Clustering and Classifying Diverse HIV Entry Inhibitors Using a Novel Consensus Shape-Based Virtual Screening Approach: Further Evidence for Multiple Binding Sites within the CCR5 Extracellular Pocket

Structure-based identification of novel scaffolds as potential HIV-1 entry inhibitors involving CCR5

C-C chemokine receptor 5 (CCR5), which is part of the chemokine receptor family, is a member of the G protein-coupled receptor superfamily. The interactions of CCR5 with HIV-1 during viral entry position it as an effective therapeutic target for designing potent antiviral therapies. The small-molecule Maraviroc was approved by the FDA as a CCR5 drug in 2007, while clinical trials failure has characterised many of the other CCR5 inhibitors. Thus, the continual identification of potential CCR5 inhibitors is, therefore, warranted. In this study, a structure-based discovery approach has been utilised to screen and retrieved novel potential CCR5 inhibitors from the Asinex antiviral compound (∼ 8,722) database. Explicit lipid-bilayer molecular dynamics simulation, in silico physicochemical and pharmacokinetic analyses, were further performed for the top compounds. A total of 23 structurally diverse compounds with binding scores higher than Maraviroc were selected. Subsequent molecular dynamics (MD) simulations analysis of the top four compounds LAS 51495192, BDB 26405401, BDB 26419079, and LAS 34154543, maintained stability at the CCR5 binding site. Furthermore, these compounds made pertinent interactions with CCR5 residues critical for the HIV-1 gp120-V3 loop binding such as Trp86, Tyr89, Phe109, Tyr108, Glu283 and Tyr251. Additionally, the predicted in silico physicochemical and pharmacokinetic descriptors of the selected compounds were within the acceptable range for drug-likeness. The results suggest positive indications that the identified molecules may represent promising CCR5 entry inhibitors. Further structural optimisations and biochemical testing of the proposed compounds may assist in the discovery of effective HIV-1 therapy.Communicated by Ramaswamy H. Sarma.

HIV-1 Entry Inhibitors: A Review of Experimental and Computational Studies

The HIV-1 life cycle consists of different events, such as cell entry and fusion, virus replication, assembly and release of the newly formed virions. The more logical way to inhibit HIV transmission among individuals is to inhibit its entry into the immune host cells rather than targeting the intracellular viral enzymes. Both viral and host cell surface receptors and co-receptors are regarded as potential targets in anti-HIV-1 drug design process. Because of the importance of this topic it was decided to summarize recent reports on small-molecule HIV-1 entry inhibitors that have not been considered in the latest released reviews. All the computational studies reported in the literature regarding HIV-1 entry inhibitors since 2014 was also considered in this review.

Advances in the Development of Shape Similarity Methods and Their Application in Drug Discovery

Molecular similarity is a key concept in drug discovery. It is based on the assumption that structurally similar molecules frequently have similar properties. Assessment of similarity between small molecules has been highly effective in the discovery and development of various drugs. Especially, two-dimensional (2D) similarity approaches have been quite popular due to their simplicity, accuracy and efficiency. Recently, the focus has been shifted toward the development of methods involving the representation and comparison of three-dimensional (3D) conformation of small molecules. Among the 3D similarity methods, evaluation of shape similarity is now gaining attention for its application not only in virtual screening but also in molecular target prediction, drug repurposing and scaffold hopping. A wide range of methods have been developed to describe molecular shape and to determine the shape similarity between small molecules. The most widely used methods include atom distance-based methods, surface-based approaches such as spherical harmonics and 3D Zernike descriptors, atom-centered Gaussian overlay based representations. Several of these methods demonstrated excellent virtual screening performance not only retrospectively but also prospectively. In addition to methods assessing the similarity between small molecules, shape similarity approaches have been developed to compare shapes of protein structures and binding pockets. Additionally, shape comparisons between atomic models and 3D density maps allowed the fitting of atomic models into cryo-electron microscopy maps. This review aims to summarize the methodological advances in shape similarity assessment highlighting advantages, disadvantages and their application in drug discovery.

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.

Using quantitative systems pharmacology for novel drug discovery

INTRODUCTION

Over the past three decades, the predominant paradigm in drug discovery was designing selective ligands for a specific target to avoid unwanted side effects. However, in the last 5 years, the aim has shifted to take into account the biological network in which they interact. Quantitative and Systems Pharmacology (QSP) is a new paradigm that aims to understand how drugs modulate cellular networks in space and time, in order to predict drug targets and their role in human pathophysiology.

AREAS COVERED

This review discusses existing computational and experimental QSP approaches such as polypharmacology techniques combined with systems biology information and considers the use of new tools and ideas in a wider 'systems-level' context in order to design new drugs with improved efficacy and fewer unwanted off-target effects.

EXPERT OPINION

The use of network biology produces valuable information such as new indications for approved drugs, drug-drug interactions, proteins-drug side effects and pathways-gene associations. However, we are still far from the aim of QSP, both because of the huge effort needed to model precisely biological network models and the limited accuracy that we are able to reach with those. Hence, moving from 'one molecule for one target to give one therapeutic effect' to the 'big systems-based picture' seems obvious moving forward although whether our current tools are sufficient for such a step is still under debate.

Modeling the allosteric modulation of CCR5 function by Maraviroc

Maraviroc is a non-peptidic, low molecular weight CC chemokine receptor 5 (CCR5) ligand that has recently been marketed for the treatment of HIV infected individuals. This review discusses recent molecular modeling studies of CCR5 by homology to CXC chemokine receptor 4, their contribution to the understanding of the allosteric mode of action of the inhibitor and their potential for the development of future drugs with improved efficiency and preservation of CCR5 biological functions.

Ligand-based virtual screening approach using a new scoring function

In this study, we aimed to develop a new ligand-based virtual screening approach using an effective shape-overlapping procedure and a more robust scoring function (denoted by the HWZ score for convenience). The HWZ score-based virtual screening approach was tested against the compounds for 40 protein targets available in the Database of Useful Decoys (DUD; dud.docking.org/jahn/ ), and the virtual screening performance was evaluated in terms of the area under the receiver operator characteristic (ROC) curve (AUC), enrichment factor (EF), and hit rate (HR), demonstrating an improved overall performance compared to other popularly used approaches examined. In particular, the HWZ score-based virtual screening led to an average AUC value of 0.84 ± 0.02 (95% confidence interval) for the 40 targets. The average HR values at the top 1% and 10% of the active compounds for the 40 targets were 46.3% ± 6.7% and 59.2% ± 4.7%, respectively. In addition, the performance of the HWZ score-based virtual screening approach is less sensitive to the choice of the target.

Computational prediction of metabolism: sites, products, SAR, P450 enzyme dynamics, and mechanisms

Metabolism of xenobiotics remains a central challenge for the discovery and development of drugs, cosmetics, nutritional supplements, and agrochemicals. Metabolic transformations are frequently related to the incidence of toxic effects that may result from the emergence of reactive species, the systemic accumulation of metabolites, or by induction of metabolic pathways. Experimental investigation of the metabolism of small organic molecules is particularly resource demanding; hence, computational methods are of considerable interest to complement experimental approaches. This review provides a broad overview of structure- and ligand-based computational methods for the prediction of xenobiotic metabolism. Current computational approaches to address xenobiotic metabolism are discussed from three major perspectives: (i) prediction of sites of metabolism (SOMs), (ii) elucidation of potential metabolites and their chemical structures, and (iii) prediction of direct and indirect effects of xenobiotics on metabolizing enzymes, where the focus is on the cytochrome P450 (CYP) superfamily of enzymes, the cardinal xenobiotics metabolizing enzymes. For each of these domains, a variety of approaches and their applications are systematically reviewed, including expert systems, data mining approaches, quantitative structure–activity relationships (QSARs), and machine learning-based methods, pharmacophore-based algorithms, shape-focused techniques, molecular interaction fields (MIFs), reactivity-focused techniques, protein–ligand docking, molecular dynamics (MD) simulations, and combinations of methods. Predictive metabolism is a developing area, and there is still enormous potential for improvement. However, it is clear that the combination of rapidly increasing amounts of available ligand- and structure-related experimental data (in particular, quantitative data) with novel and diverse simulation and modeling approaches is accelerating the development of effective tools for prediction of in vivo metabolism, which is reflected by the diverse and comprehensive data sources and methods for metabolism prediction reviewed here. This review attempts to survey the range and scope of computational methods applied to metabolism prediction and also to compare and contrast their applicability and performance.

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.

Allosteric model of maraviroc binding to CC chemokine receptor 5 (CCR5)

Maraviroc is a nonpeptidic small molecule human immunodeficiency virus type 1 (HIV-1) entry inhibitor that has just entered the therapeutic arsenal for the treatment of patients. We recently demonstrated that maraviroc binding to the HIV-1 coreceptor, CC chemokine receptor 5 (CCR5), prevents it from binding the chemokine CCL3 and the viral envelope glycoprotein gp120 by an allosteric mechanism. However, incomplete knowledge of ligand-binding sites and the lack of CCR5 crystal structures have hampered an in-depth molecular understanding of how the inhibitor works. Here, we addressed these issues by combining site-directed mutagenesis (SDM) with homology modeling and docking. Six crystal structures of G-protein-coupled receptors were compared for their suitability for CCR5 modeling. All CCR5 models had equally good geometry, but that built from the recently reported dimeric structure of the other HIV-1 coreceptor CXCR4 bound to the peptide CVX15 (Protein Data Bank code 3OE0) best agreed with the SDM data and discriminated CCR5 from non-CCR5 binders in a virtual screening approach. SDM and automated docking predicted that maraviroc inserts deeply in CCR5 transmembrane cavity where it can occupy three different binding sites, whereas CCL3 and gp120 lie on distinct yet overlapped regions of the CCR5 extracellular loop 2. Data suggesting that the transmembrane cavity remains accessible for maraviroc in CCL3-bound and gp120-bound CCR5 help explain our previous observation that the inhibitor enhances dissociation of preformed ligand-CCR5 complexes. Finally, we identified residues in the predicted CCR5 dimer interface that are mandatory for gp120 binding, suggesting that receptor dimerization might represent a target for new CCR5 entry inhibitors.

Biological profiling of anti-HIV agents and insight into CCR5 antagonist binding using in silico techniques

Molecular requirements and determinants for efficient binding to CCR5 were interpreted by computational techniques based on comparative receptor structure modeling, advanced 3D-QSAR, docking, and shape-based virtual screening of commercially available entry blockers. Results of this study may be valuable for predicting new HIV entry-blocking leads.Acquired immune deficiency syndrome (AIDS) is responsible for more than 31 million deaths, and many more people are affected by this disease worldwide. Novel ligands that are capable of blocking virus-cell fusion are emerging as promising candidate molecules against HIV-1 infection because they have the promise to overcome the major drawbacks of classical highly active antiretroviral (HAART) drugs. However, structure-based design continues to be hampered owing to the paucity of experimentally determined 3D information about HIV-1 cell-surface co-receptors. Using computational techniques based on comparative receptor structure modeling, advanced 3D-QSAR, and protein-ligand docking, we present recent results that define updated molecular requirements and determinants for efficient binding of small-molecule ligands to CCR5, a principal biological target for HIV entry blockers. These results are compared with shape- and property-based virtual screening results for commercially available entry blockers, and will be valuable for predicting new HIV entry-blocking leads.

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.