Virtual screening to enrich hit lists from high-throughput screening: a case study on small-molecule inhibitors of angiogenin

Metal-free thiolation of sulfonyl hydrazone with thiophenol: synthesis of 4-thio-chroman and diarylmethyl thioethers

A simple, efficient, and transition metal-free approach was developed for accessing 4-thio-substituted chroman and diarylmethyl thioethers from sulfonyl hydrazones. This protocol provides straightforward access to a class of diarylmethane derivatives with good to excellent yields. This operationally simple protocol exhibited good tolerance for labile functional groups, providing biologically relevant chemical libraries. This safe and feasible route is applicable to the large-scale synthesis of 4-thio-substituted chromans, which are of great synthetic interest because of their further reaction potential.

New Insights on the Activity and Selectivity of MAO-B Inhibitors through In Silico Methods

To facilitate the identification of novel MAO-B inhibitors, we elaborated a consolidated computational approach, including a pharmacophoric atom-based 3D quantitative structure-activity relationship (QSAR) model, activity cliffs, fingerprint, and molecular docking analysis on a dataset of 126 molecules. An AAHR.2 hypothesis with two hydrogen bond acceptors (A), one hydrophobic (H), and one aromatic ring (R) supplied a statistically significant 3D QSAR model reflected by the parameters: R² = 0.900 (training set); Q² = 0.774 and Pearson's R = 0.884 (test set), stability s = 0.736. Hydrophobic and electron-withdrawing fields portrayed the relationships between structural characteristics and inhibitory activity. The quinolin-2-one scaffold has a key role in selectivity towards MAO-B with an AUC of 0.962, as retrieved by ECFP4 analysis. Two activity cliffs showing meaningful potency variation in the MAO-B chemical space were observed. The docking study revealed interactions with crucial residues TYR:435, TYR:326, CYS:172, and GLN:206 responsible for MAO-B activity. Molecular docking is in consensus with and complementary to pharmacophoric 3D QSAR, ECFP4, and MM-GBSA analysis. The computational scenario provided here will assist chemists in quickly designing and predicting new potent and selective candidates as MAO-B inhibitors for MAO-B-driven diseases. This approach can also be used to identify MAO-B inhibitors from other libraries or screen top molecules for other targets involved in suitable diseases.

Application of computational methods for class A GPCR Ligand discovery

G protein-coupled receptors (GPCR) are integral membrane proteins of considerable interest as targets for drug development due to their role in transmitting cellular signals in a multitude of biological processes. Of the six classes categorizing GPCR (A, B, C, D, E, and F), class A contains the largest number of therapeutically relevant GPCR. Despite their importance as drug targets, many challenges exist for the discovery of novel class A GPCR ligands serving as drug precursors. Though knowledge of the structural and functional characteristics of GPCR has grown significantly over the past 20 years, a large portion of GPCR lack reported, experimentally determined structures. Furthermore, many GPCR have no known endogenous and/or synthetic ligands, limiting further exploration of their biochemical, cellular, and physiological roles. While many successes in GPCR ligand discovery have resulted from experimental high-throughput screening, computational methods have played an increasingly important role in GPCR ligand identification in the past decade. Here we discuss computational techniques applied to GPCR ligand discovery. This review summarizes class A GPCR structure/function and provides an overview of many obstacles currently faced in GPCR ligand discovery. Furthermore, we discuss applications and recent successes of computational techniques used to predict GPCR structure as well as present a summary of ligand- and structure-based methods used to identify potential GPCR ligands. Finally, we discuss computational hit list generation and refinement and provide comprehensive workflows for GPCR ligand identification.

FSDscore: An Effective Target-focused Scoring Criterion for Virtual Screening

Improving screening efficiency is one of the most challenging tasks of virtual screening (VS). In this work, we propose an effective target-focused scoring criterion for VS and apply it to the screening of a specific target scaffold replacement library constructed by enumeration of suitable substitution fragments and R-groups of known ligands. This criterion is based on both ligand- and structure-based scoring methods, which includes feature maps, 3D shape similarity, and the pairwise distance information between proteins and ligands (FSDscore). It is precisely due to the hybrid advantages of ligand- and structure-based approaches that FSDscore performs far better on the validation dataset than other scoring methods. We apply FSDscore to the VS of different kinase targets, MERTK (Mer tyrosine kinase) and ABL1 (tyrosine-protein kinase ABL1) in order to avoid occasionality. Finally, a VS case study shows the potential and effectiveness of our scoring criterion in drug discovery and molecular dynamics simulation further verifies its powerful ability.

Prioritizing Small Sets of Molecules for Synthesis through in-silico Tools: A Comparison of Common Ranking Methods

Prioritizing molecules for synthesis is a key role of computational methods within medicinal chemistry. Multiple tools exist for ranking molecules, from the cheap and popular molecular docking methods to more computationally expensive molecular-dynamics (MD)-based methods. It is often questioned whether the accuracy of the more rigorous methods justifies the higher computational cost and associated calculation time. Here, we compared the performance on ranking the binding of small molecules for seven scoring functions from five docking programs, one end-point method (MM/GBSA), and two MD-based free energy methods (PMX, FEP+). We investigated 16 pharmaceutically relevant targets with a total of 423 known binders. The performance of docking methods for ligand ranking was strongly system dependent. We observed that MD-based methods predominantly outperformed docking algorithms and MM/GBSA calculations. Based on our results, we recommend the application of MD-based free energy methods for prioritization of molecules for synthesis in lead optimization, whenever feasible.

Mind the Gap-Deciphering GPCR Pharmacology Using 3D Pharmacophores and Artificial Intelligence

G protein-coupled receptors (GPCRs) are amongst the most pharmaceutically relevant and well-studied protein targets, yet unanswered questions in the field leave significant gaps in our understanding of their nuanced structure and function. Three-dimensional pharmacophore models are powerful computational tools in in silico drug discovery, presenting myriad opportunities for the integration of GPCR structural biology and cheminformatics. This review highlights success stories in the application of 3D pharmacophore modeling to de novo drug design, the discovery of biased and allosteric ligands, scaffold hopping, QSAR analysis, hit-to-lead optimization, GPCR de-orphanization, mechanistic understanding of GPCR pharmacology and the elucidation of ligand-receptor interactions. Furthermore, advances in the incorporation of dynamics and machine learning are highlighted. The review will analyze challenges in the field of GPCR drug discovery, detailing how 3D pharmacophore modeling can be used to address them. Finally, we will present opportunities afforded by 3D pharmacophore modeling in the advancement of our understanding and targeting of GPCRs.

Metal-Free, Acid/Phosphine-Induced Regioselective Thiolation of p-Quinone Methides with Sodium Aryl/Alkyl Sulfinates

A simple and efficient method for the regioselective thiolation of p-quinone methides with sodium aryl/alkyl sulfinates has been established using an acid/phosphine-induced radical route under transition-metal-free conditions. A broad range of sodium aryl/alkyl sulfinates and p-quinone methides (p-QMs) are compatible for the reaction, giving the expected products with good to excellent yields. Control experiments were also performed to gain insights into the generation mechanism of thiyl radicals and hydrogen-atom transfer process. This protocol provides a safe and feasible way for the formation of carbon-sulfur bonds.

Application of In Silico and HTS Approaches to Identify Nuclear Import Inhibitors for Venezuelan Equine Encephalitis Virus Capsid Protein: A Case Study

The development of new drugs is costly and time-consuming, with estimates of over $US1 billion and 15 years for a product to reach the market. As understanding of the molecular basis of disease improves, various approaches have been used to target specific molecular interactions in the search for effective drugs. These include high-throughput screening (HTS) for novel drug identification and computer-aided drug design (CADD) to assess the properties of putative drugs before experimental work begins. We have applied conventional HTS and CADD approaches to the problem of identifying antiviral compounds to limit infection by Venezuelan equine encephalitis virus (VEEV). Nuclear targeting of the VEEV capsid (CP) protein through interaction with the host nuclear import machinery has been shown to be essential for viral pathogenicity, with viruses incapable of this interaction being greatly attenuated. Our previous conventional HTS and in silico structure-based drug design (SBDD) screens were successful in identifying novel inhibitors of CP interaction with the host nuclear import machinery, thus providing a unique opportunity to assess the relative value of the two screening approaches directly. This focused review compares and contrasts the two screening approaches, together with the properties of the inhibitors identified, as a case study for parallel use of the two approaches to identify antivirals. The utility of SBDD screens, especially when used in parallel with traditional HTS, in identifying agents of interest to target the host-pathogen interface is highlighted.

Novel TRPV1 Channel Agonists With Faster and More Potent Analgesic Properties Than Capsaicin

The transient receptor potential vanilloid 1 (TRPV1) ion channel is a member of the family of Transient Receptor Potential (TRP) channels that acts as a molecular detector of noxious signals in primary sensory neurons. Activated by capsaicin, heat, voltage and protons, it is also well known for its desensitization, which led to the medical use of topically applied TRPV1 agonist capsaicin for its long-lasting analgesic effects. Here we report three novel small molecules, which were identified using a Structure-Based Virtual Screening for TRPV1 from the ZINC database. The three compounds were tested using electrophysiological assays, which confirmed their capsaicin-like agonist activity. von Frey filaments were used to measure the analgesic effects of the compounds applied topically on tactile allodynia induced by intra-plantar carrageenan. All compounds had anti-nociceptive activity, but two of them showed faster and longer lasting analgesic effects than capsaicin. The present results suggest that TRPV1 agonists different from capsaicin could be used to develop topical analgesics with faster onset and more potent effects.

Integration of target discovery, drug discovery and drug delivery: A review on computational strategies

Most of the computational tools involved in drug discovery developed during the 1980s were largely based on computational chemistry, quantitative structure-activity relationship (QSAR) and cheminformatics. Subsequently, the advent of genomics in the 2000s gave rise to a huge number of databases and computational tools developed to analyze large quantities of data, through bioinformatics, to obtain valuable information about the genomic regulation of different organisms. Target identification and validation is a long process during which evidence for and against a target is accumulated in the pursuit of developing new drugs. Finally, the drug delivery system appears as a novel approach to improve drug targeting and releasing into the cells, leading to new opportunities to improve drug efficiency and avoid potential secondary effects. In each area: target discovery, drug discovery and drug delivery, different computational strategies are being developed to accelerate the process of selection and discovery of new tools to be applied to different scientific fields. Research on these three topics is growing rapidly, but still requires a global view of this landscape to detect the most challenging bottleneck and how computational tools could be integrated in each topic. This review describes the current state of the art in computational strategies for target discovery, drug discovery and drug delivery and how these fields could be integrated. Finally, we will discuss about the current needs in these fields and how the continuous development of databases and computational tools will impact on the improvement of those areas. This article is categorized under: Therapeutic Approaches and Drug Discovery > Emerging Technologies Therapeutic Approaches and Drug Discovery > Nanomedicine for Infectious Disease Nanotechnology Approaches to Biology > Nanoscale Systems in Biology.

Pharmacophore and molecular dynamics based activity profiling of natural products for kinases involved in lung cancer

To determine the kinase inhibitory potential of natural products that could be utilized in lung cancer therapy in the near future, a pharmacophore-based activity profiling protocol using parallel pharmacophore-based virtual screening of ZINC-a natural product database-was employed. The work presented here is based on the previously explored fact that pharmacophore-based parallel screening is a reliable in silico protocol to predict the possible biological activities of any compound, or any compound library, by screening it with a number of pharmacophore models. The present study involves ligand-based pharmacophore modeling of various kinases, including EGFR (T790 M), cMET, ErbB2, FGFR and ALK, which are well established targets of normal as well resistant lung cancer. The generated pharmacophore models were then utilized for parallel and cross screening. The profiled molecules for each target were then validated using molecular docking and molecular dynamic simulations. The results show that kinase inhibitory activity profiling of some natural product molecules was successfully achieved. Graphical abstract Pharmacophore and activity profiling of natural products for kinases involved in lung cancer.

Rce1: mechanism and inhibition

Ras converting enzyme 1 (Rce1) is an integral membrane endoprotease localized to the endoplasmic reticulum that mediates the cleavage of the carboxyl-terminal three amino acids from CaaX proteins, whose members play important roles in cell signaling processes. Examples include the Ras family of small GTPases, the γ-subunit of heterotrimeric GTPases, nuclear lamins, and protein kinases and phosphatases. CaaX proteins, especially Ras, have been implicated in cancer, and understanding the post-translational modifications of CaaX proteins would provide insight into their biological function and regulation. Many proteolytic mechanisms have been proposed for Rce1, but sequence alignment, mutational studies, topology, and recent crystallographic data point to a novel mechanism involving a glutamate-activated water and an oxyanion hole. Studies using in vivo and in vitro reporters of Rce1 activity have revealed that the enzyme cleaves only prenylated substrates and the identity of the a₂ amino residue in the Ca₁a₂X sequence is most critical for recognition, preferring Ile, Leu, or Val. Substrate mimetics can be somewhat effective inhibitors of Rce1 in vitro. Small-molecule inhibitor discovery is currently limited by the lack of structural information on a eukaryotic enzyme, but a set of 8-hydroxyquinoline derivatives has demonstrated an ability to mislocalize all three mammalian Ras isoforms, giving optimism that potent, selective inhibitors might be developed. Much remains to be discovered regarding cleavage specificity, the impact of chemical inhibition, and the potential of Rce1 as a therapeutic target, not only for cancer, but also for other diseases.

Evidence for Novel Action at the Cell-Binding Site of Human Angiogenin Revealed by Heteronuclear NMR Spectroscopy, in silico and in vivo Studies

A member of the ribonuclease A superfamily, human angiogenin (hAng) is a potent angiogenic factor. Heteronuclear NMR spectroscopy combined with induced-fit docking revealed a dual binding mode for the most antiangiogenic compound of a series of ribofuranosyl pyrimidine nucleosides that strongly inhibit hAng's angiogenic activity in vivo. While modeling suggests the potential for simultaneous binding of the inhibitors at the active and cell-binding sites, NMR studies indicate greater affinity for the cell-binding site than for the active site. Additionally, molecular dynamics simulations at 100 ns confirmed the stability of binding at the cell-binding site with the predicted protein-ligand interactions, in excellent agreement with the NMR data. This is the first time that a nucleoside inhibitor is reported to completely inhibit the angiogenic activity of hAng in vivo by exerting dual inhibitory activity on hAng, blocking both the entrance of hAng into the cell and its ribonucleolytic activity.

Current Trends in Docking Methodologies

Molecular docking was earlier considered to predict the binding affinity of the receptor and ligand molecules. With the progress in computational power and developing approaches, new horizons are now opening for accurate prediction of molecular binding affinity. In the current book chapter, recent strategies for Computer-Aided Drug Designing (CADD) including virtual screening and molecular docking, encompassing molecular dynamics simulations, and binding free energy calculation methods are discussed. Brief overview of different binding free energy methods MMPBSA, MMGBSA, LIE and TI have also been given along with the recent Relaxed Complex Scheme protocol.

NMR study of Met-1 human Angiogenin: (1)H, (13)C, (15)N backbone and side-chain resonance assignment

Here, we report the high yield expression and preliminary structural analysis via solution hetero-nuclear NMR spectroscopy of the recombinant Met-1 human Angiogenin. The analysis reveals a well folded as well as, a monomeric polypeptide. Τhe sequence-specific assignment of its (1)H, (15)N and (13)C resonances at high percentage was obtained. Also, using TALOS+ its secondary structure elements were determined.

COTI-2, a novel small molecule that is active against multiple human cancer cell lines in vitro and in vivo

Identification of novel anti-cancer compounds with high efficacy and low toxicity is critical in drug development. High-throughput screening and other such strategies are generally resource-intensive. Therefore, in silico computer-aided drug design has gained rapid acceptance and popularity. We employed our proprietary computational platform (CHEMSAS®), which uses a unique combination of traditional and modern pharmacology principles, statistical modeling, medicinal chemistry, and machine-learning technologies to discover and optimize novel compounds that could target various cancers. COTI-2 is a small molecule candidate anti-cancer drug identified using CHEMSAS. This study describes the in vitro and in vivo evaluation of COTI-2. Our data demonstrate that COTI-2 is effective against a diverse group of human cancer cell lines regardless of their tissue of origin or genetic makeup. Most treated cancer cell lines were sensitive to COTI-2 at nanomolar concentrations. When compared to traditional chemotherapy or targeted-therapy agents, COTI-2 showed superior activity against tumor cells, in vitro and in vivo. Despite its potent anti-tumor efficacy, COTI-2 was safe and well-tolerated in vivo. Although the mechanism of action of COTI-2 is still under investigation, preliminary results indicate that it is not a traditional kinase or an Hsp90 inhibitor.

Prospective performance evaluation of selected common virtual screening tools. Case study: Cyclooxygenase (COX) 1 and 2

Computational methods can be applied in drug development for the identification of novel lead candidates, but also for the prediction of pharmacokinetic properties and potential adverse effects, thereby aiding to prioritize and identify the most promising compounds. In principle, several techniques are available for this purpose, however, which one is the most suitable for a specific research objective still requires further investigation. Within this study, the performance of several programs, representing common virtual screening methods, was compared in a prospective manner. First, we selected top-ranked virtual screening hits from the three methods pharmacophore modeling, shape-based modeling, and docking. For comparison, these hits were then additionally predicted by external pharmacophore- and 2D similarity-based bioactivity profiling tools. Subsequently, the biological activities of the selected hits were assessed in vitro, which allowed for evaluating and comparing the prospective performance of the applied tools. Although all methods performed well, considerable differences were observed concerning hit rates, true positive and true negative hits, and hitlist composition. Our results suggest that a rational selection of the applied method represents a powerful strategy to maximize the success of a research project, tightly linked to its aims. We employed cyclooxygenase as application example, however, the focus of this study lied on highlighting the differences in the virtual screening tool performances and not in the identification of novel COX-inhibitors.

Techniques used for the discovery of therapeutic compounds: The case of SARS

Severe acute respiratory syndrome coronavirus (SARS-CoV) is the etiological agent of SARS disease, which has ever severely menaced humans from the end of 2002 to June 2003. To date, great efforts have been made for the discovery of therapeutic compounds by using various technologies. In this report, we present a survey of these techniques and their applications in the development of promising anti-SARS agents.:

Amino and carboxy functionalized modified nucleosides: a potential class of inhibitors for angiogenin

The 3'-amino and carboxy functionalize thymidines execute their ribonucleolytic inhibition activity for angiogenin. These modified nucleosidic molecules inhibit the ribonucleolytic activity of angiogenin in a competitive manner like the other conventional nucleotidic inhibitors, which have been confirmed from kinetic experiments. The improved inhibition constant (Ki) values 427 ± 7, 775 ± 6 μM clearly indicate modified nucleosides are an obvious option for the designing of inhibitors of angiogenesis process. The chorioallantoic membrane (CAM) assay qualitatively suggests that amino functionalized nucleosides have an effective potency to inhibited angiogenin-induced angiogenesis. Docking studies further demonstrate the interaction of their polar amino group with the P1 site residues of angiogenin, i.e., His-13 and His-114 residues.

Comparing neural-network scoring functions and the state of the art: applications to common library screening

We compare established docking programs, AutoDock Vina and Schrödinger's Glide, to the recently published NNScore scoring functions. As expected, the best protocol to use in a virtual-screening project is highly dependent on the target receptor being studied. However, the mean screening performance obtained when candidate ligands are docked with Vina and rescored with NNScore 1.0 is not statistically different than the mean performance obtained when docking and scoring with Glide. We further demonstrate that the Vina and NNScore docking scores both correlate with chemical properties like small-molecule size and polarizability. Compensating for these potential biases leads to improvements in virtual screen performance. Composite NNScore-based scoring functions suited to a specific receptor further improve performance. We are hopeful that the current study will prove useful for those interested in computer-aided drug design.

The holistic integration of virtual screening in drug discovery

During the past decade, virtual screening (VS) has come of age. In this review, we document the evolution and maturation of VS from a rather exotic, stand-alone method toward a versatile hit and lead identification technology. VS campaigns have become fully integrated into drug discovery campaigns, evenly matched and complementary to high-throughput screening (HTS) methods. Here, we propose a novel classification of VS applications to help to monitor the advances in VS and to support future improvement of computational hit and lead identification methods. Several relevant VS studies from recent publications, in both academic and industrial settings, were selected to demonstrate the progress in this area. Furthermore, we identify challenges that lie ahead for the development of integrated VS campaigns.

IspE inhibitors identified by a combination of in silico and in vitro high-throughput screening

CDP-ME kinase (IspE) contributes to the non-mevalonate or deoxy-xylulose phosphate (DOXP) pathway for isoprenoid precursor biosynthesis found in many species of bacteria and apicomplexan parasites. IspE has been shown to be essential by genetic methods and since it is absent from humans it constitutes a promising target for antimicrobial drug development. Using in silico screening directed against the substrate binding site and in vitro high-throughput screening directed against both, the substrate and co-factor binding sites, non-substrate-like IspE inhibitors have been discovered and structure-activity relationships were derived. The best inhibitors in each series have high ligand efficiencies and favourable physico-chemical properties rendering them promising starting points for drug discovery. Putative binding modes of the ligands were suggested which are consistent with established structure-activity relationships. The applied screening methods were complementary in discovering hit compounds, and a comparison of both approaches highlights their strengths and weaknesses. It is noteworthy that compounds identified by virtual screening methods provided the controls for the biochemical screens.

Application of a sequential multicomponent assembly process/huisgen cycloaddition strategy to the preparation of libraries of 1,2,3-triazole-fused 1,4-benzodiazepines

A strategy featuring a multicomponent assembly process followed by an intramolecular azide-alkyne dipolar (Huisgen) cycloaddition was implemented for the facile synthesis of three different 1,2,3-triazolo-1,4-benzodiazepine scaffolds. A diverse library of 170 compounds derived from these scaffolds was then created through N-functionalizations, palladium-catalyzed cross-coupling reactions, and several applications of α-aminonitrile chemistry.

A small molecule inhibitor of Mitf-E-box DNA binding and its depigmenting effect in melan-a cells

BACKGROUND

Microphthalmia associated transcription factor (Mitf) is a key regulatory transcriptional factor of pigmentation-related genes including tyrosinase. Inhibition of tyrosinase transcription by blocking the binding of Mitf with its promoter E-box DNA can control the pigmentation. However, no such chemicals were reported so far.

OBJECTIVE

To discover and evaluate the small molecule inhibitors of Mitf-E-box DNA.

METHODS

Candidate chemicals were screened by virtual screening from pharmacophore data followed by Mitf E-box DNA protein chip. After selecting the chemical, its inhibitory activity on binding interaction between Mitf and E-box DNA, electrophoretic mobility shift assay (EMSA) was performed. To evaluate the depigmenting activity of Compound #17, cellular melanin assa, and Western blot were performed in melan-a cells.

RESULTS

Among 27 chemicals selected from a pharmacophore data by virtual screening, Compound #17 was screened, which showed the most potent inhibitory activity against Mitf-E-box DNA binding in protein chip. EMSA results confirmed the specific inhibition of Compound #17 on Mitf-E-box DNA binding. In melan-a cells, Compound #17 reduced tyrosinase expression and melanin synthesis (62.5% at 25 μM).

CONCLUSIONS

  The results show that Compound #17 is the first small molecule inhibitor of Mitf-E-box DNA binding with depigmenting activity.

Synthesis of a drug-like focused library of trisubstituted pyrrolidines using integrated flow chemistry and batch methods

A combination of flow and batch chemistries has been successfully applied to the assembly of a series of trisubstituted drug-like pyrrolidines. This study demonstrates the efficient preparation of a focused library of these pharmaceutically important structures using microreactor technologies, as well as classical parallel synthesis techniques, and thus exemplifies the impact of integrating innovative enabling tools within the drug discovery process.

A network-based multi-target computational estimation scheme for anticoagulant activities of compounds

Background

Traditional virtual screening method pays more attention on predicted binding affinity between drug molecule and target related to a certain disease instead of phenotypic data of drug molecule against disease system, as is often less effective on discovery of the drug which is used to treat many types of complex diseases. Virtual screening against a complex disease by general network estimation has become feasible with the development of network biology and system biology. More effective methods of computational estimation for the whole efficacy of a compound in a complex disease system are needed, given the distinct weightiness of the different target in a biological process and the standpoint that partial inhibition of several targets can be more efficient than the complete inhibition of a single target.

Methodology

We developed a novel approach by integrating the affinity predictions from multi-target docking studies with biological network efficiency analysis to estimate the anticoagulant activities of compounds. From results of network efficiency calculation for human clotting cascade, factor Xa and thrombin were identified as the two most fragile enzymes, while the catalytic reaction mediated by complex IXa:VIIIa and the formation of the complex VIIIa:IXa were recognized as the two most fragile biological matter in the human clotting cascade system. Furthermore, the method which combined network efficiency with molecular docking scores was applied to estimate the anticoagulant activities of a serial of argatroban intermediates and eight natural products respectively. The better correlation (r = 0.671) between the experimental data and the decrease of the network deficiency suggests that the approach could be a promising computational systems biology tool to aid identification of anticoagulant activities of compounds in drug discovery.

Conclusions

This article proposes a network-based multi-target computational estimation method for anticoagulant activities of compounds by combining network efficiency analysis with scoring function from molecular docking.

Non-stoichiometric inhibition in biochemical high-throughput screening

INTRODUCTION

Over the last 2 decades, high-throughput screening (HTS) has become one of the key strategies for the generation of new leads. Non-stoichiometric inhibition is one of the most extensively studied mechanisms responsible for the large percentage of hit compounds from biochemical screens that cannot be developed into leads. Therefore, HTS hit lists need to be sorted rapidly and efficiently into stoichiometrically binding inhibitors and compounds that affect enzyme activity non-stoichiometrically.

AREAS COVERED

This article explores the non-stoichiometric inhibition of enzymatic activity in biochemical screens, particularly by compound aggregation, and the authors explain the terminology they use to describe such compound behavior. The paper then provides a short historical overview of both academic and industrial research on compound aggregation specifically. Finally, the article discusses the implications for industrial drug discovery and the measures that can be taken to identify non-stoichiometric and aggregating inhibitors early in this process.

EXPERT OPINION

The most pragmatic approach in a lead finding campaign is to focus on the early identification of selective and stoichiometric inhibitors. The combination of multiple approaches (assessing both activity and binding) allows the enrichment of stoichiometric inhibitors at each stage of the flowchart.

Comparative virtual screening and novelty detection for NMDA-GlycineB antagonists

Identification of novel compound classes for a drug target is a challenging task for cheminformatics and drug design when considerable research has already been undertaken and many potent lead structures have been identified, which leaves limited unclaimed chemical space for innovation. We validated and successfully applied different state-of-the-art techniques for virtual screening (Bayesian machine learning, automated molecular docking, pharmacophore search, pharmacophore QSAR and shape analysis) of 4.6 million unique and readily available chemical structures to identify promising new and competitive antagonists of the strychnine-insensitive Glycine binding site (Glycine(B) site) of the NMDA receptor. The novelty of the identified virtual hits was assessed by scaffold analysis, putting a strong emphasis on novelty detection. The resulting hits were tested in vitro and several novel, active compounds were identified. While the majority of the computational methods tested were able to partially discriminate actives from structurally similar decoy molecules, the methods differed substantially in their prospective applicability in terms of novelty detection. The results demonstrate that although there is no single best computational method, it is most worthwhile to follow this concept of focused compound library design and screening, as there still can new bioactive compounds be found that possess hitherto unexplored scaffolds and interesting variations of known chemotypes.

Virtual high throughput screening (vHTS)--a perspective

With the exponential rise in the number of viable novel drug targets, computational methods are being increasingly applied to accelerate the drug discovery process. Virtual High Throughput Screening (vHTS) is one such established methodology to identify drug candidates from large collection of compound libraries. Although it complements the expensive and time consuming High Throughput Screening (HTS) of compound libraries, vHTS possess inherent challenges. The successful vHTS requires the careful implementation of each phase of computational screening experiment right from target preparation to hit identification and lead optimization. This article discusses some of the important considerations that are imperative for designing a successful vHTS experiment.

Design of ligand binding to an engineered protein cavity using virtual screening and thermal up-shift evaluation

Proteins could be used to carry and deliver small compounds. As a tool for designing ligand binding sites in protein cores, a three-step virtual screening method is presented that has been optimised using existing data on T4 lysozyme complexes and tested in a newly engineered cavity in flavodoxin. The method can pinpoint, in large databases, ligands of specific protein cavities. In the first step, physico-chemical filters are used to screen the library and discard a majority of compounds. In the second step, a flexible, fast docking procedure is used to score and select a smaller number of compounds as potential binders. In the third step, a finer method is used to dock promising molecules of the hit list into the protein cavity, and an optimised free energy function allows discarding the few false positives by calculating the affinity of the modelled complexes. To demonstrate the portability of the method, several cavities have been designed and engineered in the flavodoxin from Anabaena PCC 7119, and the W66F/L44A double mutant has been selected as a suitable host protein. The NCI database has then been screened for potential binders, and the binding to the engineered cavity of five promising compounds and three tentative non-binders has been experimentally tested by thermal up-shift assays and spectroscopic titrations. The five tentative binders (some apolar and some polar), unlike the three tentative non-binders, are shown to bind to the host mutant and, importantly, not to bind to the wild type protein. The three-step virtual screening method developed can thus be used to identify ligands of buried protein cavities. We anticipate that the method could also be used, in a reverse manner, to identify natural or engineerable protein cavities for the hosting of ligands of interest.

How to do an evaluation: pitfalls and traps

The recent literature is replete with papers evaluating computational tools (often those operating on 3D structures) for their performance in a certain set of tasks. Most commonly these papers compare a number of docking tools for their performance in cognate re-docking (pose prediction) and/or virtual screening. Related papers have been published on ligand-based tools: pose prediction by conformer generators and virtual screening using a variety of ligand-based approaches. The reliability of these comparisons is critically affected by a number of factors usually ignored by the authors, including bias in the datasets used in virtual screening, the metrics used to assess performance in virtual screening and pose prediction and errors in crystal structures used.

Towards the development of universal, fast and highly accurate docking/scoring methods: a long way to go

Accelerating the drug discovery process requires predictive computational protocols capable of reducing or simplifying the synthetic and/or combinatorial challenge. Docking-based virtual screening methods have been developed and successfully applied to a number of pharmaceutical targets. In this review, we first present the current status of docking and scoring methods, with exhaustive lists of these. We next discuss reported comparative studies, outlining criteria for their interpretation. In the final section, we describe some of the remaining developments that would potentially lead to a universally applicable docking/scoring method.

Novel paradigms for drug discovery: computational multitarget screening

An established paradigm in current drug development is (i) to identify a single protein target whose inhibition is likely to result in the successful treatment of a disease of interest; (ii) to assay experimentally large libraries of small-molecule compounds in vitro and in vivo to identify promising inhibitors in model systems; and (iii) to determine whether the findings are extensible to humans. This complex process, which is largely based on trial and error, is risk-, time- and cost-intensive. Computational (virtual) screening of drug-like compounds simultaneously against the atomic structures of multiple protein targets, taking into account protein-inhibitor dynamics, might help to identify lead inhibitors more efficiently, particularly for complex drug-resistant diseases. Here we discuss the potential benefits of this approach, using HIV-1 and Plasmodium falciparum infections as examples. We propose a virtual drug discovery 'pipeline' that will not only identify lead inhibitors efficiently, but also help minimize side-effects and toxicity, thereby increasing the likelihood of successful therapies.

In-silico drug screening method based on the protein–compound affinity matrix using the factor selection technique

We have developed a new in-silico drug screening method, a modified version of a docking score index (DSI) method, based on a protein-compound docking affinity matrix. By using this method, the docking scores are converted to the docking score indexes by the principal component analysis (PCA) method and each compound is projected into a PCA space. In this study, we propose a method to select a set of suitable principal component axes and evaluate the database enrichment for 12 target proteins. This method selects the new active compounds or hits, which are close to the known active compounds, thereby enhancing the database enrichment.

Virtual ligand screening: strategies, perspectives and limitations

In contrast to high-throughput screening, in virtual ligand screening (VS), compounds are selected using computer programs to predict their binding to a target receptor. A key prerequisite is knowledge about the spatial and energetic criteria responsible for protein–ligand binding. The concepts and prerequisites to perform VS are summarized here, and explanations are sought for the enduring limitations of the technology. Target selection, analysis and preparation are discussed, as well as considerations about the compilation of candidate ligand libraries. The tools and strategies of a VS campaign, and the accuracy of scoring and ranking of the results, are also considered.

Promiscuous Ligands

Computational and experimental high-throughput screening are frequently used to discover new leads for drug design. Although novel ligands have been identified by these methods, it has become clear that screening hit lists are plagued by false positives. These nuisance compounds are ultimately found to be developmental dead-ends and are abandoned, often after considerable effort has been invested in them. Much work over the last decade has been devoted to exploring the origins of false-positive screening hits, and ligand promiscuity has emerged as one such cause. Well-known mechanisms of promiscuity include reactive species and privileged substructures. More recently, it has been found that some nonspecific screening hits form aggregates of 30–1000 nm in diameter. It has been proposed that these aggregate particles are responsible for the promiscuous behavior of many false positives and that aggregate-forming compounds may be widespread among screening hits. This chapter will review the known mechanisms of ligand promiscuity with an emphasis on the recently described model of aggregation. Experimental and computational methods for identifying promiscuous compounds will be described, and some outstanding questions in the field will be considered.

The binding of 3′-N-piperidine-4-carboxyl-3′-deoxy-ara-uridine to ribonuclease A in the crystal

The binding of a moderate inhibitor, 3'-N-piperidine-4-carboxyl-3'-deoxy-ara-uridine, to ribonuclease A has been studied by X-ray crystallography at 1.7A resolution. Two inhibitor molecules are bound in the central RNA binding cavity of RNase A exploiting interactions with residues from peripheral binding sites rather than from the active site of the enzyme. The uracyl moiety of the first inhibitor molecule occupies the purine-preferring site of RNase A, while the rest of the molecule projects to the solvent. The second inhibitor molecule binds with the carboxyl group at the pyrimidine recognition site and the uridine moiety exploits interactions with RNase A residues Lys66, His119 and Asp121. Comparative structural analysis of the 3'-N-piperidine-4-carboxyl-3'-deoxy-ara-uridine complex with other RNase A-ligand complexes provides a structural explanation of its potency. The crystal structure of the RNase A-3'-N-piperidine-4-carboxyl-3'-deoxy-ara-uridine complex provides evidence of a novel ligand-binding pattern in RNase A for 3'-N-aminonucleosides that was not anticipated by modelling studies, while it also suggests ways to improve the efficiency and selectivity of such compounds to develop pharmaceuticals against pathologies associated with RNase A homologues.

Minimizing false positives in kinase virtual screens

In spite of recent improvements in docking and scoring methods, high false-positive rates remain a common issue in structure-based virtual screening. In this study, the distinctive features of false positives in kinase virtual screens were investigated. A series of retrospective virtual screens on kinase targets was performed on specifically designed test sets, each combining true ligands and experimentally confirmed inactive compounds. A systematic analysis of the docking poses generated for the top-ranking compounds highlighted key aspects differentiating true hits from false positives. The most recurring feature in the poses of false positives was the absence of certain key interactions known to be required for kinase binding. A systematic analysis of 444 crystal structures of ligand-bound kinases showed that at least two hydrogen bonds between the ligand and the backbone protein atoms in the kinase hinge region are present in 90% of the complexes, with very little variability across targets. Closer inspection showed that when the two hydrogen bonds are present, one of three preferred hinge-binding motifs is involved in 96.5% of the cases. Less than 10% of the false positives satisfied these two criteria in the minimized docking poses generated by our standard protocol. Ligand conformational artifacts were also shown to contribute to the occurrence of false positives in a number of cases. Application of this knowledge in the form of docking constraints and post-processing filters provided consistent improvements in virtual screening performance on all systems. The false-positive rates were significantly reduced and the enrichment factors increased by an average of twofold. On the basis of these results, a generalized two-step protocol for virtual screening on kinase targets is suggested.

Here be dragons: docking and screening in an uncharted region of chemical space

To compare virtual and high-throughput screening in an unbiased way, 50,000 compounds were docked into the 3-dimensional structure of dihydrofolate reductase prospectively, and the results were compared to a subsequent experimental screening of the same library. Undertaking these calculations demanded careful database curation and control calculations with annotated inhibitors. These ultimately led to a ranked list of more likely and less likely inhibitors and to the prediction that relatively few inhibitors would be found in the empirical screen. The latter prediction turned out to be correct, with arguably no validated inhibitors found experimentally. Subsequent retesting of high-scoring docked molecules may have found 2 true inhibitors, although this remains uncertain due to experimental ambiguities. The implications of this study for screening campaigns are considered.

Novel curcumin- and emodin-related compounds identified by in silico 2D/3D conformer screening induce apoptosis in tumor cells

Background

Inhibition of the COP9 signalosome (CSN) associated kinases CK2 and PKD by curcumin causes stabilization of the tumor suppressor p53. It has been shown that curcumin induces tumor cell death and apoptosis. Curcumin and emodin block the CSN-directed c-Jun signaling pathway, which results in diminished c-Jun steady state levels in HeLa cells. The aim of this work was to search for new CSN kinase inhibitors analogue to curcumin and emodin by means of an in silico screening method.

Methods

Here we present a novel method to identify efficient inhibitors of CSN-associated kinases. Using curcumin and emodin as lead structures an in silico screening with our in-house database containing more than 10⁶ structures was carried out. Thirty-five compounds were identified and further evaluated by the Lipinski's rule-of-five. Two groups of compounds can be clearly discriminated according to their structures: the curcumin-group and the emodin-group. The compounds were evaluated in in vitro kinase assays and in cell culture experiments.

Results

The data revealed 3 compounds of the curcumin-group (e.g. piceatannol) and 4 of the emodin-group (e.g. anthrachinone) as potent inhibitors of CSN-associated kinases. Identified agents increased p53 levels and induced apoptosis in tumor cells as determined by annexin V-FITC binding, DNA fragmentation and caspase activity assays.

Conclusion

Our data demonstrate that the new in silico screening method is highly efficient for identifying potential anti-tumor drugs.

Comparing protein-ligand docking programs is difficult

There is currently great interest in comparing protein-ligand docking programs. A review of recent comparisons shows that it is difficult to draw conclusions of general applicability. Statistical hypothesis testing is required to ensure that differences in pose-prediction success rates and enrichment rates are significant. Numerical measures such as root-mean-square deviation need careful interpretation and may profitably be supplemented by interaction-based measures and visual inspection of dockings. Test sets must be of appropriate diversity and of good experimental reliability. The effects of crystal-packing interactions may be important. The method used for generating starting ligand geometries and positions may have an appreciable effect on docking results. For fair comparison, programs must be given search problems of equal complexity (e.g. binding-site regions of the same size) and approximately equal time in which to solve them. Comparisons based on rescoring require local optimization of the ligand in the space of the new objective function. Re-implementations of published scoring functions may give significantly different results from the originals. Ostensibly minor details in methodology may have a profound influence on headline success rates.

Overview on the use of therapeutic antibodies in drug discovery

The number of therapeutic antibodies approved by regulatory agencies as novel drugs and the number of antibodies in development has increased significantly. The modular nature of antibody structure has enabled researchers to more predictably design therapeutic antibodies by choosing appropriate functional features most appropriate for a given antibody target and clinical indication. Advances in recombinant antibody technologies have allowed the routine generation of antibodies that can satisfy stringent drug design criteria, such as low immunogenicity, high affinity, target specificity, and commercially viable manufacturing methods. Engineering design opportunities exist for both the variable and the constant regions that encompass, in addition to antigen specificity and affinity, effector functions that mediate immune complex clearance or pharmacokinetics. These are discussed in the context of relevant in vivo and in vitro technologies, such as human IgG transgenic mice, phage display, and biologics manufacturing. Finally, therapeutic antibodies are compared with traditional drugs with respect to target class, selectivity, route of administration, intellectual property issues, and lead discovery and optimization.

Scoring functions for protein-ligand interactions: a critical perspective

Scoring functions play an essential role in structure-based virtual screening. They are required to guide the docking of candidate compounds to structures of receptor binding sites, to select probable binding modes, and to discriminate binders from non-binders. Although many scoring functions have successfully been used to identify novel ligands for a wide variety of targets, much work remains to be done to avoid incorrect prediction of binding modes and high numbers of false positives. This review gives an overview of the current state of the field and outlines key issues for the further development of scoring functions.:

Streamlined System for Purifying and Quantifying a Diverse Library of Compounds and the Effect of Compound Concentration Measurements on the Accurate Interpretation of Biological Assay Results

As part of an overall systems approach to generating highly accurate screening data across large numbers of compounds and biological targets, we have developed and implemented streamlined methods for purifying and quantitating compounds at various stages of the screening process, coupled with automated "traditional" storage methods (DMSO, -20 degrees C). Specifically, all of the compounds in our druglike library are purified by LC/MS/UV and are then controlled for identity and concentration in their respective DMSO stock solutions by chemiluminescent nitrogen detection (CLND)/evaporative light scattering detection (ELSD) and MS/UV. In addition, the compound-buffer solutions used in the various biological assays are quantitated by LC/UV/CLND to determine the concentration of compound actually present during screening. Our results show that LC/UV/CLND/ELSD/MS is a widely applicable method that can be used to purify, quantitate, and identify most small organic molecules from compound libraries. The LC/UV/CLND technique is a simple and sensitive method that can be easily and cost-effectively employed to rapidly determine the concentrations of even small amounts of any N-containing compound in aqueous solution. We present data to establish error limits for concentration determination that are well within the overall variability of the screening process. This study demonstrates that there is a significant difference between the predicted amount of soluble compound from stock DMSO solutions following dilution into assay buffer and the actual amount present in assay buffer solutions, even at the low concentrations employed for the assays. We also demonstrate that knowledge of the concentrations of compounds to which the biological target is exposed is critical for accurate potency determinations. Accurate potency values are in turn particularly important for drug discovery, for understanding structure-activity relationships, and for building useful empirical models of protein-ligand interactions. Our new understanding of relative solubility demonstrates that most, if not all, decisions that are made in early discovery are based upon missing or inaccurate information. Finally, we demonstrate that careful control of compound handling and concentration, coupled with accurate assay methods, allows the use of both positive and negative data in analyzing screening data sets for structure-activity relationships that determine potency and selectivity.