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Modeling of mechanistic effects of neurotrophin modulators

Abstract (Expand)

Neurotrophins (NTs) are growth factors that are expressed in the central and peripheral nervous systems. They are implicated in different phases of the development and maintenance of the nervous system … and they can regulate neuronal survival, development, function, plasticity, as well as neuronal apoptosis. NTs can be used as therapeutics for the treatment of neurodegenerative disorders. However, their poor pharmacokinetic properties and their invasive administration to patients renders them inefficient for use as pharmaceuticals. A solution to this can be offered by small molecule NT mimetics, which can elicit NT mechanisms through binding to NT receptors, which are transmembrane (TM) glycoproteins. The mechanism of activation of NT receptors remains elusive and thus, in this thesis, I have investigated the mechanism of action of NT receptors and mimetics through molecular modeling and molecular dynamics (MD) simulations. I modeled and simulated the glycosylated state of the full extracellular (EC) domains of Tropomyosin receptor kinases A and B (TrkA, TrkB) NT receptors, which revealed that the glycans can shield the accessible surface area of the receptors and participate in the contact area between receptor and NT. Most importantly, glycosylation promoted the extended conformations of the EC domains, which might facilitate NT binding. Then, I performed coarse-grained MD simulations to study the possible arrangements of the TM helical homodimers of TrkA and TrkB receptors in micelles. The results revealed arrangements that could correspond to the active state of the receptors, while metadynamics simulations indicated a stronger binding for the TrkA helices by 10 kJ/mol compared to TrkB. Next, I modeled the full-length structures of the TrkA and TrkB receptors in their homodimeric, glycosylated state bound to their NTs. I embedded the receptors in a realistic model of a neuronal asymmetric membrane. I verified the proper behavior of the membrane and proteins with smaller systems comprising of the TM and intracellular monomers of the receptors. These test simulations revealed interactions between positively charged residues of the kinase domain of TrkA with negatively charged lipids of the inner leaflet of the membrane. These interactions were also formed in the full-length system, and they might stabilize the two kinase domains of the receptor dimer in an orientation that promotes activation, even though the kinase domains were not activated during the simulations. Also, in the full-length systems, the EC domains approached and lay down on the neuronal membrane, while interacting with membrane lipids, such as gangliosides, which are able to activate the NT receptors. Finally, I investigated the binding of small-molecule NT mimetics to the EC and TM domains of TrkA and TrkB receptors, with molecular docking and MD simulations. While plausible poses were 8 obtained, they were not able to explain the selectivity of the compounds for the receptors and the simulations showed that binding was weak. Due to the cholesterol core of the NT mimetics, I tested the ability of the compounds to enter the cell membrane with MD simulations. The compounds were able to spontaneously penetrate the membranes, indicating that their binding site could also lie in the TM region of the receptors. However, simulations with the compounds bound or close to the TM helices in the membrane environment, showed no specific binding. Further experimental exploration of the binding mechanism of these compounds is required. Overall, this thesis sheds light on the dynamic behavior of TrkA and TrkB NT receptors in membranes and the mechanistic insights provide a basis for future studies to develop NT mimetics.

Author: Christina Athanasiou

Date Published: 30th Oct 2023

Publication Type: Doctoral Thesis

Personalised metabolic whole-body models for newborns and infants predict growth and biomarkers of inherited metabolic diseases

Abstract (Expand)

Abstract Extensive whole-body models (WBMs) accounting for organ-specific dynamics have been developed to simulate adult metabolism. However, there is currently a lack of models representing infant …ls representing infant metabolism taking into consideration its special requirements in energy balance, nutrition, and growth. Here, we present a resource of organ-resolved, sex-specific, anatomically accurate models of newborn and infant metabolism, referred to as infant-whole-body models (infant-WBMs), spanning the first 180 days of life. These infant-WBMs were parameterised to represent the distinct metabolic characteristics of newborns and infants accurately. In particular, we adjusted the changes in organ weights, the energy requirements of brain development, heart function, and thermoregulation, as well as dietary requirements and energy requirements for physical activity. Subsequently, we validated the accuracy of the infant-WBMs by showing that the predicted neonatal and infant growth was consistent with the recommended growth by the World Health Organisation. We assessed the infant-WBMs’ reliability and capabilities for personalisation by simulating 10,000 newborn models, personalised with blood concentration measurements from newborn screening and birth weight. Moreover, we demonstrate that the models can accurately predict changes over time in known blood biomarkers in inherited metabolic diseases. By this, the infant-WBM resource can provide valuable insights into infant metabolism on an organ-resolved level and enable a holistic view of the metabolic processes occurring in infants, considering the unique energy and dietary requirements as well as growth patterns specific to this population. As such, the infant-WBM resource holds promise for personalised medicine, as the infant-WBMs could be a first step to digital metabolic twins for newborn and infant metabolism for personalised systematic simulations and treatment planning.

Authors: Elaine Zaunseder, Ulrike Mütze, Jürgen G. Okun, Georg F. Hoffmann, Stefan Kölker, Vincent Heuveline, Ines Thiele

Date Published: 23rd Oct 2023

Publication Type: Journal

Myristoyl's dual role in allosterically regulating and localizing Abl kinase

Abstract (Expand)

c-Abl kinase, a key signalling hub in many biological processes ranging from cell development to proliferation, is tightly regulated by two inhibitory Src homology domains. An N-terminal myristoyl-modification … can bind to a hydrophobic pocket in the kinase C-lobe, which stabilizes the auto-inhibitory assembly. Activation is triggered by myristoyl release. We used molecular dynamics simulations to show how both myristoyl and the Src homology domains are required to impose the full inhibitory effect on the kinase domain, and reveal the allosteric transmission pathway at residue-level resolution. Importantly, we find myristoyl insertion into a membrane to thermodynamically compete with binding to c-Abl. Myristoyl thus not only localizes the protein to the cellular membrane, but membrane attachment at the same time enhances activation of c-Abl by stabilizing its pre-activated state. Our data put forward a model in which lipidation tightly couples kinase localization and regulation, a scheme that currently appears to be unique for this non-receptor tyrosine kinase.

Authors: Svenja de Buhr, Frauke Gräter

Date Published: 16th Oct 2023

Publication Type: Journal

Sources of predictability of synoptic‐scale rainfall during the West African summer monsoon

Abstract

Not specified

Authors: Athul Rasheeda Satheesh, Peter Knippertz, Andreas H. Fink, Eva-Maria Walz, Tilmann Gneiting

Date Published: 16th Oct 2023

Publication Type: Journal

A Generalizable Approach for Network Flow Image Representation for Deep Learning

Abstract

Not specified

Authors: Stefan Machmeier, Manuel Trageser, Marcus Buchwald, Vincent Heuveline

Date Published: 16th Oct 2023

Publication Type: Journal

KineticNet: Deep learning a transferable kinetic energy functional for orbital-free density functional theory

Abstract (Expand)

Orbital-free density functional theory (OF-DFT) holds promise to compute ground state molecular properties at minimal cost. However, it has been held back by our inability to compute the kinetic energy …kinetic energy as a functional of electron density alone. Here, we set out to learn the kinetic energy functional from ground truth provided by the more expensive Kohn–Sham density functional theory. Such learning is confronted with two key challenges: Giving the model sufficient expressivity and spatial context while limiting the memory footprint to afford computations on a GPU and creating a sufficiently broad distribution of training data to enable iterative density optimization even when starting from a poor initial guess. In response, we introduce KineticNet, an equivariant deep neural network architecture based on point convolutions adapted to the prediction of quantities on molecular quadrature grids. Important contributions include convolution filters with sufficient spatial resolution in the vicinity of nuclear cusp, an atom-centric sparse but expressive architecture that relays information across multiple bond lengths, and a new strategy to generate varied training data by finding ground state densities in the face of perturbations by a random external potential. KineticNet achieves, for the first time, chemical accuracy of the learned functionals across input densities and geometries of tiny molecules. For two-electron systems, we additionally demonstrate OF-DFT density optimization with chemical accuracy.

Authors: R. Remme, T. Kaczun, M. Scheurer, A. Dreuw, F. A. Hamprecht

Date Published: 14th Oct 2023

Publication Type: Journal

Crafting molecular architectures with guided diffusion

Abstract

Not specified

Author: Ganna Gryn’ova

Date Published: 11th Oct 2023

Publication Type: Journal

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