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 M₂ receptors to demonstrate the existence and function of such inactive agonist·receptor 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 agonist·receptor 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 agonist·receptor 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.

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 .

Metalloenzyme arginase is a therapeutically relevant target associated with tumor growth. To fight cancer immunosuppression, arginase activity can be modulated by small chemical inhibitors binding to its catalytic center. To better understand molecular mechanisms of arginase inhibition, a careful computer-aided mechanistic structural investigation of this enzyme was conducted. Using molecular dynamics (MD) simulations in the microsecond range, key regions of the protein active site were identified and their flexibility was evaluated and compared. A cavity opening phenomenon was observed, involving three loops directly interacting with all known ligands, while metal coordinating regions remained motionless. A novel dynamic 3D pharmacophore analysis method termed dynophores has been developed that allows for the construction of a single 3D-model comprising all ligand-enzyme interactions occurring throughout a complete MD trajectory. This new technique for the in silico study of intermolecular interactions allows for loop flexibility analysis coupled with movements and conformational changes of bound ligands. Presented MD studies highlight the plasticity of the size of the arginase active site, leading to the hypothesis that larger ligands can enter the cavity of arginase. Experimental testing of a targeted fragment library substituted by different aliphatic groups validates this hypothesis, paving the way for the design of arginase inhibitors with novel binding patterns.

Summary: 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. Contact: uwe.ohler@mdc-berlin.de