G protein-coupled receptors constitute the largest family of membrane receptors and modulate almost every physiological process in humans. Binding of agonists to G protein-coupled receptors induces a shift from inactive to active receptor conformations. Biophysical studies of the dynamic equilibrium of receptors suggest that a portion of receptors can remain in inactive states even in the presence of saturating concentrations of agonist and G protein mimetic. However, the molecular details of agonist-bound inactive receptors are poorly understood.Here we use the model of bitopic orthosteric/allosteric (i.e. dualsteric) agonists for muscarinic M 2 receptors to demonstrate the existence and function of such inactive agonist⅐receptor complexes on a molecular level. Using all-atom molecular dynamics simulations, dynophores (i.e. a combination of static three-dimensional pharmacophores and molecular dynamics-based conformational sampling), ligand design, and receptor mutagenesis, we show that inactive agonist⅐receptor complexes can result from agonist binding to the allosteric vestibule alone, whereas the dualsteric binding mode produces active receptors. Each agonist forms a distinct ligand binding ensemble, and different agonist efficacies depend on the fraction of purely allosteric (i.e. inactive) versus dualsteric (i.e. active) binding modes. We propose that this concept may explain why agonist⅐receptor complexes can be inactive and that adopting multiple binding modes may be generalized also to small agonists where binding modes will be only subtly different and confined to only one binding site.Specific and coordinated cell-to-cell communication regulates the flow of information between cells, and proper information processing ensures physiological functions of biological systems. G protein-coupled receptors (GPCRs), 8 constituting the largest class of membrane proteins in mammals, are essential mediators of chemically and light-encoded information (1-4). GPCRs sense a great variety of extracellular stimuli, e.g. neurotransmitters and hormones, and subsequently translate this information into an intracellular response via G proteins, ␤-arrestins, and possibly GPCR-interacting proteins (2-5). Because of their abundance and relevance in regulating the majority of (patho-)physiological processes in humans, GPCRs have for a long time represented the most important drug targets being addressed by at least a third of all currently marketed drugs (6, 7).Agonist binding leads to receptor activation, which is followed by intracellular G protein recruitment and subsequent cell signaling. Breakthroughs in GPCR crystallography have led to inactive and active crystal structures of the same receptor protein. Among these are rhodopsin (8 -10) and more recently the ␤ 2 -adrenergic (11-14), M 2 muscarinic (15, 16), and -opioid receptors (17, 18). These structures most likely represent energetically favored, relatively stable inactive and active receptor⅐ligand complexes. Despite the diversity of crystallized receptors, a common...

Target deconvolution is a vital initial step in preclinical drug development to determine research focus and strategy. In this respect, computational target prediction is used to identify the most probable targets of an orphan ligand or the most similar targets to a protein under investigation. Applications range from the fundamental analysis of the mode-of-action over polypharmacology or adverse effect predictions to drug repositioning. Here, we provide a review on published ligand- and target-based as well as hybrid approaches for computational target prediction, together with current limitations and future directions.

Owing to the increase in freely available software and data for cheminformatics and structural bioinformatics, research for computer-aided drug design (CADD) is more and more built on modular, reproducible, and easy-to-share pipelines. While documentation for such tools is available, there are only a few freely accessible examples that teach the underlying concepts focused on CADD, especially addressing users new to the field. Here, we present TeachOpenCADD, a teaching platform developed by students for students, using open source compound and protein data as well as basic and CADD-related Python packages. We provide interactive Jupyter notebooks for central CADD topics, integrating theoretical background and practical code. TeachOpenCADD is freely available on GitHub: https://github.com/volkamerlab/TeachOpenCADD .

Ribosome profiling enables genome-wide analysis of translation with unprecedented resolution. We present Ribo-seQC, a versatile tool for the comprehensive analysis of Ribo-seq data, providing in-depth insights on data quality and translational profiles for cytoplasmic and organelle ribosomes. Ribo-seQC automatically generates platform-independent HTML reports, offering a detailed and easy-to-share basis for collaborative Ribo-seq projects.Availability: Ribo-seQC is available at https://github.com/ohlerlab/RiboseQC and submitted to Bioconductor.