Comparison of Ligand-Based and Receptor-Based Virtual Screening of HIV Entry Inhibitors for the CXCR4 and CCR5 Receptors Using 3D Ligand Shape Matching and Ligand−Receptor Docking

Applying Molecular Modeling to the Design of Innovative, Non-Symmetrical CXCR4 Inhibitors with Potent Anticancer Activity

The identification of new compounds with potential activity against CXC chemokine receptor type 4 (CXCR4) has been broadly studied, implying several chemical families, particularly AMD3100 derivatives. Molecular modeling has played a pivotal role in the identification of new active compounds. But, has its golden age ended? A virtual library of 450,000 tetraamines of general structure 8 was constructed by using five spacers and 300 diamines, which were obtained from the corresponding commercially available cyclic amines. Diversity selection was performed to guide the virtual screening of the former database and to select the most representative set of compounds. Molecular docking on the CXCR4 crystal structure allowed us to rank the selection and identify those candidate molecules with potential antitumor activity against diffuse large B-cell lymphoma (DLBCL). Among them, compound A{17,18} stood out for being a non-symmetrical structure, synthetically feasible, and with promising activity against DLBCL in in vitro experiments. The focused study of symmetrical-related compounds allowed us to identify potential pre-hits (IC50~20 µM), evidencing that molecular design is still relevant in the development of new CXCR4 inhibitor candidates.

Carbazole and tetrahydro-carboline derivatives as dopamine D3 receptor antagonists with the multiple antipsychotic-like properties

Dopamine D3 receptor (D3R) is implicated in multiple psychotic symptoms. Increasing the D3R selectivity over dopamine D2 receptor (D2R) would facilitate the antipsychotic treatments. Herein, novel carbazole and tetrahydro-carboline derivatives were reported as D3R selective ligands. Through a structure-based virtual screen, ZLG-25 (D3R Ki = 685 nmol/L; D2R Ki > 10,000 nmol/L) was identified as a novel D3R selective bitopic ligand with a carbazole scaffold. Scaffolds hopping led to the discovery of novel D3R-selective analogs with tetrahydro-β-carboline or tetrahydro-γ-carboline core. Further functional studies showed that most derivatives acted as hD3R-selective antagonists. Several lead compounds could dose-dependently inhibit the MK-801-induced hyperactivity. Additional investigation revealed that 23j and 36b could decrease the apomorphine-induced climbing without cataleptic reaction. Furthermore, 36b demonstrated unusual antidepressant-like activity in the forced swimming tests and the tail suspension tests, and alleviated the MK-801-induced disruption of novel object recognition in mice. Additionally, preliminary studies confirmed the favorable PK/PD profiles, no weight gain and limited serum prolactin levels in mice. These results revealed that 36b provided potential opportunities to new antipsychotic drugs with the multiple antipsychotic-like properties.

An Agonist of the CXCR4 Receptor Strongly Promotes Regeneration of Degenerated Motor Axon Terminals

The activation of the G-protein coupled receptor CXCR4 by its ligand CXCL12α is involved in a large variety of physiological and pathological processes, including the growth of B cells precursors and of motor axons, autoimmune diseases, stem cell migration, inflammation, and several neurodegenerative conditions. Recently, we demonstrated that CXCL12α potently stimulates the functional recovery of damaged neuromuscular junctions via interaction with CXCR4. This result prompted us to test the neuroregeneration activity of small molecules acting as CXCR4 agonists, endowed with better pharmacokinetics with respect to the natural ligand. We focused on NUCC-390, recently shown to activate CXCR4 in a cellular system. We designed a novel and convenient chemical synthesis of NUCC-390, which is reported here. NUCC-390 was tested for its capability to induce the regeneration of motor axon terminals completely degenerated by the presynaptic neurotoxin α-Latrotoxin. NUCC-390 was found to strongly promote the functional recovery of the neuromuscular junction, as assayed by electrophysiology and imaging. This action is CXCR4 dependent, as it is completely prevented by AMD3100, a well-characterized CXCR4 antagonist. These data make NUCC-390 a strong candidate to be tested in human therapy to promote nerve recovery of function after different forms of neurodegeneration.

Pharmacological modulation of CXCR4 cooperates with BET bromodomain inhibition in diffuse large B-cell lymphoma

Constitutive activation of the chemokine receptor CXCR4 has been associated with tumor progression, invasion, and chemotherapy resistance in different cancer subtypes. Although the CXCR4 pathway has recently been suggested as an adverse prognostic marker in diffuse large B-cell lymphoma, its biological relevance in this disease remains underexplored. In a homogeneous set of 52 biopsies from patients, an antibody-based cytokine array showed that tissue levels of CXCL12 correlated with high microvessel density and bone marrow involvement at diagnosis, supporting a role for the CXCL12-CXCR4 axis in disease progression. We then identified the tetra-amine IQS-01.01RS as a potent inverse agonist of the receptor, preventing CXCL12-mediated chemotaxis and triggering apoptosis in a panel of 18 cell lines and primary cultures, with superior mobilizing properties in vivo than those of the standard agent. IQS-01.01RS activity was associated with downregulation of p-AKT, p-ERK1/2 and destabilization of MYC, allowing a synergistic interaction with the bromodomain and extra-terminal domain inhibitor, CPI203. In a xenotransplant model of diffuse large B-cell lymphoma, the combination of IQS-01.01RS and CPI203 decreased tumor burden through MYC and p-AKT downregulation, and enhanced the induction of apoptosis. Thus, our results point out an emerging role of CXCL12-CXCR4 in the pathogenesis of diffuse large B-cell lymphoma and support the simultaneous targeting of CXCR4 and bromodomain proteins as a promising, rationale-based strategy for the treatment of this disease.

Maximal Unbiased Benchmarking Data Sets for Human Chemokine Receptors and Comparative Analysis

Chemokine receptors (CRs) have long been druggable targets for the treatment of inflammatory diseases and HIV-1 infection. As a powerful technique, virtual screening (VS) has been widely applied to identifying small molecule leads for modern drug targets including CRs. For rational selection of a wide variety of VS approaches, ligand enrichment assessment based on a benchmarking data set has become an indispensable practice. However, the lack of versatile benchmarking sets for the whole CRs family that are able to unbiasedly evaluate every single approach including both structure- and ligand-based VS somewhat hinders modern drug discovery efforts. To address this issue, we constructed Maximal Unbiased Benchmarking Data sets for human Chemokine Receptors (MUBD-hCRs) using our recently developed tools of MUBD-DecoyMaker. The MUBD-hCRs encompasses 13 subtypes out of 20 chemokine receptors, composed of 404 ligands and 15756 decoys so far and is readily expandable in the future. It had been thoroughly validated that MUBD-hCRs ligands are chemically diverse while its decoys are maximal unbiased in terms of "artificial enrichment", "analogue bias". In addition, we studied the performance of MUBD-hCRs, in particular CXCR4 and CCR5 data sets, in ligand enrichment assessments of both structure- and ligand-based VS approaches in comparison with other benchmarking data sets available in the public domain and demonstrated that MUBD-hCRs is very capable of designating the optimal VS approach. MUBD-hCRs is a unique and maximal unbiased benchmarking set that covers major CRs subtypes so far.

Automated Inference of Chemical Discriminants of Biological Activity

Ligand-based virtual screening has become a standard technique for the efficient discovery of bioactive small molecules. Following assays to determine the activity of compounds selected by virtual screening, or other approaches in which dozens to thousands of molecules have been tested, machine learning techniques make it straightforward to discover the patterns of chemical groups that correlate with the desired biological activity. Defining the chemical features that generate activity can be used to guide the selection of molecules for subsequent rounds of screening and assaying, as well as help design new, more active molecules for organic synthesis.The quantitative structure-activity relationship machine learning protocols we describe here, using decision trees, random forests, and sequential feature selection, take as input the chemical structure of a single, known active small molecule (e.g., an inhibitor, agonist, or substrate) for comparison with the structure of each tested molecule. Knowledge of the atomic structure of the protein target and its interactions with the active compound are not required. These protocols can be modified and applied to any data set that consists of a series of measured structural, chemical, or other features for each tested molecule, along with the experimentally measured value of the response variable you would like to predict or optimize for your project, for instance, inhibitory activity in a biological assay or ΔG_binding. To illustrate the use of different machine learning algorithms, we step through the analysis of a dataset of inhibitor candidates from virtual screening that were tested recently for their ability to inhibit GPCR-mediated signaling in a vertebrate.

Structural Analysis of Chemokine Receptor-Ligand Interactions

This review focuses on the construction and application of structural chemokine receptor models for the elucidation of molecular determinants of chemokine receptor modulation and the structure-based discovery and design of chemokine receptor ligands. A comparative analysis of ligand binding pockets in chemokine receptors is presented, including a detailed description of the CXCR4, CCR2, CCR5, CCR9, and US28 X-ray structures, and their implication for modeling molecular interactions of chemokine receptors with small-molecule ligands, peptide ligands, and large antibodies and chemokines. These studies demonstrate how the integration of new structural information on chemokine receptors with extensive structure–activity relationship and site-directed mutagenesis data facilitates the prediction of the structure of chemokine receptor–ligand complexes that have not been crystallized. Finally, a review of structure-based ligand discovery and design studies based on chemokine receptor crystal structures and homology models illustrates the possibilities and challenges to find novel ligands for chemokine receptors.

Discovery and characterization of novel small-molecule CXCR4 receptor agonists and antagonists

The chemokine CXCL12 (SDF-1) and its cognate receptor CXCR4 are involved in a large number of physiological processes including HIV-1 infectivity, inflammation, tumorigenesis, stem cell migration, and autoimmune diseases. While previous efforts have identified a number of CXCR4 antagonists, there have been no small molecule agonists reported. Herein, we describe the identification of a novel series of CXCR4 modulators, including the first small molecules to display agonist behavior against this receptor, using a combination of structure- and ligand-based virtual screening. These agonists produce robust calcium mobilization in human melanoma cell lines which can be blocked by the CXCR4-selective antagonist AMD3100. We also demonstrate the ability of these new agonists to induce receptor internalization, ERK activation, and chemotaxis, all hallmarks of CXCR4 activation. Our results describe a new series of biologically relevant small molecules that will enable further study of the CXCR4 receptor and may contribute to the development of new therapeutics.

Nitrogen positional scanning in tetramines active against HIV-1 as potential CXCR4 inhibitors

The paradigm, derived from bicyclams, by which it is necessary to use the p-phenylene moiety as the central core in order to achieve high HIV-1 antiviral activities has been reexamined for structures 4.

A systematic approach to prioritize drug targets using machine learning, a molecular descriptor-based classification model, and high-throughput screening of plant derived molecules: a case study in oral cancer

Systems-biology inspired identification of drug targets and machine learning-based screening of small molecules which modulate their activity have the potential to revolutionize modern drug discovery by complementing conventional methods. To utilize the effectiveness of such pipelines, we first analyzed the dysregulated gene pairs between control and tumor samples and then implemented an ensemble-based feature selection approach to prioritize targets in oral squamous cell carcinoma (OSCC) for therapeutic exploration. Based on the structural information of known inhibitors of CXCR4-one of the best targets identified in this study-a feature selection was implemented for the identification of optimal structural features (molecular descriptor) based on which a classification model was generated. Furthermore, the CXCR4-centered descriptor-based classification model was finally utilized to screen a repository of plant derived small-molecules to obtain potential inhibitors. The application of our methodology may assist effective selection of the best targets which may have previously been overlooked, that in turn will lead to the development of new oral cancer medications. The small molecules identified in this study can be ideal candidates for trials as potential novel anti-oral cancer agents. Importantly, distinct steps of this whole study may provide reference for the analysis of other complex human diseases.

Anti-HIV small-molecule binding in the peptide subpocket of the CXCR4:CVX15 crystal structure

The CXC chemokine receptor 4 (CXCR4) is involved in chemotaxis and serves as a coreceptor for T-tropic HIV-1 viral entry, thus making this receptor an attractive drug target. Recently, crystal structures of CXCR4 were reported as complexes with the small molecule IT1t and the CVX15 peptide. Follow-up efforts to model different antagonists into the small molecule CXCR4:IT1t crystal structure did not generate poses consistent with either the X-ray crystal structure or site-directed mutagenesis (SDM). Here, we compare the binding pockets of the two CXCR4 crystal structures, revealing differences in helices IV, V, VI, and VII, with major differences for the His203 residue buried in the binding pocket. The small molecule antagonist AMD11070 was docked into both CXCR4 crystal structures. An AMD11070 pose identified from the CXCR4:CVX15 model presented interactions with Asp171, Glu288, Trp94, and Asp97, consistent with published SDM data, thus suggesting it is the bioactive pose. A CXCR4 receptor model was optimized around this pose of AMD11070, and the resulting model correlated HIV-1 inhibition with MM-GBSA docking scores for a congeneric AMD11070-like series. Subsequent NAMFIS NMR results successfully linked the proposed binding pose to an independent experimental structure. These results strongly suggest that not all small molecules will bind to CXCR4 in a similar manner as IT1t. Instead, the CXCR4:CVX15 crystal structure may provide a binding locus for small organic molecules that is more suitable than the secondary IT1t site. This work is expected to provide modeling insights useful for future CXCR4 antagonist and X4-tropic HIV-1 based drug design efforts.

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.:

Anti-HIV drug development through computational methods

Although highly active antiretroviral therapy (HAART) is effective in controlling the progression of AIDS, the emergence of drug-resistant strains increases the difficulty of successful treatment of patients with HIV infection. Increasing numbers of patients are facing the dilemma that comes with the running out of drug combinations for HAART. Computational methods play a key role in anti-HIV drug development. A substantial number of studies have been performed in anti-HIV drug development using various computational methods, such as virtual screening, QSAR, molecular docking, and homology modeling, etc. In this review, we summarize recent advances in the application of computational methods to anti-HIV drug development for five key targets as follows: reverse transcriptase, protease, integrase, CCR5, and CXCR4. We hope that this review will stimulate researchers from multiple disciplines to consider computational methods in the anti-HIV drug development process.

Integrated computational tools for identification of CCR5 antagonists as potential HIV-1 entry inhibitors: homology modeling, virtual screening, molecular dynamics simulations and 3D QSAR analysis

Using integrated in-silico computational techniques, including homology modeling, structure-based and pharmacophore-based virtual screening, molecular dynamic simulations, per-residue energy decomposition analysis and atom-based 3D-QSAR analysis, we proposed ten novel compounds as potential CCR5-dependent HIV-1 entry inhibitors. Via validated docking calculations, binding free energies revealed that novel leads demonstrated better binding affinities with CCR5 compared to maraviroc, an FDA-approved HIV-1 entry inhibitor and in clinical use. Per-residue interaction energy decomposition analysis on the averaged MD structure showed that hydrophobic active residues Trp86, Tyr89 and Tyr108 contributed the most to inhibitor binding. The validated 3D-QSAR model showed a high cross-validated r_cv² value of 0.84 using three principal components and non-cross-validated r² value of 0.941. It was also revealed that almost all compounds in the test set and training set yielded a good predicted value. Information gained from this study could shed light on the activity of a new series of lead compounds as potential HIV entry inhibitors and serve as a powerful tool in the drug design and development machinery.

SimG: an alignment based method for evaluating the similarity of small molecules and binding sites

In this study, a Gaussian volume overlap and chemical feature based molecular similarity metric was devised, and a downhill simplex searching was carried out to evaluate the corresponding similarity. By representing the shapes of both the candidate small molecules and the binding site with chemical features and comparing the corresponding Gaussian volumes overlaps, the active compounds could be identified. These two aspects compose the proposed method named SimG which supports both structure-based and ligand-based strategies. The validity of the proposed method was examined by analyzing the similarity score variation between actives and decoys as well as correlation among distinct reference methods. A retrospective virtual screening test was carried out on DUD data sets, demonstrating that the performance of structure-based shape matching virtual screening in DUD data sets is substantially dependent on some physical properties, especially the solvent-exposure extent of the binding site: The enrichments of targets with less solvent-exposed binding sites generally exceeds that of the one with more solvent-exposed binding sites and even surpasses the corresponding ligand-based virtual screening.

Biasing conformational ensembles towards bioactive-like conformers for ligand-based drug design

IMPORTANCE OF THE FIELD

In silico or virtual screening has become a common practice in contemporary computer-aided drug discovery efforts and currently constitutes a reasonably mature paradigm. Application of ligand-based approaches to virtual screening requires the ability to identify the bioactive conformers of drug-like compounds as these conformers are expected to elicit the biological activity. However, given the complexity of the energy potential surfaces of such ligands and in particular those exhibiting some degree of flexibility and the limitation of contemporary energy functions, this is not an easy task.

AREAS COVERED IN THIS REVIEW

The current contribution provides an in-depth review of recent developments in the field of generating conformational ensembles of drug-like compounds with a particular emphasis of focusing such ensembles on bioactive conformers using both energy and structural criteria. The literature reviewed in this manuscript roughly covers the last decade.

WHAT THE READER WILL GAIN

Readers of this review will gain an appreciation for the complexity of identifying bioactive conformers of drug-like compounds and an exposure to the different computational methods which were developed in order to tackle this problem as well as to the remaining challenges in this field.

TAKE HOME MESSAGE

The identification of ensembles of bioactive conformers of drug-like compounds is far from being a solved problem. Recent research has advanced the field to the point where bioactive conformers could be readily identified from within conformational ensembles generated by contemporary computational tools. However, as such conformers are inevitably accompanied by many other non-relevant conformations, a focusing mechanism is required. New methods in this field are showing promise but more work is clearly needed. New research lines are proposed which are believed to enhance the performances and with it the usefulness of 3D ligand-based methods in drug discovery and development.

A ligand-based approach for the in silico discovery of multi-target inhibitors for proteins associated with HIV infection

Acquired immunodeficiency syndrome (AIDS) is a dangerous disease, which damages the immune system cells to the point that the immune system can no longer fight against other infections that it would usually be able to prevent. The causal agent is the human immunodeficiency virus (HIV), and for this reason, the search for more effective chemotherapies against HIV is a challenge for the scientific community. Chemoinformatics and Quantitative Structure-Activity Relationship (QSAR) studies have played an essential role in the design of potent inhibitors for proteins associated with the HIV infection. However, all previous studies took into consideration the discovery of future drug candidates using homogeneous series of compounds against only one protein. This fact limits the use of more efficient anti-HIV chemotherapies. In this work, we develop the first ligand-based approach for the in silico design of multi-target (mt) inhibitors for seven key proteins associated with the HIV infection. Two mt-QSAR models were constructed from a large and heterogeneous database of compounds. The first model was based on linear discriminant analysis (mt-QSAR-LDA) employing fragment-based descriptors. The second model was obtained using artificial neural networks (mt-QSAR-ANN) with global 2D descriptors. Both models correctly classified more than 90% of active and inactive compounds in training and prediction sets. Some fragments were extracted and their contributions to anti-HIV activity through inhibition of the different proteins were calculated using the mt-QSAR-LDA model. New molecules designed from fragments with positive contributions were suggested and correctly predicted by the two models as possible potent and versatile anti-HIV agents.

Identifying and characterizing promiscuous targets: implications for virtual screening

INTRODUCTION

Ligand-based shape matching approaches have become established as important and popular virtual screening (VS) techniques. However, despite their relative success, the question of how to best choose the initial query compounds and their conformations remains largely unsolved. This issue gains importance when dealing with promiscuous targets, that is, proteins that bind multiple ligand scaffold families in one or more binding site. Conventional shape matching VS approaches assume that there is only one binding mode for a given protein target. This may be true for some targets, but it is certainly not true in all cases. Several recent studies have shown that some protein targets bind to different ligands in different ways.

AREAS COVERED

The authors discuss the concept of promiscuity in the context of virtual drug screening, and present and analyze several examples of promiscuous targets. The article also reports on the impact of the query conformation on the performance of shape-based VS and the potential to improve VS performance by using consensus shape clustering techniques.

EXPERT OPINION

The notion of polypharmacology is becoming highly relevant in drug discovery. Understanding and exploiting promiscuity present challenges and opportunities for drug discovery endeavors. The examples of promiscuity presented here suggest that promiscuous targets and ligands are much more common than previously assumed, and this should be taken into account in practical VS protocols. Although some progress has been made, there is a need to develop more sophisticated computational techniques and protocols that can identify and characterize promiscuous targets on a genomic scale.

Classification of anti-HIV compounds using counterpropagation artificial neural networks and decision trees

The main aim of the present work was to collect and categorize anti-HIV molecules in order to identify general structure-activity relationships. In this respect, a total of 5580 drugs and drug-like molecules was collected from 256 different articles published between 1992 and 2010. An algorithm called genetic algorithm-pattern search counterpropagation artificial neural networks (GPS-CPANN) was proposed for the classification of compounds. In addition, the CART (classification and regression trees) method was used for construction of decision trees and finding the best molecular descriptors. The results revealed that the developed CPANN models and decision tree can correctly classify the molecules according to their inhibition mechanisms and activities. Some general parameters such as molecular weight, average molecular weight, number of hydrogen atoms and number of hydroxyl groups were found to be important for describing the inhibition behaviour of anti-HIV agents. The developed classifier models in this work can be used to screen large libraries of compounds to identify those likely to display activity as anti-HIV agents.

A novel, customizable and optimizable parameter method using spherical harmonics for molecular shape similarity comparisons

A novel molecular shape similarity comparison method, namely SHeMS, derived from spherical harmonic (SH) expansion, is presented in this study. Through weight optimization using genetic algorithms for a customized reference set, the optimal combination of weights for the translationally and rotationally invariant (TRI) SH shape descriptor, which can specifically and effectively distinguish overall and detailed shape features according to the molecular surface, is obtained for each molecule. This method features two key aspects: firstly, the SH expansion coefficients from different bands are weighted to calculate similarity, leading to a distinct contribution of overall and detailed features to the final score, and thus can be better tailored for each specific system under consideration. Secondly, the reference set for optimization can be totally configured by the user, which produces great flexibility, allowing system-specific and customized comparisons. The directory of useful decoys (DUD) database was adopted to validate and test our method, and principal component analysis (PCA) reveals that SH descriptors for shape comparison preserve sufficient information to separate actives from decoys. The results of virtual screening indicate that the proposed method based on optimal SH descriptor weight combinations represents a great improvement in performance over original SH (OSH) and ultra-fast shape recognition (USR) methods, and is comparable to many other popular methods. Through combining efficient shape similarity comparison with SH expansion method, and other aspects such as chemical and pharmacophore features, SHeMS can play a significant role in this field and can be applied practically to virtual screening by means of similarity comparison with 3D shapes of known active compounds or the binding pockets of target proteins.

Application of QSAR and shape pharmacophore modeling approaches for targeted chemical library design

Optimization of chemical library composition affords more efficient identification of hits from biological screening experiments. The optimization could be achieved through rational selection of reagents used in combinatorial library synthesis. However, with a rapid advent of parallel synthesis methods and availability of millions of compounds synthesized by many vendors, it may be more efficient to design targeted libraries by means of virtual screening of commercial compound collections. This chapter reviews the application of advanced cheminformatics approaches such as quantitative structure-activity relationships (QSAR) and pharmacophore modeling (both ligand and structure based) for virtual screening. Both approaches rely on empirical SAR data to build models; thus, the emphasis is placed on achieving models of the highest rigor and external predictive power. We present several examples of successful applications of both approaches for virtual screening to illustrate their utility. We suggest that the expert use of both QSAR and pharmacophore models, either independently or in combination, enables users to achieve targeted libraries enriched with experimentally confirmed hit compounds.

Novel monocyclam derivatives as HIV entry inhibitors: Design, synthesis, anti-HIV evaluation, and their interaction with the CXCR4 co-receptor

The CXCR4 receptor has been shown to interact with the human immunodeficiency virus (HIV) envelope glycoprotein gp120, leading to fusion of viral and cell membranes. Therefore, ligands that can attach to this receptor represent an important class of therapeutic agents against HIV, thus inhibiting the first step in the cycle of viral infection: the virus-cell entry/fusion. Herein we describe the in silico design, synthesis, and biological evaluation of novel monocyclam derivatives as HIV entry inhibitors. In vitro activity testing of these compounds in cell cultures against HIV strains revealed EC(50) values in the low micromolar range without cytotoxicity at the concentrations tested. Docking and molecular dynamics simulations were performed to predict the binding interactions between CXCR4 and the novel monocyclam derivatives. A binding mode of these compounds is proposed which is consistent with the main existing site-directed mutagenesis data on the CXCR4 co-receptor. Moreover, molecular modeling comparisons were performed between these novel monocyclams, previously reported non-cyclam compounds from which the monocyclams are derived, and the well-known AMD3100 bicyclam CXCR4 inhibitors. Our results suggest that these three structurally diverse CXCR4 inhibitors bind to overlapping but not identical amino acid residues in the transmembrane regions of the receptor.

Ligand-guided optimization of CXCR4 homology models for virtual screening using a multiple chemotype approach

CXCR4 is a G-protein coupled receptor for CXCL12 that plays an important role in human immunodeficiency virus infection, cancer growth and metastasization, immune cell trafficking and WHIM syndrome. In the absence of an X-ray crystal structure, theoretical modeling of the CXCR4 receptor remains an important tool for structure-function analysis and to guide the discovery of new antagonists with potential clinical use. In this study, the combination of experimental data and molecular modeling approaches allowed the development of optimized ligand-receptor models useful for elucidation of the molecular determinants of small molecule binding and functional antagonism. The ligand-guided homology modeling approach used in this study explicitly re-shaped the CXCR4 binding pocket in order to improve discrimination between known CXCR4 antagonists and random decoys. Refinement based on multiple test-sets with small compounds from single chemotypes provided the best early enrichment performance. These results provide an important tool for structure-based drug design and virtual ligand screening of new CXCR4 antagonists.

Comparative evaluation of 3D virtual ligand screening methods: impact of the molecular alignment on enrichment

In the early stage of drug discovery programs, when the structure of a complex involving a target and a small molecule is available, structure-based virtual ligand screening methods are generally preferred. However, ligand-based strategies like shape-similarity search methods can also be applied. Shape-similarity search methods consist in exploring a pseudo-binding-site derived from the known small molecule used as a reference. Several of these methods use conformational sampling algorithms which are also shared by corresponding docking methods: for example Surflex-dock/Surflex-sim, FlexX/FlexS, ICM, and OMEGA-FRED/OMEGA-ROCS. Using 11 systems issued from the challenging "own" subsets of the Directory of Useful Decoys (DUD-own), we evaluated and compared the performance of the above-cited programs in terms of molecular alignment accuracy, enrichment in active compounds, and enrichment in different chemotypes (scaffold-hopping). Since molecular alignment is a crucial aspect of performance for the different methods, we have assessed its impact on enrichment. We have also illustrated the paradox of retrieving active compounds with good scores even if they are inaccurately positioned. Finally, we have highlighted possible positive aspects of using shape-based approaches in drug-discovery protocols when the structure of the target in complex with a small molecule is known.

HPPD: ligand- and target-based virtual screening on a herbicide target

Hydroxyphenylpyruvate dioxygenase (HPPD) has proven to be a very successful target for the development of herbicides with bleaching properties, and today HPPD inhibitors are well established in the agrochemical market. Syngenta has a long history of HPPD-inhibitor research, and HPPD was chosen as a case study for the validation of diverse ligand- and target-based virtual screening approaches to identify compounds with inhibitory properties. Two-dimensional extended connectivity fingerprints, three-dimensional shape-based tools (ROCS, EON, and Phase-shape) and a pharmacophore approach (Phase) were used as ligand-based methods; Glide and Gold were used as target-based. Both the virtual screening utility and the scaffold-hopping ability of the screening tools were assessed. Particular emphasis was put on the specific pitfalls to take into account for the design of a virtual screening campaign in an agrochemical context, as compared to a pharmaceutical environment.

Advanced in silico Approaches in Antiviral Research

Computer-aided drug design has seen constantly increasing application over the past two decades in every area of drug discovery. It can offer significant advantages over conventional approaches, being far less expensive and faster than conventional methods, or offering the possibility to predict molecular behaviours that cannot be elucidated in any other way. Recent developments in software and hardware make it possible to simulate increasingly complex molecular environments, widening the applicability of in silico studies from the interactions of small molecules with key protein residues, to the simulation of the dynamic evolution of complex biological systems with atomic resolution. Antiviral research offers several open challenges, from a biological, biochemical and pharmaceutical point of view. Computational approaches are already providing some answers and will undoubtedly give more in the near future. Here, we present a brief overview of the cutting-edge computational methods that play a major role in present and future antiviral research.

Comparison of structure- and ligand-based virtual screening protocols considering hit list complementarity and enrichment factors

Structure- and ligand-based virtual-screening methods (docking, 2D- and 3D-similarity searching) were analysed for their effectiveness in virtual screening against four different targets: angiotensin-converting enzyme (ACE), cyclooxygenase 2 (COX-2), thrombin and human immunodeficiency virus 1 (HIV-1) protease. The relative performance of the tools was compared by examining their ability to recognise known active compounds from a set of actives and nonactives. Furthermore, we investigated whether the application of different virtual-screening methods in parallel provides complementary or redundant hit lists. Docking was performed with GOLD, Glide, FlexX and Surflex. The obtained docking poses were rescored by using nine different scoring functions in addition to the scoring functions implemented as objective functions in the docking algorithms. Ligand-based virtual screening was done with ROCS (3D-similarity searching), Feature Trees and Scitegic Functional Fingerprints (2D-similarity searching). The results show that structure- and ligand-based virtual-screening methods provide comparable enrichments in detecting active compounds. Interestingly, the hit lists that are obtained from different virtual-screening methods are generally highly complementary. These results suggest that a parallel application of different structure- and ligand-based virtual-screening methods increases the chance of identifying more (and more diverse) active compounds from a virtual-screening campaign.

LigMatch: a multiple structure-based ligand matching method for 3D virtual screening

We have developed a new virtual screening (VS) method called LigMatch and evaluated its performance on 13 protein targets using a filtered and clustered version of the directory of useful decoys (DUD). The method uses 3D structural comparison to a crystallographically determined ligand in a bioactive 'template' conformation, using a geometric hashing method, in order to prioritize each database compound. We show that LigMatch outperforms several other widely used VS methods on the 13 DUD targets. We go on to demonstrate that improved VS performance can be gained from using multiple, structurally diverse templates rather than a single template ligand for a particular protein target. In this case, a 2D fingerprint-based method is used to select a ligand template from a set of known bioactive conformations. Furthermore, we show that LigMatch performs well even in the absence of 2D similarity to the template ligands, thereby demonstrating its robustness with respect to purely 2D methods and its potential for scaffold hopping.

Ligand shape emerges in solvent dipole ordering region at ligand binding site of protein

Solvent dipole ordering (SDO), introduced by Higo et al. (Proteins Struct Funct Genet 2000, 40, 193), is an entity that captures an aspect of hydration structure. We have studied SDO in the ligand binding site of two proteins (FK506 binding protein and dihydrofolate reductase) and found that the high SDO regions overlap significantly with the 3D structures of known inhibitors bound to the proteins. Thus, the SDO region might be used to predict the preferred molecular shape of ligands that bind to a protein. Based on this finding, we propose a novel docking procedure using model molecules that mimic the shape of the SDO region. To prove the validity of thisapproach, we performed a redocking experiment for p38 mitogen-activated protein kinase ligands using model molecules for search queries; we succeeded in identifying the binding conformations and binding modes of known inhibitors.

Discovery of novel HIV entry inhibitors for the CXCR4 receptor by prospective virtual screening

The process of HIV entry begins with the binding of the viral envelope glycoprotein gp120 to both the CD4 receptor and one of CXCR4 or CCR5 chemokine coreceptors. There is currently considerable interest in developing novel ligands which can attach to these coreceptors and hence block virus-cell fusion. This article compares the application of structure-based (docking) and ligand-based (QSAR analyses, pharmacophore modeling, and shape matching) virtual screening tools to find new potential HIV entry inhibitors for the CXCR4 receptor. The comparison is based on retrospective virtual screening of a library containing different known CXCR4 inhibitors from the literature, a smaller set of active CXCR4 inhibitors selected from a large combinatorial virtual library and synthesized by us, and some druglike presumed inactive molecules as the reference set. The enrichment factors and diversity of the retrieved molecular scaffolds in the virtual hit lists was determined. Once the different virtual screening approaches had been validated and the best parameters had been selected, prospective virtual screening of our virtual library was applied to identify new anti-HIV compounds using the same protocol as in the retrospective virtual screening analysis. The compounds selected using these computational tools were subsequently synthesized and assayed and showed activity values ranging from 4 to 0.022 microg/mL.

Chemokine receptors and other G protein-coupled receptors

PURPOSE OF REVIEW

Class A G protein-coupled receptors (GPCRs), including the chemokine receptors, CCR5 and CXCR4, share a seven transmembrane-spanning alpha-helix architecture that accommodates signal propagation from across biological membranes. CXCR4 and CCR5 are utilized as co-receptors during HIV viral entry and, therefore, crystal structures of GPCRs aid in the understanding of their function in viral entry.

RECENT FINDINGS

Recent progress in structure determination of class A GPCRs, which include vertebrate and invertebrate rhodopsin as well as adrenergic and adenosine receptors, provides molecular templates for how this diverse group of transmembrane receptors functions. Each of these GPCRs differs in how specific ligands bind to the transmembrane core, underscoring that additional structures of GPCRs from other subfamilies are needed to facilitate rational drug design. More recent studies also indicate a need to consider the more complex character of GPCRs, such as their oligomerization and dynamics.

SUMMARY

Recently, the atomic structures of invertebrate rhodopsin, beta1-adrenergic and beta2-adrenergic receptors and the A(2A)-adenosine receptor have been solved via X-ray crystallography. The impact that these structures have on the biochemistry of viral entry and signal transduction is discussed. Because the chemokine receptors have proven refractory to structural studies thus far, further structural study of the chemokine receptors will be essential to understanding ligand binding, activation and function as co-receptors during viral entry.

Discovery of novel non-cyclam polynitrogenated CXCR4 coreceptor inhibitors

HIV cell fusion and entry have been validated as targets for therapeutic intervention against infection. Bicyclams were the first low-molecular-weight compounds to show specific interaction with CXCR4. The most potent bicyclam was AMD3100, in which the two cyclam moieties are tethered by a 1,4-phenylenebis(methylene) bridge. It was withdrawn from clinical trials owing to its lack of oral bioavailability and cardiotoxicity. We have designed a combinatorial library of non-cyclam polynitrogenated compounds by preserving the main features of AMD3100. At least two nitrogen atoms on each side of the p-phenylene moiety, one in the benzylic position and the other(s) in the heterocyclic system were maintained, and the distances between them were similar to the nitrogen atom distances in cyclam. A selection of diverse compounds from this library were prepared, and their in vitro activity was tested in cell cultures against HIV strains. This led to the identification of novel potent CXCR4 coreceptor inhibitors without cytotoxicity at the tested concentrations.