Targeting multifunctional proteins by virtual screening: structurally diverse cytohesin inhibitors with differentiated biological functions

Arf6 as a therapeutic target: Structure, mechanism, and inhibitors

ADP-ribosylation factor 6 (Arf6), a small G-protein of the Ras superfamily, plays pivotal roles in multiple cellular events, including exocytosis, endocytosis, actin remodeling, plasma membrane reorganization and vesicular transport. Arf6 regulates the progression of cancer through the activation of cell motility and invasion. Aberrant Arf6 activation is a potential therapeutic target. This review aims to understand the comprehensive function of Arf6 for future cancer therapy. The Arf6 GEFs, protein structure, and roles in cancer have been summarized. Comprehending the mechanism underlying Arf6-mediated cancer cell growth and survival is essential. The structural features of Arf6 and its efforts are discussed and may be contributed to the discovery of future novel protein-protein interaction inhibitors. In addition, Arf6 inhibitors and mechanism of action are listed in the table. This review further emphasizes the crucial roles in drug resistance and attempts to offer an outlook of Arf6 in cancer therapy.

Machine learning-based predictive modeling, virtual screening and biological evaluation studies for identification of potential inhibitors of MMP-13

Matrix Metalloproteinase-13 (MMP-13) is a collagenase that regulates the homeostasis of the extracellular matrix (ECM) and basement membrane, as well as the breakdown of type II collagen. Recent research studies on the molecular and cellular mechanisms of cartilage degradation suggest that MMP-13 overexpression triggers osteoarthritis and is considered a promising target for osteoarthritis treatment. The present work employs machine learning-based virtual screening and structure-based rational drug design approaches to identify potential inhibitors of MMP-13 with diverse chemical scaffolds. The twelve top-scoring screened compounds were subjected to biological evaluation to validate the robustness and predictive modeling of ML-based Virtual Screening. It was observed that eight compounds exhibited approximately 44%-60% inhibition at 0.1 µM concentration, and the IC50 lies in the range of 1.9-2.3 µM against MMP-13. Interestingly, two of the compounds, DP01473 and RH01617, showed potent dose-dependent inhibitory activity. Compound DP01473 inhibited MMP-13 by 44%, 50%, and 70%, while compound RH01617 inhibited MMP-13 by 54%, 55%, and 57% at 0.1 μM, 1 μM, and 10 μM concentrations, respectively, and can be further optimized for the design and development of more potent MMP-13 inhibitors.Communicated by Ramaswamy H. Sarma.

Targeting the Small GTPase Superfamily through Their Regulatory Proteins

The Ras superfamily of small GTPases are guanine-nucleotide-dependent switches essential for numerous cellular processes. Mutations or dysregulation of these proteins are associated with many diseases, but unsuccessful attempts to target the small GTPases directly have resulted in them being classed as "undruggable". The GTP-dependent signaling of these proteins is controlled by their regulators; guanine nucleotide exchange factors (GEFs), GTPase activating proteins (GAPs), and in the Rho and Rab subfamilies, guanine nucleotide dissociation inhibitors (GDIs). This review covers the recent small molecule and biologics strategies to target the small GTPases through their regulators. It seeks to critically re-evaluate recent chemical biology practice, such as the presence of PAINs motifs and the cell-based readout using compounds that are weakly potent or of unknown specificity. It highlights the vast scope of potential approaches for targeting the small GTPases in the future through their regulatory proteins.

Reactions of secondary propargylamines with heteroallenes for the synthesis of diverse heterocycles

This focused review aims to summarize recent developments in the processes involving additions of secondary propargylamines to various heteroallenes and subsequent transition metal-catalyzed or electrophile-mediated cyclizations.

New Structural Templates for Clinically Validated and Novel Targets in Antimicrobial Drug Research and Development

The development of bacterial resistance against current antibiotic drugs necessitates a continuous renewal of the arsenal of efficacious drugs. This imperative has not been met by the output of antibiotic research and development of the past decades for various reasons, including the declining efforts of large pharma companies in this area. Moreover, the majority of novel antibiotics are chemical derivatives of existing structures that represent mostly step innovations, implying that the available chemical space may be exhausted. This review negates this impression by showcasing recent achievements in lead finding and optimization of antibiotics that have novel or unexplored chemical structures. Not surprisingly, many of the novel structural templates like teixobactins, lysocin, griselimycin, or the albicidin/cystobactamid pair were discovered from natural sources. Additional compounds were obtained from the screening of synthetic libraries and chemical synthesis, including the gyrase-inhibiting NTBI's and spiropyrimidinetrione, the tarocin and targocil inhibitors of wall teichoic acid synthesis, or the boronates and diazabicyclo[3.2.1]octane as novel β-lactamase inhibitors. A motif that is common to most clinically validated antibiotics is that they address hotspots in complex biosynthetic machineries, whose functioning is essential for the bacterial cell. Therefore, an introduction to the biological targets-cell wall synthesis, topoisomerases, the DNA sliding clamp, and membrane-bound electron transport-is given for each of the leads presented here.

Structure-based virtual screening for drug discovery: principles, applications and recent advances

Structure-based drug discovery (SBDD) is becoming an essential tool in assisting fast and cost-efficient lead discovery and optimization. The application of rational, structure-based drug design is proven to be more efficient than the traditional way of drug discovery since it aims to understand the molecular basis of a disease and utilizes the knowledge of the three-dimensional structure of the biological target in the process. In this review, we focus on the principles and applications of Virtual Screening (VS) within the context of SBDD and examine different procedures ranging from the initial stages of the process that include receptor and library pre-processing, to docking, scoring and post-processing of topscoring hits. Recent improvements in structure-based virtual screening (SBVS) efficiency through ensemble docking, induced fit and consensus docking are also discussed. The review highlights advances in the field within the framework of several success studies that have led to nM inhibition directly from VS and provides recent trends in library design as well as discusses limitations of the method. Applications of SBVS in the design of substrates for engineered proteins that enable the discovery of new metabolic and signal transduction pathways and the design of inhibitors of multifunctional proteins are also reviewed. Finally, we contribute two promising VS protocols recently developed by us that aim to increase inhibitor selectivity. In the first protocol, we describe the discovery of micromolar inhibitors through SBVS designed to inhibit the mutant H1047R PI3Kα kinase. Second, we discuss a strategy for the identification of selective binders for the RXRα nuclear receptor. In this protocol, a set of target structures is constructed for ensemble docking based on binding site shape characterization and clustering, aiming to enhance the hit rate of selective inhibitors for the desired protein target through the SBVS process.

Hierarchical virtual screening approaches in small molecule drug discovery

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

Computational methods in drug discovery

Computer-aided drug discovery/design methods have played a major role in the development of therapeutically important small molecules for over three decades. These methods are broadly classified as either structure-based or ligand-based methods. Structure-based methods are in principle analogous to high-throughput screening in that both target and ligand structure information is imperative. Structure-based approaches include ligand docking, pharmacophore, and ligand design methods. The article discusses theory behind the most important methods and recent successful applications. Ligand-based methods use only ligand information for predicting activity depending on its similarity/dissimilarity to previously known active ligands. We review widely used ligand-based methods such as ligand-based pharmacophores, molecular descriptors, and quantitative structure-activity relationships. In addition, important tools such as target/ligand data bases, homology modeling, ligand fingerprint methods, etc., necessary for successful implementation of various computer-aided drug discovery/design methods in a drug discovery campaign are discussed. Finally, computational methods for toxicity prediction and optimization for favorable physiologic properties are discussed with successful examples from literature.

Molecular crime scene investigation - dusting for fingerprints

In chemoinformatics and drug design, fingerprints (FPs) are defined as string representations of molecular structure and properties and are popular descriptors for similarity searching. FPs are generally characterized by the simplicity of their design and ease of use. Despite a long history in chemoinformatics, the potential and limitations of FP searching are often not well under- stood. Standard FPs can also be subjected to engineering techniques to tune them for specific search applications.

Molecular similarity in medicinal chemistry

Similarity is a subjective and multifaceted concept, regardless of whether compounds or any other objects are considered. Despite its intrinsically subjective nature, attempts to quantify the similarity of compounds have a long history in chemical informatics and drug discovery. Many computational methods employ similarity measures to identify new compounds for pharmaceutical research. However, chemoinformaticians and medicinal chemists typically perceive similarity in different ways. Similarity methods and numerical readouts of similarity calculations are probably among the most misunderstood computational approaches in medicinal chemistry. Herein, we evaluate different similarity concepts, highlight key aspects of molecular similarity analysis, and address some potential misunderstandings. In addition, a number of practical aspects concerning similarity calculations are discussed.

Fragment-based identification of a locus in the Sec7 domain of Arno for the design of protein-protein interaction inhibitors

By virtual screening using a fragment-based drug design (FBDD) approach, 33 fragments were selected within small pockets around interaction hot spots on the Sec7 surface of the nucleotide exchange factor Arno, and then their ability to interfere with the Arno-catalyzed nucleotide exchange on the G-protein Arf1 was evaluated. By use of SPR, NMR, and fluorescence assays, the direct binding of three of the identified fragments to Arno Sec7 domain was demonstrated and the promiscuous aggregate behavior evaluated. Then the binding mode of one fragment and of a more active analogue was solved by X-ray crystallography. This highlighted the role of stable and transient pockets at the Sec7 domain surface in the discovery and binding of interfering compounds. These results provide structural information on how small organic compounds can interfere with the Arf1-Arno Sec7 domain interaction and may guide the rational drug design of competitive inhibitors of Arno enzymatic activity.

Cationic dirhodium carboxylate-catalyzed synthesis of dihydropyrimidones from propargyl ureas

Cationic Rh(II) complexes are able to catalyze the regioselective hydroamination of propargyl ureas in a 6-endo fashion. This transformation permits access to interesting substitution patterns of dihydropyrimidines which have found use as nucleotide exchange factor inhibitors.

Significance estimation for sequence-based chemical similarity searching (PhAST) and application to AuroraA kinase inhibitors

BACKGROUND

Chemical similarity searching allows the retrieval of preferred screening molecules from a compound database. Candidates are ranked according to their similarity to a reference compound (query). Assessing the statistical significance of chemical similarity scores helps prioritizing significant hits, and identifying cases where the database does not contain any promising compounds.

METHOD

Our text-based similarity measure, Pharmacophore Alignment Search Tool (PhAST), employs pair-wise sequence alignment. We adapted the concept of E-values as significance estimates and employed a sampling technique that incorporates the principle of importance sampling in a Markov chain Monte Carlo simulation to generate distributions of random alignment scores. These distributions were used to compute significance estimates for similarity scores in a preliminary prospective virtual screen for inhibitors of Aurora A kinase.

CONCLUSION

Assessing the significance of compound similarity computed with PhAST allows for a statistically motivated identification of candidate screening compounds. Inhibitors of Aurora A kinase were retrieved from a large compound library.

Small inhibitors of ADP-ribosylation factor activation and function in mammalian cells

Small GTP-binding proteins of the ADP-ribosylation factor (Arf) family control various cell functional responses including protein transport and recycling between different cellular compartments, phagocytosis, proliferation, cytoskeletal remodelling, and migration. The activity of Arfs is tightly regulated. GTPase-activating proteins (GAPs) inactivate Arfs by stimulating GTP hydrolysis, and guanine nucleotide exchange factors (GEFs) stimulate the conversion of inactive GDP-bound Arf to the active GTP-bound conformation. There is increasing evidence that Arf small GTPases contribute to cancer growth and invasion. Increased expression of Arf6 and of Arf-GEPs, or deregulation Arf-GAP functions have been correlated with enhanced invasive capacity of tumor cells and metastasis. The spatiotemporal specificity of Arf activation is dictated by their GEFs that integrate various signals in stimulated cells. Brefeldin A (BFA), which inactivates a subset of Arf-GEFs, has been very useful for assessing the function of Golgi-localized Arfs. However, specific inhibitors to investigate the individual function of BFA-sensitive and insensitive Arf-GEFs are lacking. In recent years, specific screens have been developed, and new inhibitors with improved selectivity and potency to study cell functional responses regulated by BFA-sensitive and BFA-insensitive Arf pathways have been identified. These inhibitors have been instrumental for our understanding of the spatiotemporal activation of Arf proteins in cells and demonstrate the feasibility of developing small molecules interfering with Arf activation to prevent tumor invasion and metastasis.

Virtual compound screening in drug discovery

Virtual screening (VS) methods are applied in both academia and drug discovery, and can be divided into ligand- and target structure-based approaches. The VS field is still evolving and is characterized by scientific heterogeneity. The value of virtual compound screening for drug discovery is often debated, in particular, given the large investments made in experimental high-throughput screening technologies. The current state-of-the-art in the VS field is discussed. Despite its limitations, VS applications have often succeeded in identifying novel hits including first-in-class active compounds and novel chemotypes. VS has its place in pharmaceutical research, but there is still much room for further improvements including method evaluation and drug discovery applications. The potential of VS is currently underutilized because its complementarity to high-throughput screening is not sufficiently exploited. Building close interfaces between computational and experimental screening would further streamline the hit identification process.

Anti-proliferative effect of cytohesin inhibition in gefitinib-resistant lung cancer cells

Epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKI), such as gefitinib, have been proven to efficiently inhibit the proliferation of a subset of non small-cell lung cancers (NSCLC). Unfortunately, the majority of NSCLC expressing wild type EGFR is primarily resistant to EGFR-TKI treatment. Here, we show that the proliferation of the gefitinib-resistant NSCLC cell lines H460 and A549 is reduced by the small molecule SecinH3 which indirectly attenuates EGFR activation by inhibition of cytohesins, a class of recently discovered cytoplasmic EGFR activators. SecinH3 and gefitinib showed a synergistic antiproliferative effect, which correlated with a profound inhibition of Akt activation and survivin expression. Treating mice bearing H460 xenografts with SecinH3 showed the antiproliferative and pro-apoptotic effect of SecinH3 in vivo. Our data suggest that targeting the EGFR indirectly by inhibiting its cytoplasmic activators, the cytohesins, has the potential to improve the treatment of primarily EGFR-TKI resistant lung cancers.

Large-scale similarity search profiling of ChEMBL compound data sets

A large-scale similarity search investigation has been carried out on 266 well-defined compound activity classes extracted from the ChEMBL database. The analysis was performed using two widely applied two-dimensional (2D) fingerprints that mark opposite ends of the current performance spectrum of these types of fingerprints, i.e., MACCS structural keys and the extended connectivity fingerprint with bond diameter four (ECFP4). For each fingerprint, three nearest neighbor search strategies were applied. On the basis of these search calculations, a similarity search profile of the ChEMBL database was generated. Overall, the fingerprint search campaign was surprisingly successful. In 203 of 266 test cases (∼76%), a compound recovery rate of at least 50% was observed with at least the better performing fingerprint and one search strategy. The similarity search profile also revealed several general trends. For example, fingerprint searching was often characterized by an early enrichment of active compounds in database selection sets. In addition, compound activity classes have been categorized according to different similarity search performance levels, which helps to put the results of benchmark calculations into perspective. Therefore, a compendium of activity classes falling into different search performance categories is provided. On the basis of our large-scale investigation, the performance range of state-of-the-art 2D fingerprinting has been delineated for compound data sets directed against a wide spectrum of pharmaceutical targets.

A homogeneous fluorescence resonance energy transfer system for monitoring the activation of a protein switch in real time

A homogeneous fluorescence resonance energy transfer (FRET) system for the real-time monitoring of exchange factor-catalyzed activation of a ras-like small GTPase is described. The underlying design is based on supramolecular template effects exerted by protein-protein interactions between the GTPase adenosine diphosphate ribosylation factor (ARF) and its effector protein GGA3. The GTPase is activated when bound to guanosine triphosphate (GTP) and switched off in its guanosine diphosphate (GDP)-bound state. Both states are accompanied by severe conformational changes that are recognized by GGA3, which only binds the GTPase "on" state. GDP-to-GTP exchange, i.e., GTPase activation, is catalyzed by the guanine nucleotide exchange factor cytohesin-2. When GGA3 and the GTPase ARF1 are labeled with thoroughly selected FRET probes, with simultaneous recording of the fluorescence of an internal tryptophan residue in ARF1, the conformational changes during the activation of the GTPase can be monitored in real time. We applied the FRET system to a multiplex format that allows the simultaneous identification and distinction of small-molecule inhibitors that interfere with the cytohesin-catalyzed ARF1 activation and/or with the interaction between activated ARF1-GTP and GGA3. By screening a library of potential cytohesin inhibitors, predicted by in silico modeling, we identified new inhibitors for the cytohesin-catalyzed GDP/GTP exchange on ARF1 and verified their increased potency in a cell proliferation assay.

State-of-the-art in ligand-based virtual screening

Virtual screening is a much discussed topic in chemoinformatics and medicinal chemistry, and widely applied in pharmaceutical research. Here, we provide an in-depth analysis of currently available ligand-based virtual screening applications. We formulate several scientific quality criteria for prospective ligand-based virtual screens and analyze, in detail, the information provided by currently available peer-reviewed publications. The results presented herein provide a detailed view of the current state-of-the-art in this field and point at several problematic issues but also opportunities for further advances.