On the applicability of GPCR homology models to computer-aided drug discovery: a comparison between in silico and crystal structures of the beta2-adrenergic receptor

The Flexible, Extended Coil of the PDZ-Binding Motif of the Three Deadly Human Coronavirus E Proteins Plays a Role in Pathogenicity

The less virulent human (h) coronaviruses (CoVs) 229E, NL63, OC43, and HKU1 cause mild, self-limiting respiratory tract infections, while the more virulent SARS-CoV-1, MERS-CoV, and SARS-CoV-2 have caused severe outbreaks. The CoV envelope (E) protein, an important contributor to the pathogenesis of severe hCoV infections, may provide insight into this disparate severity of the disease. We, therefore, generated full-length E protein models for SARS-CoV-1 and -2, MERS-CoV, HCoV-229E, and HCoV-NL63 and docked C-terminal peptides of each model to the PDZ domain of the human PALS1 protein. The PDZ-binding motif (PBM) of the SARS-CoV-1 and -2 and MERS-CoV models adopted a more flexible, extended coil, while the HCoV-229E and HCoV-NL63 models adopted a less flexible alpha helix. All the E peptides docked to PALS1 occupied the same binding site and the more virulent hCoV E peptides generally interacted more stably with PALS1 than the less virulent ones. We hypothesize that the increased flexibility of the PBM in the more virulent hCoVs facilitates more stable binding to various host proteins, thereby contributing to more severe disease. This is the first paper to model full-length 3D structures for both the more virulent and less virulent hCoV E proteins, providing novel insights for possible drug and/or vaccine development.

Efficiency of Homology Modeling Assisted Molecular Docking in G-protein Coupled Receptors

BACKGROUND

Molecular docking is in regular practice to assess ligand affinity on a target protein crystal structure. In the absence of protein crystal structure, the homology modeling or comparative modeling is the best alternative to elucidate the relationship details between a ligand and protein at the molecular level. The development of accurate homology modeling (HM) and its integration with molecular docking (MD) is essential for successful, rational drug discovery.

OBJECTIVE

The G-protein coupled receptors (GPCRs) are attractive therapeutic targets due to their immense role in human pharmacology. The GPCRs are membrane-bound proteins with the complex constitution, and the understanding of their activation and inactivation mechanisms is quite challenging. Over the past decade, there has been a rapid expansion in the number of solved G-protein-coupled receptor (GPCR) crystal structures; however, the majority of the GPCR structures remain unsolved. In this context, HM guided MD has been widely used for structure-based drug design (SBDD) of GPCRs.

METHODS

The focus of this review is on the recent (i) developments on HM supported GPCR drug discovery in the absence of GPCR crystal structures and (ii) application of HM in understanding the ligand interactions at the binding site, virtual screening, determining receptor subtype selectivity and receptor behaviour in comparison with GPCR crystal structures.

RESULTS

The HM in GPCRs has been extremely challenging due to the scarcity in template structures. In such a scenario, it is difficult to get accurate HM that can facilitate understanding of the ligand-receptor interactions. This problem has been alleviated to some extent by developing refined HM based on incorporating active /inactive ligand information and inducing protein flexibility. In some cases, HM proteins were found to outscore crystal structures.

CONCLUSION

The developments in HM have been highly operative to gain insights about the ligand interaction at the binding site and receptor functioning at the molecular level. Thus, HM guided molecular docking may be useful for rational drug discovery for the GPCRs mediated diseases.

Influence of the Structural Accuracy of Homology Models on Their Applicability to Docking-Based Virtual Screening: The β2 Adrenergic Receptor as a Case Study

How accurate do structures of the β₂ adrenergic receptor (β₂AR) need to be to effectively serve as platforms for docking-based virtual screening campaigns? To answer this research question, here, we targeted through controlled virtual screening experiments 23 homology models of the β₂AR endowed with different levels of structural accuracy. Subsequently, we studied the correlation between virtual screening performance and structural accuracy of the targeted models. Moreover, we studied the correlation between virtual screening performance and template/target receptor sequence identity. Our study demonstrates that docking-based virtual screening campaigns targeting homology models of the β₂AR, in the majority of the cases, yielded results that exceeded random expectations in terms of area under the receiver operating characteristic curve (ROC AUC). Moreover, with the most effective scoring method, over one-third and one-quarter of the models yielded results that exceeded random expectation also in terms of enrichment factors (EF1, EF5, and EF10) and BEDROC (α = 160.9), respectively. Not surprisingly, we found a detectable linear correlation between virtual screening performance and structural accuracy of the ligand-binding cavity. We also found a detectable linear correlation between virtual screening performance and structural accuracy of the second extracellular loop (EL2). Finally, our data indicate that, although there is no detectable linear correlation between virtual screening performance and template/β₂AR sequence identity, models built on the basis of templates that show high sequence identity with the β₂AR, especially within the ligand-biding cavity, performed consistently well. Conversely, models with lower sequence identity displayed performance levels that ranged from very good to random, with no apparent correlation with the sequence identity itself.

Assessing GPCR homology models constructed from templates of various transmembrane sequence identities: Binding mode prediction and docking enrichment

GPCR crystal structures have become more readily accessible in recent years. However, homology models of GPCRs continue to play an important role as many GPCR structures remain unsolved. The new crystal structures now available provide not only additional templates for homology modelling but also the opportunity to assess the performance of homology models against their respective crystal structures and gain insight into the performance of such models. In this study we have constructed homology models from templates of various transmembrane sequence identities for eight GPCR targets to better understand the relationship between transmembrane sequence identity and model quality. Model quality was assessed relative to the crystal structure in terms of structural accuracy as well as performance in two typical structure-based drug design applications: ligand binding pose prediction and docking enrichment in virtual screening. Crystal structures significantly outperformed homology models in both assessments. Accurate ligand binding pose prediction was possible but difficult to achieve using homology models, even with the use of induced fit docking. In virtual screening using homology models still conferred significant enrichment compared to random selection, with a clear benefit also observed in using models optimized through induced fit docking. Our results indicate that while homology models that are reasonably accurate structurally can be constructed, without significant refinement homology models will be outperformed by crystal structures in ligand binding pose prediction and docking enrichment regardless of the template used, primarily due to the extremely high level of structural accuracy needed for such applications.

Compound Ranking Based on Fuzzy Three-Dimensional Similarity Improves the Performance of Docking into Homology Models of G-Protein-Coupled Receptors

Ligand docking into homology models of G-protein-coupled receptors (GPCRs) is a widely used approach in computational compound screening. The generation of "double-hypothetical" models of ligand-target complexes has intrinsic accuracy limitations that further complicate compound ranking and selection compared to those of X-ray structures. Given these uncertainties, we have explored "fuzzy 3D similarity" between hypothetical binding modes of known ligands in homology models and docking poses of database compounds as an alternative to conventional scoring schemes. Therefore, GPCR homology models at varying accuracy levels were generated and used for docking. Increases in recall performance were observed for fuzzy 3D similarity ranking using single or multiple ligand poses compared to that of conventional scoring functions and interaction fingerprints. Fuzzy similarity ranking was also successfully applied to docking into an external model of a GPCR for which no experimental structure is currently available. Taken together, our results indicate that the use of putative ligand poses, albeit approximate at best, increases the odds of identifying active compounds in docking screens of GPCR homology models.

G-Protein coupled receptors: answers from simulations

Molecular-dynamics (MD) simulations are playing an increasingly important role in research into the modes of action of G-protein coupled receptors (GPCRs). In this field, MD simulations are unusually important as, because of the difficult experimental situation, they often offer the only opportunity to determine structural and mechanistic features in atomistic detail. Modern combinations of soft- and hardware have made MD simulations a powerful tool in GPCR research. This is important because GPCRs are targeted by approximately half of the drugs on the market, so that computer-aided drug design plays a major role in GPCR research.

Ligand binding pocket of a novel Allatostatin receptor type C of stick insect, Carausius morosus

Allatostatins (AST) are neuropeptides with variable function ranging from regulation of developmental processes to the feeding behavior in insects. They exert their effects by binding to cognate GPCRs, called Allatostatin receptors (AlstR), which emerge as promising targets for pesticide design. However, AlstRs are rarely studied. This study is the first reported structural study on AlstR-AST interaction. In this work, the first C type AlstR from the stick insect Carausius morosus (CamAlstR-C) was identified and its interaction with type C AST peptide was shown to be physically consistent with the experimental results. The proposed structure of CamAlstR-C revealed a conserved motif within the third extracellular loop, which, together with the N-terminus is essential for ligand binding. In this work, computational studies were combined with molecular and nano-scale approaches in order to introduce an unknown GPCR-ligand system. Consequently, the data obtained provided a reliable target region for future agonist/inverse agonist studies on AlstRs.

Homology modeling of a Class A GPCR in the inactive conformation: A quantitative analysis of the correlation between model/template sequence identity and model accuracy

With the present work we quantitatively studied the modellability of the inactive state of Class A G protein-coupled receptors (GPCRs). Specifically, we constructed models of one of the Class A GPCRs for which structures solved in the inactive state are available, namely the β₂ AR, using as templates each of the other class members for which structures solved in the inactive state are also available. Our results showed a detectable linear correlation between model accuracy and model/template sequence identity. This suggests that the likely accuracy of the homology models that can be built for a given receptor can be generally forecasted on the basis of the available templates. We also probed whether sequence alignments that allow for the presence of gaps within the transmembrane domains to account for structural irregularities afford better models than the classical alignment procedures that do not allow for the presence of gaps within such domains. As our results indicated, although the overall differences are very subtle, the inclusion of internal gaps within the transmembrane domains has a noticeable a beneficial effect on the local structural accuracy of the domain in question.

Structural modeling of G-protein coupled receptors: An overview on automatic web-servers

Despite the significant efforts and discoveries during the last few years in G protein-coupled receptor (GPCR) expression and crystallization, the receptors with known structures to date are limited only to a small fraction of human GPCRs. The lack of experimental three-dimensional structures of the receptors represents a strong limitation that hampers a deep understanding of their function. Computational techniques are thus a valid alternative strategy to model three-dimensional structures. Indeed, recent advances in the field, together with extraordinary developments in crystallography, in particular due to its ability to capture GPCRs in different activation states, have led to encouraging results in the generation of accurate models. This, prompted the community of modelers to render their methods publicly available through dedicated databases and web-servers. Here, we present an extensive overview on these services, focusing on their advantages, drawbacks and their role in successful applications. Future challenges in the field of GPCR modeling, such as the predictions of long loop regions and the modeling of receptor activation states are presented as well.

Human coronavirus OC43 3CL protease and the potential of ML188 as a broad-spectrum lead compound: homology modelling and molecular dynamic studies

Background

The coronavirus 3 chymotrypsin-like protease (3CL^pro) is a validated target in the design of potential anticoronavirus inhibitors. The high degree of homology within the protease’s active site and substrate conservation supports the identification of broad spectrum lead compounds. A previous study identified the compound ML188, also termed 16R, as an inhibitor of the Severe Acute Respiratory Syndrome coronavirus (SARS-CoV) 3CL^pro. This study will detail the generation of a homology model of the 3CL^pro of the human coronavirus OC43 and determine the potential of 16R to form a broad-spectrum lead compound. MODELLER was used to generate a suitable three-dimensional model of the OC43 3CL^pro and the Prime module of Schrӧdinger predicted the binding conformation and free energy of binding of 16R within the 3CL^pro active site. Molecular dynamics further confirmed ligand stability and hydrogen bonding networks.

Results

A high quality homology model of the OC43 3CL^pro was successfully generated in an active conformation. Further studies reproduced the binding pose of 16R within the active site of the generated model, where its free energy of binding was shown to equal that of the 3CL^pro of SARS-CoV, a receptor it is experimentally proven to inhibit. The stability of the ligand was subsequently confirmed by molecular dynamics.

Conclusion

The lead compound 16R may represent a broad-spectrum inhibitor of the 3CL^pro of OC43 and potentially other coronaviruses. This study provides an atomistic structure of the 3CL^pro of OC43 and supports further experimental validation of the inhibitory effects of 16R. These findings further confirm that the 3CL^pro of coronaviruses can be inhibited by broad spectrum lead compounds.

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.

Computational studies to predict or explain G protein coupled receptor polypharmacology

Since G protein-coupled receptors (GPCRs) belong to a very large superfamily of evolutionarily related receptors (>800 members in humans), and due to the rapid progress on their structural biology, they are ideal candidates for polypharmacology studies. Broad screening and bioinformatics/chemoinformatics have been applied to understanding off-target effects of GPCR ligands. It is now feasible to approach the question of GPCR polypharmacology using molecular modeling and the available X-ray GPCR structures. As an example, large and sterically constrained adenosine derivatives (potent adenosine receptor ligands with low conformational freedom and multiple extended substituents) were screened for binding at diverse receptors. Unanticipated off-target interactions, including at biogenic amine receptors, were then modeled using a structure-based approach to provide a consistent understanding of recognition. A conserved Asp in TM3 changed its role from counterion for biogenic amines to characteristic H-bonding to adenosine. The same systematic approach could potentially be applied to many GPCRs or other receptors using other sets of congeneric ligands.

Structure and putative signaling mechanism of Protease activated receptor 2 (PAR2) - a promising target for breast cancer

Experimental evidences have observed enhanced expression of protease activated receptor 2 (PAR2) in breast cancer consistently. However, it is not yet recognized as an important therapeutic target for breast cancer as the primary molecular mechanisms of its activation are not yet well-defined. Nevertheless, recent reports on the mechanism of GPCR activation and signaling have given new insights to GPCR functioning. In the light of these details, we attempted to understand PAR2 structure & function using molecular modeling techniques. In this work, we generated averaged representative stable models of PAR2, using protease activated receptor 1 (PAR1) as a template and selected conformation based on their binding affinity with PAR2 specific agonist, GB110. Further, the selected model was used for studying the binding affinity of putative ligands. The selected ligands were based on a recent publication on phylogenetic analysis of Class A rhodopsin family of GPCRs. This study reports putative ligands, their interacting residues, binding affinity and molecular dynamics simulation studies on PAR2-ligand complexes. The results reported from this study would be useful for researchers and academicians to investigate PAR2 function as its physiological role is still hypothetical. Further, this information may provide a novel therapeutic scheme to manage breast cancer.

Monte Carlo loop refinement and virtual screening of the thyroid-stimulating hormone receptor transmembrane domain

Metropolis Monte Carlo (MMC) loop refinement has been performed on the three extracellular loops (ECLs) of rhodopsin and opsin-based homology models of the thyroid-stimulating hormone receptor transmembrane domain, a class A type G protein-coupled receptor. The Monte Carlo sampling technique, employing torsion angles of amino acid side chains and local moves for the six consecutive backbone torsion angles, has previously reproduced the conformation of several loops with known crystal structures with accuracy consistently less than 2 Å. A grid-based potential map, which includes van der Waals, electrostatics, hydrophobic as well as hydrogen-bond potentials for bulk protein environment and the solvation effect, has been used to significantly reduce the computational cost of energy evaluation. A modified sigmoidal distance-dependent dielectric function has been implemented in conjunction with the desolvation and hydrogen-bonding terms. A long high-temperature simulation with 2 kcal/mol repulsion potential resulted in extensive sampling of the conformational space. The slow annealing leading to the low-energy structures predicted secondary structure by the MMC technique. Molecular docking with the reported agonist reproduced the binding site within 1.5 Å. Virtual screening performed on the three lowest structures showed that the ligand-binding mode in the inter-helical region is dependent on the ECL conformations.

The GPCR crystallography boom: providing an invaluable source of structural information and expanding the scope of homology modeling

G protein-coupled receptors (GPCRs) are integral membrane proteins of high pharmaceutical interest. Until relatively recently, their structures have been particularly elusive, and rhodopsin has been for many years the only member of the superfamily with experimentally elucidated structures. However, a number of recent technical and scientific advancements made the determination of GPCR structures more feasible, thus leading to the solution of the structures of several receptors. Besides providing direct structural information, these experimental GPCR structures also provide templates for the construction of GPCR models. In depth studies have been performed to probe the accuracy of these models, in particular with respect to the interactions with their ligands, and to assess their applicability the rational discovery of GPCR modulators. Given the current state of the art and the pace of the field, the future of GPCR structural studies is likely to be characterized by a landscape populated by an increasingly higher number of experimental and theoretical structures.

G protein-coupled receptors: computer-aided ligand discovery and computational structural analyses in the 2010s

G protein-coupled receptors, or GPCRs, are a large superfamily of proteins found on the plasma membrane of cells. They are involved in most physiological and pathophysiological functions and constitute the target of the majority of marketed drugs. Although these receptors have been historically elusive to attempts of structural determination, GPCR crystallography is now in full blossom, opening the way to structure-based drug discovery and enabling homology modeling. This thematic issue of the journal In Silico Pharmacology, which illustrates how the expanding body of structural knowledge is fostering complex computational analyses of the structure-function relationships of the receptors and their interactions with their ligands, stems from the 31st Camerino-Cyprus-Noordwijkerhout Symposium held in Italy, in May 2013, at the University of Camerino. Specifically, it originates from a session of the symposium entitled “Structure-Based Discovery of Ligands of G Protein-Coupled Receptors: Finally a Reality”, and features a mix of research articles and reviews on the application of computational modeling to the analysis of the structure of GPCRs and the interactions of the receptors with their ligands.

Current progress in Structure-Based Rational Drug Design marks a new mindset in drug discovery

The past decade has witnessed a paradigm shift in preclinical drug discovery with structure-based drug design (SBDD) making a comeback while high-throughput screening (HTS) methods have continued to generate disappointing results. There is a deficit of information between identified hits and the many criteria that must be fulfilled in parallel to convert them into preclinical candidates that have a real chance to become a drug. This gap can be bridged by investigating the interactions between the ligands and their receptors. Accurate calculations of the free energy of binding are still elusive; however progresses were made with respect to how one may deal with the versatile role of water. A corpus of knowledge combining X-ray structures, bioinformatics and molecular modeling techniques now allows drug designers to routinely produce receptor homology models of increasing quality. These models serve as a basis to establish and validate efficient rationales used to tailor and/or screen virtual libraries with enhanced chances of obtaining hits. Many case reports of successful SBDD show how synergy can be gained from the combined use of several techniques. The role of SBDD with respect to two different classes of widely investigated pharmaceutical targets: (a) protein kinases (PK) and (b) G-protein coupled receptors (GPCR) is discussed. Throughout these examples prototypical situations covering the current possibilities and limitations of SBDD are presented.

Homology model-assisted elucidation of binding sites in GPCRs

G protein-coupled receptors (GPCRs) are important mediators of cell signaling and a major family of drug targets. Despite recent breakthroughs, experimental elucidation of GPCR structures remains a formidable challenge. Homology modeling of 3D structures of GPCRs provides a practical tool for elucidating the structural determinants governing the interactions of these important receptors with their ligands. The working model of the binding site can then be used for virtual screening of additional ligands that may fit this site, for determining and comparing specificity profiles of related receptors, and for structure-based design of agonists and antagonists. The current review presents the protocol and enumerates the steps for modeling and validating the residues involved in ligand binding. The main stages include (a) modeling the receptor structure using an automated fragment-based approach, (b) predicting potential binding pockets, (c) docking known binders, (d) analyzing predicted interactions and comparing with positions that have been shown to bind ligands in other receptors, (e) validating the structural model by mutagenesis.

Probing GPCR structure: adenosine and P2Y nucleotide receptors

The adenosine receptors (ARs) provide an example of how to accurately predict ligand recognition, even prior to the availability of a crystallographic structure. Homology modeling has been used to gain structural insight, in conjunction with site-directed mutagenesis, and structure-activity relationships of small molecular ligands. Recent X-ray structures greatly improved the accuracy of knowledge of AR ligand recognition and furthermore characterized conformational changes induced by receptor activation. Now, homology modeling extends these structural insights to related GPCRs and suggests new ligand structures. This strategy is also being applied to the eight subtypes of P2Y receptors for extracellular nucleotides, which lack X-ray structures and are best modeled by homology to the CXCR4 (peptide) receptor. Neoceptors, as studied for three of the four AR subtypes, create a molecular complementarity between a mutant receptor and a chemically tailored agonist ligand to selectively enhance affinity, implying direct physical contact and thus validating docking hypotheses.

Application of Monte Carlo-based receptor ensemble docking to virtual screening for GPCR ligands

Receptor ensemble docking (RED) is an effective strategy to account for receptor flexibility in the course of a docking-based virtual screening campaign. Such an approach can be applied when multiple crystal structures of a receptor have been solved, but it can also be applied when only a single crystal structure is available. In this case, alternative structures can be generated from the latter by computational means and subsequently applied to RED. Here, we illustrate how such conformers can be generated by subjecting a crystal structure to Monte Carlo conformational searches. Through a controlled virtual screening experiment, we then show the applicability of such a strategy to the identification of ligands of the β(2) adrenergic receptor, a G protein-coupled receptor activated by epinephrine. Requiring the availability of one crystal structure only, this strategy is applicable to all systems for which multiple experimentally elucidated structures are not available.

Homology modeling a fast tool for drug discovery: current perspectives

Major goal of structural biology involve formation of protein-ligand complexes; in which the protein molecules act energetically in the course of binding. Therefore, perceptive of protein-ligand interaction will be very important for structure based drug design. Lack of knowledge of 3D structures has hindered efforts to understand the binding specificities of ligands with protein. With increasing in modeling software and the growing number of known protein structures, homology modeling is rapidly becoming the method of choice for obtaining 3D coordinates of proteins. Homology modeling is a representation of the similarity of environmental residues at topologically corresponding positions in the reference proteins. In the absence of experimental data, model building on the basis of a known 3D structure of a homologous protein is at present the only reliable method to obtain the structural information. Knowledge of the 3D structures of proteins provides invaluable insights into the molecular basis of their functions. The recent advances in homology modeling, particularly in detecting and aligning sequences with template structures, distant homologues, modeling of loops and side chains as well as detecting errors in a model contributed to consistent prediction of protein structure, which was not possible even several years ago. This review focused on the features and a role of homology modeling in predicting protein structure and described current developments in this field with victorious applications at the different stages of the drug design and discovery.

Modeling of mammalian olfactory receptors and docking of odorants

Olfactory receptors (ORs) belong to the superfamily of G protein-coupled receptors (GPCRs), the second largest class of genes after those related to immunity, and account for about 3 % of mammalian genomes. ORs are present in all multicellular organisms and represent more than half the GPCRs in mammalian species (e.g., the mouse OR repertoire contains >1,000 functional genes). ORs are mainly expressed in the olfactory epithelium where they detect odorant molecules, but they are also expressed in a number of other cells, such as sperm cells, although their functions in these cells remain mostly unknown. It has recently been reported that ORs are present in tumoral tissues where they are expressed at different levels than in healthy tissues. A specific OR is over-expressed in prostate cancer cells, and activation of this OR has been shown to inhibit the proliferation of these cells. Odorant stimulation of some of these receptors results in inhibition of cell proliferation. Even though their biological role has not yet been elucidated, these receptors might constitute new targets for diagnosis and therapeutics. It is important to understand the activation mechanism of these receptors at the molecular level, in particular to be able to predict which ligands are likely to activate a particular receptor ('deorphanization') or to design antagonists for a given receptor. In this review, we describe the in silico methodologies used to model the three-dimensional (3D) structure of ORs (in the more general framework of GPCR modeling) and to dock ligands into these 3D structures.

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.

New insights for drug design from the X-ray crystallographic structures of G-protein-coupled receptors

Methodological advances in X-ray crystallography have made possible the recent solution of X-ray structures of pharmaceutically important G protein-coupled receptors (GPCRs), including receptors for biogenic amines, peptides, a nucleoside, and a sphingolipid. These high-resolution structures have greatly increased our understanding of ligand recognition and receptor activation. Conformational changes associated with activation common to several receptors entail outward movements of the intracellular side of transmembrane helix 6 (TM6) and movements of TM5 toward TM6. Movements associated with specific agonists or receptors have also been described [e.g., extracellular loop (EL) 3 in the A(2A) adenosine receptor]. The binding sites of different receptors partly overlap but differ significantly in ligand orientation, depth, and breadth of contact areas in TM regions and the involvement of the ELs. A current challenge is how to use this structural information for the rational design of novel potent and selective ligands. For example, new chemotypes were discovered as antagonists of various GPCRs by subjecting chemical libraries to in silico docking in the X-ray structures. The vast majority of GPCR structures and their ligand complexes are still unsolved, and no structures are known outside of family A GPCRs. Molecular modeling, informed by supporting information from site-directed mutagenesis and structure-activity relationships, has been validated as a useful tool to extend structural insights to related GPCRs and to analyze docking of other ligands in already crystallized GPCRs.

Biogenic amines and the control of neuromuscular signaling in schistosomes

Biogenic amines are small cationic monoamines that function broadly as neurotransmitters and/or neuromodulators in every animal phylum. They include such ubiquitous substances as serotonin, dopamine and invertebrate-specific phenolamines (tyramine, octopamine), among others. Biogenic amines are important neuroactive agents in all the flatworms, including blood flukes of the genus Schistosoma, the etiological agents of human schistosomiasis. A large body of evidence spanning nearly five decades identifies biogenic amines as major modulators of neuromuscular function in schistosomes, controlling movement, attachment to the host and other fundamental behaviors. Recent advances in schistosome genomics have made it possible to dissect the molecular mechanisms responsible for these effects and to identify the proteins involved. These efforts have already provided important new information about the mode of action of amine transmitters in the parasite. Moreover, these advances are continuing, as the field moves into a post-genomics era, and new molecular tools for gene and protein analysis are becoming available. Here, we review the current status of this research and discuss future prospects. In particular, we focus our attention on the receptors that mediate biogenic amine activity, their structural characteristics, functional properties and "druggability" potential. One of the themes that will emerge from this discussion is that schistosomes have a rich diversity of aminergic receptors, many of which share little sequence homology with those of the human host, making them ideally suited for selective drug targeting. Strategies for the characterization of these important parasite proteins will be discussed.

Computational modeling-based discovery of novel classes of anti-inflammatory drugs that target lanthionine synthetase C-like protein 2

Background

Lanthionine synthetase component C-like protein 2 (LANCL2) is a member of the eukaryotic lanthionine synthetase component C-Like protein family involved in signal transduction and insulin sensitization. Recently, LANCL2 is a target for the binding and signaling of abscisic acid (ABA), a plant hormone with anti-diabetic and anti-inflammatory effects.

Methodology/Principal Findings

The goal of this study was to determine the role of LANCL2 as a potential therapeutic target for developing novel drugs and nutraceuticals against inflammatory diseases. Previously, we performed homology modeling to construct a three-dimensional structure of LANCL2 using the crystal structure of lanthionine synthetase component C-like protein 1 (LANCL1) as a template. Using this model, structure-based virtual screening was performed using compounds from NCI (National Cancer Institute) Diversity Set II, ChemBridge, ZINC natural products, and FDA-approved drugs databases. Several potential ligands were identified using molecular docking. In order to validate the anti-inflammatory efficacy of the top ranked compound (NSC61610) in the NCI Diversity Set II, a series of in vitro and pre-clinical efficacy studies were performed using a mouse model of dextran sodium sulfate (DSS)-induced colitis. Our findings showed that the lead compound, NSC61610, activated peroxisome proliferator-activated receptor gamma in a LANCL2- and adenylate cyclase/cAMP dependent manner in vitro and ameliorated experimental colitis by down-modulating colonic inflammatory gene expression and favoring regulatory T cell responses.

Conclusions/Significance

LANCL2 is a novel therapeutic target for inflammatory diseases. High-throughput, structure-based virtual screening is an effective computational-based drug design method for discovering anti-inflammatory LANCL2-based drug candidates.

Do crystal structures obviate the need for theoretical models of GPCRs for structure-based virtual screening?

Recent highly expected structural characterizations of agonist-bound and antagonist-bound beta-2 adrenoreceptor (β2AR) by X-ray crystallography have been widely regarded as critical advances to enable more effective structure-based discovery of GPCRs ligands. It appears that this very important development may have undermined many previous efforts to develop 3D theoretical models of GPCRs. To address this question directly, we have compared several historical β2AR models versus the inactive state and nanobody-stabilized active state of β2AR crystal structures in terms of their structural similarity and effectiveness of use in virtual screening for β2AR specific agonists and antagonists. Theoretical models, incluing both homology and de novo types, were collected from five different groups who have published extensively in the field of GPCRs modeling. All models were built before X-ray structures became available. In general, β2AR theoretical models differ significantly from the crystal structure in terms of TMH definition and the global packing. Nevertheless, surprisingly, several models afforded hit rates resulting from virtual screening of large chemical library enriched by known β2AR ligands that exceeded those using X-ray structures. The hit rates were particularly higher for agonists. Furthemore, the screening performance of models is associated with local structural quality, such as the RMSDs for binding pocket residues and the ability to capture accurately, most if not all critical protein/ligand interactions. These results suggest that carefully built models of GPCRs could capture critical chemical and structural features of the binding pocket, and thus may be even more useful for practical structure-based drug discovery than X-ray structures.

Structure and activation of rhodopsin

Rhodopsin is the first G-protein-coupled receptor (GPCR) with its three-dimensional structure solved by X-ray crystallography. The crystal structure of rhodopsin has revealed the molecular mechanism of photoreception and signal transduction in the visual system. Although several other GPCR crystal structures have been reported over the past few years, the rhodopsin structure remains an important model for understanding the structural and functional characteristics of other GPCRs. This review summarizes the structural features, the photoactivation, and the G protein signal transduction of rhodopsin.

Predicting the biological activities through QSAR analysis and docking-based scoring

Numerous computational methodologies have been developed to facilitate the process of drug discovery. Broadly, they can be classified into ligand-based approaches, which are solely based on the calculation of the molecular properties of compounds, and structure-based approaches, which are based on the study of the interactions between compounds and their target proteins. This chapter deals with two major categories of ligand-based and structure-based methods for the prediction of biological activities of chemical compounds, namely quantitative structure-activity relationship (QSAR) analysis and docking-based scoring. QSAR methods are endowed with robustness and good ranking ability when applied to the prediction of the activity of closely related analogs; however, their great dependence on training sets significantly limits their applicability to the evaluation of diverse compounds. Instead, docking-based scoring, although not very effective in ranking active compounds on the basis of their affinities or potencies, offer the great advantage of not depending on training sets and have proven to be suitable tools for the distinction of active from inactive compounds, thus providing feasible platforms for virtual screening campaigns. Here, we describe the basic principles underlying the prediction of biological activities on the basis of QSAR and docking-based scoring, as well as a method to combine two or more individual predictions into a consensus model. Finally, we describe an example that illustrates the applicability of QSAR and molecular docking to G protein-coupled receptor (GPCR) projects.

Homology modeling of class a G protein-coupled receptors

G protein-coupled receptors (GPCRs) are a large superfamily of membrane bound signaling proteins that hold great pharmaceutical interest. Since experimentally elucidated structures are available only for a very limited number of receptors, homology modeling has become a widespread technique for the construction of GPCR models intended to study the structure-function relationships of the receptors and aid the discovery and development of ligands capable of modulating their activity. Through this chapter, various aspects involved in the constructions of homology models of the serpentine domain of the largest class of GPCRs, known as class A or rhodopsin family, are illustrated. In particular, the chapter provides suggestions, guidelines, and critical thoughts on some of the most crucial aspect of GPCR modeling, including: collection of candidate templates and a structure-based alignment of their sequences; identification and alignment of the transmembrane helices of the query receptor to the corresponding domains of the candidate templates; selection of one or more templates receptor; election of homology or de novo modeling for the construction of specific extracellular and intracellular domains; construction of the 3D models, with special consideration to extracellular regions, disulfide bridges, and interhelical cavity; validation of the models through controlled virtual screening experiments.

In silico screening for agonists and blockers of the β(2) adrenergic receptor: implications of inactive and activated state structures

Ten crystal structures of the β(2) adrenergic receptor have been published, reflecting different signaling states. Here, through controlled-docking experiments, we examined the implications of using inactive or activated structures on the in silico screening for agonists and blockers of the receptor. Specifically, we targeted the crystal structures solved in complex with carazolol (2RH1), the neutral antagonist alprenalol, the irreversible agonist FAUC50 (3PDS), and the full agonist BI-167017 (3P0G). Our results indicate that activated structures favor agonists over blockers, whereas inactive structures favor blockers over agonists. This tendency is more marked for activated than for inactive structures. Additionally, agonists tend to receive more favorable docking scores when docked at activated rather than inactive structures, while blockers do the opposite. Hence, the difference between the docking scores attained with an activated and an inactive structure is an excellent means for the classification of ligands into agonists and blockers as we determined through receiver operating characteristic curves and linear discriminant analysis. With respect to virtual screening, all structures prioritized well agonists and blockers over nonbinders. However, inactive structures worked better for blockers and activated structures worked better for agonists, respectively. Notably, the combination of individual docking experiments through receptor ensemble docking resulted in an excellent performance in the retrieval of both agonists and blockers. Finally, we demonstrated that the induced-fit docking of agonists is a viable way of modifying an inactive crystal structure and bias it toward the in silico recognition of agonists rather than blockers.

Structural aspects of M₃ muscarinic acetylcholine receptor dimer formation and activation

To explore the structural mechanisms underlying the assembly and activation of family A GPCR dimers, we used the rat M(3) muscarinic acetylcholine receptor (M3R) as a model system. Studies with Cys-substituted mutant M3Rs expressed in COS-7 cells led to the identification of several mutant M3Rs that exclusively existed as cross-linked dimers under oxidizing conditions. The cross-linked residues were located at the bottom of transmembrane domain 5 (TM5) and within the N-terminal portion of the third intracellular loop (i3 loop). Studies with urea-stripped membranes demonstrated that M3R disulfide cross-linking did not require the presence of heterotrimeric G proteins. Molecular modeling studies indicated that the cross-linking data were in excellent agreement with the existence of a low-energy M3R dimer characterized by a TM5-TM5 interface. [(35)S]GTPγS binding/Gα(q/11) immunoprecipitation assays revealed that an M3R dimer that was cross-linked within the N-terminal portion of the i3 loop (264C) was functionally severely impaired (∼50% reduction in receptor-G-protein coupling, as compared to control M3R). These data support the novel concept that agonist-induced activation of M3R dimers requires a conformational change of the N-terminal segment of the i3 loop. Given the high degree of structural homology among family A GPCRs, these findings should be of broad significance.

Software and resources for computational medicinal chemistry

Computer-aided drug design plays a vital role in drug discovery and development and has become an indispensable tool in the pharmaceutical industry. Computational medicinal chemists can take advantage of all kinds of software and resources in the computer-aided drug design field for the purposes of discovering and optimizing biologically active compounds. This article reviews software and other resources related to computer-aided drug design approaches, putting particular emphasis on structure-based drug design, ligand-based drug design, chemical databases and chemoinformatics tools.

Homology model and docking studies on porcine β₂ adrenoceptor: description of two binding sites

The affinity of the classical β(2) adrenoceptor-selective inverse agonist ICI118,551 is notoriously lower for porcine β(2) adrenoceptors (p(2)βAR) than for human β(2) adrenoceptors (hβ(2)AR) but molecular mechanisms for this difference are still unclear. Homology 3-D models of pβ(2)AR can be useful in predicting similarities and differences, which might in turn increase the comparative understanding of ligand interactions with the hβ(2)AR. In this work, the pβ(2)AR amino acid sequence was used to carry out homology modeling. The selected pβ(2)AR 3-D structure was structurally and energetically optimized and used as a model for further theoretical study. The homology model of pβ(2)AR has a 3-D structure very similar to the crystal structures of recently studied hβ(2)AR. This was also corroborated by sequence identity, RMSD, Ramachandran map, TM-score and docking results. Upon performing molecular docking simulations with the AutoDock4.0.1 program on pβ(2)AR, it was found that a set of well-known β(2)AR ligands reach two distinct binding sites on pβ(2)AR. Whereas one of these sites is similar to that reported on the hβ(2)AR crystal structure, the other can explain some important experimental observations. Additionally, the theoretical affinity estimated for ICI118,551 closely agrees with affinities estimated from experimental in vitro data. The experimental differences between the human/porcine β(2)ARs in relation to ligand affinity can in part be elucidated by observations in this molecular modeling study.