Peter, Christine

Lade...
Profilbild
E-Mail-Adresse
Geburtsdatum
Forschungsvorhaben
Organisationseinheiten
Berufsbeschreibung
Nachname
Peter
Vorname
Christine
Name

Suchergebnisse Publikationen

Gerade angezeigt 1 - 10 von 42
Vorschaubild nicht verfügbar
Veröffentlichung

Specifying conformational heterogeneity of multi-domain proteins at atomic resolution

2023-10, Schneider, Tobias, Sawade, Kevin, Berner, Frederic, Peter, Christine, Kovermann, Michael

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.

Vorschaubild nicht verfügbar
Veröffentlichung

Machine Learning Force Fields and Coarse-Grained Variables in Molecular Dynamics : Application to Materials and Biological Systems

2020-08-11, Gkeka, Paraskevi, Stoltz, Gabriel, Barati Farimani, Amir, Belkacemi, Zineb, Ceriotti, Michele, Chodera, John D., Dinner, Aaron R., Ferguson, Andrew L., Maillet, Jean-Bernard, Peter, Christine

Machine learning encompasses tools and algorithms that are now becoming popular in almost all scientific and technological fields. This is true for molecular dynamics as well, where machine learning offers promises of extracting valuable information from the enormous amounts of data generated by simulation of complex systems. We provide here a review of our current understanding of goals, benefits, and limitations of machine learning techniques for computational studies on atomistic systems, focusing on the construction of empirical force fields from ab initio databases and the determination of reaction coordinates for free energy computation and enhanced sampling.

Vorschaubild nicht verfügbar
Veröffentlichung

EncoderMap(II) : Visualizing important molecular motions with improved generation of protein conformations

2019-11-25, Lemke, Tobias, Berg, Andrej, Jain, Alok, Peter, Christine

Dimensionality reduction can be used to project high-dimensional molecular data into a simplified, low-dimensional map. One feature of our recently introduced dimensionality reduction technique EncoderMap, which relies on the combination of an autoencoder with multidimensional scaling, is its ability to do the reverse. It is able to generate conformations for any selected points in the low-dimensional map. This transfers the simplified, low-dimensional map back into the high-dimensional conformational space. Although the output is again high-dimensional, certain aspects of the simplification are preserved. The generated conformations only mirror the most dominant conformational differences that determine the positions of conformational states in the low-dimensional map. This allows to depict such differences and - in consequence - visualize molecular motions and gives a unique perspective on high-dimensional conformational data. In our previous work protein conformations described in backbone dihedral angle space were used as input for EncoderMap, and conformations were also generated in this space. For large proteins, however, the generation of conformations is inaccurate with this approach due to the local character of backbone dihedral angles. Here, we present an improved variant of EncoderMap which is able to generate large protein conformations that are accurate in short-range and long-range order. This is achieved by differentiable reconstruction of Cartesian coordinates from the generated dihedrals, which allows to add a contribution to the cost function that monitors the accuracy of all pairwise distances between the C α -atoms of the generated conformations. The improved capabilities to generate conformations of large, even multidomain, proteins are demonstrated for two examples: diubiquitin and a part of the Ssa1 Hsp70 yeast chaperone. We show that the improved variant of EncoderMap can nicely visualize motions of protein domains relative to each other but is also able to highlight important conformational changes within the individual domains.

Vorschaubild nicht verfügbar
Veröffentlichung

Coarse-Grained Simulation of CaCO3 Aggregation and Crystallization Made Possible by Non-Bonded Three-Body Interactions

2019-02-07, King, Michael, Pasler, Simon, Peter, Christine

Calcium-containing minerals are key model systems to investigate fundamental principles of nucleation and mineral formation both experimentally and by simulation. Due to the rare event character of nucleation, the different dimensions of pre- and postnucleation stages and the possible relevance of non-classical nucleation pathways, such investigations require advanced sampling techniques and simulation models on a range of resolution levels. To this end we have developed coarse-grained (CG) models for calcium carbonate. We present a strategy to devise CG parameters - including non-bonded angular-dependent terms - such that the model correctly represents the calcite phase along with properties of the constituents in solution. We show how the CG interactions affect the crystallization pathways by stabilizing different intermediates - spanning a wide range of degrees of crystallinity and water content. This will allow us to investigate contributions to crystallization transitions and link them to experimentally observed non-classical crystallization intermediates.

Vorschaubild nicht verfügbar
Veröffentlichung

On the Binding Mechanisms of Calcium Ions to Polycarboxylates: Effects of Molecular Weight, Side Chain, and Backbone Chemistry

2022-11-29, Gindele, Maxim Benjamin, Malaszuk, Krzysztof K., Peter, Christine, Gebauer, Denis

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.

Vorschaubild nicht verfügbar
Veröffentlichung

Machine Learning Driven Analysis of Large Scale Simulations Reveals Conformational Characteristics of Ubiquitin Chains

2020-05-12, Berg, Andrej, Franke, Leon, Scheffner, Martin, Peter, Christine

Understanding the conformational characteristics of protein complexes in solution is crucial for a deeper insight in their biological function. Molecular dynamics simulations performed on high performance computing plants and with modern simulation techniques can be used to obtain large data sets that contain conformational and thermodynamic information about biomolecular systems. While this can in principle give a detailed picture of protein-protein interactions in solution and therefore complement experimental data, it also raises the challenge of processing exceedingly large high-dimensional data sets with several million samples. Here we present a novel method for the characterization of protein-protein interactions, which combines a neural network based dimensionality reduction technique to obtain a two-dimensional representation of the conformational space with a density based clustering algorithm for state detection and a metric which assesses the (dis)similarity between different conformational spaces. This method is highly scalable and therefore makes the analysis of massive data sets computationally tractable. We demonstrate the power of this approach to large scale data analysis by characterizing highly dynamic conformational phase spaces of differently linked ubiquitin (Ub) oligomers from coarse-grained simulations. We are able to extract a protein-protein interaction model for two unlinked Ub proteins which is then used to determine how the Ub-Ub interaction pattern is altered in Ub oligomers by the introduction of a covalent linkage. We find that the Ub chain conformational ensemble depends highly on the linkage type and for some cases also on the Ub chain length. By this, we obtain insight into the conformational characteristics of different Ub chains and how this may contribute to linkage type and chain length specific recognition.

Vorschaubild nicht verfügbar
Veröffentlichung

Anisotropic Extended-Chain Polymer Nanocrystals

2019-08-27, Rank, Christina, Häußler, Manuel, Rathenow, Patrick, King, Michael, Globisch, Christoph, Peter, Christine, Mecking, Stefan

As a concept for distinct shape polymer nanoparticles, nanoscale single crystals composed of a crystallizable chain with lyophilic end groups are explored. This differs from much studied block copolymer nanoparticles and nanostructures, in which the second (noncrystalline) blocks’ spacial demand impacts the overall structure and blurs the cores’ anisotropic shape. For precise C48 polyethylene telechelics X(CH2)46X (X = COOM+ or CH2SO3M+, with M+ = Na+, K+, or Cs+) as a relevant model system, a combined experimental and atomistic-level simulation study reveals them to form extended-chain, single-crystalline nanoparticles sandwiched by a layer of head groups. Their microscopic structure, order, and the resulting overall shape are decisively impacted by the mutual repulsion of the head groups, itself determined by the degree of ion pairing with the counterions and the size of the head groups. This leads to the bending of the chains at the lateral side of the crystal, preventing the particles from agglomeration, and to a chain tilt of the monolayer, thus reducing its thickness. By comparison, for a shorter analogue Cs+ OOC(CH2)21COO Cs+, the attractive van der Waals interactions between the hydrocarbon chains are not sufficient to overcome the head group repulsion, resulting in nanoparticle break up. These insights are instrumental for understanding and designing anisotropic organic polymer particles exploiting the principles of polymer crystallinity, which are also predestined for particle assembly.

Vorschaubild nicht verfügbar
Veröffentlichung

Multiscale simulations of protein and membrane systems

2021-12-22, Sawade, Kevin, Peter, Christine

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.

Vorschaubild nicht verfügbar
Veröffentlichung

Editorial overview: Theory and simulation : Progress, yes; revolutions, no

2020, Mark, Alan Edward, Peter, Christine

Vorschaubild nicht verfügbar
Veröffentlichung

Coarse grained simulations of peptide nanoparticle formation : the role of local structure and nonbonded interactions

2019-02-12, Jain, Alok, Globisch, Christoph, Verma, Sandeep, Peter, Christine

Biocompatible nanostructures play an important role in drug delivery and tissue engineering applications. A controlled growth of peptide based nanoparticles with specific morphology needs an understanding of the role of the sequence and solvation properties. In a previous combined experimental-computational study we identified factors that govern the formation of well-defined aggregates by self-assembled pentapeptides, using single amino acid substitution (Mishra, N. K.; Jain, A.; Peter, C.; Verma, S. J. Phys. Chem. B 2017, 121, 8155-8161). The atomistic simulation study suggested a subtle interplay between various peptide properties like igidity/flexibility, hydrogen-bonding, partitioning of aromatic residues and dimerization of peptides that determine the different morphologies, while the overall aggregation propensity was mostly determined by the composition of the methanol/water solvent mixture. The size of the simulated aggregates and the timescales were rather restricted due to the atomistic character of the study. Here, we present an extension to a coarse grained representation which allows for much larger system sizes and longer time scales. To this end, we have optimized a MARTINI model so that it can deal with a system that relies on local structure formation. We combine information on local behavior from atomistic studies and apply supportive dihedral angles together with local adjustment of the bead types to find the right interplay of solvent and peptides. Finally, to mimic the dimers, an introduction of additional bonds between the monomers was necessary. By adding the modifications stepwise we were able to disentangle the influences of the various contributions, like rigidity/flexibility of the peptides, the dimer formation, or the non-bonded properties of the beads, on the overall aggregation propensity and morphology of the nanoparticles. The obtained models resemble the experimental and atomistic behavior and are able to provide mechanistic insight into peptide nanoparticle formation.