Structure-Based Virtual Screening for Ligands of G Protein-Coupled Receptors: What Can Molecular Docking Do for You?

G protein-coupled receptors (GPCRs) constitute the largest family of membrane proteins in the human genome and are important therapeutic targets. During the last decade, the number of atomic-resolution structures of GPCRs has increased rapidly, providing insights into drug binding at the molecular level. These breakthroughs have created excitement regarding the potential of using structural information in ligand design and initiated a new era of rational drug discovery for GPCRs. The molecular docking method is now widely applied to model the three-dimensional structures of GPCR-ligand complexes and screen for chemical probes in large compound libraries. In this review article, we first summarize the current structural coverage of the GPCR superfamily and the understanding of receptor-ligand interactions at atomic resolution. We then present the general workflow of structure-based virtual screening and strategies to discover GPCR ligands in chemical libraries. We assess the state of the art of this research field by summarizing prospective applications of virtual screening based on experimental structures. Strategies to identify compounds with specific efficacy and selectivity profiles are discussed, illustrating the opportunities and limitations of the molecular docking method. Our overview shows that structure-based virtual screening can discover novel leads and will be essential in pursuing the next generation of GPCR drugs. SIGNIFICANCE STATEMENT: Extraordinary advances in the structural biology of G protein-coupled receptors have revealed the molecular details of ligand recognition by this large family of therapeutic targets, providing novel avenues for rational drug design. Structure-based docking is an efficient computational approach to identify novel chemical probes from large compound libraries, which has the potential to accelerate the development of drug candidates.

Use of molecular docking computational tools in drug discovery

Molecular docking has become an important component of the drug discovery process. Since first being developed in the 1980s, advancements in the power of computer hardware and the increasing number of and ease of access to small molecule and protein structures have contributed to the development of improved methods, making docking more popular in both industrial and academic settings. Over the years, the modalities by which docking is used to assist the different tasks of drug discovery have changed. Although initially developed and used as a standalone method, docking is now mostly employed in combination with other computational approaches within integrated workflows. Despite its invaluable contribution to the drug discovery process, molecular docking is still far from perfect. In this chapter we will provide an introduction to molecular docking and to the different docking procedures with a focus on several considerations and protocols, including protonation states, active site waters and consensus, that can greatly improve the docking results.

Can molecular dynamics simulations improve the structural accuracy and virtual screening performance of GPCR models?

The determination of G protein-coupled receptor (GPCR) structures at atomic resolution has improved understanding of cellular signaling and will accelerate the development of new drug candidates. However, experimental structures still remain unavailable for a majority of the GPCR family. GPCR structures and their interactions with ligands can also be modelled computationally, but such predictions have limited accuracy. In this work, we explored if molecular dynamics (MD) simulations could be used to refine the accuracy of in silico models of receptor-ligand complexes that were submitted to a community-wide assessment of GPCR structure prediction (GPCR Dock). Two simulation protocols were used to refine 30 models of the D3 dopamine receptor (D3R) in complex with an antagonist. Close to 60 μs of simulation time was generated and the resulting MD refined models were compared to a D3R crystal structure. In the MD simulations, the receptor models generally drifted further away from the crystal structure conformation. However, MD refinement was able to improve the accuracy of the ligand binding mode. The best refinement protocol improved agreement with the experimentally observed ligand binding mode for a majority of the models. Receptor structures with improved virtual screening performance, which was assessed by molecular docking of ligands and decoys, could also be identified among the MD refined models. Application of weak restraints to the transmembrane helixes in the MD simulations further improved predictions of the ligand binding mode and second extracellular loop. These results provide guidelines for application of MD refinement in prediction of GPCR-ligand complexes and directions for further method development.

PyPLIF HIPPOS-Assisted Prediction of Molecular Determinants of Ligand Binding to Receptors

Identification of molecular determinants of receptor-ligand binding could significantly increase the quality of structure-based virtual screening protocols. In turn, drug design process, especially the fragment-based approaches, could benefit from the knowledge. Retrospective virtual screening campaigns by employing AutoDock Vina followed by protein-ligand interaction fingerprinting (PLIF) identification by using recently published PyPLIF HIPPOS were the main techniques used here. The ligands and decoys datasets from the enhanced version of the database of useful decoys (DUDE) targeting human G protein-coupled receptors (GPCRs) were employed in this research since the mutation data are available and could be used to retrospectively verify the prediction. The results show that the method presented in this article could pinpoint some retrospectively verified molecular determinants. The method is therefore suggested to be employed as a routine in drug design and discovery.

Molecular Modeling of Histamine Receptors-Recent Advances in Drug Discovery

The recent developments of fast reliable docking, virtual screening and other algorithms gave rise to discovery of many novel ligands of histamine receptors that could be used for treatment of allergic inflammatory disorders, central nervous system pathologies, pain, cancer and obesity. Furthermore, the pharmacological profiles of ligands clearly indicate that these receptors may be considered as targets not only for selective but also for multi-target drugs that could be used for treatment of complex disorders such as Alzheimer's disease. Therefore, analysis of protein-ligand recognition in the binding site of histamine receptors and also other molecular targets has become a valuable tool in drug design toolkit. This review covers the period 2014-2020 in the field of theoretical investigations of histamine receptors mostly based on molecular modeling as well as the experimental characterization of novel ligands of these receptors.

PyPLIF HIPPOS: A Molecular Interaction Fingerprinting Tool for Docking Results of AutoDock Vina and PLANTS

We describe here our tool named PyPLIF HIPPOS, which was newly developed to analyze the docking results of AutoDock Vina and PLANTS. Its predecessor, PyPLIF (https://github.com/radifar/pyplif), is a molecular interaction fingerprinting tool for the docking results of PLANTS, exclusively. Unlike its predecessor, PyPLIF HIPPOS speeds up the computational times by separating the reference generation and docking analysis. PyPLIF HIPPOS also offers more options compared to PyPLIF. PyPLIF HIPPOS for Linux is stored as the Supporting Information in this application note and can be accessed in GitHub (https://github.com/radifar/PyPLIF-HIPPOS). Additionally, we present here the application of the tool in a retrospective structure-based virtual screening campaign targeting neuraminidase.

Enigmatic Histamine Receptor H4 for Potential Treatment of Multiple Inflammatory, Autoimmune, and Related Diseases

The histamine H4 receptor, belonging to the family of G-protein coupled receptors, is an increasingly attractive drug target. It plays an indispensable role in many cellular pathways, and numerous H4R ligands are being studied for the treatment of several inflammatory, allergic, and autoimmune disorders, including pulmonary fibrosis. Activation of H4R is involved in cytokine production and mediates mast cell activation and eosinophil chemotaxis. The importance of this receptor has also been shown in inflammatory models: peritonitis, respiratory tract inflammation, colitis, osteoarthritis, and rheumatoid arthritis. Recent studies suggest that H4R acts as a modulator in cancer, neuropathic pain, vestibular disorders, and type-2 diabetes, however, its role is still not fully understood.

Performance of virtual screening against GPCR homology models: Impact of template selection and treatment of binding site plasticity

Rational drug design for G protein-coupled receptors (GPCRs) is limited by the small number of available atomic resolution structures. We assessed the use of homology modeling to predict the structures of two therapeutically relevant GPCRs and strategies to improve the performance of virtual screening against modeled binding sites. Homology models of the D2 dopamine (D2R) and serotonin 5-HT2A receptors (5-HT2AR) were generated based on crystal structures of 16 different GPCRs. Comparison of the homology models to D2R and 5-HT2AR crystal structures showed that accurate predictions could be obtained, but not necessarily using the most closely related template. Assessment of virtual screening performance was based on molecular docking of ligands and decoys. The results demonstrated that several templates and multiple models based on each of these must be evaluated to identify the optimal binding site structure. Models based on aminergic GPCRs showed substantial ligand enrichment and there was a trend toward improved virtual screening performance with increasing binding site accuracy. The best models even yielded ligand enrichment comparable to or better than that of the D2R and 5-HT2AR crystal structures. Methods to consider binding site plasticity were explored to further improve predictions. Molecular docking to ensembles of structures did not outperform the best individual binding site models, but could increase the diversity of hits from virtual screens and be advantageous for GPCR targets with few known ligands. Molecular dynamics refinement resulted in moderate improvements of structural accuracy and the virtual screening performance of snapshots was either comparable to or worse than that of the raw homology models. These results provide guidelines for successful application of structure-based ligand discovery using GPCR homology models.

Data of small peptides in SMILES and three-dimensional formats for virtual screening campaigns

The data presented in this article are structures of dipeptides, tripeptides and tetrapeptides constructed from all possible combinations of 20 natural and common amino acids. In total, the data contains 168400 peptides. The structures are available in their simplified molecular-input line-entry system (SMILES) and three-dimensional (3D) formats. The type of data are text files, which could be accessed and modified either by text editor applications (e.g. Notepad++) or by molecule visualization softwares (e.g., YASARA View). These structures could be used further in virtual screening campaigns in the early stage of drug discovery projects.

Unpacking the Pathogen Box-An Open Source Tool for Fighting Neglected Tropical Disease

The Pathogen Box is a 400-strong collection of drug-like compounds, selected for their potential against several of the world's most important neglected tropical diseases, including trypanosomiasis, leishmaniasis, cryptosporidiosis, toxoplasmosis, filariasis, schistosomiasis, dengue virus and trichuriasis, in addition to malaria and tuberculosis. This library represents an ensemble of numerous successful drug discovery programmes from around the globe, aimed at providing a powerful resource to stimulate open source drug discovery for diseases threatening the most vulnerable communities in the world. This review seeks to provide an in-depth analysis of the literature pertaining to the compounds in the Pathogen Box, including structure-activity relationship highlights, mechanisms of action, related compounds with reported activity against different diseases, and, where appropriate, discussion on the known and putative targets of compounds, thereby providing context and increasing the accessibility of the Pathogen Box to the drug discovery community.

Development and Testing of Druglike Screening Libraries

Although significant advances in experimental high throughput screening (HTS) have been made for drug lead identification, in silico virtual screening (VS) is indispensable owing to its unique advantage over experimental HTS, target-focused, cheap, and efficient, albeit its disadvantage of producing false positive hits. For both experimental HTS and VS, the quality of screening libraries is crucial and determines the outcome of those studies. In this paper, we first reviewed the recent progress on screening library construction. We realized the urgent need for compiling high-quality screening libraries in drug discovery. Then we compiled a set of screening libraries from about 20 million druglike ZINC molecules by running fingerprint-based similarity searches against known drug molecules. Lastly, the screening libraries were objectively evaluated using 5847 external actives covering more than 2000 drug targets. The result of the assessment is very encouraging. For example, with the Tanimoto coefficient being set to 0.75, 36% of external actives were retrieved and the enrichment factor was 13. Additionally, drug target family specific screening libraries were also constructed and evaluated. The druglike screening libraries are available for download from https://mulan.pharmacy.pitt.edu .

Structure-based exploration and pharmacological evaluation of N-substituted piperidin-4-yl-methanamine CXCR4 chemokine receptor antagonists

Using the available structural information of the chemokine receptor CXCR4, we present hit finding and hit exploration studies that make use of virtual fragment screening, design, synthesis and structure-activity relationship (SAR) studies. Fragment 2 was identified as virtual screening hit and used as a starting point for the exploration of 31 N-substituted piperidin-4-yl-methanamine derivatives to investigate and improve the interactions with the CXCR4 binding site. Additionally, subtle structural ligand changes lead to distinct interactions with CXCR4 resulting in a full to partial displacement of CXCL12 binding and competitive and/or non-competitive antagonism. Three-dimensional quantitative structure-activity relationship (3D-QSAR) and binding model studies were used to identify important hydrophobic interactions that determine binding affinity and indicate key ligand-receptor interactions.

Discovery of a Novel Highly Selective Histamine H4 Receptor Antagonist for the Treatment of Atopic Dermatitis

The histamine H4 receptor (H4R), a member of the G-protein coupled receptor family, has been considered as a potential therapeutic target for treating atopic dermatitis (AD). A large number of H4R antagonists have been disclosed, but no efficient agents controlling both pruritus and inflammation in AD have been developed yet. Here, we have discovered a novel class of orally available H4R antagonists showing strong anti-itching and anti-inflammation activity as well as excellent selectivity against off-targets. A pharmacophore-based virtual screening system constructed in-house successfully identified initial hit compound 9, and the subsequent homology model-guided optimization efficiently led us to discover pyrido[2,3- e]tetrazolo[1,5- a]pyrazine analogue 48 as a novel chemotype of a potent and highly selective H4R antagonist. Importantly, orally administered compound 48 exhibits remarkable efficacy on antipruritus and anti-inflammation with a favorable pharmacokinetic (PK) profile in several mouse models of AD. Thus, these data strongly suggest that our compound 48 is a promising clinical candidate for treatment of AD.

Methods for Virtual Screening of GPCR Targets: Approaches and Challenges

Virtual screening (VS) has become an integral part of the drug discovery process and is a valuable tool for finding novel chemical starting points for GPCR targets. Ligand-based VS makes use of biochemical data for known, active compounds and has been applied successfully to many diverse GPCRs. Recent progress in GPCR X-ray crystallography has made it possible to incorporate detailed structural information into the VS process. This chapter outlines the latest VS techniques along with examples that highlight successful applications of these methods. Best practices for increasing the likelihood of VS success, as well as ongoing challenges, are also discussed.

Protein-Ligand Docking Methodologies and Its Application in Drug Discovery

Molecular docking is a key tool in structural biology and computer-assisted drug design. Molecular docking is a method which predicts the preferred orientation of a ligand when bound in an active site to form a stable complex. It is the most common method used as a structure-based drug design. Here, the authors intend to discuss the various types of docking methods and their development and applications in modern drug discovery. The important basic theories such as sampling algorithm and scoring functions have been discussed briefly. The performances of the different available docking software have also been discussed. This chapter also includes some application examples of docking studies in modern drug discovery such as targeted drug delivery using carbon nanotubes, docking of nucleic acids to find the binding modes and a comparative study between high-throughput screening and structure-based virtual screening.

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.

Fragment-Based Discovery of Subtype-Selective Adenosine Receptor Ligands from Homology Models

Fragment-based lead discovery (FBLD) holds great promise for drug discovery, but applications to G protein-coupled receptors (GPCRs) have been limited by a lack of sensitive screening techniques and scarce structural information. If virtual screening against homology models of GPCRs could be used to identify fragment ligands, FBLD could be extended to numerous important drug targets and contribute to efficient lead generation. Access to models of multiple receptors may further enable the discovery of fragments that bind specifically to the desired target. To investigate these questions, we used molecular docking to screen >500 000 fragments against homology models of the A3 and A1 adenosine receptors (ARs) with the goal to discover A3AR-selective ligands. Twenty-one fragments with predicted A3AR-specific binding were evaluated in live-cell fluorescence-based assays; of eight verified ligands, six displayed A3/A1 selectivity, and three of these had high affinities ranging from 0.1 to 1.3 μM. Subsequently, structure-guided fragment-to-lead optimization led to the identification of a >100-fold-selective antagonist with nanomolar affinity from commercial libraries. These results highlight that molecular docking screening can guide fragment-based discovery of selective ligands even if the structures of both the target and antitarget receptors are unknown. The same approach can be readily extended to a large number of pharmaceutically important targets.

Combinatorial Consensus Scoring for Ligand-Based Virtual Fragment Screening: A Comparative Case Study for Serotonin 5-HT(3)A, Histamine H(1), and Histamine H(4) Receptors

In the current study we have evaluated the applicability of ligand-based virtual screening (LBVS) methods for the identification of small fragment-like biologically active molecules using different similarity descriptors and different consensus scoring approaches. For this purpose, we have evaluated the performance of 14 chemical similarity descriptors in retrospective virtual screening studies to discriminate fragment-like ligands of three membrane-bound receptors from fragments that are experimentally determined to have no affinity for these proteins (true inactives). We used a complete fragment affinity data set of experimentally determined ligands and inactives for two G protein-coupled receptors (GPCRs), the histamine H1 receptor (H1R) and the histamine H4 receptor (H4R), and one ligand-gated ion channel (LGIC), the serotonin receptor (5-HT3AR), to validate our retrospective virtual screening studies. We have exhaustively tested consensus scoring strategies that combine the results of multiple actives (group fusion) or combine different similarity descriptors (similarity fusion), and for the first time systematically evaluated different combinations of group fusion and similarity fusion approaches. Our studies show that for these three case study protein targets both consensus scoring approaches can increase virtual screening enrichments compared to single chemical similarity search methods. Our cheminformatics analyses recommend to use a combination of both group fusion and similarity fusion for prospective ligand-based virtual fragment screening.

Expanding the horizons of G protein-coupled receptor structure-based ligand discovery and optimization using homology models

We show the key role of structural homology models in GPCR structure-based lead discovery and optimization, highlighting methodological aspects, recent progress and future directions.

Discovery of trace amine-associated receptor 1 ligands by molecular docking screening against a homology model

An in silico screen of a TAAR1 homology model identifies novel ligands.