initiated. Furthermore, we apply a recently introduced conformational clustering algorithm to the back-mapped atomistic ensemble. Thus, we obtain insight into the structural composition of the 2D landscape and illustrate that the dimensionality reduction algorithm separates different conformational characteristics very well into different regions of the map. This cluster analysis allows us to show how atomistic trajectories sample conformational states, move through the projection space and in sum converge to an atomistic conformational landscape that slightly differs from the original CG map, indicating a correction of flaws in the CG template.
Electrostatic and steric effects underlie acetylation-induced changes in ubiquitin structure and function
Covalent attachment of ubiquitin (Ub) to proteins is a highly versatile posttranslational modification. Moreover, Ub is not only a modifier but itself is modified by phosphorylation and lysine acetylation. However, the functional consequences of Ub acetylation are poorly understood. By generation and comprehensive characterization of all seven possible mono-acetylated Ub variants, we show that each acetylation site has a particular impact on Ub structure. This is reflected in selective usage of the acetylated variants by different E3 ligases and overlapping but distinct interactomes, linking different acetylated variants to different cellular pathways. Notably, not only electrostatic but also steric effects contribute to acetylation-induced changes in Ub structure and, thus, function. Finally, we provide evidence that p300 acts as a position-specific Ub acetyltransferase and HDAC6 as a general Ub deacetylase. Our findings provide intimate insights into the structural and functional consequences of Ub acetylation and highlight the general importance of Ub acetylation.
Titin kinase ubiquitination aligns autophagy receptors with mechanical signals in the sarcomere
Striated muscle undergoes remodelling in response to mechanical and physiological stress, but little is known about the integration of such varied signals in the myofibril. The interaction of the elastic kinase region from sarcomeric titin (A168-M1) with the autophagy receptors Nbr1/p62 and MuRF E3 ubiquitin ligases is well suited to link mechanosensing with the trophic response of the myofibril. To investigate the mechanisms of signal cross-talk at this titin node, we elucidated its 3D structure, analysed its response to stretch using steered molecular dynamics simulations and explored its functional relation to MuRF1 and Nbr1/p62 using cellular assays. We found that MuRF1-mediated ubiquitination of titin kinase promotes its scaffolding of Nbr1/p62 and that the process can be dynamically down-regulated by the mechanical unfolding of a linker sequence joining titin kinase with the MuRF1 receptor site in titin. We propose that titin ubiquitination is sensitive to the mechanical state of the sarcomere, the regulation of sarcomere targeting by Nbr1/p62 being a functional outcome. We conclude that MuRF1/Titin Kinase/Nbr1/p62 constitutes a distinct assembly that predictably promotes sarcomere breakdown in inactive muscle.
Visualizing the Residue Interaction Landscape of Proteins by Temporal Network Embedding
Characterizing the structural dynamics of proteins with heterogeneous conformational landscapes is crucial to understanding complex biomolecular processes. To this end, dimensionality reduction algorithms are used to produce low-dimensional embeddings of the high-dimensional conformational phase space. However, identifying a compact and informative set of input features for the embedding remains an ongoing challenge. Here, we propose to harness the power of Residue Interaction Networks (RINs) and their centrality measures, established tools to provide a graph theoretical view on molecular structure. Specifically, we combine the closeness centrality, which captures global features of the protein conformation at residue-wise resolution, with EncoderMap, a hybrid neural-network autoencoder/multidimensional-scaling like dimensionality reduction algorithm. We find that the resulting low-dimensional embedding is a meaningful visualization of the residue interaction landscape that resolves structural details of the protein behavior while retaining global interpretability. This feature-based graph embedding of temporal protein graphs makes it possible to apply the general descriptive power of RIN formalisms to the analysis of protein simulations of complex processes such as protein folding and multidomain interactions requiring no protein-specific input. We demonstrate this on simulations of the fast folding protein Trp-Cage and the multidomain signaling protein FAT10. Due to its generality and modularity, the presented approach can easily be transferred to other protein systems.
On the Binding Mechanisms of Calcium Ions to Polycarboxylates: Effects of Molecular Weight, Side Chain, and Backbone Chemistry
We experimentally determined the characteristics and Langmuir parameters of the binding of calcium ions to different polycarboxylates. By using potentiometric titrations and isothermal titration calorimetry, the effects of side chain chemistry, pH value, and chain length were systematically investigated using the linear polymers poly(aspartic acid), poly(glutamic acid), and poly(acrylic acid). We demonstrate that for polymers with high polymerization degrees, the binding process is governed by higher-order effects, such as the change of apparent pKa of carboxyl groups, and contributions arising from the whole polymer chain while the chemistry of the monomer unit constituting the polymer plays a subordinate role. In addition, primary binding sites need to be present in the polymer, thus rendering the abundance and sequential arrangement of protonated and deprotonated groups important. The detection of higher-order effects contradicts the assumptions posed by the Langmuir model of noninteracting binding sites and puts a question mark on whether ion binding to polycarboxylates can be described using solely a Langmuir binding model. No single uniform mechanism fits all investigated systems, and the whole polymer chain, including terminal groups, needs to be considered for the interpretation of binding data. Therefore, one needs to be careful when explaining ion binding to polymers solely based on studies on monomers or oligomers.
Solvent-mediated Isotope Effects Strongly Influence the Early Stages of Calcium Carbonate Formation : Exploring D2O vs. H2O in a Combined Computational and Experimental Approach
In experimental studies, heavy water (D2O) is employed, e.g., so as to shift the spectroscopic solvent background, but any potential effects of this solvent exchange on reaction pathways are often neglected. While the important role of light water (H2O) during the early stages of calcium carbonate formation has been realized, studies into the actual effects of aqueous solvent exchanges are scarce. Here, we present a combined computational and experimental approach to start to fill this gap. We extended a suitable force field for molecular dynamics (MD) simulations. Experimentally, we utilised advanced titration assays and time-resolved attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy. We find distinct effects in various mixtures of the two aqueous solvents, and in pure H2O or D2O. Disagreements between the computational results and experimental data regarding the stabilities of ion associates might be due to the unexplored role of HDO, or an unprobed complex phase behaviour of the solvent mixtures in the simulations. Altogether, however, our data suggest that calcium carbonate formation might proceed “more classically” in D2O. Also, there are indications for the formation of new structures in amorphous and crystalline calcium carbonates. There is huge potential towards further improving the understanding of mineralization mechanisms by studying solvent-mediated isotope effects, also beyond calcium carbonate. Last, it must be appreciated that H2O and D2O have significant, distinct effects on mineralization mechanisms, and that care has to be taken when experimental data from D2O studies are used, e.g., for the development of H2O-based computer models.
Fast conformational clustering of extensive molecular dynamics simulation data
We present an unsupervised data processing workflow that is specifically designed to obtain a fast conformational clustering of long molecular dynamics simulation trajectories. In this approach, we combine two dimensionality reduction algorithms (cc_analysis and encodermap) with a density-based spatial clustering algorithm (hierarchical density-based spatial clustering of applications with noise). The proposed scheme benefits from the strengths of the three algorithms while avoiding most of the drawbacks of the individual methods. Here, the cc_analysis algorithm is applied for the first time to molecular simulation data. The encodermap algorithm complements cc_analysis by providing an efficient way to process and assign large amounts of data to clusters. The main goal of the procedure is to maximize the number of assigned frames of a given trajectory while keeping a clear conformational identity of the clusters that are found. In practice, we achieve this by using an iterative clustering approach and a tunable root-mean-square-deviation-based criterion in the final cluster assignment. This allows us to find clusters of different densities and different degrees of structural identity. With the help of four protein systems, we illustrate the capability and performance of this clustering workflow: wild-type and thermostable mutant of the Trp-cage protein (TC5b and TC10b), NTL9, and Protein B. Each of these test systems poses their individual challenges to the scheme, which, in total, give a nice overview of the advantages and potential difficulties that can arise when using the proposed method.
Lipid-mediated activation of plasma membrane-localized deubiquitylating enzymes modulate endosomal trafficking
The abundance of plasma membrane-resident receptors and transporters has to be tightly regulated by ubiquitin-mediated endosomal degradation for the proper coordination of environmental stimuli and intracellular signaling. Arabidopsis OVARIAN TUMOR PROTEASE (OTU) 11 and OTU12 are plasma membrane-localized deubiquitylating enzymes (DUBs) that bind to phospholipids through a polybasic motif in the OTU domain. Here we show that the DUB activity of OTU11 and OTU12 towards K63-linked ubiquitin is stimulated by binding to lipid membranes containing anionic lipids. In addition, we show that the DUB activity of OTU11 against K6- and K11-linkages is also stimulated by anionic lipids, and that OTU11 and OTU12 can modulate the endosomal degradation of a model cargo and the auxin efflux transporter PIN2-GFP in vivo. Our results suggest that the catalytic activity of OTU11 and OTU12 is tightly connected to their ability to bind membranes and that OTU11 and OTU12 are involved in the fine-tuning of plasma membrane proteins in Arabidopsis.
Multiscale simulations of protein and membrane systems
Classical multiscale simulations are perfectly suited to investigate biological soft matter systems. Owing to the bridging between microscopically realistic and lower-resolution models or the integration of a hierarchy of subsystems, one gets access to biologically relevant system sizes and timescales. In recent years, increasingly complex systems and processes have come into focus such as multidomain proteins, phase separation processes in biopolymer solutions, multicomponent biomembranes, or multiprotein complexes up to entire viruses. The review shows factors that have contributed to this progress - from improved models to machine-learning-based analysis and scale-bridging methods.