Recent Trends and Applications of Molecular Modeling in GPCR⁻Ligand Recognition and Structure-Based Drug Design

In silico investigations and molecular insights for designing tRNA-encoded peptides as potential therapeutics for targeting over-expressed receptors in breast cancer

tRNA- Encoded Peptides (tREPs) have recently been discovered as new functional peptides and hold promise as therapeutics for anti-parasitic applications. In this study, in silico investigations were conducted to design tRNA-encoded peptides with the potential to target over-expressed receptors in breast cancer cells. tRNA genes were translated into corresponding peptides (tREPs) using computational tools. The tREPs, which were predicted as anticancer peptides, were then screened for various ADMET properties. Molecular docking studies were conducted for three cancer target receptors, the Estrogen Receptor (ER), Peroxisome Proliferator-Activated Receptor (PPAR) and the Epidermal Growth Factor Receptor (EGFR). Based on the docking results, specific tREPs were screened and molecular dynamics simulations were performed, and the binding energies were further explored using MMPBSA calculations. The peptide Pep1 (DWIAWRHHNDIVSWLTCGPRFKSWS) and Pep2 (GFIAWWSRHLELAQTRFKSWWS) exhibited a good binding affinity against the Estrogen Receptor (ER) and the Peroxisome Proliferator-Activated Receptor Alpha (PPAR) cancer target. The Pep1-ER and Pep1-PPAR complex maintained an average of two hydrogen bonds throughout the simulation and demonstrated a higher negative binding free energy of -72.27 kcal/mol and -65.16 kcal/mol respectively, as calculated by MMPBSA. Therefore, the tREPs designed as anticancer peptides in this study provide novel approaches for potential anticancer therapeutic modalities.Communicated by Ramaswamy H. Sarma.

L-DOPA and Droxidopa: From Force Field Development to Molecular Docking into Human β2-Adrenergic Receptor

The increasing interest in the molecular mechanism of the binding of different agonists and antagonists to β₂-adrenergic receptor (β₂AR) inactive and active states has led us to investigate protein–ligand interactions using molecular docking calculations. To perform this study, the 3.2 Å X-ray crystal structure of the active conformation of human β₂AR in the complex with the endogenous agonist adrenaline has been used as a template for investigating the binding of two exogenous catecholamines to this adrenergic receptor. Here, we show the derivation of L-DOPA and Droxidopa OPLS all atom (AA) force field (FF) parameters via quantum mechanical (QM) calculations, molecular dynamics (MD) simulations in aqueous solutions of the two catecholamines and the molecular docking of both ligands into rigid and flexible β₂AR models. We observe that both ligands share with adrenaline similar experimentally observed binding anchor sites, which are constituted by Asp113/Asn312 and Ser203/Ser204/Ser207 side chains. Moreover, both L-DOPA and Droxidopa molecules exhibit binding affinities comparable to that predicted for adrenaline, which is in good agreement with previous experimental and computational results. L-DOPA and Droxidopa OPLS AA FFs have also been tested by performing MD simulations of these ligands docked into β₂AR proteins embedded in lipid membranes. Both hydrogen bonds and hydrophobic interaction networks observed over the 1 μs MD simulation are comparable with those derived from molecular docking calculations and MD simulations performed with the CHARMM FF.

In Silico Studies of Tumor Targeted Peptide-Conjugated Natural Products for Targeting Over-Expressed Receptors in Breast Cancer Cells Using Molecular Docking, Molecular Dynamics and MMGBSA Calculations

In this work, in silico studies were carried out for the design of diterpene and polyphenol-peptide conjugates to potentially target over-expressed breast tumor cell receptors. Four point mutations were induced into the known tumor-targeting peptide sequence YHWYGYTPQN at positions 1, 2, 8 and 10, resulting in four mutated peptides. Each peptide was separately conjugated with either chlorogenate, carnosate, gallate, or rosmarinate given their known anti-tumor activities, creating dual targeting compounds. Molecular docking studies were conducted with the epidermal growth factor receptor (EGFR), to which the original peptide sequence is known to bind, as well as the estrogen receptor (ERα) and peroxisome proliferator-activated receptor (PPARα) using both Autodock Vina and FireDock. Based on docking results, peptide conjugates and peptides were selected and subjected to molecular dynamics simulations. MMGBSA calculations were used to further probe the binding energies. ADME studies revealed that the compounds were not CYP substrates, though most were Pgp substrates. Additionally, most of the peptides and conjugates showed MDCK permeability. Our results indicated that several of the peptide conjugates enhanced binding interactions with the receptors and resulted in stable receptor-ligand complexes; Furthermore, they may successfully target ERα and PPARα in addition to EGFR and may be further explored for synthesis and biological studies for therapeutic applications.

Analysis of L-DOPA and droxidopa binding to human β2-adrenergic receptor

Over the last two decades, an increasing number of studies has been devoted to a deeper understanding of the molecular process involved in the binding of various agonists and antagonists to active and inactive conformations of β2-adrenergic receptor (β2AR). The 3.2 Å x-ray crystal structure of human β2AR active state in combination with the endogenous low affinity agonist adrenaline offers an ideal starting structure for studying the binding of various catecholamines to adrenergic receptors. We show that molecular docking of levodopa (L-DOPA) and droxidopa into rigid and flexible β2AR models leads for both ligands to binding anchor sites comparable to those experimentally reported for adrenaline, namely D113/N312 and S203/S204/S207 side chains. Both ligands have a hydrogen bond network that is extremely similar to those of noradrenaline and dopamine. Interestingly, redocking neutral and protonated versions of adrenaline to rigid and flexible β2AR models results in binding poses that are more energetically stable and distinct from the x-ray crystal structure. Similarly, lowest energy conformations of noradrenaline and dopamine generated by docking into flexible β2AR models had binding free energies lower than those of best poses in rigid receptor models. Furthermore, our findings show that L-DOPA and droxidopa molecules have binding affinities comparable to those predicted for adrenaline, noradrenaline, and dopamine, which are consistent with previous experimental and computational findings and supported by the molecular dynamics simulations of β2AR-ligand complexes performed here.

Mechanistic Understanding from Molecular Dynamics in Pharmaceutical Research 2: Lipid Membrane in Drug Design

We review the use of molecular dynamics (MD) simulation as a drug design tool in the context of the role that the lipid membrane can play in drug action, i.e., the interaction between candidate drug molecules and lipid membranes. In the standard "lock and key" paradigm, only the interaction between the drug and a specific active site of a specific protein is considered; the environment in which the drug acts is, from a biophysical perspective, far more complex than this. The possible mechanisms though which a drug can be designed to tinker with physiological processes are significantly broader than merely fitting to a single active site of a single protein. In this paper, we focus on the role of the lipid membrane, arguably the most important element outside the proteins themselves, as a case study. We discuss work that has been carried out, using MD simulation, concerning the transfection of drugs through membranes that act as biological barriers in the path of the drugs, the behavior of drug molecules within membranes, how their collective behavior can affect the structure and properties of the membrane and, finally, the role lipid membranes, to which the vast majority of drug target proteins are associated, can play in mediating the interaction between drug and target protein. This review paper is the second in a two-part series covering MD simulation as a tool in pharmaceutical research; both are designed as pedagogical review papers aimed at both pharmaceutical scientists interested in exploring how the tool of MD simulation can be applied to their research and computational scientists interested in exploring the possibility of a pharmaceutical context for their research.

Uncovering the signalling, structure and function of the 20-HETE-GPR75 pairing: Identifying the chemokine CCL5 as a negative regulator of GPR75

BACKGROUND AND PURPOSE

The G-protein-coupled receptor GPR75 (Gq) and its ligand, the cytochrome P450-derived vasoactive eicosanoid 20-hydroxyeicosatetraenoic acid (20-HETE), are involved in the activation of pro-inflammatory and hypertensive signalling cascades contributing to diabetes, obesity, vascular dysfunction/remodelling, hypertension and cardiovascular disease. Little is known as to how, where and with what affinity 20-HETE interacts with GPR75.

EXPERIMENTAL APPROACH

To better understand the pairing of 20-HETE and its receptor (GPR75), we used surface plasmon resonance (SPR) to determine binding affinity/kinetics. The PRESTO-Tango receptor-ome methodology for GPR75 overexpression was coupled with FLIPR Calcium 6 assays, homogeneous time-resolved fluorescence (HTRF) IP-1 and β-arrestin recruitment assays to determine receptor activation and downstream signalling events.

KEY RESULTS

SPR confirmed 20-HETE binding to GPR75 with an estimated K_D of 1.56 × 10^-10  M. In GPR75-transfected HTLA cells, 20-HETE stimulated intracellular Ca²⁺ levels, IP-1 accumulation and β-arrestin recruitment, all of which were negated by known 20-HETE functional antagonists. Computational modelling of the putative ligand-binding pocket and mutation of Thr212 within the putative 20-HETE binding site abolished 20-HETE's ability to stimulate GPR75 activation. Knockdown of GPR75 in human endothelial cells nullified 20-HETE-stimulated intracellular Ca²⁺ . The chemokine CCL5, a suggested GPR75 ligand, binds to GPR75 (K_D of 5.85 × 10^-10  M) yet fails to activate GPR75; however, it inhibited 20-HETE's ability to activate GPR75 signalling.

CONCLUSIONS AND IMPLICATIONS

We have identified 20-HETE as a high-affinity ligand for GPR75 and CCL5 as a low-affinity negative regulator of GPR75, providing additional evidence for the deorphanization of GPR75 as a 20-HETE receptor.

The Potential of 19F NMR Application in GPCR Biased Drug Discovery

Although structure-based virtual drug discovery is revolutionizing the conventional high-throughput cell-based screening system, its limitation is obvious, together with other critical challenges. In particular, the resolved static snapshots fail to represent a full free-energy landscape due to homogenization in structural determination processing. The loss of conformational heterogeneity and related functional diversity emphasize the necessity of developing an approach that can fill this space. In this regard, NMR holds undeniable potential. However, outstanding questions regarding the NMR application remain. This review summarizes the limitations of current drug discovery and explores the potential of ¹⁹F NMR in establishing a conformation-guided drug screening system, advancing the cell- and structure-based discovery strategy for G protein-coupled receptor (GPCR) biased drug screening.

G-Protein coupled receptors: structure and function in drug discovery

The G-protein coupled receptors (GPCRs) superfamily comprise similar proteins arranged into families or classes thus making it one of the largest in the mammalian genome. GPCRs take part in many vital physiological functions making them targets for numerous novel drugs. GPCRs share some distinctive features, such as the seven transmembrane domains, they also differ in the number of conserved residues in their transmembrane domain. Here we provide an introductory and accessible review detailing the computational advances in GPCR pharmacology and drug discovery. An overview is provided on family A-C GPCRs; their structural differences, GPCR signalling, allosteric binding and cooperativity. The dielectric constant (relative permittivity) of proteins is also discussed in the context of site-specific environmental effects.

Multiscale Molecular Modeling in G Protein-Coupled Receptor (GPCR)-Ligand Studies

G protein-coupled receptors (GPCRs) are major drug targets due to their ability to facilitate signal transduction across cell membranes, a process that is vital for many physiological functions to occur. The development of computational technology provides modern tools that permit accurate studies of the structures and properties of large chemical systems, such as enzymes and GPCRs, at the molecular level. The advent of multiscale molecular modeling permits the implementation of multiple levels of theories on a system of interest, for instance, assigning chemically relevant regions to high quantum mechanics (QM) level of theory while treating the rest of the system using classical force field (molecular mechanics (MM) potential). Multiscale QM/MM molecular modeling have far-reaching applications in the rational design of GPCR drugs/ligands by affording precise ligand binding configurations through the consideration of conformational plasticity. This enables the identification of key binding site residues that could be targeted to manipulate GPCR function. This review will focus on recent applications of multiscale QM/MM molecular simulations in GPCR studies that could boost the efficiency of future structure-based drug design (SBDD) strategies.

In silico identification of novel 5-HT2A antagonists supported with ligand- and target-based drug design methodologies

A wide range of neuropsychological disorders is caused by serotonin 5-HT_2A receptor (5-HT_2AR) malfunction. Therefore, this receptor had been frequently used as target in CNS drug research. To design novel potent 5-HT_2AR antagonists, we have combined ligand-based and target-based approaches. This study was performed on wide range of structurally diverse antagonists that were divided into three different clusters: clozapine, ziprasidone, and ChEMBL240876 derivatives. By performing the 50 ns long molecular dynamic simulations with each cluster representative in complex with 5-HT_2A receptor, we have obtained virtually bioactive conformations of the ligands and three different antagonist-bound, inactive, conformations of the 5-HT_2AR. These three 5-HT_2AR conformations were further used for docking studies and generation of the bioactive conformations of the data set ligands in each cluster. Subsequently, selected conformers were used for 3D-Quantitative Structure Activity Relationship (3D-QSAR) modelling and pharmacophore analysis. The reliability and predictive power of the created model was assessed using an external test set compounds and showed reasonable external predictability. Statistically significant variables were used to define the most important structural features required for 5-HT_2A antagonistic activity. Conclusions obtained from performed ligand-based (3D-QSAR) and target-based (molecular docking and molecular dynamics) methods were compiled and used as guidelines for rational drug design of novel 5-HT_2AR antagonists.Communicated by Ramaswamy H. Sarma.

Conformational Searching with Quantum Mechanics

Estimating the range of three-dimensional structures (conformations) that are available to a molecule is a key component of computer-aided drug design. Quantum mechanical simulation offers improved accuracy over forcefield methods, but at a high computational cost. The question is whether this increased cost can be justified in a context in which high-throughput analysis of large numbers of molecules is often key. This chapter discusses the application of quantum mechanics to conformational searching, with a focus on three key challenges: (1) the generation of ensembles that include a good approximation to a molecule's bioactive conformation at as prominent a ranking as possible; (2) rational analysis and modification of a pre-established bioactive conformation in terms of its energetics; and (3) approximation of real solution-phase conformational ensembles in tandem with NMR data. The impact of QM on the high-throughput application (1) is debatable, meaning that for the moment its primary application is still lower-throughput applications such as (2) and (3). The optimal choice of QM method is also discussed. Rigorous benchmarking suggests that DFT methods are only acceptable when used with large basis sets, but a trickle of papers continue to obtain useful results with relatively low-cost methods, leading to a dilemma that the literature has yet to fully resolve.

The Biological and Clinical Relevance of G Protein-Coupled Receptors to the Outcomes of Hematopoietic Stem Cell Transplantation: A Systematized Review

Hematopoietic stem cell transplantation (HSCT) remains the only curative treatment for several malignant and non-malignant diseases at the cost of serious treatment-related toxicities (TRTs). Recent research on extending the benefits of HSCT to more patients and indications has focused on limiting TRTs and improving immunological effects following proper mobilization and engraftment. Increasing numbers of studies report associations between HSCT outcomes and the expression or the manipulation of G protein-coupled receptors (GPCRs). This large family of cell surface receptors is involved in various human diseases. With ever-better knowledge of their crystal structures and signaling dynamics, GPCRs are already the targets for one third of the current therapeutic arsenal. The present paper assesses the current status of animal and human research on GPCRs in the context of selected HSCT outcomes via a systematized survey and analysis of the literature.

Importance of protein dynamics in the structure-based drug discovery of class A G protein-coupled receptors (GPCRs)

Demand for novel GPCR modulators is increasing as the association between the GPCR signaling pathway and numerous diseases such as cancers, psychological and metabolic disorders continues to be established. In silico structure-based drug design (SBDD) offers an outlet where researchers could exploit the accumulating structural information of GPCR to expedite the process of drug discovery. The coupling of structure-based approaches such as virtual screening and molecular docking with molecular dynamics and/or Monte Carlo simulation aids in reflecting the dynamics of proteins in nature into previously static docking studies, thus enhancing the accuracy of rationally designed ligands. This review will highlight recent computational strategies that incorporate protein flexibility into SBDD of GPCR-targeted ligands.

Exploring the Potential of Spherical Harmonics and PCVM for Compounds Activity Prediction

Biologically active chemical compounds may provide remedies for several diseases. Meanwhile, Machine Learning techniques applied to Drug Discovery, which are cheaper and faster than wet-lab experiments, have the capability to more effectively identify molecules with the expected pharmacological activity. Therefore, it is urgent and essential to develop more representative descriptors and reliable classification methods to accurately predict molecular activity. In this paper, we investigate the potential of a novel representation based on Spherical Harmonics fed into Probabilistic Classification Vector Machines classifier, namely SHPCVM, to compound the activity prediction task. We make use of representation learning to acquire the features which describe the molecules as precise as possible. To verify the performance of SHPCVM ten-fold cross-validation tests are performed on twenty-one G protein-coupled receptors (GPCRs). Experimental outcomes (accuracy of 0.86) assessed by the classification accuracy, precision, recall, Matthews’ Correlation Coefficient and Cohen’s kappa reveal that using our Spherical Harmonics-based representation which is relatively short and Probabilistic Classification Vector Machines can achieve very satisfactory performance results for GPCRs.

Characterising GPCR-ligand interactions using a fragment molecular orbital-based approach

There has been fantastic progress in solving GPCR crystal structures. However, the ability of X-ray crystallography to guide the drug discovery process for GPCR targets is limited by the availability of accurate tools to explore receptor-ligand interactions. Visual inspection and molecular mechanics approaches cannot explain the full complexity of molecular interactions. Quantum mechanical approaches (QM) are often too computationally expensive, but the fragment molecular orbital (FMO) method offers an excellent solution that combines accuracy, speed and the ability to reveal key interactions that would otherwise be hard to detect. Integration of GPCR crystallography or homology modelling with FMO reveals atomistic details of the individual contributions of each residue and water molecule towards ligand binding, including an analysis of their chemical nature.

Applications of machine learning in GPCR bioactive ligand discovery

GPCRs constitute the largest druggable family having targets for 475 Food and Drug Administration (FDA) approved drugs. As GPCRs are of great interest to pharmaceutical industry, enormous efforts are being expended to find relevant and potent GPCR ligands as lead compounds. There are tens of millions of compounds present in different chemical databases. In order to scan this immense chemical space, computational methods, especially machine learning (ML) methods, are essential components of GPCR drug discovery pipelines. ML approaches have applications in both ligand-based and structure-based virtual screening. We present here a cheminformatics overview of ML applications to different stages of GPCR drug discovery. Focusing on olfactory receptors, which are the largest family of GPCRs, a case study for predicting agonists for an ectopic olfactory receptor, OR1G1, compares four classical ML methods.

Synthesis, biological evaluation, and computational studies of novel fused six-membered O-containing heterocycles as potential acetylcholinesterase inhibitors

An efficient, borax-catalyzed protocol for the synthesis of novel 4-aryl-substituted-4H-pyran derivatives fused to α-pyrone ring in a one-pot is described. By this achievement, some novel 4-aryl substituted 4H-pyrans fused to the α-pyrone ring as potential acetylcholinesterase inhibitors (AChEIs) with good to excellent yields are obtained from a one-pot three-component reaction between various aryl aldehydes, 4-hydroxy-6-methyl-2H-pyran-2-one and malononitrile. The method is a facile, inexpensive, practical and highly efficient one to obtain target compounds. The chemical structures of all compounds were characterized by FT-IR, FT-¹³CNMR and FT-¹HNMR, MS spectroscopy and also elemental analyses data. Furthermore, the purity of all novel compounds was checked by HPLC. In addition, both molecular modelling studies and Absorption, Distribution, Metabolism, Excretion and Toxicity (ADMETox) prediction nominated all compounds as good acetylcholinesterase inhibitors to the potential treatment of Alzheimer, Parkinson and Autism diseases that among them compound 4f showed the best activity against acetylcholinesterase enzyme.