Márton Vass

Virtual fragment screening on GPCRs: A case study on dopamine D3 and histamine H4 receptors

Prospective structure based virtual fragment screening methodologies on two GPCR targets namely the dopamine D3 and the histamine H4 receptors with a library of 12,905 fragments were evaluated. Fragments were docked to the X-ray structure and the homology model of the D3 and H4 receptors, respectively. Representative receptor conformations for ensemble docking were obtained from molecular dynamics trajectories. Inxa0vitro confirmed hit rates ranged from 16% to 32%. Hits had high ligand efficiency (LE) values in the range of 0.31-0.74 and also acceptable lipophilic efficiency. The X-ray structure, the homology model and structural ensembles were all found suitable for docking based virtual screening of fragments against these GPCRs. However, there was little overlap among different hit sets and methodologies were thus complementary to each other.

The impact of molecular dynamics sampling on the performance of virtual screening against GPCRs.

The formation of ligand-protein complexes requires simultaneous adaptation of the binding partners. In structure based virtual screening, high throughput docking approaches typically consider the ligand flexibility, but the conformational freedom of the protein is usually taken into account in a limited way. The goal of this study is to elaborate a methodology for incorporating protein flexibility to improve the virtual screening enrichments on GPCRs. Explicit-solvated molecular dynamics simulations (MD) were carried out in lipid bilayers to generate an ensemble of protein conformations for the X-ray structures and homology models of both aminergic and peptidergic GPCRs including the chemokine CXCR4, dopamine D3, histamine H4, and serotonin 5HT6 holo receptor complexes. The quality of the receptor models was assessed by enrichment studies to compare X-ray structures, homology models, and snapshots from the MD trajectory. According to our results, selected frames from the MD trajectory can outperform X-ray structures and homology models in terms of enrichment factor and AUC values. Significant changes were observed considering EF1% values: comparing the original CXCR4, D3, and H4 targets and the additional 5HT6 initial models to that of the best MD frame resulted in 0 to 6.7, 0.32 to 3.5 (10×), 13.3 to 26.7 (2×), and 0 to 14.1 improvements, respectively. It is worth noting that rank-average based ensemble evaluation calculated for different ensemble sizes could not improve the results further. We propose here that MD simulation can capture protein conformations representing the key interacting points of the receptor but less biased toward one specific chemotype. These conformations are useful for the identification of a consensus binding site with improved performance in virtual screening.

Multiple Fragment Docking and Linking in Primary and Secondary Pockets of Dopamine Receptors

A sequential docking methodology was applied to computationally predict starting points for fragment linking using the human dopamine D3 receptor crystal structure and a human dopamine D2 receptor homology model. Two focused fragment libraries were docked in the primary and secondary binding sites, and best fragment combinations were enumerated. Similar top scoring fragments were found for the primary site, while secondary site fragments were predicted to convey selectivity. Three linked compounds were synthesized that had 9-, 39-, and 55-fold selectivity in favor of D3 and the subtype selectivity of the compounds was assessed on a structural basis.

Molecular interaction fingerprint approaches for GPCR drug discovery

Protein-ligand interaction fingerprints (IFPs) are binary 1D representations of the 3D structure of protein-ligand complexes encoding the presence or absence of specific interactions between the binding pocket amino acids and the ligand. Various implementations of IFPs have been developed and successfully applied for post-processing molecular docking results for G Protein-Coupled Receptor (GPCR) ligand binding mode prediction and virtual ligand screening. Novel interaction fingerprint methods enable structural chemogenomics and polypharmacology predictions by complementing the increasing amount of GPCR structural data. Machine learning methods are increasingly used to derive relationships between bioactivity data and fingerprint descriptors of chemical and structural information of binding sites, ligands, and protein-ligand interactions. Factors that influence the application of IFPs include structure preparation, binding site definition, fingerprint similarity assessment, and data processing and these factors pose challenges as well possibilities to optimize interaction fingerprint methods for GPCR drug discovery.

Multiple ligand docking by Glide: implications for virtual second-site screening

Performance of Glide was evaluated in a sequential multiple ligand docking paradigm predicting the binding modes of 129 protein–ligand complexes crystallized with clusters of 2–6 cooperative ligands. Three sampling protocols (single precision—SP, extra precision—XP, and SP without scaling ligand atom radii—SP hard) combined with three different scoring functions (GlideScore, Emodel and Glide Energy) were tested. The effects of ligand number, docking order and druglikeness of ligands and closeness of the binding site were investigated. On average 36xa0% of all structures were reproduced with RMSDs lower than 2xa0Å. Correctly docked structures reached 50xa0% when docking druglike ligands into closed binding sites by the SP hard protocol. Cooperative binding to metabolic and transport proteins can dramatically alter pharmacokinetic parameters of drugs. Analyzing the cytochrome P450 subset the SP hard protocol with Emodel ranking reproduced two-thirds of the structures well. Multiple ligand binding is also exploited by the fragment linking approach in lead discovery settings. The HSP90 subset from real life fragment optimization programs revealed that Glide is able to reproduce the positions of multiple bound fragments if conserved water molecules are considered. These case studies assess the utility of Glide in sequential multiple docking applications.

3D-e-Chem-VM: Structural Cheminformatics Research Infrastructure in a Freely Available Virtual Machine

3D-e-Chem-VM is an open source, freely available Virtual Machine (http://3d-e-chem.github.io/3D-e-Chem-VM/) that integrates cheminformatics and bioinformatics tools for the analysis of protein–ligand interaction data. 3D-e-Chem-VM consists of software libraries, and database and workflow tools that can analyze and combine small molecule and protein structural information in a graphical programming environment. New chemical and biological data analytics tools and workflows have been developed for the efficient exploitation of structural and pharmacological protein–ligand interaction data from proteomewide databases (e.g., ChEMBLdb and PDB), as well as customized information systems focused on, e.g., G protein-coupled receptors (GPCRdb) and protein kinases (KLIFS). The integrated structural cheminformatics research infrastructure compiled in the 3D-e-Chem-VM enables the design of new approaches in virtual ligand screening (Chemdb4VS), ligand-based metabolism prediction (SyGMa), and structure-based protein binding site comparison and bioisosteric replacement for ligand design (KRIPOdb).

Fragments to link. A multiple docking strategy for second site binders

Fragment based drug discovery employs growing and linking strategies for optimization. Here we report the binding mode prediction of multiple fragments bound to a single target using a sequential docking methodology employing Glide to support the identification and linking of fragment hits. Sampling and scoring accuracy for the first and second site binders in self- and cross-docking situations is assessed.

Chemical Diversity in the G Protein-Coupled Receptor Superfamily

G protein-coupled receptors (GPCRs) are the largest family of cell signaling transmembrane proteins that can be modulated by a plethora of chemical compounds. Systematic cheminformatics analysis of structurally and pharmacologically characterized GPCR ligands shows that cocrystallized GPCR ligands cover a significant part of chemical ligand space, despite their limited number. Many GPCR ligands and substructures interact with multiple receptors, providing a basis for polypharmacological ligand design. Experimentally determined GPCR structures represent a variety of binding sites and receptor-ligand interactions that can be translated to chemically similar ligands for which structural data are lacking. This integration of structural, pharmacological, and chemical information on GPCR-ligand interactions enables the extension of the structural GPCR-ligand interactome and the structure-based design of novel modulators of GPCR function.

Dynamics and structural determinants of ligand recognition of the 5-HT6 receptor

In order to identify molecular models of the human 5-HT6 receptor suitable for virtual screening, homology modeling and membrane-embedded molecular dynamics simulations were performed. Structural requirements for robust enrichment were assessed by an unbiased chemometric analysis of enrichments from retrospective virtual screening studies. The two main structural features affecting enrichment are the outward movement of the second extracellular loop and the formation of a hydrophobic cavity deep in the binding site. These features appear transiently in the trajectories and furthermore the stretches of uniformly high enrichment may only last 4–10xa0ps. The formation of the inner hydrophobic cavity was also linked to the active-like to inactive-like transition of the receptor, especially the so-called connector region. The best structural models provided significant and robust enrichment over three independent ligand sets.

Cell-based and virtual fragment screening for adrenergic α2C receptor agonists.

Fragment-based drug discovery has emerged as an alternative to conventional lead identification and optimization strategies generally supported by biophysical detection techniques. Membrane targets like G protein-coupled receptors (GPCRs), however, offer challenges in lack of generic immobilization or stabilization methods for the dynamic, membrane-bound supramolecular complexes. Also modeling of different functional states of GPCRs proved to be a challenging task. Here we report a functional cell-based high concentration screening campaign for the identification of adrenergic α2C receptor agonists compared with the virtual screening of the same ligand set against an active-like homology model of the α2C receptor. The conventional calcium mobilization-based assay identified active fragments with a similar incidence to several other reported fragment screens on GPCRs. 16 out of 3071 screened fragments turned out as specific ligands of α2C, two of which were identified by virtual screening as well and several of the hits possessed surprisingly high affinity and ligand efficiency. Our results indicate that in vitro biological assays can be utilized in the fragment hit identification process for GPCR targets.