Molecular Modeling of the Three-Dimensional Structure of Dopamine 3 (D3) Subtype Receptor: Discovery of Novel and Potent D3 Ligands through a Hybrid Pharmacophore- and Structure-Based Database Searching Approach

Photoinduced C-C bond cleavage for the synthesis of 2,4-disubstituted-1-naphthols from indenone derivatives and sulfoxonium ylide

The synthesis of 2,4-disubstituted-1-naphthols has been developed employing photomediated C-C bond cleavage (UV-LED 390 nm) of cyclopropane fused-indanones generated in situ from the reaction between indenones and trimethylsulfoxonium chloride under basic conditions at room temperature. Seventeen substrates were examined in this study. The results showed that indenone precursors containing aryl substituents could smoothly provide the desired products in up to 81% yield.

Appraisal of the Role of In silico Methods in Pyrazole Based Drug Design

Pyrazole and its derivatives are a pharmacologically and significantly active scaffolds that have innumerable physiological and pharmacological activities. They can be very good targets for the discovery of novel anti-bacterial, anti-cancer, anti-inflammatory, anti-fungal, anti-tubercular, antiviral, antioxidant, antidepressant, anti-convulsant and neuroprotective drugs. This review focuses on the importance of in silico manipulations of pyrazole and its derivatives for medicinal chemistry. The authors have discussed currently available information on the use of computational techniques like molecular docking, structure-based virtual screening (SBVS), molecular dynamics (MD) simulations, quantitative structure activity relationship (QSAR), comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) to drug design using pyrazole moieties. Pyrazole based drug design is mainly dependent on the integration of experimental and computational approaches. The authors feel that more studies need to be done to fully explore the pharmacological potential of the pyrazole moiety and in silico method can be of great help.

ReSimNet: drug response similarity prediction using Siamese neural networks

MOTIVATION

Traditional drug discovery approaches identify a target for a disease and find a compound that binds to the target. In this approach, structures of compounds are considered as the most important features because it is assumed that similar structures will bind to the same target. Therefore, structural analogs of the drugs that bind to the target are selected as drug candidates. However, even though compounds are not structural analogs, they may achieve the desired response. A new drug discovery method based on drug response, which can complement the structure-based methods, is needed.

RESULTS

We implemented Siamese neural networks called ReSimNet that take as input two chemical compounds and predicts the CMap score of the two compounds, which we use to measure the transcriptional response similarity of the two compounds. ReSimNet learns the embedding vector of a chemical compound in a transcriptional response space. ReSimNet is trained to minimize the difference between the cosine similarity of the embedding vectors of the two compounds and the CMap score of the two compounds. ReSimNet can find pairs of compounds that are similar in response even though they may have dissimilar structures. In our quantitative evaluation, ReSimNet outperformed the baseline machine learning models. The ReSimNet ensemble model achieves a Pearson correlation of 0.518 and a precision@1% of 0.989. In addition, in the qualitative analysis, we tested ReSimNet on the ZINC15 database and showed that ReSimNet successfully identifies chemical compounds that are relevant to a prototype drug whose mechanism of action is known.

AVAILABILITY AND IMPLEMENTATION

The source code and the pre-trained weights of ReSimNet are available at https://github.com/dmis-lab/ReSimNet.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

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

G protein-coupled receptors represent the largest family of human membrane proteins and are modulated by a variety of drugs and endogenous ligands. Molecular modeling techniques, especially enhanced sampling methods, have provided significant insight into the mechanism of GPCR–ligand recognition. Notably, the crucial role of the membrane in the ligand-receptor association process has earned much attention. Additionally, docking, together with more accurate free energy calculation methods, is playing an important role in the design of novel compounds targeting GPCRs. Here, we summarize the recent progress in the computational studies focusing on the above issues. In the future, with continuous improvement in both computational hardware and algorithms, molecular modeling would serve as an indispensable tool in a wider scope of the research concerning GPCR–ligand recognition as well as drug design targeting GPCRs.

Current Therapeutic Molecules and Targets in Neurodegenerative Diseases Based on in silico Drug Design

BACKGROUND

As the number of elderly persons increases, neurodegenerative diseases are becoming ubiquitous. There is currently a great need for knowledge concerning management of oldage neurodegenerative diseases; the most important of which are: Alzheimer's disease, Parkinson's disease, Amyotrophic Lateral Sclerosis, and Huntington's disease.

OBJECTIVE

To summarize the potential of computationally predicted molecules and targets against neurodegenerative diseases.

METHOD

Review of literature published since 1997 against neurodegenerative diseases, utilizing as keywords: in silico, Alzheimer's disease, Parkinson's disease, Amyotrophic Lateral Sclerosis ALS, and Huntington's disease was conducted.

RESULTS AND CONCLUSION

Due to the costs associated with experimentation and current ethical law, performing experiments directly on living organisms has become much more difficult. In this scenario, in silico techniques have been successful and have become powerful tools in the search to cure disease. Researchers use the Computer Aided Drug Design pipeline which: 1) generates 3- dimensional structures of target proteins through homology modeling 2) achieves stabilization through molecular dynamics simulation, and 3) exploits molecular docking through large compound libraries. Next generation sequencing is continually producing enormous amounts of raw sequence data while neuroimaging is producing a multitude of raw image data. To solve such pressing problems, these new tools and algorithms are required. This review elaborates precise in silico tools and techniques for drug targets, active molecules, and molecular docking studies, together with future prospects and challenges concerning possible breakthroughs in Alzheimer's, Parkinson's, Amyotrophic Lateral Sclerosis, and Huntington's disease.

Rational Drug Design Rational Drug Design

For the past three decades rationale drug design (RDD) has been developing as an innovative, rapid and successful way to discover new drug candidates. Many strategies have been followed and several targets with diverse structures and different biological roles have been investigated. Despite the variety of computational tools available, one can broadly divide them into two major classes that can be adopted either separately or in combination. The first class involves structure-based drug design, when the target's 3-dimensional structure is available or it can be computationally generated using homology modeling. On the other hand, when only a set of active molecules is available, and the structure of the target is unknown, ligand-based drug design tools are usually used. This review describes some recent advances in rational drug design, summarizes a number of their practical applications, and discusses both the advantages and shortcomings of the various techniques used.

Function-specific virtual screening for GPCR ligands using a combined scoring method

The ability of scoring functions to correctly select and rank docking poses of small molecules in protein binding sites is highly target dependent, which presents a challenge for structure-based drug discovery. Here we describe a virtual screening method that combines an energy-based docking scoring function with a molecular interaction fingerprint (IFP) to identify new ligands based on G protein-coupled receptor (GPCR) crystal structures. The consensus scoring method is prospectively evaluated by: 1) the discovery of chemically novel, fragment-like, high affinity histamine H₁ receptor (H₁R) antagonists/inverse agonists, 2) the selective structure-based identification of ß₂-adrenoceptor (ß₂R) agonists, and 3) the experimental validation and comparison of the combined and individual scoring approaches. Systematic retrospective virtual screening simulations allowed the definition of scoring cut-offs for the identification of H₁R and ß₂R ligands and the selection of an optimal ß-adrenoceptor crystal structure for the discrimination between ß₂R agonists and antagonists. The consensus approach resulted in the experimental validation of 53% of the ß₂R and 73% of the H₁R virtual screening hits with up to nanomolar affinities and potencies. The selective identification of ß₂R agonists shows the possibilities of structure-based prediction of GPCR ligand function by integrating protein-ligand binding mode information.

Hierarchical virtual screening approaches in small molecule drug discovery

Virtual screening has played a significant role in the discovery of small molecule inhibitors of therapeutic targets in last two decades. Various ligand and structure-based virtual screening approaches are employed to identify small molecule ligands for proteins of interest. These approaches are often combined in either hierarchical or parallel manner to take advantage of the strength and avoid the limitations associated with individual methods. Hierarchical combination of ligand and structure-based virtual screening approaches has received noteworthy success in numerous drug discovery campaigns. In hierarchical virtual screening, several filters using ligand and structure-based approaches are sequentially applied to reduce a large screening library to a number small enough for experimental testing. In this review, we focus on different hierarchical virtual screening strategies and their application in the discovery of small molecule modulators of important drug targets. Several virtual screening studies are discussed to demonstrate the successful application of hierarchical virtual screening in small molecule drug discovery.

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.

Structure-based virtual screening for fragment-like ligands of the G protein-coupled histamine H4 receptor

Structure-based virtual screening using H₁R- and β₂R-based histamine H₄R homology models identified 9 fragments with an affinity ranging from 0.14 to 6.3 μm for H₄R.

Beyond small-molecule SAR: using the dopamine D3 receptor crystal structure to guide drug design

The dopamine D3 receptor is a target of pharmacotherapeutic interest in a variety of neurological disorders including schizophrenia, restless leg syndrome, and drug addiction. The high protein sequence homology between the D3 and D2 receptors has posed a challenge to developing D3 receptor-selective ligands whose behavioral actions can be attributed to D3 receptor engagement, in vivo. However, through primarily small-molecule structure-activity relationship (SAR) studies, a variety of chemical scaffolds have been discovered over the past two decades that have resulted in several D3 receptor-selective ligands with high affinity and in vivo activity. Nevertheless, viable clinical candidates remain limited. The recent determination of the high-resolution crystal structure of the D3 receptor has invigorated structure-based drug design, providing refinements to the molecular dynamic models and testable predictions about receptor-ligand interactions. This chapter will highlight recent preclinical and clinical studies demonstrating potential utility of D3 receptor-selective ligands in the treatment of addiction. In addition, new structure-based rational drug design strategies for D3 receptor-selective ligands that complement traditional small-molecule SAR to improve the selectivity and directed efficacy profiles are examined.

Understanding the Structural Requirements of Hybrid (S)-6-((2-(4-Phenylpiperazin-1-yl)ethyl)(propyl)amino)-5,6,7,8-tetrahydronaphthalen-1-ol and its Analogs as D2/D3 Receptor Ligands: A Three-Dimensional Quantitative Structure-Activity Relationship (3D QSAR) Investigation

To gain insights into the structural requirements for dopamine D2 and D3 agonists in the treatment of Parkinson's disease (PD) and to elucidate the basis of selectivity for D3 over D2 (D2/D3), 3D quantitative structure-activity relationship (3D QSAR) investigations using CoMFA (comparative molecular field analysis) and CoMSIA (comparative molecular similarity indices analysis) methods were performed on a series of 45 structurally related D2 and D3 dopaminergic ligands. Two alignment methods (atom-based and flexible) and two charge calculation methods (Gasteiger-Hückel and AM1) were used in the present study. Overall, D2 affinity and selectivity (D2/D3) models performed better with r2cv values of 0.71 and 0.63 for CoMFA and 0.71 and 0.79 for CoMSIA, respectively. The corresponding predictive r2 values for the CoMFA and CoMSIA models were 0.92 and 0.86 and 0.91 and 0.78, respectively. The CoMFA models generated using flexible alignment outperformed the models with the atom-based alignment in terms of relevant statistics and interpretability of the generated contour maps while CoMSIA models obtained using atom-based alignment showed superiority in terms of internal and external predictive abilities. The presence of carbonyl group (C=O) attached to the piperazine ring and the hydrophobic biphenyl ring were found to be the most important features responsible for the D3 selectivity over D2. This study can be further utilized to design and develop selective and potent dopamine agonists to treat PD.

Structure-based discovery of selective serotonin 5-HT(1B) receptor ligands

The development of safe and effective drugs relies on the discovery of selective ligands. Serotonin (5-hydroxytryptamine [5-HT]) G protein-coupled receptors are therapeutic targets for CNS disorders but are also associated with adverse drug effects. The determination of crystal structures for the 5-HT1B and 5-HT2B receptors provided an opportunity to identify subtype selective ligands using structure-based methods. From docking screens of 1.3 million compounds, 22 molecules were predicted to be selective for the 5-HT1B receptor over the 5-HT2B subtype, a requirement for safe serotonergic drugs. Nine compounds were experimentally verified as 5-HT1B-selective ligands, with up to 300-fold higher affinities for this subtype. Three of the ligands were agonists of the G protein pathway. Analysis of state-of-the-art homology models of the two 5-HT receptors revealed that the crystal structures were critical for predicting selective ligands. Our results demonstrate that structure-based screening can guide the discovery of ligands with specific selectivity profiles.

C(X)CR in silico: Computer-aided prediction of chemokine receptor-ligand interactions

This review will focus on the construction, refinement, and validation of chemokine receptor models for the purpose of structure-based virtual screening and ligand design. The review will present a comparative analysis of ligand binding pockets in chemokine receptors, including a review of the recently released CXCR4 X-ray structures, and their implication on chemokine receptor (homology) modeling. The recommended protein-ligand modeling procedure as well as the use of experimental anchors to steer the modeling procedure is discussed and an overview of several successful structure-based ligand discovery and design studies is provided. This review shows that receptor models, despite structural inaccuracies, can be efficiently used to find novel ligands for chemokine receptors.:

Tranylcypromine substituted cis-hydroxycyclobutylnaphthamides as potent and selective dopamine D₃ receptor antagonists

We report a class of potent and selective dopamine D₃ receptor antagonists based upon tranylcypromine. Although tranylcypromine has a low affinity for the rat D₃ receptor (K_i = 12.8 μM), our efforts have yielded (1R,2S)-11 (CJ-1882), which has K_i values of 2.7 and 2.8 nM at the rat and human dopamine D₃ receptors, respectively, and displays respective selectivities of >10000-fold and 223-fold over the rat and human D₂ receptors. Evaluation in a β-arrestin functional assay showed that (1R,2S)-11 is a potent and competitive antagonist at the human D₃ receptor.

Structural modifications of neuroprotective anti-Parkinsonian (-)-N6-(2-(4-(biphenyl-4-yl)piperazin-1-yl)-ethyl)-N6-propyl-4,5,6,7-tetrahydrobenzo[d]thiazole-2,6-diamine (D-264): an effort toward the improvement of in vivo efficacy of the parent molecule

In our overall goal to develop multifunctional dopamine D2/D3 agonist drugs for the treatment of Parkinson's disease (PD), we previously synthesized potent D3 preferring agonist D-264 (1a), which exhibited neuroprotective properties in two animal models of PD. To enhance the in vivo efficacy of 1a, a structure-activity relationship study was carried out. Competitive binding and [(35)S]GTPγS functional assays identified compound (-)-9b as one of the lead molecules with preferential D3 agonist activity (EC50(GTPγS); D3 = 0.10 nM; D2/D3 (EC50): 159). Compounds (-)-9b and (-)-8b exhibited high in vivo activity in two PD animal models, reserpinized and 6-hydroxydopamine (OHDA)-induced unilateral lesioned rats. On the other hand, 1a failed to show any in vivo activity in these models unless the compound was dissolved in 5-10% beta-hydroxy propyl cyclodextrin solution. Lead compounds exhibited appreciable radical scavenging activity. In vitro experiments with dopaminergic MN9D cells indicated neuroprotection by both 1a and (-)-9b from toxicity of MPP+.

Rational Drug Design

For the past three decades rationale drug design (RDD) has been developing as an innovative, rapid and successful way to discover new drug candidates. Many strategies have been followed and several targets with diverse structures and different biological roles have been investigated. Despite the variety of computational tools available, one can broadly divide them into two major classes that can be adopted either separately or in combination. The first class involves structure-based drug design, when the target's 3-dimensional structure is available or it can be computationally generated using homology modeling. On the other hand, when only a set of active molecules is available, and the structure of the target is unknown, ligand-based drug design tools are usually used. This review describes some recent advances in rational drug design, summarizes a number of their practical applications, and discusses both the advantages and shortcomings of the various techniques used.

Modification of agonist binding moiety in hybrid derivative 5/7-{[2-(4-aryl-piperazin-1-yl)-ethyl]-propyl-amino}-5,6,7,8-tetrahydro-naphthalen-1-ol/-2-amino versions: impact on functional activity and selectivity for dopamine D2/D3 receptors

The goal of the present study was to explore, in our previously developed hybrid template, the effect of introduction of additional heterocyclic rings (mimicking catechol hydroxyl groups as bioisosteric replacement) on selectivity and affinity for the D3 versus D2 receptor. In addition, we wanted to explore the effect of derivatization of functional groups of the agonist binding moiety in compounds developed by us earlier from the hybrid template. Binding affinity (K(i)) of the new compounds was measured with tritiated spiperone as the radioligand and HEK-293 cells expressing either D2 or D3 receptors. Functional activity of selected compounds was assessed in the GTPγS binding assay. In the imidazole series, compound 10a exhibited the highest D3 affinity whereas the indole derivative 13 exhibited similar high D3 affinity. Functionalization of the amino group in agonist (+)-9d with different sulfonamides derivatives improved the D3 affinity significantly with (+)-14f exhibiting the highest affinity. However, functionalization of the hydroxyl and amino groups of 15 and (+)-9d, known agonist and partial agonist, to sulfonate ester and amide in general modulated the affinity. In both cases loss of agonist potency resulted from such derivatization.

A Strategy Combining Differential Low-Throughput Screening and Virtual Screening (DLS-VS) Accelerating the Discovery of new Modulators for the Orphan GPR34 Receptor

The DLS-VS strategy was developed as an integrated method for identifying chemical modulators for orphan GPCRs. It combines differential low-throughput screening (DLS) and virtual screening (VS). The two cascaded techniques offer complementary advantages and allow the experimental testing of a minimal number of compounds. First, DLS identifies modulators specific for the considered receptor among a set of receptors, through the screening of a small library with diverse chemical compounds. Then, an active molecular model of the receptor is built by homology to a validated template, and it is progressively refined by rotamers modification for key side-chains, by VS of the already screened library, and by iterative selection of the model generating the best enrichment. The refined active model is finally used for the VS of a large chemical library and the selection of a small set of compounds for experimental testing. Applied to the orphan receptor GPR34, the DLS-VS strategy combined the experimental screening of 20 000 compounds and the virtual screening of 1 250 000 compounds. It identified one agonist and eight inverse agonists, showing a high chemical diversity. We describe the method. The strategy can be applied to other GPCRs.

Potential human cholesterol esterase inhibitor design: benefits from the molecular dynamics simulations and pharmacophore modeling studies

Human pancreatic cholesterol esterase (hCEase) is one of the lipases found to involve in the digestion of large and broad spectrum of substrates including triglycerides, phospholipids, cholesteryl esters, etc. The presence of bile salts is found to be very important for the activation of hCEase. Molecular dynamic simulations were performed for the apoform and bile salt complexed form of hCEase using the co-ordinates of two bile salts from bovine CEase. The stability of the systems throughout the simulation time was checked and two representative structures from the highly populated regions were selected using cluster analysis. These two representative structures were used in pharmacophore model generation. The generated pharmacophore models were validated and used in database screening. The screened hits were refined for their drug-like properties based on Lipinski's rule of five and ADMET properties. The drug-like compounds were further refined by molecular docking simulation using GOLD program based on the GOLD fitness score, mode of binding, and molecular interactions with the active site amino acids. Finally, three hits of novel scaffolds were selected as potential leads to be used in novel and potent hCEase inhibitor design. The stability of binding modes and molecular interactions of these final hits were re-assured by molecular dynamics simulations.

Functional Rescue of F508del-CFTR Using Small Molecule Correctors

High-throughput screens for small molecules that are effective in “correcting” the functional expression of F508del-CFTR have yielded several promising hits. Two such compounds are currently in clinical trial. Despite this success, it is clear that further advances will be required in order to restore 50% or greater of wild-type CFTR function to the airways of patients harboring the F508del-CFTR protein. Progress will be enhanced by our better understanding of the molecular and cellular defects caused by the F508del mutation, present in 90% of CF patients. The goal of this chapter is to review the current understanding of defects caused by F508del in the CFTR protein and in CFTR-mediated interactions important for its biosynthesis, trafficking, channel function, and stability at the cell surface. Finally, we will discuss the gaps in our knowledge regarding the mechanism of action of existing correctors, the unmet need to discover compounds which restore proper CFTR structure and function in CF affected tissues and new strategies for therapy development.

High-affinity and selective dopamine D₃ receptor full agonists

We have designed, synthesized and evaluated a series of new compounds with the goal to identify potent and selective D(3) ligands. The two most potent and selective new D(3) ligands are compounds 38 and 52, which bind to the D(3) receptors with a K(i) value of <nM and display a selectivity of 450-494 times over the D(2) receptors and >10,000 times over the D(1) receptors. Both 38 and 52 are full agonists with high potency at the D(3) receptor in a D(3) functional assay.

Life beyond kinases: structure-based discovery of sorafenib as nanomolar antagonist of 5-HT receptors

Of great interest in recent years has been computationally predicting the novel polypharmacology of drug molecules. Here, we applied an "induced-fit" protocol to improve the homology models of 5-HT(2A) receptor, and we assessed the quality of these models in retrospective virtual screening. Subsequently, we computationally screened the FDA approved drug molecules against the best induced-fit 5-HT(2A) models and chose six top scoring hits for experimental assays. Surprisingly, one well-known kinase inhibitor, sorafenib, has shown unexpected promiscuous 5-HTRs binding affinities, K(i) = 1959, 56, and 417 nM against 5-HT(2A), 5-HT(2B), and 5-HT(2C), respectively. Our preliminary SAR exploration supports the predicted binding mode and further suggests sorafenib to be a novel lead compound for 5HTR ligand discovery. Although it has been well-known that sorafenib produces anticancer effects through targeting multiple kinases, carefully designed experimental studies are desirable to fully understand whether its "off-target" 5-HTR binding activities contribute to its therapeutic efficacy or otherwise undesirable side effects.

Diversity-oriented synthesis of polycyclic diazinic scaffolds

An efficient and versatile synthesis of a polycyclic diazinic system starting from oxazine has been developed using a two-step Michael/retro Michael and cyclization sequence. The substrates were synthesized with good to high yields giving rapid access to molecular diversity.

Crystal structure-based virtual screening for fragment-like ligands of the human histamine H(1) receptor

The recent crystal structure determinations of druggable class A G protein-coupled receptors (GPCRs) have opened up excellent opportunities in structure-based ligand discovery for this pharmaceutically important protein family. We have developed and validated a customized structure-based virtual fragment screening protocol against the recently determined human histamine H(1) receptor (H(1)R) crystal structure. The method combines molecular docking simulations with a protein-ligand interaction fingerprint (IFP) scoring method. The optimized in silico screening approach was successfully applied to identify a chemically diverse set of novel fragment-like (≤22 heavy atoms) H(1)R ligands with an exceptionally high hit rate of 73%. Of the 26 tested fragments, 19 compounds had affinities ranging from 10 μM to 6 nM. The current study shows the potential of in silico screening against GPCR crystal structures to explore novel, fragment-like GPCR ligand space.

Ligand discovery from a dopamine D3 receptor homology model and crystal structure

G protein-coupled receptors (GPCRs) are intensely studied as drug targets and for their role in signaling. With the determination of the first crystal structures, interest in structure-based ligand discovery increased. Unfortunately, for most GPCRs no experimental structures are available. The determination of the D(3) receptor structure and the challenge to the community to predict it enabled a fully prospective comparison of ligand discovery from a modeled structure versus that of the subsequently released crystal structure. Over 3.3 million molecules were docked against a homology model, and 26 of the highest ranking were tested for binding. Six had affinities ranging from 0.2 to 3.1 μM. Subsequently, the crystal structure was released and the docking screen repeated. Of the 25 compounds selected, five had affinities ranging from 0.3 to 3.0 μM. One of the new ligands from the homology model screen was optimized for affinity to 81 nM. The feasibility of docking screens against modeled GPCRs more generally is considered.

In Silico Veritas: The Pitfalls and Challenges of Predicting GPCR-Ligand Interactions

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.

Studies of new fused benzazepine as selective dopamine D3 receptor antagonists using 3D-QSAR, molecular docking and molecular dynamics

In recent years, great interest has been paid to the development of compounds with high selectivity for central dopamine (DA) D3 receptors, an interesting therapeutic target in the treatment of different neurological disorders. In the present work, based on a dataset of 110 collected benzazepine (BAZ) DA D3 antagonists with diverse kinds of structures, a variety of in silico modeling approaches, including comparative molecular field analysis (CoMFA), comparative similarity indices analysis (CoMSIA), homology modeling, molecular docking and molecular dynamics (MD) were carried out to reveal the requisite 3D structural features for activity. Our results show that both the receptor-based (Q(2) = 0.603, R(2) (ncv) = 0.829, R(2) (pre) = 0.690, SEE = 0.316, SEP = 0.406) and ligand-based 3D-QSAR models (Q(2) = 0.506, R(2) (ncv) =0.838, R(2) (pre) = 0.794, SEE = 0.316, SEP = 0.296) are reliable with proper predictive capacity. In addition, a combined analysis between the CoMFA, CoMSIA contour maps and MD results with a homology DA receptor model shows that: (1) ring-A, position-2 and R(3) substituent in ring-D are crucial in the design of antagonists with higher activity; (2) more bulky R(1) substituents (at position-2 of ring-A) of antagonists may well fit in the binding pocket; (3) hydrophobicity represented by MlogP is important for building satisfactory QSAR models; (4) key amino acids of the binding pocket are CYS101, ILE105, LEU106, VAL151, PHE175, PHE184, PRO254 and ALA251. To our best knowledge, this work is the first report on 3D-QSAR modeling of the new fused BAZs as DA D3 antagonists. These results might provide information for a better understanding of the mechanism of antagonism and thus be helpful in designing new potent DA D3 antagonists.

Computational modeling toward understanding agonist binding on dopamine 3

The dopamine 3 (D3) receptor is a promising therapeutic target for the treatment of nervous system disorders, such as Parkinson's disease, and current research interests primarily focus on the discovery/design of potent D3 agonists. Herein, a well-designed computational protocol, which combines pharmacophore identification, homology modeling, molecular docking, and molecular dynamics (MD) simulations, was employed to understand the agonist binding on D3 aiming to provide insights into the development of novel potent D3 agonists. We (1) identified the chemical features required in effective D3 agonists by pharmacophore modeling based upon 18 known diverse D3 agonists; (2) constructed the three-dimensional (3D) structure of D3 based on homology modeling and the pharmacophore hypothesis; (3) identified the binding modes of the agonists to D3 by the correlation between the predicted binding free energies and the experimental values; and (4) investigated the induced fit of D3 upon agonist binding through MD simulations. The pharmacophore models of the D3 agonists and the 3D structure of D3 can be used for either ligand- or receptor-based drug design. Furthermore, the MD simulations further give the insight that the long and flexible EL2 acts as a "door" for agonist binding, and the "ionic lock" at the bottom of TM3 and TM6 is essential to transduce the activation signal.

Interaction of novel hybrid compounds with the D3 dopamine receptor: Site-directed mutagenesis and homology modeling studies

The dopamine D3 receptor has been implicated as a potential target for drug development in various complex psychiatric disorders including psychosis, drug dependence, and Parkinson's disease. In our overall goal to develop molecules with preferential affinity at D3 receptors, we undertook a hybrid drug development approach by combining a known dopamine agonist moiety with a substituted piperazine fragment. In the present study, three compounds produced this way with preferential D3 agonist activity, were tested at D3 receptors with mutations in the agonist binding pocket of three residues known to be important for agonist binding activity. At S192A and T369V, the hybrid agonist compounds produced an interaction profile in [(3)H]spiperone binding assays similar to that of the parent 5-OH-DPAT and 7-OH-DPAT molecules. The loss of affinity at the S192A mutant was most prominent for 5-OH-DPAT and its corresponding hybrid compound D237. D110N did not show any radioligand binding. Homology modeling indicated that 7-OH-DPAT-derived D315 uniquely shares H-bonding with Tyr365 which produced favorable interaction and no loss of H-bonding in the S192A mutant, suggesting that agonist activity may not be solely controlled by residues in the binding pocket.

GPCR 3D homology models for ligand screening: lessons learned from blind predictions of adenosine A2a receptor complex

Proteins of the G-protein coupled receptor (GPCR) family present numerous attractive targets for rational drug design, but also a formidable challenge for identification and conformational modeling of their 3D structure. A recently performed assessment of blind predictions of adenosine A2a receptor (AA2AR) structure in complex with ZM241385 (ZMA) antagonist provided a first example of unbiased evaluation of the current modeling algorithms on a GPCR target with approximately 30% sequence identity to the closest structural template. Several of the 29 groups participating in this assessment exercise (Michino et al., doi: 10.1038/nrd2877) successfully predicted the overall position of the ligand ZMA in the AA2AR ligand binding pocket, however models from only three groups captured more than 40% the ligand-receptor contacts. Here we describe two of these top performing approaches, in which all-atom models of the AA2AR were generated by homology modeling followed by ligand guided backbone ensemble receptor optimization (LiBERO). The resulting AA2AR-ZMA models, along with the best models from other groups are assessed here for their vitual ligand screening (VLS) performance on a large set of GPCR ligands. We show that ligand guided optimization was critical for improvement of both ligand-receptor contacts and VLS performance as compared to the initial raw homology models. The best blindly predicted models performed on par with the crystal structure of AA2AR in selecting known antagonists from decoys, as well as from antagonists for other adenosine subtypes and AA2AR agonists. These results suggest that despite certain inaccuracies, the optimized homology models can be useful in the drug discovery process.

Using ligand-based virtual screening to allosterically stabilize the activated state of a GPCR

G-protein coupled receptors play an essential role in many biological processes. Despite an increase in the number of solved X-ray crystal structures of G-protein coupled receptors, capturing a G-protein coupled receptor in its activated state for structural analysis has proven to be difficult. An unexplored paradigm is stabilization of one or more conformational states of a G-protein coupled receptor via binding a small molecule to the intracellular loops. A short tetrazole peptidomimetic based on the photoactivated state of rhodopsin-bound structure of Gt(alpha)(340-350) was previously designed and shown to stabilize the photoactivated state of rhodopsin, the G-protein coupled receptor involved in vision. A pharmacophore model derived from the designed tetrazole tetrapeptide was used for ligand-based virtual screening to enhance the possible discovery of novel scaffolds. Maybridge Hitfinder and National Cancer Institute diversity libraries were screened for compounds containing the pharmacophore. Forty-seven compounds resulted from virtually screening the Maybridge library, whereas no hits resulted with the National Cancer Institute library. Three of the 47 Maybridge compounds were found to stabilize the MII state. As these compounds did not inhibit binding of transducin to photoactivated state of rhodopsin, they were assumed to be allosteric ligands. These compounds are potentially useful for crystallographic studies where complexes with these compounds might capture rhodopsin in its activated conformational state.

Analysis of hydrophobic interactions of antagonists with the beta2-adrenergic receptor

The adrenergic receptors mediate a wide variety of physiological responses, including vasodilatation and vasoconstriction, heart rate modulation, and others. Beta-adrenergic antagonists ('beta-blockers') thus constitute a widely used class of drugs in cardiovascular medicine as well as in management of anxiety, migraine, and glaucoma. The importance of the hydrophobic effect has been evidenced for a wide range of beta-blocker properties. To better understand the role of the hydrophobic effect in recognition of beta-blockers by their receptor, we carried out a molecular docking study combined with an original approach to estimate receptor-ligand hydrophobic interactions. The proposed method is based on automatic detection of molecular fragments in ligands and the analysis of their interactions with receptors separately. A series of beta-blockers, based on phenylethanolamines and phenoxypropanolamines, were docked to the beta2-adrenoceptor binding site in the crystal structure. Hydrophobic complementarity between the ligand and the receptor was calculated using the PLATINUM web-server (http://model.nmr.ru/platinum). Based on the analysis of the hydrophobic match for molecular fragments of beta-blockers, we have developed a new scoring function which efficiently predicts dissociation constant (pKd) with strong correlations (r(2) approximately 0.8) with experimental data.

Investigation of various N-heterocyclic substituted piperazine versions of 5/7-{[2-(4-aryl-piperazin-1-yl)-ethyl]-propyl-amino}-5,6,7,8-tetrahydro-naphthalen-2-ol: effect on affinity and selectivity for dopamine D3 receptor

Here we report on the design and synthesis of several heterocyclic analogues belonging to the 5/7-{[2-(4-aryl-piperazin-1-yl)-ethyl]-propyl-amino}-5,6,7,8-tetrahydro-naphthalen-2-ol series of molecules. Compounds were subjected to [(3)H]spiperone binding assays, carried out with HEK-293 cells expressing either D2 or D3 dopamine receptors, in order to evaluate their inhibition constant (K(i)) at these receptors. Results indicate that N-substitution on the piperazine ring can accommodate various substituted indole rings. The results also show that in order to maintain high affinity and selectivity for the D3 receptor the heterocyclic ring does not need to be connected directly to the piperazine ring as the majority of compounds included here are linked either via an amide or a methylene linker to the heterocyclic moiety. The enantiomers of the most potent racemic compound 10e exhibited differential activity with (-)-10e (K(i); D2=47.5 nM, D3=0.57 nM) displaying higher affinity at both D2 and D3 receptors compared to its enantiomer (+)-10e (K(i); D2=113 nM, D3=3.73 nM). Additionally, compound (-)-10e was more potent and selective for the D3 receptor compared to either 7-OH-DPAT or 5-OH-DPAT. Among the bioisosteric derivatives, the indazole derivative 10g and benzo[b]thiophene derivative 10i exhibited the highest affinity for D2 and D3 receptors. In the functional GTPgammaS binding study, one of the lead molecules, (-)-15, exhibited potent agonist activity at both D2 and D3 receptors with preferential affinity at D3.

Identifying conformational changes of the beta(2) adrenoceptor that enable accurate prediction of ligand/receptor interactions and screening for GPCR modulators

The new beta(2) Adrenoceptor (beta(2)AR) crystal structures provide a high-resolution snapshot of receptor interactions with two particular partial inverse agonists, (-)-carazolol and timolol. However, both experimental and computational studies of GPCR structure are significantly complicated by the existence of multiple conformational states coupled to ligand type and receptor activity. Agonists and antagonists induce or stabilize distinct changes in receptor structure that mediate a range of pharmacological activities. In this work, we (1) established that the existing beta(2)AR crystallographic conformers can be extended to describe ligand/receptor interactions for additional antagonist types, (2) generated agonist-bound receptor conformations, and (3) validated these models for agonist and antagonist virtual ligand screening (VLS). Using a ligand directed refinement protocol, we derived a single agonist-bound receptor conformation that selectively retrieved a diverse set of full and partial beta(2)AR agonists in VLS trials. Additionally, the impact of extracellular loop two conformation on VLS was assessed by docking studies with rhodopsin-based beta(2)AR homology models, and loop-deleted receptor models. A general strategy for constructing and selecting agonist-bound receptor pocket conformations is presented, which may prove broadly useful in creating agonist and antagonist bound models for other GPCRs.