Inhibitors of dihydrodipicolinate reductase, a key enzyme of the diaminopimelate pathway of Mycobacterium tuberculosis

In silico virtual screening for the identification of novel inhibitors against dihydrodipicolinate reductase (DapB) of Mycobacterium tuberculosis, a key enzyme of diaminopimelate pathway

IMPORTANCE

Non-compliance to lengthy antituberculosis (TB) treatment regimen, associated side effects, and emergence of drug-resistant strains of Mycobacterium tuberculosis (M. tb) emphasize the need to develop more effective anti-TB drugs. Here, we have evaluated the role of M. tb dihydrodipicolinate reductase (DapB), a component of the diaminopimelate pathway, which is involved in the biosynthesis of both lysine and mycobacterial cell wall. We showed that DapB is essential for the in vitro as well as intracellular growth of M. tb. We further utilized M. tb DapB, as a target for identification of inhibitors by employing in silico virtual screening, and conducted various in vitro screening assays to identify inhibitors with potential to inhibit DapB activity and in vitro and intracellular growth of M. tb with no significant cytotoxicity against various mammalian cell lines. Altogether, M. tb DapB serves as an important drug target and a hit molecule, namely, 4-(3-Phenylazoquinoxalin-2-yl) butanoic acid methyl ester has been identified as an antimycobacterial molecule in our study.

Repurposed inhibitor of bacterial dihydrodipicolinate reductase exhibits effective herbicidal activity

Herbicide resistance represents one of the biggest threats to our natural environment and agricultural sector. Thus, new herbicides are urgently needed to tackle the rise in herbicide-resistant weeds. Here, we employed a novel strategy to repurpose a 'failed' antibiotic into a new and target-specific herbicidal compound. Specifically, we identified an inhibitor of bacterial dihydrodipicolinate reductase (DHDPR), an enzyme involved in lysine biosynthesis in plants and bacteria, that exhibited no antibacterial activity but severely attenuated germination of the plant Arabidopsis thaliana. We confirmed that the inhibitor targets plant DHDPR orthologues in vitro, and exhibits no toxic effects against human cell lines. A series of analogues were then synthesised with improved efficacy in germination assays and against soil-grown A. thaliana. We also showed that our lead compound is the first lysine biosynthesis inhibitor with activity against both monocotyledonous and dicotyledonous weed species, by demonstrating its effectiveness at reducing the germination and growth of Lolium rigidum (rigid ryegrass) and Raphanus raphanistrum (wild radish). These results provide proof-of-concept that DHDPR inhibition may represent a much-needed new herbicide mode of action. Furthermore, this study exemplifies the untapped potential of repurposing 'failed' antibiotic scaffolds to fast-track the development of herbicide candidates targeting the respective plant enzymes.

The coordinated action of the enzymes in the L-lysine biosynthetic pathway and how to inhibit it for antibiotic targets

BACKGROUND

Antimicrobial resistance is a global health issue that requires immediate attention in terms of new antibiotics and new antibiotic targets. The l-lysine biosynthesis pathway (LBP) is a promising avenue for drug discovery as it is essential for bacterial growth and survival and is not required by human beings.

SCOPE OF REVIEW

The LBP involves a coordinated action of fourteen different enzymes distributed over four distinct sub-pathways. The enzymes involved in this pathway belong to different classes, such as aspartokinase, dehydrogenase, aminotransferase, epimerase, etc. This review provides a comprehensive account of the secondary and tertiary structure, conformational dynamics, active site architecture, mechanism of catalytic action, and inhibitors of all enzymes involved in LBP of different bacterial species.

MAJOR CONCLUSIONS

LBP offers a wide scope for novel antibiotic targets. The enzymology of a majority of the LBP enzymes is well understood, although these enzymes are less widely studied in the critical pathogens (according to the 2017 WHO report) that require immediate attention. In particular, the enzymes in the acetylase pathway, DapAT, DapDH, and Aspartokinase in critical pathogens have received little attention. High throughput screening for inhibitor design against the enzymes of lysine biosynthetic pathway is rather limited, both in number and in the extent of success.

GENERAL SIGNIFICANCE

This review can serve as a guide for the enzymology of LBP and help in identifying new drug targets and designing potential inhibitors.

Colorimetric ortho-aminobenzaldehyde assay developed for the high-throughput chemical screening of inhibitors against dihydrodipicolinate synthase from pathogenic bacteria

In search of a new class of antibacterial agents, compounds that target the essential bacterial enzyme, dihydrodipicolinate synthase (DHDPS), are of interest to drug discovery efforts. DHDPS catalyzes the first committed step in the diaminopimelate (DAP) pathway to the biosynthesis of lysine in bacteria and plants. The ortho-aminobenzaldehyde (o-ABA) assay is typically used as a qualitative tool for identifying fractions containing DHDPS during purification. This report is about the development of a high-throughput o-ABA assay format for the quantification of DHDPS enzyme activity using multi-well plates. The colorimetric assay is suitable for determining enzymatic parameters (K _M and V_max) and identifying inhibitors of DHDPS in a high-throughput screen.

A Nucleophilic Activity-Based Probe Enables Profiling of PLP-Dependent Enzymes

PLP-dependent enzymes represent an important class of highly "druggable" enzymes that perform a wide array of critical reactions to support all organisms. Inhibition of individual members of this family of enzymes has been validated as a therapeutic target for pathologies ranging from infection with Mycobacterium tuberculosis to epilepsy. Given the broad nature of the activities within this family of enzymes, we envisioned a universally acting probe to characterize existing and putative members of the family that also includes the necessary chemical moieties to enable activity-based protein profiling experiments. Hence, we developed a probe that contains an N-hydroxyalanine warhead that acts as a covalent inhibitor of PLP-dependent enzymes, a linear diazirine for UV crosslinking, and an alkyne moiety to enable enrichment of crosslinked proteins. Our molecule was used to study PLP-dependent enzymes in vitro as well as look at whole-cell lysates of M. tuberculosis and assess inhibitory activity. The probe was able to enrich and identify LysA, a PLP-dependent enzyme crucial for lysine biosynthesis, through mass spectrometry. Overall, our study shows the utility of this trifunctional first-generation probe. We anticipate further optimization of probes for PLP-dependent enzymes will enable the characterization of rationally designed covalent inhibitors of PLP-dependent enzymes, which will expedite the preclinical characterization of these important therapeutic targets.

The Design, Synthesis, and Evaluation of Diaminopimelic Acid Derivatives as Potential dapF Inhibitors Preventing Lysine Biosynthesis for Antibacterial Activity

We created thiazole and oxazole analogues of diaminopimelic acid (DAP) by replacing its carboxyl groups and substituting sulphur for the central carbon atom. Toxicity, ADME, molecular docking, and in vitro antimicrobial studies of the synthesized compounds were carried out. These compounds displayed significant antibacterial efficacy, with MICs of 70-80 µg/mL against all tested bacteria. Comparative values of the MIC, MBC, and ZOI of the synthesized compound were noticed when compared with ciprofloxacin. At 200 µg/mL, thio-DAP (1) had a ZOI of 22.67 ± 0.58, while ciprofloxacin had a ZOI of 23.67 ± 0.58. To synthesize thio-DAP (1) and oxa-DAP (2), l-cysteine was used as a precursor for the L-stereocenter (l-cysteine), which is recognized by the dapF enzyme's active site and selectively binds to the ligand's L-stereocenter. Docking studies of these compounds were carried out using the programme version 11.5 Schrodinger to reveal the hydrophobic and hydrophilic properties of these complexes. The docking scores of compounds one and two were -9.823 and -10.098 kcal/mol, respectively, as compared with LL-DAP (-9.426 kcal/mol.). This suggests that compounds one and two interact more precisely with dapF than LL-DAP. Chemicals one and two were synthesized via the SBDD (structure-based drug design) approach and these act as inhibitors of the dapF in the lysine pathway of bacterial cell wall synthesis.

Low-cost anti-mycobacterial drug discovery using engineered E. coli

Whole-cell screening for Mycobacterium tuberculosis (Mtb) inhibitors is complicated by the pathogen's slow growth and biocontainment requirements. Here we present a synthetic biology framework for assaying Mtb drug targets in engineered E. coli. We construct Target Essential Surrogate E. coli (TESEC) in which an essential metabolic enzyme is deleted and replaced with an Mtb-derived functional analog, linking bacterial growth to the activity of the target enzyme. High throughput screening of a TESEC model for Mtb alanine racemase (Alr) revealed benazepril as a targeted inhibitor, a result validated in whole-cell Mtb. In vitro biochemical assays indicated a noncompetitive mechanism unlike that of clinical Alr inhibitors. We establish the scalability of TESEC for drug discovery by characterizing TESEC strains for four additional targets.

Designing of potent anti-diabetic molecules by targeting SIK2 using computational approaches

Diabetes mellitus (DM) is one of the major health problems worldwide. WHO have estimated that 439 million people may have DM by the year 2030. Several classes of drugs such as sulfonylureas, meglitinides, thiazolidinediones etc. are available to manage this disease, however, there is no cure for this disease. Salt inducible kinase 2 (SIK2) is expressed several folds in adipose tissue than in normal tissues and thus SIK2 is one of the attractive targets for DM treatment. SIK2 inhibition improves glucose homeostasis. Several analogues have been reported and experimentally proven against SIK for DM treatment. But, identifying potential SIK2 inhibitors with improved efficacy and good pharmacokinetic profiles will be helpful for the effective treatment of DM. The objective of the present study is to identify selective SIK2 inhibitors with good pharmacokinetic profiles. Due to the unavailability of SIK2 structure, the modeled structure of SIK2 will be an important to understand the atomic level of SIK2 inhibitors in the binding site pocket. In this study, different molecular modeling studies such as Homology Modeling, Molecular Docking, Pharmacophore-based virtual screening, MD simulations, Density Functional Theory calculations and WaterMap analysis were performed to identify potential SIK2 inhibitors. Five molecules from different databases such as Binding_4067, TosLab_837067, NCI_349155, Life chemicals_ F2565-0113, Enamine_7623111186 molecules were identified as possible SIK2 inhibitors.

Expression of a novel mycobacterial phosphodiesterase successfully lowers cAMP levels resulting in reduced tolerance to cell wall-targeting antimicrobials

cAMP and antimicrobial susceptibility in mycobacteriaAntimicrobial tolerance, the ability to survive exposure to antimicrobials via transient nonspecific means, promotes the development of antimicrobial resistance (AMR). The study of the molecular mechanisms that result in antimicrobial tolerance is therefore essential for the understanding of AMR. In gram-negative bacteria, the second messenger molecule 3'',5''-cAMP has been previously shown to be involved in AMR. In mycobacteria, however, the role of cAMP in antimicrobial tolerance has been difficult to probe due to its particular complexity. In order to address this difficulty, here, through unbiased biochemical approaches consisting in the fractionation of clear protein lysate from a mycobacterial strain deleted for the known cAMP phosphodiesterase (Rv0805c) combined with mass spectrometry techniques, we identified a novel cyclic nucleotide-degrading phosphodiesterase enzyme (Rv1339) and developed a system to significantly decrease intracellular cAMP levels through plasmid expression of Rv1339 using the constitutive expression system, pVV16. In Mycobacterium smegmatis mc²155, we demonstrate that recombinant expression of Rv1339 reduced cAMP levels threefold and resulted in altered gene expression, impaired bioenergetics, and a disruption in peptidoglycan biosynthesis leading to decreased tolerance to antimicrobials that target cell wall synthesis such as ethambutol, D-cycloserine, and vancomycin. This work increases our understanding of the role of cAMP in mycobacterial antimicrobial tolerance, and our observations suggest that nucleotide signaling may represent a new target for the development of antimicrobial therapies.

IMB-XMA0038, a new inhibitor targeting aspartate-semialdehyde dehydrogenase of Mycobacterium tuberculosis

The emergence of drug-resistant tuberculosis (TB) constitutes a major challenge to TB control programmes. There is an urgent need to develop effective anti-TB drugs with novel mechanisms of action. Aspartate-semialdehyde dehydrogenase (ASADH) is the second enzyme in the aspartate metabolic pathway. The absence of the pathway in humans and the absolute requirement of aspartate in bacteria make ASADH a highly attractive drug target. In this study, we used ASADH coupled with Escherichia coli type III aspartate kinase (LysC) to establish a high-throughput screening method to find new anti-TB inhibitors. IMB-XMA0038 was identified as an inhibitor of MtASADH with an IC₅₀ value of 0.59 μg/mL through screening. The interaction between IMB-XMA0038 and MtASADH was confirmed by surface plasmon resonance (SPR) assay and molecular docking analysis. Furthermore, IMB-XMA0038 was found to inhibit various drug-resistant MTB strains potently with minimal inhibitory concentrations (MICs) of 0.25–0.5 μg/mL. The conditional mutant strain MTB::asadh cultured with different concentrations of inducer (10⁻⁵ or 10⁻¹ μg/mL pristinamycin) resulted in a maximal 16 times difference in MICs. At the same time, IMB-XMA0038 showed low cytotoxicity in vitro and vivo. In mouse model, it encouragingly declined the MTB colony forming units (CFU) in lung by 1.67 log10 dosed at 25 mg/kg for 15 days. In conclusion, our data demonstrate that IMB-XMA0038 is a promising lead compound against drug-resistant tuberculosis.

A Systematic Strategy to Find Potential Therapeutic Targets for Pseudomonas aeruginosa Using Integrated Computational Models

Pseudomonas aeruginosa is an opportunistic human pathogen that has been a constant global health problem due to its ability to cause infection at different body sites and its resistance to a broad spectrum of clinically available antibiotics. The World Health Organization classified multidrug-resistant Pseudomonas aeruginosa among the top-ranked organisms that require urgent research and development of effective therapeutic options. Several approaches have been taken to achieve these goals, but they all depend on discovering potential drug targets. The large amount of data obtained from sequencing technologies has been used to create computational models of organisms, which provide a powerful tool for better understanding their biological behavior. In the present work, we applied a method to integrate transcriptome data with genome-scale metabolic networks of Pseudomonas aeruginosa. We submitted both metabolic and integrated models to dynamic simulations and compared their performance with published in vitro growth curves. In addition, we used these models to identify potential therapeutic targets and compared the results to analyze the assumption that computational models enriched with biological measurements can provide more selective and (or) specific predictions. Our results demonstrate that dynamic simulations from integrated models result in more accurate growth curves and flux distribution more coherent with biological observations. Moreover, identifying drug targets from integrated models is more selective as the predicted genes were a subset of those found in the metabolic models. Our analysis resulted in the identification of 26 non-host homologous targets. Among them, we highlighted five top-ranked genes based on lesser conservation with the human microbiome. Overall, some of the genes identified in this work have already been proposed by different approaches and (or) are already investigated as targets to antimicrobial compounds, reinforcing the benefit of using integrated models as a starting point to selecting biologically relevant therapeutic targets.

Targeting amino acid metabolism of Mycobacterium tuberculosis for developing inhibitors to curtail its survival

Tuberculosis caused by the bacterium, Mycobacterium tuberculosis (Mtb), continues to remain one of the most devastating infectious diseases afflicting humans. Although there are several drugs for treating tuberculosis available currently, the emergence of the drug resistant forms of this pathogen has made its treatment and eradication a challenging task. While the replication machinery, protein synthesis and cell wall biogenesis of Mtb have been targeted often for anti-tubercular drug development a number of essential metabolic pathways crucial to its survival have received relatively less attention. In this context a number of amino acid biosynthesis pathways have recently been shown to be essential for the survival and pathogenesis of Mtb. Many of these pathways and or their key enzymes homologs are absent in humans hence they could be harnessed for anti-tubercular drug development. In this review, we describe comprehensively the amino acid metabolic pathways essential in Mtb and the key enzymes involved therein that are being investigated for developing inhibitors that compromise the survival and pathogenesis caused by this pathogen.

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.

Hybrid Dynamic Pharmacophore Models as Effective Tools to Identify Novel Chemotypes for Anti-TB Inhibitor Design: A Case Study With Mtb-DapB

Antimicrobial resistance (AMR) is one of the most serious global public health threats as it compromises the successful treatment of deadly infectious diseases like tuberculosis. New therapeutics are constantly needed but it takes a long time and is expensive to explore new biochemical space. One way to address this issue is to repurpose the validated targets and identify novel chemotypes that can simultaneously bind to multiple binding pockets of these targets as a new lead generation strategy. This study reports such a strategy, dynamic hybrid pharmacophore model (DHPM), which represents the combined interaction features of different binding pockets contrary to the conventional approaches, where pharmacophore models are generated from single binding sites. We have considered Mtb-DapB, a validated mycobacterial drug target, as our model system to explore the effectiveness of DHPMs to screen novel unexplored compounds. Mtb-DapB has a cofactor binding site (CBS) and an adjacent substrate binding site (SBS). Four different model systems of Mtb-DapB were designed where, either NADPH/NADH occupies CBS in presence/absence of an inhibitor 2, 6-PDC in the adjacent SBS. Two more model systems were designed, where 2, 6-PDC was linked to NADPH and NADH to form hybrid molecules. The six model systems were subjected to 200 ns molecular dynamics simulations and trajectories were analyzed to identify stable ligand-receptor interaction features. Based on these interactions, conventional pharmacophore models (CPM) were generated from the individual binding sites while DHPMs were created from hybrid-molecules occupying both binding sites. A huge library of 1,563,764 publicly available molecules were screened by CPMs and DHPMs. The screened hits obtained from both types of models were compared based on their Hashed binary molecular fingerprints and 4-point pharmacophore fingerprints using Tanimoto, Cosine, Dice and Tversky similarity matrices. Molecules screened by DHPM exhibited significant structural diversity, better binding strength and drug like properties as compared to the compounds screened by CPMs indicating the efficiency of DHPM to explore new chemical space for anti-TB drug discovery. The idea of DHPM can be applied for a wide range of mycobacterial or other pathogen targets to venture into unexplored chemical space.

Comparative structural and mechanistic studies of 4-hydroxy-tetrahydrodipicolinate reductases from Mycobacterium tuberculosis and Vibrio vulnificus

BACKGROUND

The products of the lysine biosynthesis pathway, meso-diaminopimelate and lysine, are essential for bacterial survival. This paper focuses on the structural and mechanistic characterization of 4-hydroxy-tetrahydrodipicolinate reductase (DapB), which is one of the enzymes from the lysine biosynthesis pathway. DapB catalyzes the conversion of (2S, 4S)-4-hydroxy-2,3,4,5-tetrahydrodipicolinate (HTPA) to 2,3,4,5-tetrahydrodipicolinate in an NADH/NADPH dependent reaction. Genes coding for DapBs were identified as essential for many pathogenic bacteria, and therefore DapB is an interesting new target for the development of antibiotics.

METHODS

We have combined experimental and computational approaches to provide novel insights into mechanism of the DapB catalyzed reaction.

RESULTS

Structures of DapBs originating from Mycobacterium tuberculosis and Vibrio vulnificus in complexes with NAD⁺, NADP⁺, as well as with inhibitors, were determined and described. The structures determined by us, as well as currently available structures of DapBs from other bacterial species, were compared and used to elucidate a mechanism of reaction catalyzed by this group of enzymes. Several different computational methods were used to provide a detailed description of a plausible reaction mechanism.

CONCLUSIONS

This is the first report presenting the detailed mechanism of reaction catalyzed by DapB.

GENERAL SIGNIFICANCE

Structural data in combination with information on the reaction mechanism provide a background for development of DapB inhibitors, including transition-state analogues.

Applications of Molecular Simulation in the Discovery of Antituberculosis Drugs: A Review

After decades of efforts, tuberculosis has been well controlled in most places. The existing drugs are no longer sufficient for the treatment of drug-resistant Mycobacterium tuberculosis due to significant toxicity and selective pressure, especially for XDR-TB. In order to accelerate the development of high-efficiency, low-toxic antituberculosis drugs, it is particularly important to use Computer Aided Drug Design (CADD) for rational drug design. Here, we systematically reviewed the specific role of molecular simulation in the discovery of new antituberculosis drugs. The purpose of this review is to overview current applications of molecular simulation methods in the discovery of antituberculosis drugs. Furthermore, the unique advantages of molecular simulation was discussed in revealing the mechanism of drug resistance. The comprehensive use of different molecular simulation methods will help reveal the mechanism of drug resistance and improve the efficiency of rational drug design. With the help of molecular simulation methods such as QM/MM method, the mechanisms of biochemical reactions catalyzed by enzymes at atomic level in Mycobacterium tuberculosis has been deeply analyzed. QSAR and virtual screening both accelerate the development of highefficiency, low-toxic potential antituberculosis drugs. Improving the accuracy of existing algorithms and developing more efficient new methods for CADD will always be a hot topic in the future. It is of great value to utilize molecular dynamics simulation to investigate complex systems that cannot be studied in experiments, especially for drug resistance of Mycobacterium tuberculosis.

Virtual Screening Meets Deep Learning

BACKGROUND

Automated compound testing is currently the de facto standard method for drug screening, but it has not brought the great increase in the number of new drugs that was expected. Computer- aided compounds search, known as Virtual Screening, has shown the benefits to this field as a complement or even alternative to the robotic drug discovery. There are different methods and approaches to address this problem and most of them are often included in one of the main screening strategies. Machine learning, however, has established itself as a virtual screening methodology in its own right and it may grow in popularity with the new trends on artificial intelligence.

OBJECTIVE

This paper will attempt to provide a comprehensive and structured review that collects the most important proposals made so far in this area of research. Particular attention is given to some recent developments carried out in the machine learning field: the deep learning approach, which is pointed out as a future key player in the virtual screening landscape.

Molecular Docking and Quantitative Structure Activity Relationship (QSAR) Studies of Some Newly Synthesized Poly (Azomethine) Esters

Molecular docking procedure is well known for the investigation of small molecules; however, for macromolecules, it has attained limited success so far. Thus, in an attempt, a series of poly (azomethine) esters was synthesized in a laboratory, and their model oligomer units were studied by computer-aided computational MOE software package to investigate, specifically, binding modes that could influence their anticancer activities. Poly (azomethine) ester (PAME) was prepared by solution phase polycondensation of a preformed Schiff base (SB) 4-((4-(4-(4-hydroxybenzylideneamino)phenoxy)phenylimino)methyl) phenol with terephthaloyl chloride (TC). Terpolymers (PAMEF, PAMEB, PAMESi, PAMEPr, and PAMEH) were synthesized by the incorporation of various moieties along with TC and SB in the main chain. Structural elucidation was carried out by spectroscopic studies and elemental analysis. Docking procedure, adopted to investigate anticancer activity, showed that material was docked in the same pocket of active site as by anticancer protein complex (PDB code: 1T69). Molecular docking along with the quantitative structure activity relationship (QSAR) investigations showed groove binding as a preferred mode between the material and double-stranded DNA (PDB ID-1BNA). Binding strength indicated worthy correlation with various physicochemical parameters of the material like hydrophobic surface area (Vsurf), EHOMO, ELUMO, log P, and molar refractivity (MR). Calculated values for the formation constant (Kf) showed good binding strength for polymer-DNA complex. Consequently, the synthesized material is expected to exhibit anticancer activities and could be studied further as anticancer drugs.

Crystal structure of dihydrodipicolinate reductase (PaDHDPR) from Paenisporosarcina sp. TG-14: structural basis for NADPH preference as a cofactor

Dihydrodipicolinate reductase (DHDPR) is a key enzyme in the diaminopimelate- and lysine-synthesis pathways that reduces DHDP to tetrahydrodipicolinate. Although DHDPR uses both NADPH and NADH as a cofactor, the structural basis for cofactor specificity and preference remains unclear. Here, we report that Paenisporosarcina sp. TG-14 PaDHDPR has a strong preference for NADPH over NADH, as determined by isothermal titration calorimetry and enzymatic activity assays. We determined the crystal structures of PaDHDPR alone, with its competitive inhibitor (dipicolinate), and the ternary complex of the enzyme with dipicolinate and NADPH, with results showing that only the ternary complex had a fully closed conformation and suggesting that binding of both substrate and nucleotide cofactor is required for enzymatic activity. Moreover, NADPH binding induced local conformational changes in the N-terminal long loop (residues 34–59) of PaDHDPR, as the His35 and Lys36 residues in this loop interacted with the 2′-phosphate group of NADPH, possibly accounting for the strong preference of PaDHDPR for NADPH. Mutation of these residues revealed reduced NADPH binding and enzymatic activity, confirming their importance in NADPH binding. These findings provide insight into the mechanism of action and cofactor selectivity of this important bacterial enzyme.

Recent advances of imidazole-containing derivatives as anti-tubercular agents

Tuberculosis still remains one of the most common, communicable, and leading deadliest diseases known to mankind throughout the world. Drug-resistance in Mycobacterium tuberculosis which threatens to worsen the global tuberculosis epidemic has caused great concern in recent years. To overcome the resistance, the development of new drugs with novel mechanisms of actions is of great importance. Imidazole-containing derivatives endow with various biological properties, and some of them demonstrated excellent anti-tubercular activity. As the most emblematic example, 4-nitroimidazole delamanid has already received approval for treatment of multidrug-resistant tuberculosis infected patients. Thus, imidazole-containing derivatives have caused great interests in discovery of new anti-tubercular agents. Numerous of imidazole-containing derivatives were synthesized and screened for their in vitro and in vivo anti-mycobacterial activities against both drug-sensitive and drug-resistant Mycobacterium tuberculosis pathogens. This review aims to outline the recent advances of imidazole-containing derivatives as anti-tubercular agents, and summarize the structure-activity relationship of these derivatives. The enriched structure-activity relationship may pave the way for the further rational development of imidazole-containing derivatives as anti-tubercular agents.

Inhibition of Mycobacterium tuberculosis dihydrodipicolinate synthase by alpha-ketopimelic acid and its other structural analogues

The Mycobacterium tuberculosis dihydrodipicolinate synthase (Mtb-dapA) is an essential gene. Mtb-DapA catalyzes the aldol condensation between pyruvate and L-aspartate-beta-semialdehyde (ASA) to yield dihydrodipicolinate. In this work we tested the inhibitory effects of structural analogues of pyruvate on recombinant Mtb-DapA (Mtb-rDapA) using a coupled assay with recombinant dihydrodipicolinate reductase (Mtb-rDapB). Alpha-ketopimelic acid (α-KPA) showed maximum inhibition of 88% and IC50 of 21 μM in the presence of pyruvate (500 μM) and ASA (400 μM). Competition experiments with pyruvate and ASA revealed competition of α-KPA with pyruvate. Liquid chromatography-mass spectrometry (LC-MS) data with multiple reaction monitoring (MRM) showed that the relative abundance peak of final product, 2,3,4,5-tetrahydrodipicolinate, was decreased by 50%. Thermal shift assays showed 1 °C Tm shift of Mtb-rDapA upon binding α-KPA. The 2.4 Å crystal structure of Mtb-rDapA-α-KPA complex showed the interaction of critical residues at the active site with α-KPA. Molecular dynamics simulations over 500 ns of pyruvate docked to Mtb-DapA and of α-KPA-bound Mtb-rDapA revealed formation of hydrogen bonds with pyruvate throughout in contrast to α-KPA. Molecular descriptors analysis showed that ligands with polar surface area of 91.7 Å(2) are likely inhibitors. In summary, α-hydroxypimelic acid and other analogues could be explored further as inhibitors of Mtb-DapA.

Docking Screens for Novel Ligands Conferring New Biology

It is now plausible to dock libraries of 10 million molecules against targets over several days or weeks. When the molecules screened are commercially available, they may be rapidly tested to find new leads. Although docking retains important liabilities (it cannot calculate affinities accurately nor even reliably rank order high-scoring molecules), it can often can distinguish likely from unlikely ligands, often with hit rates above 10%. Here we summarize the improvements in libraries, target quality, and methods that have supported these advances, and the open access resources that make docking accessible. Recent docking screens for new ligands are sketched, as are the binding, crystallographic, and in vivo assays that support them. Like any technique, controls are crucial, and key experimental ones are reviewed. With such controls, docking campaigns can find ligands with new chemotypes, often revealing the new biology that may be docking's greatest impact over the next few years.

Multiscale modelling of relationships between protein classes and drug behavior across all diseases using the CANDO platform

We have examined the effect of eight different protein classes (channels, GPCRs, kinases, ligases, nuclear receptors, proteases, phosphatases, transporters) on the benchmarking performance of the CANDO drug discovery and repurposing platform (http://protinfo.org/cando). The first version of the CANDO platform utilizes a matrix of predicted interactions between 48278 proteins and 3733 human ingestible compounds (including FDA approved drugs and supplements) that map to 2030 indications/diseases using a hierarchical chem and bio-informatic fragment based docking with dynamics protocol (> one billion predicted interactions considered). The platform uses similarity of compound-proteome interaction signatures as indicative of similar functional behavior and benchmarking accuracy is calculated across 1439 indications/diseases with more than one approved drug. The CANDO platform yields a significant correlation (0.99, p-value < 0.0001) between the number of proteins considered and benchmarking accuracy obtained indicating the importance of multitargeting for drug discovery. Average benchmarking accuracies range from 6.2 % to 7.6 % for the eight classes when the top 10 ranked compounds are considered, in contrast to a range of 5.5 % to 11.7 % obtained for the comparison/control sets consisting of 10, 100, 1000, and 10000 single best performing proteins. These results are generally two orders of magnitude better than the average accuracy of 0.2% obtained when randomly generated (fully scrambled) matrices are used. Different indications perform well when different classes are used but the best accuracies (up to 11.7% for the top 10 ranked compounds) are achieved when a combination of classes are used containing the broadest distribution of protein folds. Our results illustrate the utility of the CANDO approach and the consideration of different protein classes for devising indication specific protocols for drug repurposing as well as drug discovery.

Identification of Small-Molecule Inhibitors against Meso-2, 6-Diaminopimelate Dehydrogenase from Porphyromonas gingivalis

Species-specific antimicrobial therapy has the potential to combat the increasing threat of antibiotic resistance and alteration of the human microbiome. We therefore set out to demonstrate the beginning of a pathogen-selective drug discovery method using the periodontal pathogen Porphyromonas gingivalis as a model. Through our knowledge of metabolic networks and essential genes we identified a “druggable” essential target, meso-diaminopimelate dehydrogenase, which is found in a limited number of species. We adopted a high-throughput virtual screen method on the ZINC chemical library to select a group of potential small-molecule inhibitors. Meso-diaminopimelate dehydrogenase from P. gingivalis was first expressed and purified in Escherichia coli then characterized for enzymatic inhibitor screening studies. Several inhibitors with similar structural scaffolds containing a sulfonamide core and aromatic substituents showed dose-dependent inhibition. These compounds were further assayed showing reasonable whole-cell activity and the inhibition mechanism was determined. We conclude that the establishment of this target and screening strategy provides a model for the future development of new antimicrobials.

Targeting xenobiotic receptors PXR and CAR in human diseases

Nuclear receptors such as the pregnane X receptor (PXR) and constitutive androstane receptor (CAR) are xenobiotic receptors regulating not only drug metabolism and disposition but also various human diseases such as cancer, diabetes, inflammatory disease, metabolic disease and liver diseases, suggesting that PXR and CAR are promising targets for drug discovery. Consequently, there is an urgent need to discover and develop small molecules that target these PXR- and/or CAR-mediated human-disease-related pathways for relevant therapeutic applications. This review proposes approaches to target PXR and CAR, either individually or simultaneously, in the context of various human diseases, taking into consideration the structural differences between PXR and CAR.

Virtual screening: an in silico tool for interlacing the chemical universe with the proteome

In silico screening both in the forward (traditional virtual screening) and reverse sense (inverse virtual screening (IVS)) are helpful techniques for interlacing the chemical universe of small molecules with the proteome. The former, which is using a protein structure and a large chemical database, is well-known by the scientific community. We have chosen here to provide an overview on the latter, focusing on validation and target prioritization strategies. By comparing it to complementary or alternative wet-lab approaches, we put IVS in the broader context of chemical genomics, target discovery and drug design. By giving examples from the literature and an own example on how to validate the approach, we provide guidance on the issues related to IVS.

High-throughput and in silico screenings in drug discovery

BACKGROUND

In the current situation of weak drug pipelines, impending patent expiration of several blockbuster drugs, industry consolidation and changing business models that target special diseases like cancer, diabetes, Alzheimer's and obesity, the pharmaceutical industry is under intense pressure to generate a strong drug pipeline distinguished by better productivity, diversity and cost effectiveness. The goal is discovering high-quality leads in the initial stages of the development cycle, to minimize the costs associated with failures at later ones.

OBJECTIVE

Thus, there is a great amount of interest in further developing and optimizing high-throughput screening and in silico screening, the two methods responsible for generating most of the lead compounds. Although high-throughput screening is the predominant starting point for discovery programs, in silico methods have gradually made inroads by their more rational approach, to expedite the drug discovery and development process.

CONCLUSION

Modern drug discovery strategies include both methods in tandem or in an iterative way. This review primarily provides a succinct overview and comparison of experimental and in silico screening techniques, selected case studies where both methods were used in concert to investigate their performance and complementary nature and a statement on the developments in experimental and in silico approaches in the near future.

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.

Investigation of rifampicin resistance mechanisms in Brucella abortus using MS-driven comparative proteomics

Mutations in the rpoB gene have already been shown to contribute to rifampicin resistance in many bacterial strains including Brucella species. Resistance against this antibiotic easily occurs and resistant strains have already been detected in human samples. We here present the first research project that combines proteomic, genomic, and microbiological analysis to investigate rifampicin resistance in an in vitro developed rifampicin resistant strain of Brucella abortus 2308. In silico analysis of the rpoB gene was performed and several antibiotics used in the therapy of Brucellosis were used for cross resistance testing. The proteomic profiles were examined and compared using MS-driven comparative proteomics. The resistant strain contained an already described mutation in the rpoB gene, V154F. A correlation between rifampicin resistance and reduced susceptibility on trimethoprim/sulfamethoxazole was detected by E-test and supported by the proteomics results. Using 12 836 MS/MS spectra we identified 6753 peptides corresponding to 456 proteins. The resistant strain presented 39 differentially regulated proteins most of which are involved in various metabolic pathways. Results from our research suggest that rifampicin resistance in Brucella mostly involves mutations in the rpoB gene, excitation of several metabolic processes, and perhaps the use of the already existing secretion mechanisms at a more efficient level.

Structure and function of Mycobacterium tuberculosis meso-diaminopimelic acid (DAP) biosynthetic enzymes

Because of an increased emergence of resistance to current antitubercular drugs, there is a need for new antitubercular agents directed against novel targets. Diaminopimelic acid (DAP) biosynthetic enzymes are unique to bacteria and are absent in mammals and provide a rich source of essential targets for antitubercular chemotherapy. Herein, we review the structure and function of the mycobacterial DAP biosynthetic enzymes.

Molecular docking simulations for macromolecularly imprinted polymers

Molecularly imprinted polymers are fully synthetic antibody mimics prepared via the crosslinking of organic monomers in the presence of an analyte. This general procedure is now well developed for small molecule templates; however, attempts to extend the same techniques to the macromolecular regime have achieved limited success to date. We employ molecular docking simulations to investigate the interactions between albumin, a common protein template, and frequently employed ligands used in the literature at the molecular level. Specifically, we determine the most favorable binding sites for these ligands on albumin and determine the types of non-covalent interactions taking place based on the amino acids present nearby this binding pocket. Our results show that hydrogen bonding, electrostatic interactions, and hydrophobic interactions occur between amino acids side chains and ligands. Several interactions are also taking place with the polypeptide backbone, potentially disrupting the protein's secondary structure. We show that several of the ligands preferentially bind to the same sites on the protein, which indicates that if multiple monomers are used during synthesis then competition for the same amino acids could lead to non-specific recognition. Both of these results provide rational explanations for the lack of success to date in the field.

Profile-QSAR: a novel meta-QSAR method that combines activities across the kinase family to accurately predict affinity, selectivity, and cellular activity

Profile-QSAR is a novel 2D predictive model building method for kinases. This "meta-QSAR" method models the activity of each compound against a new kinase target as a linear combination of its predicted activities against a large panel of 92 previously studied kinases comprised from 115 assays. Profile-QSAR starts with a sparse incomplete kinase by compound (KxC) activity matrix, used to generate Bayesian QSAR models for the 92 "basis-set" kinases. These Bayesian QSARs generate a complete "synthetic" KxC activity matrix of predictions. These synthetic activities are used as "chemical descriptors" to train partial-least squares (PLS) models, from modest amounts of medium-throughput screening data, for predicting activity against new kinases. The Profile-QSAR predictions for the 92 kinases (115 assays) gave a median external R²(ext) = 0.59 on 25% held-out test sets. The method has proven accurate enough to predict pairwise kinase selectivities with a median correlation of R²(ext) = 0.61 for 958 kinase pairs with at least 600 common compounds. It has been further expanded by adding a "C(k)XC" cellular activity matrix to the KxC matrix to predict cellular activity for 42 kinase driven cellular assays with median R²(ext) = 0.58 for 24 target modulation assays and R²(ext) = 0.41 for 18 cell proliferation assays. The 2D Profile-QSAR, along with the 3D Surrogate AutoShim, are the foundations of an internally developed iterative medium-throughput screening (IMTS) methodology for virtual screening (VS) of compound archives as an alternative to experimental high-throughput screening (HTS). The method has been applied to 20 actual prospective kinase projects. Biological results have so far been obtained in eight of them. Q² values ranged from 0.3 to 0.7. Hit-rates at 10 uM for experimentally tested compounds varied from 25% to 80%, except in K5, which was a special case aimed specifically at finding "type II" binders, where none of the compounds were predicted to be active at 10 μM. These overall results are particularly striking as chemical novelty was an important criterion in selecting compounds for testing. The method is completely automated. Predicted activities for nearly 4 million internal and commercial compounds across 115 kinase assays and 42 cellular assays are stored in the corporate database. Like computed physical properties, this predicted kinase activity profile can be computed and stored as each compound is registered.

Catalytic mechanism and cofactor preference of dihydrodipicolinate reductase from methicillin-resistant Staphylococcus aureus

Given the rapid rise in antibiotic resistance, including methicillin resistance in Staphylococcus aureus (MRSA), there is an urgent need to characterize novel drug targets. Enzymes of the lysine biosynthesis pathway in bacteria are examples of such targets, including dihydrodipicolinate reductase (DHDPR, E.C. 1.3.1.26), which is the product of an essential bacterial gene. DHDPR catalyzes the NAD(P)H-dependent reduction of dihydrodipicolinate (DHDP) to tetrahydrodipicolinate (THDP) in the lysine biosynthesis pathway. We show that MRSA-DHDPR exhibits a unique nucleotide specificity utilizing NADPH (K(m)=12μM) as a cofactor more effectively than NADH (K(m)=26μM). However, the enzyme is inhibited by high concentrations of DHDP when using NADPH as a cofactor, but not with NADH. Isothermal titration calorimetry (ITC) studies reveal that MRSA-DHDPR has ∼20-fold greater binding affinity for NADPH (K(d)=1.5μM) relative to NADH (K(d)=29μM). Kinetic investigations in tandem with ITC studies show that the enzyme follows a compulsory-order ternary complex mechanism; with inhibition by DHDP through the formation of a nonproductive ternary complex with NADP(+). This work describes, for the first time, the catalytic mechanism and cofactor preference of MRSA-DHDPR, and provides insight into rational approaches to inhibiting this valid antimicrobial target.

SwissDock, a protein-small molecule docking web service based on EADock DSS

Most life science processes involve, at the atomic scale, recognition between two molecules. The prediction of such interactions at the molecular level, by so-called docking software, is a non-trivial task. Docking programs have a wide range of applications ranging from protein engineering to drug design. This article presents SwissDock, a web server dedicated to the docking of small molecules on target proteins. It is based on the EADock DSS engine, combined with setup scripts for curating common problems and for preparing both the target protein and the ligand input files. An efficient Ajax/HTML interface was designed and implemented so that scientists can easily submit dockings and retrieve the predicted complexes. For automated docking tasks, a programmatic SOAP interface has been set up and template programs can be downloaded in Perl, Python and PHP. The web site also provides an access to a database of manually curated complexes, based on the Ligand Protein Database. A wiki and a forum are available to the community to promote interactions between users. The SwissDock web site is available online at http://www.swissdock.ch. We believe it constitutes a step toward generalizing the use of docking tools beyond the traditional molecular modeling community.

NMR studies uncover alternate substrates for dihydrodipicolinate synthase and suggest that dihydrodipicolinate reductase is also a dehydratase

Despite extensive effort, the drug target dihydrodipicolinate synthase (DHDPS) continues to evade effective inhibition. We used NMR spectroscopy to examine the substrate specificity of this enzyme and found that two pyruvate analogues previously classified as weak competitive inhibitors were turned over productively by DHDPS. Four other analogues were confirmed not to be substrates. Finally, our examination of the natural product of DHDPS and its degradation revealed that dihydrodipicolinate reductase (DHDPR) possesses previously unrecognized dehydratase activity.

The structure of dihydrodipicolinate reductase (DapB) from Mycobacterium tuberculosis in three crystal forms

Dihydrodipicolinate reductase (DHDPR, DapB) is an enzyme that belongs to the L-lysine biosynthetic pathway. DHDPR reduces the alpha,beta-unsaturated cyclic imine 2,3-dihydrodipicolinic acid to yield the compound 2,3,4,5-tetrahydrodipicolinic acid in a pyridine nucleotide-dependent reaction. The substrate of this reaction is the unstable product of the preceding enzyme dihydrodipicolinate synthase (DHDPS, DapA). Here, the structure of apo-DHDPR from Mycobacterium tuberculosis is reported in two orthorhombic crystal forms, as well as the structure of DHDPR from M. tuberculosis in complex with NADH in a monoclinic crystal form. A comparison of the results with previously solved structures of this enzyme shows that DHDPR undergoes a major conformational change upon binding of its cofactor. This conformational change can be interpreted as one of the low-frequency normal modes of the structure.

Docking and chemoinformatic screens for new ligands and targets

Computer-based docking screens are now widely used to discover new ligands for targets of known structure; in the last two years alone, the discovery of ligands for more than 20 proteins has been reported. Recently, investigators have also turned to predicting new substrates for enzymes of unknown function, taking docking in a wholly new direction. Increasingly, the hit rates, the true-positives, and the false-positives from the docking screens are being compared to those from empirical, high-throughput screens, revealing the strengths, weaknesses, and complementarities of both techniques. The recent efflorescence of GPCR structures has made these quintessential drug targets available to structure-based approaches. Consistent with their 'druggability', the docking screens have returned high hit rates and potent molecules. Finally, in the last several years, an approach almost exactly opposite to docking has also appeared; this pharmacological network approach begins not with the structure of the target but rather those of drug molecules and asks, given a pattern of chemistry in the ligands, what targets may a particular drug bind to? This method, which returns to an older, pharmacology logic, has been surprisingly successful in predicting new 'off-targets' for established drugs.

targetTB: a target identification pipeline for Mycobacterium tuberculosis through an interactome, reactome and genome-scale structural analysis

Background

Tuberculosis still remains one of the largest killer infectious diseases, warranting the identification of newer targets and drugs. Identification and validation of appropriate targets for designing drugs are critical steps in drug discovery, which are at present major bottle-necks. A majority of drugs in current clinical use for many diseases have been designed without the knowledge of the targets, perhaps because standard methodologies to identify such targets in a high-throughput fashion do not really exist. With different kinds of 'omics' data that are now available, computational approaches can be powerful means of obtaining short-lists of possible targets for further experimental validation.

Results

We report a comprehensive in silico target identification pipeline, targetTB, for Mycobacterium tuberculosis. The pipeline incorporates a network analysis of the protein-protein interactome, a flux balance analysis of the reactome, experimentally derived phenotype essentiality data, sequence analyses and a structural assessment of targetability, using novel algorithms recently developed by us. Using flux balance analysis and network analysis, proteins critical for survival of M. tuberculosis are first identified, followed by comparative genomics with the host, finally incorporating a novel structural analysis of the binding sites to assess the feasibility of a protein as a target. Further analyses include correlation with expression data and non-similarity to gut flora proteins as well as 'anti-targets' in the host, leading to the identification of 451 high-confidence targets. Through phylogenetic profiling against 228 pathogen genomes, shortlisted targets have been further explored to identify broad-spectrum antibiotic targets, while also identifying those specific to tuberculosis. Targets that address mycobacterial persistence and drug resistance mechanisms are also analysed.

Conclusion

The pipeline developed provides rational schema for drug target identification that are likely to have high rates of success, which is expected to save enormous amounts of money, resources and time in the drug discovery process. A thorough comparison with previously suggested targets in the literature demonstrates the usefulness of the integrated approach used in our study, highlighting the importance of systems-level analyses in particular. The method has the potential to be used as a general strategy for target identification and validation and hence significantly impact most drug discovery programmes.

Binding synergy and cooperativity in dihydrodipicolinate reductase: implications for mechanism and the design of biligand inhibitors

Dihydrodipicolinate reductase (DHPR) is a homotetramer that catalyzes reduction of dihydrodipicolinate (DHP). We recently reported a biligand inhibitor ( K i = 100 nM) of DHPR, comprised of fragments that bind in the NADH (CRAA = catechol rhodanine acetic acid) and DHP (PDC = pyridine dicarboxylate) binding sites. Herein, we characterize binding synergy and cooperativity for ligand binding to Escherichia coli DHPR: NADH or CRAA and PDC (stable analog of DHP). While K d values indicate little synergy between NADH and PDC, (1)H- (15)N HSQC chemical shift perturbation and saturation transfer difference (STD) titrations indicate that PDC induces a more dramatic conformational change than NADH, consistent with a role in domain closure. PDC binds cooperatively (Hill coefficient = 2), while NADH does not, based on STD titrations that monitor only fast exchange processes. However, HSQC titrations monitoring Trp253 (located between monomers) indicate that NADH binds in two steps, with high affinity binding to only one of the monomers. Therefore, DHPR binds cofactor via a sequential model, with negative cooperativity. These results, interpreted in light of steady-state data, suggest that DHPR activity requires NADH binding at only one of the four monomers. Implications of our results for fragment assembly are discussed, using CRAA tethering to PDC as a model biligand: (a) if one fragment (ex. PDC) must induce a large structural change before the other fragment is brought proximal, this must be screened for upfront, and (b) cooperative or synergistic interactions between binding sites can lead to unexpected and misleading effects in NMR-based screening.

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.

Computer-aided drug discovery and development (CADDD): in silico-chemico-biological approach

It is generally recognized that drug discovery and development are very time and resources consuming processes. There is an ever growing effort to apply computational power to the combined chemical and biological space in order to streamline drug discovery, design, development and optimization. In biomedical arena, computer-aided or in silico design is being utilized to expedite and facilitate hit identification, hit-to-lead selection, optimize the absorption, distribution, metabolism, excretion and toxicity profile and avoid safety issues. Commonly used computational approaches include ligand-based drug design (pharmacophore, a 3D spatial arrangement of chemical features essential for biological activity), structure-based drug design (drug-target docking), and quantitative structure-activity and quantitative structure-property relationships. Regulatory agencies as well as pharmaceutical industry are actively involved in development of computational tools that will improve effectiveness and efficiency of drug discovery and development process, decrease use of animals, and increase predictability. It is expected that the power of CADDD will grow as the technology continues to evolve.

Probing molecular docking in a charged model binding site

A model binding site was used to investigate charge-charge interactions in molecular docking. This simple site, a small (180A(3)) engineered cavity in cyctochrome c peroxidase (CCP), is negatively charged and completely buried from solvent, allowing us to explore the balance between electrostatic energy and ligand desolvation energy in a system where many of the common approximations in docking do not apply. A database with about 5300 molecules was docked into this cavity. Retrospective testing with known ligands and decoys showed that overall the balance between electrostatic interaction and desolvation energy was captured. More interesting were prospective docking scre"ens that looked for novel ligands, especially those that might reveal problems with the docking and energy methods. Based on screens of the 5300 compound database, both high-scoring and low-scoring molecules were acquired and tested for binding. Out of 16 new, high-scoring compounds tested, 15 were observed to bind. All of these were small heterocyclic cations. Binding constants were measured for a few of these, they ranged between 20microM and 60microM. Crystal structures were determined for ten of these ligands in complex with the protein. The observed ligand geometry corresponded closely to that predicted by docking. Several low-scoring alkyl amino cations were also tested and found to bind. The low docking score of these molecules owed to the relatively high charge density of the charged amino group and the corresponding high desolvation penalty. When the complex structures of those ligands were determined, a bound water molecule was observed interacting with the amino group and a backbone carbonyl group of the cavity. This water molecule mitigates the desolvation penalty and improves the interaction energy relative to that of the "naked" site used in the docking screen. Finally, six low-scoring neutral molecules were also tested, with a view to looking for false negative predictions. Whereas most of these did not bind, two did (phenol and 3-fluorocatechol). Crystal structures for these two ligands in complex with the cavity site suggest reasons for their binding. That these neutral molecules do, in fact bind, contradicts previous results in this site and, along with the alkyl amines, provides instructive false negatives that help identify weaknesses in our scoring functions. Several improvements of these are considered.

Comparative virtual and experimental high-throughput screening for glycogen synthase kinase-3beta inhibitors

Glycogen synthase kinase-3beta (GSK-3beta) is a serine/threonine kinase that has recently emerged as a key target for neurodegenerative diseases and diabetes. As an initial step of our lead discovery program, we developed a virtual screen to discriminate known GSK-3beta inhibitors and inactive compounds using FlexX, FlexX-Pharm, and FlexE. The maximal enrichment factor (EF = 28) suggests that our protocol identifies potential GSK-3beta inhibitors effectively from large compound collections. The effectiveness of our screening protocol was further investigated by comparative experimental and virtual high-throughput screens (HTSs) performed for the same subset of our corporate library. Enrichment factors, the significantly higher hit rate of virtual screening (12.9%) than that of the HTS (0.55%), and also the comparison of active clusters suggest that our virtual screening protocol is an effective tool in GSK-3beta-based library focusing. Head-to-head comparison of true/false positives and negatives revealed the two approaches to be complementary rather than competitive.

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.

Decoys for docking

Molecular docking is widely used to predict novel lead compounds for drug discovery. Success depends on the quality of the docking scoring function, among other factors. An imperfect scoring function can mislead by predicting incorrect ligand geometries or by selecting nonbinding molecules over true ligands. These false-positive hits may be considered "decoys". Although these decoys are frustrating, they potentially provide important tests for a docking algorithm; the more subtle the decoy, the more rigorous the test. Indeed, decoy databases have been used to improve protein structure prediction algorithms and protein-protein docking algorithms. Here, we describe 20 geometric decoys in five enzymes and 166 "hit list" decoys-i.e., molecules predicted to bind by our docking program that were tested and found not to do so-for beta-lactamase and two cavity sites in lysozyme. Especially in the cavity sites, which are very simple, these decoys highlight particular weaknesses in our scoring function. We also consider the performance of five other widely used docking scoring functions against our geometric and hit list decoys. Intriguingly, whereas many of these other scoring functions performed better on the geometric decoys, they typically performed worse on the hit list decoys, often highly ranking molecules that seemed to poorly complement the model sites. Several of these "hits"from the other scoring functions were tested experimentally and found, in fact, to be decoys. Collectively, these decoys provide a tool for the development and improvement of molecular docking scoring functions. Such improvements may, in turn, be rapidly tested experimentally against these and related experimental systems, which are well-behaved in assays and for structure determination.

Heterocyclic inhibitors of dihydrodipicolinate synthase are not competitive

A series of piperidine- and pyridine-2,6-dicarboxylate derivatives has been evaluated as potential inhibitors of dihydrodipicolinate synthase (DHDPS). The compounds were designed with oxygen functionality at the C-4 position in order to mimic the putative enzyme product HTHDP. Most compounds displayed weak-moderate inhibition of DHDPS. Additionally, the most potent inhibitors were shown not to be competitive, indicating they do not bind at the active site. Discrepancies between the two common assay systems-the imidazole assay and the coupled assay-were observed which are attributed to inherent problems in the imidazole assay, leading to artefactually low K(i) measurements.

Virtual screening of chemical libraries

Virtual screening uses computer-based methods to discover new ligands on the basis of biological structures. Although widely heralded in the 1970s and 1980s, the technique has since struggled to meet its initial promise, and drug discovery remains dominated by empirical screening. Recent successes in predicting new ligands and their receptor-bound structures, and better rates of ligand discovery compared to empirical screening, have re-ignited interest in virtual screening, which is now widely used in drug discovery, albeit on a more limited scale than empirical screening.