Structure-Activity Relationship Study of the High-Affinity Neuropeptide Y4 Receptor Positive Allosteric Modulator VU0506013

Positive allosteric modulators targeting the Y₄ receptor (Y₄R), a G protein-coupled receptor (GPCR) involved in the regulation of satiety, offer great potential in anti-obesity research. In this study, we selected 603 compounds by using quantitative structure-activity relationship (QSAR) models and tested them in high-throughput screening (HTS). Here, the novel positive allosteric modulator (PAM) VU0506013 was identified, which exhibits nanomolar affinity and pronounced selectivity toward the Y₄R in engineered cell lines and mouse descending colon mucosa natively expressing the Y₄R. Based on this lead structure, we conducted a systematic SAR study in two regions of the scaffold and presented a series of 27 analogues with modifications in the N- and C-terminal heterocycles of the molecule to obtain insight into functionally relevant positions. By mutagenesis and computational docking, we present a potential binding mode of VU0506013 in the transmembrane core of the Y₄R. VU0506013 presents a promising scaffold for developing in vivo tools to move toward anti-obesity drug research focused on the Y₄R.

Introduction to the BioChemical Library (BCL): An Application-Based Open-Source Toolkit for Integrated Cheminformatics and Machine Learning in Computer-Aided Drug Discovery

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.

Binding of Natural Peptide Ligands to the Neuropeptide Y5 Receptor

The binding mode of natural peptide ligands to the Y5 G protein-coupled receptor (Y5 R), an attractive therapeutic target for the treatment of obesity, is largely unknown. Here, we apply complementary biochemical and computational approaches, including scanning of the receptor surface with a genetically encoded crosslinker, Ala-scanning of the ligand and double-cycle mutagenesis, to map interactions in the ligand-receptor interface and build a structural model of the NPY-Y5 R complex 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 α-helical segment of the ligand drapes over ECL1 and is tethered towards ECL2 by a hydrophobic cluster. We further show that the other two natural ligands of Y5 R, peptide YY (PYY) and pancreatic polypeptide (PP) dock to the receptor in a similar pose.

Mapping the binding sites of UDP and prostaglandin E2 glyceryl ester in the nucleotide receptor P2Y6

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 P2Y₆ was identified as a target for the prostaglandin E2 glycerol ester (PGE₂‐G). Here, we show that UDP and PGE₂‐G are evolutionary conserved endogenous agonists at vertebrate P2Y₆ orthologs. Using sequence comparison of P2Y₆ orthologs, homology modeling, and ligand docking studies, we proposed several receptor positions participating in agonist binding. Site‐directed mutagenesis and functional analysis of these P2Y₆ mutants revealed that both UDP and PGE₂‐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.

The Structural Basis of Peptide Binding at Class A G Protein-Coupled Receptors

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.

Structural Perspective on Ancient Neuropeptide Y-like System reveals Hallmark Features for Peptide Recognition and Receptor Activation

The neuropeptide Y (NPY) family is a peptide-activated G protein-coupled receptor system conserved across all bilaterians, and is involved in food intake, learning, and behavior. We hypothesized that comparing the NPY system in evolutionarily ancient organisms can reveal structural determinants of peptide recognition and receptor activation conserved in evolution. To test this hypothesis, we investigated the homologous FLP/NPR system of the protostome C.elegans. For three prototypic peptide-receptor complexes representing different ligand types, we integrate extensive functional data into structural models of the receptors. Common features include acidic patches in the extracellular loops (ECLs) of the receptors that cooperatively 'draw' the peptide into the binding pocket, which was functionally validated in vivo. A structurally conserved glutamate in the ECL2 anchors the peptides by a conserved salt bridge to the arginine of the RFamide motif. Beyond this conserved interaction, peptide binding show variability enabled by receptor-specific interactions. The family-conserved residue Q^3.32 is a key player for peptide binding and receptor activation. Altered interaction patterns at Q^3.32 may drastically increase the efficacy to activate the receptor.

Highly Selective Y4 Receptor Antagonist Binds in an Allosteric Binding Pocket

Human neuropeptide Y receptors (Y₁R, Y₂R, Y₄R, and Y₅R) 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 Y₄R 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 Y₄R function and binds in an allosteric site located below the binding pocket of the endogenous ligand pancreatic polypeptide in the core of the Y₄R transmembrane domains. Taken together, our studies provide a first-of-its-kind tool for probing Y₄R 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).

BCL::Mol2D-a robust atom environment descriptor for QSAR modeling and lead optimization

Comparing fragment based molecular fingerprints of drug-like molecules is one of the most robust and frequently used approaches in computer-assisted drug discovery. 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 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 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 inhibitors.

Consensus queries in ligand-based virtual screening experiments

Background

In 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.

Results

We 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.

Conclusions

The 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.