The beta2-adrenergic receptor (B2AR) belongs to the family of G protein-coupled receptors, one of the major drug targets. G protein-coupled receptors are integral membrane proteins that convert external signals into intracellular responses. Two already known drugs employed in the treatment of several respiratory diseases are salmeterol and salbutamol. They show a high affinity to B2AR, however, their binding pathways have not yet been fully characterized. Along this project we will shed light on the binding process by means of coarse-grained molecular dynamics simulations using the Martini 3.0 force field. This methodology enables us to study the binding pathway of both drugs in an unbiased way.
Das interdisziplinäre FIGSS Seminar findet während des Semesters jeden zweiten Montag von 12:00-13:00 statt. Die Doktoranden der Graduiertenschule erhalten hier die Möglichkeit, ihre Forschungsergebnisse vorzustellen und zu diskutieren. Von Zeit zu Zeit werden auch externe Sprecher eingeladen.
Das FIGSS Seminar findet dieses Semester wieder in Präsenz statt. Jeden zweiten Montag von 12-13 Uhr werden wir uns im Hörsaal treffen. An jedem Termin wird es 1-2 Vorträge unserer Mitglieder geben.
Zusätzlich zu den Fachvorträgen gibt es an einigen Termin einen kurzen Impulsvortrag über ein FIAS spezifisches Thema. Vorschläge hierzu nimmt Doris gerne entgegen.
In all eukaryotes, the Unfolded Protein Response (UPR) is a molecular program that maintains the protein folding homeostasis in the endoplasmic reticulum (ER). The UPR plays a crucial role in health and disease. Stress sensors proteins on the ER membrane activate the UPR. The evolutionary most conserved sensor is the protein IRE1, which activates the UPR by forming dimers and larger assemblies. In particular, IRE1's luminal domain (LD) interacts with unfolded proteins and these interactions promote oligomerization by an unresolved mechanism. The direct binding mode of peptides is still not understood.
My work aims to elucidate the structure and assembly mechanism of large supramolecular assemblies of human IRE1 and probe its binding to unfolded proteins. These events are crucial for IRE1's functions but are not yet understood.
We employed a multiscale approach, performing atomistic and coarse-grained (CG) molecular dynamic (MD) simulations.
For investigating the formation of clusters of dimers of IRE1, we used the coarse-grained Martini 3 force field. We obtained encouraging results: hIRE cLD dimers can form clusters where contacts are mediated by disordered regions.
Peptide binding experiments, in atomistic and CG, were successful and led us to propose a new model for the direct binding of peptides and unfolded proteins.
Further analysis will be needed to extrapolate relevant dimer-dimer conformations from our simulations and to assess the effect of peptides on the dynamics of hIRE1 cLD dimer.
We outline a new model in which generalised uncertainty relations are obtained without modified commutation relations. While existing models introduce modified phase space volumes for the canonical degrees of freedom, we introduce new degrees of freedom for the background geometry. The background is treated as a genuinely quantum object, with an associated state vector, and the model naturally gives rise to the extended generalised uncertainty principle (EGUP). Importantly, this approach solves (or rather, evades) well known problems associated with modified commutators, including violation of the equivalence principle, the ‘soccer ball’ problem for multi-particle states, and the velocity dependence of the minimum length. However, it implies two radical conclusions. The first is that space must be quantised on a different scale to matter and the second is that the fundamental quanta of geometry are fermions. We explain how, in the context of the model, this gives rise to an effective dark energy density, without contradicting established results including the no go theorems for multiple quantisation constants, which still hold for species of material particles, and the spin-2 nature of gravitons.
Modern supervised machine learning (ML) techniques have demonstrated great utility for the classification of electroencephalography (EEG) signals. However, the large amounts of annotated training data typically required are not available in many medical settings. In this talk, we will discuss a few possibilities applying the unsupervised learning approaches for EEG representation learning in the context of epilepsy. Specifically, we apply a variational autoencoder (VAE) network to learn a compressed representation of the input EEG signals. This could be potentially used in the tasks of early diagnosis of epilepsy, treatment effect detection, or disease progression understanding. We also show large intersubject variability in the data that imposes great challenges.
The b-value from the Gutenberg-Richter relation captures the ratio between small and large earthquakes triggering for a region. Several researchers have observed fluctuation in b-values prior to an earthquake, especially during the aftershock sequence analysis. By leveraging the ever-increasing earthquake database and the development in the field of deep learning computations, this study focuses on analyzing the spatial-temporal b-value series for Japan.
We follow a two step approach, in the first step a spatial-temporal b-value series is calculated from the earthquake catalog, which takes the shape of a series of [32 × 32] pixel images of spatial b-value distributions. On this we train an autoencoder, which compresses and then decompresses the input to learn the normal behavior and relationships within the data. In the second step, we then take the pixel by pixel reconstruction error as input for a Dilated ResNet-like classifier, which predicts an earthquake probability. We expect that this study will improve the understanding of earthquake occurrences which could further be beneficial for Early Warning, rapid response and mitigation plans, especially for sustainable human habitats in earthquake-prone regions.
Die FIGSS Seminare des Wintersemesters 2021/21
Die FIGSS Seminare des Wintersemesters 2018/19.
Die FIGSS Seminare des Sommersemesters 2018.
Die FIGSS Seminare des Wintersemesters 2017/18.
Die FIGSS Seminare des Sommersemesters 2017.
Die FIGSS Seminare des Wintersemesters 2016/17.