Ultra-large make-on-demand compound libraries now contain billions of readily available compounds. This represents a golden opportunity for in-silico drug discovery. One challenge, however, is the time and computational cost of an exhaustive screen of such large libraries when receptor flexibility is taken into account. We propose an evolutionary algorithm to search combinatorial make-on-demand chemical space efficiently without enumerating all molecules. We exploit the feature of make-on-demand compound libraries, namely that they are constructed from lists of substrates and chemical reactions. Our algorithm RosettaEvolutionaryLigand (REvoLd) explores the vast search space of combinatorial libraries for protein-ligand docking with full ligand and receptor flexibility through RosettaLigand. A benchmark of REvoLd on five drug targets showed improvements in hit rates by factors between 869 and 1622 compared to random selections. REvoLd is available as an application within the Rosetta software suite ( https://docs.rosettacommons.org/docs/latest/revold ). This work formulates an evolutionary algorithm for optimization and exploration of ultra-large make-on-demand libraries. We demonstrate that our approach results in strong and stable enrichment, offering the most efficient algorithm for drug discovery in ultra-large chemical space to date.

G protein-coupled receptors (GPCRs) are the largest class of drug targets and undergo substantial conformational changes in response to ligand binding. Despite recent progress in GPCR structure determination, static snapshots fail to reflect the conformational space of putative binding pocket geometries to which small molecule ligands can bind. In comparative modeling of GPCRs in the absence of a ligand, often a shrinking of the orthosteric binding pocket is observed. However, the exact prediction of the flexible orthosteric binding site is crucial for adequate structure-based drug discovery. In order to improve ligand docking and guide virtual screening experiments in computer-aided drug discovery, we developed RosettaGPCRPocketSize. The algorithm creates a conformational ensemble of biophysically realistic conformations of the GPCR binding pocket between the TM bundle, which is consistent with a knowledge base of expected pocket geometries. Specifically, tetrahedral volume restraints are defined based on information about critical residues in the orthosteric binding site and their experimentally observed range of C_α-C_α-distances. The output of RosettaGPCRPocketSize is an ensemble of binding pocket geometries that are filtered by energy to ensure biophysically probable arrangements, which can be used for docking simulations. In a benchmark set, pocket shrinkage observed in the default RosettaGPCR was reduced by up to 80% and the binding pocket volume range and geometric diversity were increased. Compared to models from four different GPCR homology model databases (RosettaGPCR, GPCR-Tasser, GPCR-SSFE, and GPCRdb), the here-created models showed more accurate volumes of the orthosteric pocket when evaluated with respect to the crystallographic reference structure. Furthermore, RosettaGPCRPocketSize was able to generate an improved realistic pocket distribution. However, while being superior to other homology models, the accuracy of generated model pockets was comparable to AlphaFold2 models. Furthermore, in a docking benchmark using small-molecule ligands with a higher molecular weight between 400 and 700 Da, a higher success rate in creating native-like binding poses was observed. In summary, RosettaGPCRPocketSize can generate GPCR models with realistic orthosteric pocket volumes, which are useful for structure-based drug discovery applications.

RF‐amide peptide receptors including the neuropeptide FF receptor 1 (NPFFR1) are G protein‐coupled receptors (GPCRs) that modulate diverse physiological functions. High conservation of endogenous ligands and receptors makes the identification of selective ligands challenging. Previously identified antagonists mimic the C‐terminus of peptide ligands and lack selectivity towards the closely related neuropeptide FF receptor 2 (NPFFR2) or the neuropeptide Y₁ receptor (Y₁R). In a high‐throughput screening, we identified the pentacyclic triterpenoid hederagenin (1) as a novel selective antagonist for the NPFFR1. Hederagenin (1) is a natural product isolated from Hedera helix (ivy). We characterized its mode of activity using in vitro and in silico methods, revealing an overlapping binding site of the small molecule with the orthosteric peptide agonists. Despite the high similarity of the orthosteric binding pockets of NPFFR1 and NPFFR2, hederagenin (1) shows strong subtype selectivity, particularly caused by slight differences in the shape of the binding pockets and the rigidity of the small molecule. Several residues inhibiting the activity of hederagenin (1) at the NPFFR2 were identified. As NPFFR1 antagonists are discussed as potential candidates for the treatment of chronic pain, these insights into the structural determinants governing subtype specificity will facilitate the development of next‐generation analgesics with improved safety and efficacy.

Several studies have linked chemerin/chemokine-like receptor 1 (CMKLR1) to inflammation, leukocyte recruitment, and obesity. Reduced cellular activation may reduce inflammation in adipose tissues. High-throughput screening identified a novel antagonist (VU0514009), which was optimized to compound 16 as a full and competitive antagonist (IC₅₀ = 37 μM). Mutagenesis studies elucidated relevant interactions of compound 16 at CMKLR1 residues Y6.51 and L7.35 as well as F7.31, S7.32, and T7.39 forming the binding pocket. Based on active CMKLR1/chemerin-9 structures and the inactive AlphaFold model, in silico docking was performed in the inactive model, with compound 16 most likely binding orthosterically. Considering the sequence similarity of CMKLR1 and GPR1, compound 16 was docked to GPR1, indicating a similar binding. At GPR1, compound 16 showed a slightly lower effect on chemerin-9-mediated arrestin recruitment and internalization.

The Notch pathway is a highly conserved signaling cascade across metazoans that regulates numerous physiological processes, including cell proliferation, differentiation, and fate determination. Given its fundamental roles, the pathway is tightly regulated by diverse molecules through multiple mechanisms. Here, we identify the Adhesion GPCR latrophilin (LPHN/ADGRL) as a positive modulator of Notch signaling, which increases Notch receptor activation and the translocation of its intracellular domain into the nucleus. Physiologically, this latrophilin role is crucial for balancing the number of proliferating cells in the gonadal stem cell niche of the nematode C. elegans. In silico, in vitro, and in vivo analyses demonstrate that the C. elegans latrophilin homolog LAT-1 directly interacts with the DSL protein/Notch ligand LAG-2 on the same cell. This interaction is mediated by LAT-1’s conserved GAIN and the RBL domain. Importantly, the modulatory effect depends solely on the receptor’s N terminus and is independent of G protein signaling. Finally, we explore the implications of this fine-tuning of Notch signaling by an aGPCR.