Human neuropeptide Y receptors (Y 1 R, Y 2 R, Y 4 R, and Y 5 R) belong to the superfamily of G protein-coupled receptors and play an important role in the regulation of food intake and energy metabolism. We identified and characterized the first selective Y 4 R allosteric antagonist (S)-VU0637120, an important step toward validating Y receptors as therapeutic targets for metabolic diseases. To obtain insight into the antagonistic mechanism of (S)-VU0637120, we conducted a variety of in vitro, ex vivo, and in silico studies. These studies revealed that (S)-VU0637120 selectively inhibits native Y 4 R function and binds in an allosteric site located below the binding pocket of the endogenous ligand pancreatic polypeptide in the core of the Y 4 R transmembrane domains. Taken together, our studies provide a first-of-its-kind tool for probing Y 4 R function and improve the general understanding of allosteric modulation, ultimately contributing to the rational development of allosteric modulators for peptide-activated G protein-coupled receptors (GPCRs).

The BioChemical Library (BCL) cheminformatics toolkit is an application-based academic open-source software package designed to integrate traditional small molecule cheminformatics tools with machine learning-based quantitative structure-activity/property relationship (QSAR/QSPR) modeling. In this pedagogical article we provide a detailed introduction to core BCL cheminformatics functionality, showing how traditional tasks (e.g., computing chemical properties, estimating druglikeness) can be readily combined with machine learning. In addition, we have included multiple examples covering areas of advanced use, such as reaction-based library design. We anticipate that this manuscript will be a valuable resource for researchers in computer-aided drug discovery looking to integrate modular cheminformatics and machine learning tools into their pipelines.

G protein-coupled receptors (GPCRs) represent the largest membrane protein family and a significant target class for therapeutics. Receptors from GPCRs’ largest class, class A, influence virtually every aspect of human physiology. About 45% of the members of this family endogenously bind flexible peptides or peptides segments within larger protein ligands. While many of these peptides have been structurally characterized in their solution state, the few studies of peptides in their receptor-bound state suggest that these peptides interact with a shared set of residues and undergo significant conformational changes. For the purpose of understanding binding dynamics and the development of peptidomimetic drug compounds, further studies should investigate the peptide ligands that are complexed to their cognate receptor.

BackgroundIn ligand-based virtual screening experiments, a known active ligand is used in similarity searches to find putative active compounds for the same protein target. When there are several known active molecules, screening using all of them is more powerful than screening using a single ligand. A consensus query can be created by either screening serially with different ligands before merging the obtained similarity scores, or by combining the molecular descriptors (i.e. chemical fingerprints) of those ligands.ResultsWe report on the discriminative power and speed of several consensus methods, on two datasets only made of experimentally verified molecules. The two datasets contain a total of 19 protein targets, 3776 known active and ~ 2 × 106 inactive molecules. Three chemical fingerprints are investigated: MACCS 166 bits, ECFP4 2048 bits and an unfolded version of MOLPRINT2D. Four different consensus policies and five consensus sizes were benchmarked.ConclusionsThe best consensus method is to rank candidate molecules using the maximum score obtained by each candidate molecule versus all known actives. When the number of actives used is small, the same screening performance can be approached by a consensus fingerprint. However, if the computational exploration of the chemical space is limited by speed (i.e. throughput), a consensus fingerprint allows to outperform this consensus of scores.

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The binding mode of natural peptide ligands to the Y 5 Gprotein-coupled receptor (Y 5 R), an attractive therapeutic target for the treatment of obesity,islargely unknown. Here,we apply complementary biochemical and computational approaches,i ncluding scanning of the receptor surface with ag enetically encoded crosslinker,A la-scanning of the ligand and double-cycle mutagenesis,t om ap interactions in the ligand-receptor interface and build as tructural model of the NPY-Y 5 Rc omplex guided by the experimental data. In the model, the carboxyl (C)-terminus of bound NPY is placed close to the extracellular loop (ECL) 3, whereas the characteristic a-helical segment of the ligand drapes over ECL1 and is tethered towards ECL2 by ah ydrophobic cluster.W ef urther show that the other two natural ligands of Y 5 R, peptide YY (PYY) and pancreatic polypeptide (PP) dock to the receptor in asimilar pose.

Comparing fragment based molecular fingerprints of drug-like molecules is one of the most robust and frequently used approaches in computer-assisted drug discovery (CADD). Molprint2D, a popular atom environment (AE) descriptor, yielded the best enrichment of active compounds across a diverse set of targets in a recent large-scale study. We present here BCL::Mol2D descriptors that outperformed Molprint2D on nine PubChem datasets spanning a wide range of protein classes. Because BCL::Mol2D records the number of AEs from a universal AE library, a novel aspect of BCL::Mol2D over the Molprint2D is its reversibility. This property enables decomposition of prediction from machine learning models to particular molecular substructures. Artificial neural networks (ANNs) with dropout, when trained on BCL::Mol2D descriptors outperform those trained on Molprint2D descriptors by up to 26% in logAUC metric. When combined with the Reduced Short Range (RSR) descriptor set, our previously published set of descriptors optimized for QSARs, BCL::Mol2D yields a modest improvement. Finally, we demonstrate how the reversibility of BCL::Mol2D enables visualization of a 'pharmacophore map' that could guide lead optimization for serine/threonine kinase 33 (STK33) inhibitors.

Cyclooxygenase-2 catalyzes the biosynthesis of prostaglandins from arachidonic acid and the biosynthesis of prostaglandin glycerol esters (PG-Gs) from 2-arachidonoylglycerol. PG-Gs are mediators of several biological actions such as macrophage activation, hyperalgesia, synaptic plasticity, and intraocular pressure. Recently, the human UDP receptor P2Y 6 was identified as a target for the prostaglandin E2 glycerol ester (PGE 2 -G).Here, we show that UDP and PGE 2 -G are evolutionary conserved endogenous agonists at vertebrate P2Y 6 orthologs. Using sequence comparison of P2Y 6 orthologs, homology modeling, and ligand docking studies, we proposed several receptor positions participating in agonist binding. Site-directed mutagenesis and functional analysis of these P2Y 6 mutants revealed that both UDP and PGE 2 -G share in parts one ligand-binding site. Thus, the convergent signaling of these two chemically very different agonists has already been manifested in the evolutionary design of the ligand-binding pocket.