Pseudoreceptor-based pocket selection in a molecular dynamics simulation of the histamine H4 receptor

There is a renewed interest in pseudoreceptor models which enable computational chemists to bridge the gap of ligand- and receptor-based drug design. We developed a pseudoreceptor model for the histamine H4 receptor (H4R) based on five potent antagonists representing different chemotypes. Here we present the selection of potential ligand binding pockets that occur during molecular dynamics (MD) simulations of a homology-based receptor model. We present a method for prioritizing receptor models according to their match with the consensus ligand-binding mode represented by the pseudoreceptor. In this way, ligand information can be transferred to receptor-based modelling. We use Geometric Hashing to match three-dimensional points in Cartesion space. This allows for the rapid translation- and rotation-free comparison of atom coordinates, which also permits partial matching. The only prerequisite is a hash table, which uses distance triplets as hash keys. Each time a distance triplet occurring in the candidate point set which corresponds to an existing key, the match is represented by a vote of the respective key. Finally, the global match of both point sets can be easily extracted by selection of voted distance triplets. The results revealed a preferred ligand-binding pocket in H4R, which would not have been identified using an unrefined homology model of the protein. The key idea was to rely on ligand information by pseudoreceptor modelling

Two polymorphisms facilitate differences in plasticity between two chicken major histocompatibility complex class I proteins

Major histocompatibility complex class I molecules (MHC I) present peptides to cytotoxic T-cells at the surface of almost all nucleated cells. The function of MHC I molecules is to select high affinity peptides from a large intracellular pool and they are assisted in this process by co-factor molecules, notably tapasin. In contrast to mammals, MHC homozygous chickens express a single MHC I gene locus, termed BF2, which is hypothesised to have co-evolved with the highly polymorphic tapasin within stable haplotypes. The BF2 molecules of the B15 and B19 haplotypes have recently been shown to differ in their interactions with tapasin and in their peptide selection properties. This study investigated whether these observations might be explained by differences in the protein plasticity that is encoded into the MHC I structure by primary sequence polymorphisms. Furthermore, we aimed to demonstrate the utility of a complimentary modelling approach to the understanding of complex experimental data. Combining mechanistic molecular dynamics simulations and the primary sequence based technique of statistical coupling analysis, we show how two of the eight polymorphisms between BF2*15:01 and BF2*19:01 facilitate differences in plasticity. We show that BF2*15:01 is intrinsically more plastic than BF2*19:01, exploring more conformations in the absence of peptide. We identify a protein sector of contiguous residues connecting the membrane bound ?3 domain and the heavy chain peptide binding site. This sector contains two of the eight polymorphic residues. One is residue 22 in the peptide binding domain and the other 220 is in the ?3 domain, a putative tapasin binding site. These observations are in correspondence with the experimentally observed functional differences of these molecules and suggest a mechanism for how modulation of MHC I plasticity by tapasin catalyses peptide selection allosterically

Punctuality Predictions in Public Transportation: Quantifying the Effect of External Factors

Increasing availability of large-scale datasets for automatic vehicle location (AVL) in public transportation (PT) encouraged researchers to investigate data-driven punctuality prediction models (PPMs). PPMs promise to accelerate the mobility transition through more accurate prediction delays, increased customer service levels, and more efficient and forward-looking planning by mobility providers. While several PPMs show promising results for buses and long-distance trains, a comprehensive study on external factors\u27 effect on tram services is missing. Therefore, we implement four machine learning (ML) models to predict departure delays and elaborate on the performance increase by adding real-world weather and holiday data for three consecutive years. For our best model (XGBoost) the average MAE performance increased by 17.33 % compared to the average model performance when only trained on AVL data enriched by timetable characteristics. The results provide strong evidence that adding information-bearing features improves the forecast quality of PPMs

Combining Predicted and Live Traffic with Time-Dependent A* Potentials

We study efficient and exact shortest path algorithms for routing on road networks with realistic traffic data. For navigation applications, both current (i.e., live) traffic events and predictions of future traffic flows play an important role in routing. While preprocessing-based speedup techniques have been employed successfully to both settings individually, a combined model poses significant challenges. Supporting predicted traffic typically requires expensive preprocessing while live traffic requires fast updates for regular adjustments. We propose an A*-based solution to this problem. By generalizing A* potentials to time dependency, i.e. the estimate of the distance from a vertex to the target also depends on the time of day when the vertex is visited, we achieve significantly faster query times than previously possible. Our evaluation shows that our approach enables interactive query times on continental-sized road networks while allowing live traffic updates within a fraction of a minute. We achieve a speedup of at least two orders of magnitude over Dijkstra's algorithm and up to one order of magnitude over state-of-the-art time-independent A* potentials.Comment: 19 pages, 5 figures. Full version of ESA22 pape

Electronic excitation spectra of the five-orbital Anderson impurity model: From the atomic limit to itinerant atomic magnetism

We study the competition of Coulomb interaction and hybridization effects in the five-orbital Anderson impurity model by means of continuous time quantum Monte Carlo, exact diagonalization, and Hartree-Fock calculations. The dependence of the electronic excitation spectra and thermodynamic ground-state properties on the hybridization strength and the form of the Coulomb interaction is systematically investigated for impurity occupation number N≈6. With increasing hybridization strength, a Kondo resonance emerges, broadens and merges with some of the upper and lower Hubbard peaks. Concomitantly, there is an increase of charge fluctuations at the impurity site. In contrast to the single-orbital model, some atomic multiplet peaks and exchange split satellites persist despite strong charge fluctuations. We find that Hund's coupling leads to a state that may be characterized as an itinerant single atom magnet. As the filling is increased, the magnetic moment decreases, but the spin freezing phenomenon persists up to N≈8. When the hybridization is weak, the positions of atomic ionization peaks are rather sensitive to shifts of the impurity on-site energies. This allows to distinguish atomic ionization peaks from quasiparticle peaks or satellites in the electronic excitation spectra. On the methodological side we show that a comparison between the spectra obtained from Monte Carlo and exact diagonalization calculations is possible if the charge fluctuations are properly matched

Multi-orbital Kondo physics of Co in Cu hosts

We investigate the electronic structure of cobalt atoms on a copper surface and in a copper host by combining density functional calculations with a numerically exact continuous-time quantum Monte Carlo treatment of the five-orbital impurity problem. In both cases we find low energy resonances in the density of states of all five Co $d$-orbitals. The corresponding self-energies indicate the formation of a Fermi liquid state at low temperatures. Our calculations yield the characteristic energy scale -- the Kondo temperature -- for both systems in good agreement with experiments. We quantify the charge fluctuations in both geometries and suggest that Co in Cu must be described by an Anderson impurity model rather than by a model assuming frozen impurity valency at low energies. We show that fluctuations of the orbital degrees of freedom are crucial for explaining the Kondo temperatures obtained in our calculations and measured in experiments.Comment: 10 pages, 10 figure

oaBricksle – Ein knuffiger Roboter

In der Woche vom 17.02. bis 13.02. wurde oaBricksle erschaffen. Dieser LEGO Mindstorms Roboter wurde mit MATlab programmiert und soll die knuffige Seite von Robotern zeigen. Er findet seinen eigenen Weg und verlangt nach einer gewissen Zeit nach Aufmerksamkeit. OaBricksle wurde als etwas tolpatschig programmiert und macht auf sich aufmerksam, wenn er gestreichelt werden m¨ochte