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A modelling study has been carried out, investigating the binding of histamine (Hist), 2-methylhistamine (2-MeHist) and 2-phenylhistamine (2-PhHist) at two postulated agonistic binding sites on transmembrane domain 5 (TM5) of the histamine H1-receptor. For this purpose a conformational analysis study was performed on three particular residues of TM5, i.e., Lys200, Thr203 and Asn207, for which a functional role in binding has been proposed. The most favourable results were obtained for the interaction between Hist and the Lys200/Asn207 pair. Therefore, Lys200 was subsequently mutated and converted to an alanine, resulting in a 50-fold decrease of H1-receptor stimulation by histamine. Altogether, the data suggest that the Lys200/Asn207 pair is important for activation of the H1-receptor by histamine. In contrast, analogues of 2-PhHist seem to belong to a distinct subclass of histamine agonists and an alternative mode of binding is proposed in which the 2-phenyl ring binds to the same receptor location as one of the aromatic rings of classical histamine H1-antagonists. Subsequently, the binding modes of the agonists Hist, 2-MeHist and 2-PhHist and the H1-antagonist cyproheptadine were evaluated in three different seven-alpha-helical models of the H1-receptor built in homology with bacteriorhodopsin, but using three different alignments. Our findings suggest that the position of the carboxylate group of Asp116 (TM3) within the receptor pocket depends on whether an agonist or an antagonist binds to the protein; a conformational change of this aspartate residue upon agonist binding is expected to play an essential role in receptor stimulation.

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Uncertainty assessment has gained rapid interest in medical image analysis. A popular technique to compute epistemic uncertainty is the Monte-Carlo (MC) dropout technique. From a network with MC dropout and a single input, multiple outputs can be sampled. Various methods can be used to obtain epistemic uncertainty maps from those multiple outputs. In the case of multi-class segmentation, the number of methods is even larger as epistemic uncertainty can be computed voxelwise per class or voxelwise per image.This paper highlights a systematic approach to define and quantitatively compare those methods in two different contexts: class-specific epistemic uncertainty maps (one value per image, voxel and class) and combined epistemic uncertainty maps (one value per image and voxel). We applied this quantitative analysis to a multi-class segmentation of the carotid artery lumen and vessel wall, on a multi-center, multi-scanner, multi-sequence dataset of Magnetic Resonance (MR) images. We validated our analysis over 144 sets of hyperparameters of a model.Our main analysis considers the relationship between the order of the voxels sorted according to their epistemic uncertainty values and the misclassification of the prediction. Under this consideration, the comparison of combined uncertainty maps reveals that the multi-class entropy and the multi-class mutual information statistically out-perform the other combined uncertainty maps under study (the averaged entropy, the averaged variance, the similarity Bhattacharya coefficient and the similarity Kullback-Leibler divergence). In a class-specific scenario, the one-versus-all entropy statistically out-performs the class-wise entropy, the class-wise variance and the one versus all mutual information. The classwise entropy statistically out-performs the other class-specific uncertainty maps in term of calibration. We made a python package available to reproduce our analysis on different data and tasks.

New molecular modeling tools were developed to construct a qualitative pharmacophore model for histamine H3 receptor antagonists. The program SLATE superposes ligands assuming optimum hydrogen bond geometry. One or two ligands are allowed to flex in the procedure, thereby enabling the determination of the bioactive conformation of flexible H3 antagonists. In the derived model, four hydrogen-bonding site points and two hydrophobic pockets available for binding antagonists are revealed. The model results in a better understanding of the structure-activity relationships of H3 antagonists. To validate the model, a series of new antagonists was synthesized. The compounds were designed to interact with all four hydrogen-bonding site points and the two hydrophobic pockets simultaneously. These ligands have high H3 receptor affinity, thereby illustrating how the model can be used in the design of new classes of H3 antagonists.