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

In silico trials to design potent inhibitors against matrilysin (MMP-7)

The study deals with structure-based rational drug design against the chief zinc-rely endopeptidase called matrilysin (MMP-7) that is involved in inflammatory and metastasis process of several carcinomas. Hyperactivated matrilysin of human was targeted, because of its hydrolytic actions on extracellular matrix (ECM) protein components constitutes fibrillar collagens, gelatins, fibronectins and it also activates zymogen forms of vital matrix metalloproteinases (gelatinase A-MMP-2 and B-MMP-9) responsible for ECM destruction in many cancers. In the present work, e-pharmacophores were generated for the respective five co-crystal structures of human matrilysin by mapping ligand's pharmacophoric features. During the lead-optimization campaign, the five e-pharmacophores-based shape screening against an in-house library of >21 million compounds created a dataset of 5000 structural analogs. The subsequent three different docking strategies, including rigid-receptor docking, quantum-polarized-ligand docking, induced-fit docking and free energy binding calculations resulted four leads as novel and potent MMP-7 binders. These four leads were observed with good pharmacological features and good receiver operating characteristics curve metrics (ROC: 0.93) in post-docking evaluations against five existing co-crystal inhibitors and 1000 decoy molecules with MMP-7. Moreover, stability and dynamics behavior of matrilysin-lead1 complex and matrilysin-cocrystal ligand (TQJ) complex were analyzed in natural physiological milieu of 1000 ns or 1 µs molecular dynamics simulations. Lead1-MMP-7 complex was found with an average Cα root-mean-square deviation (RMSD) of 2.35 Å, average ligand root-mean-square fluctuations (RMSF) of 0.66 Å and the strong metallic interactions with E220, a key residue for proteolytic action thereby hinders ECM proteolysis that in turn can halt metastatic cancerous condition.Communicated by Ramaswamy H. Sarma.

Ligand-Based Modeling of CXC Chemokine Receptor 4 and Identification of Inhibitors of Novel Chemotypes as Potential Leads towards New Anti-COVID-19 Treatments

BACKGROUND

Chemokines are involved in several human diseases and in different stages of COVID-19 infection and play critical role in the pathophysiology of the associated acute respiratory disease syndrome, a major complication leading to death among COVID-19 patients. In particular, CXC chemokine receptor 4 (CXCR4) was found to be highly expressed in COVID-19 patients.

METHODS

We herein describe a computational workflow based on combining pharmacophore modeling and QSAR analysis towards the discovery of novel CXCR4 inhibitors. Subsequent virtual screening identified two promising CXCR4 inhibitors from the National Cancer Institute (NCI) list of compounds. The most active hit showed in vitro IC50 value of 24.4 µM.

RESULTS AND CONCLUSION

These results prove the validity of the QSAR model and associated pharmacophore models as means to screen virtual databases towards new CXCR4 inhibitors as leads for the development of new COVID-19 therapies.

Artificial intelligence to deep learning: machine intelligence approach for drug discovery

Drug designing and development is an important area of research for pharmaceutical companies and chemical scientists. However, low efficacy, off-target delivery, time consumption, and high cost impose a hurdle and challenges that impact drug design and discovery. Further, complex and big data from genomics, proteomics, microarray data, and clinical trials also impose an obstacle in the drug discovery pipeline. Artificial intelligence and machine learning technology play a crucial role in drug discovery and development. In other words, artificial neural networks and deep learning algorithms have modernized the area. Machine learning and deep learning algorithms have been implemented in several drug discovery processes such as peptide synthesis, structure-based virtual screening, ligand-based virtual screening, toxicity prediction, drug monitoring and release, pharmacophore modeling, quantitative structure-activity relationship, drug repositioning, polypharmacology, and physiochemical activity. Evidence from the past strengthens the implementation of artificial intelligence and deep learning in this field. Moreover, novel data mining, curation, and management techniques provided critical support to recently developed modeling algorithms. In summary, artificial intelligence and deep learning advancements provide an excellent opportunity for rational drug design and discovery process, which will eventually impact mankind. The primary concern associated with drug design and development is time consumption and production cost. Further, inefficiency, inaccurate target delivery, and inappropriate dosage are other hurdles that inhibit the process of drug delivery and development. With advancements in technology, computer-aided drug design integrating artificial intelligence algorithms can eliminate the challenges and hurdles of traditional drug design and development. Artificial intelligence is referred to as superset comprising machine learning, whereas machine learning comprises supervised learning, unsupervised learning, and reinforcement learning. Further, deep learning, a subset of machine learning, has been extensively implemented in drug design and development. The artificial neural network, deep neural network, support vector machines, classification and regression, generative adversarial networks, symbolic learning, and meta-learning are examples of the algorithms applied to the drug design and discovery process. Artificial intelligence has been applied to different areas of drug design and development process, such as from peptide synthesis to molecule design, virtual screening to molecular docking, quantitative structure-activity relationship to drug repositioning, protein misfolding to protein-protein interactions, and molecular pathway identification to polypharmacology. Artificial intelligence principles have been applied to the classification of active and inactive, monitoring drug release, pre-clinical and clinical development, primary and secondary drug screening, biomarker development, pharmaceutical manufacturing, bioactivity identification and physiochemical properties, prediction of toxicity, and identification of mode of action.

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.

Chemokine Receptor Crystal Structures: What Can Be Learned from Them?

Chemokine receptors belong to the class A of G protein-coupled receptors (GPCRs) and are implicated in a wide variety of physiologic functions, mostly related to the homeostasis of the immune system. Chemokine receptors are also involved in multiple pathologic processes, including immune and autoimmune diseases, as well as cancer. Hence, several members of this GPCR subfamily are considered to be very relevant therapeutic targets. Since drug discovery efforts can be significantly reinforced by the availability of crystal structures, substantial efforts in the area of chemokine receptor structural biology could dramatically increase the outcome of drug discovery campaigns. This short review summarizes the available data on chemokine receptor crystal structures, discusses the numerous applications from chemokine receptor structures that can enhance the daily work of molecular pharmacologists, and describes the challenges and pitfalls to consider when relying on crystal structures for further research applications. SIGNIFICANCE STATEMENT: This short review summarizes the available data on chemokine receptor crystal structures, discusses the numerous applications from chemokine receptor structures that can enhance the daily work of molecular pharmacologists, and describes the challenges and pitfalls to consider when relying on crystal structures for further research applications.

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.

Methods for Virtual Screening of GPCR Targets: Approaches and Challenges

Virtual screening (VS) has become an integral part of the drug discovery process and is a valuable tool for finding novel chemical starting points for GPCR targets. Ligand-based VS makes use of biochemical data for known, active compounds and has been applied successfully to many diverse GPCRs. Recent progress in GPCR X-ray crystallography has made it possible to incorporate detailed structural information into the VS process. This chapter outlines the latest VS techniques along with examples that highlight successful applications of these methods. Best practices for increasing the likelihood of VS success, as well as ongoing challenges, are also discussed.

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.

Human Immunodeficiency Virus Immune Cell Receptors, Coreceptors, and Cofactors: Implications for Prevention and Treatment

In the last three decades, extensive research on human immunodeficiency virus (HIV) has highlighted its capability to exploit a variety of strategies to enter and infect immune cells. Although CD4(+) T cells are well known as the major HIV target, with infection occurring through the canonical combination of the cluster of differentiation 4 (CD4) receptor and either the C-C chemokine receptor type 5 (CCR5) or C-X-C chemokine receptor type 4 (CXCR4) coreceptors, HIV has also been found to enter other important immune cell types such as macrophages, dendritic cells, Langerhans cells, B cells, and granulocytes. Interestingly, the expression of distinct cellular cofactors partially regulates the rate in which HIV infects each distinct cell type. Furthermore, HIV can benefit from the acquisition of new proteins incorporated into its envelope during budding events. While several publications have investigated details of how HIV manipulates particular cell types or subtypes, an up-to-date comprehensive review on HIV tropism for different immune cells is lacking. Therefore, this review is meant to focus on the different receptors, coreceptors, and cofactors that HIV exploits to enter particular immune cells. Additionally, prophylactic approaches that have targeted particular molecules associated with HIV entry and infection of different immune cells will be discussed. Unveiling the underlying cellular receptors and cofactors that lead to HIV preference for specific immune cell populations is crucial in identifying novel preventative/therapeutic targets for comprehensive strategies to eliminate viral infection.

Inhibitor design against JNK1 through e-pharmacophore modeling docking and molecular dynamics simulations

c-Jun-NH2 terminal kinases (JNKs) come under a class of serine/threonine protein kinases and are encoded by three genes, namely JNK1, JNK2 and JNK3. Human JNK1 is a cytosolic kinase belonging to mitogen-activated protein kinase (MAPK) family, which plays a major role in intracrinal signal transduction cascade mechanism. Overexpressed human JNK1, a key kinase interacts with other kinases involved in the etiology of many cancers, such as skin cancer, liver cancer, breast cancer, brain tumors, leukemia, multiple myeloma and lymphoma. Thus, to unveil a novel human JNK1 antagonist, receptor-based pharmacophore modeling was performed with the available eighteen cocrystal structures of JNK1 in the protein data bank. Eighteen e-pharmacophores were generated from the 18 cocrystal structures. Four common e-pharmacophores were developed from the 18 e-pharmacophores, which were used as three-dimensional (3D) query for shape-based similarity screening against more than one million small molecules to generate a JNK1 ligand library. Rigid receptor docking (RRD) performed using GLIDE v6.3 for the 1683 compounds from in-house library and 18 cocrystal ligands with human JNK1 from lower stringency to higher stringency revealed 17 leads. Further to derive the best leads, dock complexes obtained from RRD were studied further with quantum-polarized ligand docking (QPLD), induced fit docking (IFD) and molecular mechanics/generalized Born surface area (MM-GBSA). Four leads have showed lesser binding free energy and better binding affinity towards JNK1 compared to 18 cocrystal ligands. Additionally, JNK1-lead1 complex interaction stability was reasserted using 50 ns MD simulations run and also compared with the best resolute cocrystal structure using Desmond v3.8. Thus, the results obtained from RRD, QPLD, IFD and MD simulations indicated that lead1 might be used as a potent antagonist toward human JNK1 in cancer therapeutics.

Performance evaluation of structure based and ligand based virtual screening methods on ten selected anti-cancer targets

Virtual screening has become an important tool in drug discovery process. Structure based and ligand based approaches are generally used in virtual screening process. To date, several benchmark sets for evaluating the performance of the virtual screening tool are available. In this study, our aim is to compare the performance of both structure based and ligand based virtual screening methods. Ten anti-cancer targets and their corresponding benchmark sets from 'Demanding Evaluation Kits for Objective In silico Screening' (DEKOIS) library were selected. X-ray crystal structures of protein-ligand complexes were selected based on their resolution. Openeye tools such as FRED, vROCS were used and the results were carefully analyzed. At EF1%, vROCS produced better results but at EF5% and EF10%, both FRED and ROCS produced almost similar results. It was noticed that the enrichment factor values were decreased while going from EF1% to EF5% and EF10% in many cases.

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.:

Rational design of HIV-1 entry inhibitors

This chapter reviews studies that have used in silico techniques to design or identify potential HIV-1 entry inhibitors targeting cellular receptors CD4, CCR5, and CXCR4 and envelope glycoproteins, gp120 and gp41 of HIV-1. Both structure- and ligand-based design techniques have been used in those studies by applying diverse modeling techniques such as quantitative structure-activity relationship analysis, conformational analysis, molecular dynamics, pharmacophore generation, docking, virtual screening (using docking software and also shape-based ROCS techniques), and fragment-based design.

Pharmacological modulation of chemokine receptor function

G protein-coupled chemokine receptors and their peptidergic ligands are interesting therapeutic targets due to their involvement in various immune-related diseases, including rheumatoid arthritis, multiple sclerosis, inflammatory bowel disease, chronic obstructive pulmonary disease, HIV-1 infection and cancer. To tackle these diseases, a lot of effort has been focused on discovery and development of small-molecule chemokine receptor antagonists. This has been rewarded by the market approval of two novel chemokine receptor inhibitors, AMD3100 (CXCR4) and Maraviroc (CCR5) for stem cell mobilization and treatment of HIV-1 infection respectively. The recent GPCR crystal structures together with mutagenesis and pharmacological studies have aided in understanding how small-molecule ligands interact with chemokine receptors. Many of these ligands display behaviour deviating from simple competition and do not interact with the chemokine binding site, providing evidence for an allosteric mode of action. This review aims to give an overview of the evidence supporting modulation of this intriguing receptor family by a range of ligands, including small molecules, peptides and antibodies. Moreover, the computer-assisted modelling of chemokine receptor-ligand interactions is discussed in view of GPCR crystal structures. Finally, the implications of concepts such as functional selectivity and chemokine receptor dimerization are considered.

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.

In silico virtual screening approaches for anti-viral drug discovery

Despite the considerable advances in medical and pharmaceutical research during the past years, diseases caused by viruses have remained a major burden to public health. Virtual in silico screening has repeatedly proven to be useful to meet the special challenges of antiviral drug discovery. Large virtual compound libraries are filtered by different computational screening methods such as docking, ligand-based similarity searches or pharmacophore-based screening, reducing the number of candidate molecules to a smaller set of promising candidates that are then tested biologically. This rational approach makes the drug discovery process more goal-oriented and saves resources in terms of time and money. In this review we discuss how different virtual screening techniques can be applied to antiviral drug discovery, present recent success stories in this field and finally address the main differences between the methods.:

Pharmacophore-based virtual screening

This chapter is a review of the most recent developments in the field of pharmacophore modeling, covering both methodology and application. Pharmacophore-based virtual screening is nowadays a mature technology, very well accepted in the medicinal chemistry laboratory. Nevertheless, like any empirical approach, it has specific limitations and efforts to improve the methodology are still ongoing. Fundamentally, the core idea of "stripping" functional groups of their actual chemical nature in order to classify them into very few pharmacophore types, according to their dominant physico-chemical features, is both the main advantage and the main drawback of pharmacophore modeling. The advantage is the one of simplicity - the complex nature of noncovalent ligand binding interactions is rendered intuitive and comprehensible by the human mind. Although computers are much better suited for comparisons of pharmacophore patterns, a chemist's intuition is primarily scaffold-oriented. Its underlying simplifications render pharmacophore modeling unable to provide perfect predictions of ligand binding propensities - not even if all its subsisting technical problems would be solved. Each step in pharmacophore modeling and exploitation has specific drawbacks: from insufficient or inaccurate conformational sampling to ambiguities in pharmacophore typing (mainly due to uncertainty regarding the tautomeric/protonation status of compounds), to computer time limitations in complex molecular overlay calculations, and to the choice of inappropriate anchoring points in active sites when ligand cocrystals structures are not available. Yet, imperfections notwithstanding, the approach is accurate enough in order to be practically useful and actually is the most used virtual screening technique in medicinal chemistry - notably for "scaffold hopping" approaches, allowing the discovery of new chemical classes carriers of a desired biological activity.

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.

Theoretical targets for TCDD: a bioinformatics approach

Dioxins are a group of highly toxic molecules that exert their toxicity through the activation of the aryl hydrocarbon receptor (AhR). The most important agonist of the AhR, 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) is a highly toxic compound. Although most of the effects related to TCDD exposure have been linked to the activation of AhR, the objective of this work was to use a bioinformatics approach to identify possible new targets for TCDD. The Target Fishing Docking (TarFisDock) Server was used to find target proteins for TCDD. This virtual screening allowed the identification of binding sites with high affinity for TCDD in diverse proteins, such as metallopeptidases 8 and 3, oxidosqualene cyclase, and myeloperoxidase. Some of these proteins are well known for their biochemical role in some pathological effects of dioxin exposure, including endometriosis, diabetes, inflammation and liver damage. These results suggest that TCDD could also be interacting with cellular targets though AhR-independent pathways.