Comparison of structure- and ligand-based scoring functions for deep generative models: a GPCR case study.

Deep generative models have shown the ability to devise both valid and novel chemistry, which could significantly accelerate the identification of bioactive compounds. Many current models, however, use molecular descriptors or ligand-based predictive methods to guide molecule generation towards a desirable property space. This restricts their application to relatively data-rich targets, neglecting those where little data is available to sufficiently train a predictor. Moreover, ligand-based approaches often bias molecule generation towards previously established chemical space, thereby limiting their ability to identify truly novel chemotypes. In this work, we assess the ability of using molecular docking via Glide-a structure-based approach-as a scoring function to guide the deep generative model REINVENT and compare model performance and behaviour to a ligand-based scoring function. Additionally, we modify the previously published MOSES benchmarking dataset to remove any induced bias towards non-protonatable groups. We also propose a new metric to measure dataset diversity, which is less confounded by the distribution of heavy atom count than the commonly used internal diversity metric. With respect to the main findings, we found that when optimizing the docking score against DRD2, the model improves predicted ligand affinity beyond that of known DRD2 active molecules. In addition, generated molecules occupy complementary chemical and physicochemical space compared to the ligand-based approach, and novel physicochemical space compared to known DRD2 active molecules. Furthermore, the structure-based approach learns to generate molecules that satisfy crucial residue interactions, which is information only available when taking protein structure into account. Overall, this work demonstrates the advantage of using molecular docking to guide de novo molecule generation over ligand-based predictors with respect to predicted affinity, novelty, and the ability to identify key interactions between ligand and protein target. Practically, this approach has applications in early hit generation campaigns to enrich a virtual library towards a particular target, and also in novelty-focused projects, where de novo molecule generation either has no prior ligand knowledge available or should not be biased by it

Comparison of structure- and ligand-based scoring functions for deep generative models: a GPCR case study.

Deep generative models have shown the ability to devise both valid and novel chemistry, which could significantly accelerate the identification of bioactive compounds. Many current models, however, use molecular descriptors or ligand-based predictive methods to guide molecule generation towards a desirable property space. This restricts their application to relatively data-rich targets, neglecting those where little data is available to sufficiently train a predictor. Moreover, ligand-based approaches often bias molecule generation towards previously established chemical space, thereby limiting their ability to identify truly novel chemotypes. In this work, we assess the ability of using molecular docking via Glide-a structure-based approach-as a scoring function to guide the deep generative model REINVENT and compare model performance and behaviour to a ligand-based scoring function. Additionally, we modify the previously published MOSES benchmarking dataset to remove any induced bias towards non-protonatable groups. We also propose a new metric to measure dataset diversity, which is less confounded by the distribution of heavy atom count than the commonly used internal diversity metric. With respect to the main findings, we found that when optimizing the docking score against DRD2, the model improves predicted ligand affinity beyond that of known DRD2 active molecules. In addition, generated molecules occupy complementary chemical and physicochemical space compared to the ligand-based approach, and novel physicochemical space compared to known DRD2 active molecules. Furthermore, the structure-based approach learns to generate molecules that satisfy crucial residue interactions, which is information only available when taking protein structure into account. Overall, this work demonstrates the advantage of using molecular docking to guide de novo molecule generation over ligand-based predictors with respect to predicted affinity, novelty, and the ability to identify key interactions between ligand and protein target. Practically, this approach has applications in early hit generation campaigns to enrich a virtual library towards a particular target, and also in novelty-focused projects, where de novo molecule generation either has no prior ligand knowledge available or should not be biased by it

Advances in therapeutic peptides targeting G protein-coupled receptors.

Dysregulation of peptide-activated pathways causes a range of diseases, fostering the discovery and clinical development of peptide drugs. Many endogenous peptides activate G protein-coupled receptors (GPCRs) - nearly 50 GPCR peptide drugs have been approved to date, most of them for metabolic disease or oncology, and more than 10 potentially first-in-class peptide therapeutics are in the pipeline. The majority of existing peptide therapeutics are agonists, which reflects the currently dominant strategy of modifying the endogenous peptide sequence of ligands for peptide-binding GPCRs. Increasingly, novel strategies are being employed to develop both agonists and antagonists, to both introduce chemical novelty and improve drug-like properties. Pharmacodynamic improvements are evolving to allow biasing ligands to activate specific downstream signalling pathways, in order to optimize efficacy and reduce side effects. In pharmacokinetics, modifications that increase plasma half-life have been revolutionary. Here, we discuss the current status of the peptide drugs targeting GPCRs, with a focus on evolving strategies to improve pharmacokinetic and pharmacodynamic properties.Wellcome Trust [WT107715/Z/15/Z], Cambridge Biomedical Research Centre Biomedical Resources Gran

Dynamic tuneable G protein-coupled receptor monomer-dimer populations

G protein-coupled receptors (GPCRs) are the largest class of membrane receptors, playing a key role in the regulation of processes as varied as neurotransmission and immune response. Evidence for GPCR oligomerisation has been accumulating that challenges the idea that GPCRs function solely as monomeric receptors; however, GPCR oligomerisation remains controversial primarily due to the difficulties in comparing evidence from very different types of structural and dynamic data. Using a combination of single-molecule and ensemble FRET, double electron–electron resonance spectroscopy, and simulations, we show that dimerisation of the GPCR neurotensin receptor 1 is regulated by receptor density and is dynamically tuneable over the physiological range. We propose a “rolling dimer” interface model in which multiple dimer conformations co-exist and interconvert. These findings unite previous seemingly conflicting observations, provide a compelling mechanism for regulating receptor signalling, and act as a guide for future physiological studies

Modulators of CXCR4 and CXCR7/ACKR3 Function

Copyright © 2019 by The Author(s). The two G protein-coupled receptors (GPCRs) C-X-C chemokine receptor type 4 (CXCR4) and atypical chemokine receptor 3 (ACKR3) are part of the class A chemokine GPCR family and represent important drug targets for human immunodeficiency virus (HIV) infection, cancer, and inflammation diseases. CXCR4 is one of only three chemokine receptors with a US Food and Drug Administration approved therapeutic agent, the small-molecule modulator AMD3100. In this review, known modulators of the two receptors are discussed in detail. Initially, the structural relationship between receptors and ligands is reviewed on the basis of common structural motifs and available crystal structures. To date, no atypical chemokine receptor has been crystallized, which makes ligand design and predictions for these receptors more difficult. Next, the selectivity, receptor activation, and the resulting ligand-induced signaling output of chemokines and other peptide ligands are reviewed. Binding of pepducins, a class of lipid-peptides whose basis is the internal loop of a GPCR, to CXCR4 is also discussed. Finally, small-molecule modulators of CXCR4 and ACKR3 are reviewed. These modulators have led to the development of radio- and fluorescently labeled tool compounds, enabling the visualization of ligand binding and receptor characterization both in vitro and in vivo. SIGNIFICANCE STATEMENT: To investigate the pharmacological modulation of CXCR4 and ACKR3, significant effort has been focused on the discovery and development of a range of ligands, including small-molecule modulators, pepducins, and synthetic peptides. Imaging tools, such as fluorescent probes, also play a pivotal role in the field of drug discovery. This review aims to provide an overview of the aforementioned modulators that facilitate the study of CXCR4 and ACKR3 receptors

Advanced fluorescence microscopy reveals disruption of dynamic CXCR4 dimerization by subpocket-specific inverse agonists

Funding: This research was funded by European Union’s Horizon2020 Marie Skłodowska-Curie Actions (MSCA) Program under Grant Agreement 641833 (ONCORNET) and European Cooperation in Science and Technology (COST) Action CA18133 European Research Network on Signal Transduction (ERNEST). A. Inoue was funded by the Leading Advanced Projects for Medical Innovation (LEAP) JP19gm0010004 from the Japan Agency for Medical Research and Development.Although class A G protein−coupled receptors (GPCRs) can function as monomers, many of them form dimers and oligomers, but the mechanisms and functional relevance of such oligomerization is ill understood. Here, we investigate this problem for the CXC chemokine receptor 4 (CXCR4), a GPCR that regulates immune and hematopoietic cell trafficking, and a major drug target in cancer therapy. We combine single-molecule microscopy and fluorescence fluctuation spectroscopy to investigate CXCR4 membrane organization in living cells at densities ranging from a few molecules to hundreds of molecules per square micrometer of the plasma membrane. We observe that CXCR4 forms dynamic, transient homodimers, and that the monomer−dimer equilibrium is governed by receptor density. CXCR4 inverse agonists that bind to the receptor minor pocket inhibit CXCR4 constitutive activity and abolish receptor dimerization. A mutation in the minor binding pocket reduced the dimer-disrupting ability of these ligands. In addition, mutating critical residues in the sixth transmembrane helix of CXCR4 markedly diminished both basal activity and dimerization, supporting the notion that CXCR4 basal activity is required for dimer formation. Together, these results link CXCR4 dimerization to its density and to its activity. They further suggest that inverse agonists binding to the minor pocket suppress both dimerization and constitutive activity and may represent a specific strategy to target CXCR4.Publisher PDFPeer reviewe

Identification of Phosphorylation Sites Altering Pollen Soluble Inorganic Pyrophosphatase Activity

Protein phosphorylation regulates numerous cellular processes. Identifying the substrates and protein kinases involved is vital to understand how these important posttranslational modifications modulate biological function in eukaryotic cells. Pyrophosphatases catalyze the hydrolysis of inorganic phosphate (PPi) to inorganic phosphate Pi, driving biosynthetic reactions; they are essential for low cytosolic inorganic phosphate. It was suggested recently that posttranslational regulation of Family I soluble inorganic pyrophosphatases (sPPases) may affect their activity. We previously demonstrated that two pollen-expressed sPPases, Pr-p26.1a and Pr-p26.1b, from the flowering plant Papaver rhoeas were inhibited by phosphorylation. Despite the potential significance, there is a paucity of data on sPPase phosphorylation and regulation. Here, we used liquid chromatographic tandem mass spectrometry to map phosphorylation sites to the otherwise divergent amino-terminal extensions on these pollen sPPases. Despite the absence of reports in the literature on mapping phosphorylation sites on sPPases, a database survey of various proteomes identified a number of examples, suggesting that phosphorylation may be a more widely used mechanism to regulate these enzymes. Phosphomimetic mutants of Pr-p26.1a/b significantly and differentially reduced PPase activities by up to 2.5-fold at pH 6.8 and 52% in the presence of Ca2+ and hydrogen peroxide over unmodified proteins. This indicates that phosphoregulation of key sites can inhibit the catalytic responsiveness of these proteins in concert with key intracellular events. As sPPases are essential for many metabolic pathways in eukaryotic cells, our findings identify the phosphorylation of sPPases as a potential master regulatory mechanism that could be used to attenuate metabolism

Principals in Programming Languages: Technical Results

This is the companion technical report for ``Principals in Programming Languages'' [20]. See that document for a more readable version of these results. In this paper, we describe two variants of the simply typed $\lambda$-calculus extended with a notion of {\em principal}. The results are languages in which intuitive statements like ``the client must call $\mathtt{open}$ to obtain a file handle'' can be phrased and proven formally. The first language is a two-agent calculus with references and recursive types, while the second language explores the possibility of multiple agents with varying amounts of type information. We use these calculi to give syntactic proofs of some type abstraction results that traditionally require semantic arguments

In Silico Veritas: The Pitfalls and Challenges of Predicting

Recently the first community-wide assessments of the prediction of the structures of complexes between proteins and small molecule ligands have been reported in the so-called GPCR Dock 2008 and 2010 assessments. In the current review we discuss the different steps along the protein-ligand modeling workflow by critically analyzing the modeling strategies we used to predict the structures of protein-ligand complexes we submitted to the recent GPCR Dock 2010 challenge. These representative test cases, focusing on the pharmaceutically relevant G Protein-Coupled Receptors, are used to demonstrate the strengths and challenges of the different modeling methods. Our analysis indicates that the proper performance of the sequence alignment, introduction of structural adjustments guided by experimental data, and the usage of experimental data to identify protein-ligand interactions are critical steps in the protein-ligand modeling protocol. © 2011 by the authors; licensee MDPI, Basel, Switzerland

Free energies of binding of R- and S-propranolol to wild-type and F483A mutant cytochrome P450 2D6 from molecular dynamics simulations

Detailed molecular dynamics (MD) simulations have been performed to reproduce and rationalize the experimental finding that the F483A mutant of CYP2D6 has lower affinity for R-propranolol than for S-propranolol. Wild-type (WT) CYP2D6 does not show this stereospecificity. Four different approaches to calculate the free energy differences have been investigated and were compared to the experimental binding data. From the differences between calculations based on forward and backward processes and the closure of thermodynamic cycles, it was clear that not all simulations converged sufficiently. The approach that calculates the free energies of exchanging R-propranolol with S-propranolol in the F483A mutant relative to the exchange free energy in WT CYP2D6 accurately reproduced the experimental binding data. Careful inspection of the end-points of the MD simulations involved in this approach, allowed for a molecular interpretation of the observed differences