Protein flexibility poses a major challenge to docking of potential ligands in that the binding site can adopt different shapes. Docking algorithms usually keep the protein rigid and only allow the ligand to be treated as flexible. However, a wrong assessment of the shape of the binding pocket can prevent a ligand from adapting a correct pose. Ensemble docking is a simple yet promising method to solve this problem: Ligands are docked into multiple structures, and the results are subsequently merged. Selection of protein structures is a significant factor for this approach. In this work we perform a comprehensive and comparative study evaluating the impact of structure selection on ensemble docking. We perform ensemble docking with several crystal structures and with structures derived from molecular dynamics simulations of renin, an attractive target for antihypertensive drugs. Here, 500 ns of MD simulations revealed binding site shapes not found in any available crystal structure. We evaluate the importance of structure selection for ensemble docking by comparing binding pose prediction, ability to rank actives above nonactives (screening utility), and scoring accuracy. As a result, for ensemble definition k-means clustering appears to be better suited than hierarchical clustering with average linkage. The best performing ensemble consists of four crystal structures and is able to reproduce the native ligand poses better than any individual crystal structure. Moreover this ensemble outperforms 88% of all individual crystal structures in terms of screening utility as well as scoring accuracy. Similarly, ensembles of MD-derived structures perform on average better than 75% of any individual crystal structure in terms of scoring accuracy at all inspected ensembles sizes.

Human blood group B galactosyltransferase (GTB) catalyzes the galactosylation of the H antigen and is responsible for the formation of the blood group antigen of phenotype B. The ABO blood group system is well studied and routinely serotyped before transfusion and transplantation. Blood type subgroups have been repeatedly linked to an increased occurrence of diseases (e.g., a highly increased incidence rate for pancreatic cancer for individuals with blood group phenotype B). 3‐Phenyl‐5‐(piperazin‐1‐yl)‐1,2,4‐thiadiazole 1 has previously been described to inhibit GTB with a Ki value of 800 μm. In this work, we describe a computer‐guided fragment‐growing approach for the optimization of this fragment that was subsequently realized by synthesizing the most promising ligands. Enlarging the phenyl moiety of fragment 1 to a naphthyl moiety resulted in ligand 3‐(naphthalene‐1‐yl)‐5‐(piperazin‐1‐yl)‐1,2,4‐thiadiazole 2 a, which shows a threefold improvement in binding affinity (Ki=271 μm).

Core fucosylation of N‐glycans is catalyzed by fucosyltransferase 8 and is associated with various types of cancer. Most reported fucosyltransferase inhibitors contain non‐drug‐like features, such as charged groups. New starting points for the development of inhibitors of fucosyltransferase 8 using a fragment‐based strategy are presented. Firstly, we discuss the potential of a new putative binding site of fucosyltransferase 8 that, according to a molecular dynamics (MD) simulation, is made accessible by a significant motion of the SH3 domain. This might enable the design of completely new inhibitor types for fucosyltransferase 8. Secondly, we have performed a docking study targeting the donor binding site of fucosyltransferase 8, and this yielded two fragments that were linked and trimmed in silico. The resulting ligand was synthesized. Saturation transfer difference (STD) NMR confirmed binding of the ligand featuring a pyrazole core that mimics the guanine moiety. This ligand represents the first low‐molecular‐weight compound for the development of inhibitors of fucosyltransferase 8 with drug‐like properties.

Food authenticity is becoming increasingly important but challenges existing analytical methods. In this study, we analyze the mango cultivar Alphonso with regard to authenticity using 1H-NMR spectroscopy. This cultivar has been termed “the king of mangoes” due to its unique flavor. Regarding its metabolites however, little is known about unique constellations that allow for differentiation of the Alphonso cultivar. We find that the Alphonso cultivar is distinguished by high levels of niacin, trigonelline, and histidine but features relatively low levels of alanine. Furthermore, we develop a model based on the local outlier factor algorithm that effectively detects admixture of non-Alphonso cultivars to Alphonso purée. This task is highly challenging because we identified no metabolites that are unique or uniquely absent in the Alphonso cultivar compared to other mango cultivars analyzed in this study. Our model shows promising results on a test set: Admixtures consisting of 35% non-Alphonso and 65% Alphonso mango purée were uncovered with a sensitivity of 88%. At the same time, our model verified Alphonso samples with a good specificity of 86%.