Discovery of a Hidden Trypanosoma cruzi Spermidine Synthase Binding Site and Inhibitors through In Silico, In Vitro, and X-ray Crystallography.

In drug discovery research, the selection of promising binding sites and understanding the binding mode of compounds are crucial fundamental studies. The current understanding of the proteins-ligand binding model extends beyond the simple lock and key model to include the induced-fit model, which alters the conformation to match the shape of the ligand, and the pre-existing equilibrium model, selectively binding structures with high binding affinity from a diverse ensemble of proteins. Although methods for detecting target protein binding sites and virtual screening techniques using docking simulation are well-established, with numerous studies reported, they only consider a very limited number of structures in the diverse ensemble of proteins, as these methods are applied to a single structure. Molecular dynamics (MD) simulation is a method for predicting protein dynamics and can detect potential ensembles of protein binding sites and hidden sites unobservable in a single-point structure. In this study, to demonstrate the utility of virtual screening with protein dynamics, MD simulations were performed on Trypanosoma cruzi spermidine synthase to obtain an ensemble of dominant binding sites with a high probability of existence. The structure of the binding site obtained through MD simulation revealed pockets in addition to the active site that was present in the initial structure. Using the obtained binding site structures, virtual screening of 4.8 million compounds by docking simulation, in vitro assays, and X-ray analysis was conducted, successfully identifying two hit compounds.

In silico, in vitro, X-ray crystallography, and integrated strategies for discovering spermidine synthase inhibitors for Chagas disease.

Chagas disease results from infection by Trypanosoma cruzi and is a neglected tropical disease (NTD). Although some treatment drugs are available, their use is associated with severe problems, including adverse effects and limited effectiveness during the chronic disease phase. To develop a novel anti-Chagas drug, we virtually screened 4.8 million small molecules against spermidine synthase (SpdSyn) as the target protein using our super computer "TSUBAME2.5" and conducted in vitro enzyme assays to determine the half-maximal inhibitory concentration values. We identified four hit compounds that inhibit T. cruzi SpdSyn (TcSpdSyn) by in silico and in vitro screening. We also determined the TcSpdSyn-hit compound complex structure using X-ray crystallography, which shows that the hit compound binds to the putrescine-binding site and interacts with Asp171 through a salt bridge.

Pharmacophore modeling for anti-Chagas drug design using the fragment molecular orbital method.

BACKGROUND: Chagas disease, caused by the parasite Trypanosoma cruzi, is a neglected tropical disease that causes severe human health problems. To develop a new chemotherapeutic agent for the treatment of Chagas disease, we predicted a pharmacophore model for T. cruzi dihydroorotate dehydrogenase (TcDHODH) by fragment molecular orbital (FMO) calculation for orotate, oxonate, and 43 orotate derivatives. METHODOLOGY/PRINCIPAL FINDINGS: Intermolecular interactions in the complexes of TcDHODH with orotate, oxonate, and 43 orotate derivatives were analyzed by FMO calculation at the MP2/6-31G level. The results indicated that the orotate moiety, which is the base fragment of these compounds, interacts with the Lys43, Asn67, and Asn194 residues of TcDHODH and the cofactor flavin mononucleotide (FMN), whereas functional groups introduced at the orotate 5-position strongly interact with the Lys214 residue. CONCLUSIONS/SIGNIFICANCE: FMO-based interaction energy analyses revealed a pharmacophore model for TcDHODH inhibitor. Hydrogen bond acceptor pharmacophores correspond to Lys43 and Lys214, hydrogen bond donor and acceptor pharmacophores correspond to Asn67 and Asn194, and the aromatic ring pharmacophore corresponds to FMN, which shows important characteristics of compounds that inhibit TcDHODH. In addition, the Lys214 residue is not conserved between TcDHODH and human DHODH. Our analysis suggests that these orotate derivatives should preferentially bind to TcDHODH, increasing their selectivity. Our results obtained by pharmacophore modeling provides insight into the structural requirements for the design of TcDHODH inhibitors and their development as new anti-Chagas drugs.

Distinct properties of telmisartan on agonistic activities for peroxisome proliferator-activated receptor γ among clinically used angiotensin II receptor blockers: drug-target interaction analyses.

A proportion of angiotensin II type 1 receptor blockers (ARBs) improves glucose dyshomeostasis and insulin resistance in a clinical setting. Of these ARBs, telmisartan has the unique property of being a partial agonist for peroxisome proliferator-activated receptor γ (PPARγ). However, the detailed mechanism of how telmisartan acts on PPARγ and exerts its insulin-sensitizing effect is poorly understood. In this context, we investigated the agonistic activity of a variety of clinically available ARBs on PPARγ using isothermal titration calorimetry (ITC) and surface plasmon resonance (SPR) system. Based on physicochemical data, we then reevaluated the metabolically beneficial effects of telmisartan in cultured murine adipocytes. ITC and SPR assays demonstrated that telmisartan exhibited the highest affinity of the ARBs tested. Distribution coefficient and parallel artificial membrane permeability assays were used to assess lipophilicity and cell permeability, for which telmisartan exhibited the highest levels of both. We next examined the effect of each ARB on insulin-mediated glucose metabolism in 3T3-L1 preadipocytes. To investigate the impact on adipogenesis, 3T3-L1 preadipocytes were differentiated with each ARB in addition to standard inducers of differentiation for adipogenesis. Telmisartan dose-dependently facilitated adipogenesis and markedly augmented the mRNA expression of adipocyte fatty acid-binding protein (aP2), accompanied by an increase in the uptake of 2-deoxyglucose and protein expression of glucose transporter 4 (GLUT4). In contrast, other ARBs showed only marginal effects in these experiments. In accordance with its highest affinity of binding for PPARγ as well as the highest cell permeability, telmisartan superbly activates PPARγ among the ARBs tested, thereby providing a fresh avenue for treating hypertensive patients with metabolic derangement.

Discovery of a novel nicotinamide phosphoribosyl transferase (NAMPT) inhibitor via in silico screening.

Nicotinamide phosphoribosyl transferase (NAMPT) is a key enzyme in the salvage pathway of mammalian nicotinamide adenine dinucleotide (NAD) biosynthesis, catalyzing the synthesis of nicotinamide mononucleotide from nicotinamide (Nam). The diverse functions of NAD suggest that NAMPT inhibitors are potential drug candidates as anticancer agents, immunomodulators, or other agents. However, difficulty in conducting high-throughput NAMPT assay with good sensitivity has hampered the discovery of novel anti-NAMPT drugs with improved profiles. We combined an in silico screening strategy with a radioisotope (RI)-based enzyme assay and rationally identified promising NAMPT inhibitors with novel structures. AS1604498 was the most potent inhibitor, with an IC50 of 44 nM, and inhibited THP-1 and K562 cell line growth with the IC50 of 198 nM and 673 nM, respectively. The mode of action was found to reduce intracellular NAD following apoptosis, suggesting that these compounds inhibit NAMPT in cell-based assay. This strategy can be used to discover new drug candidates with targets which are difficult to assess through high-throughput screening. Our hit compounds may be used as seed compounds for developing new therapeutics with NAMPT.

Accelerating quantum chemistry calculations with graphical processing units - toward in high-density (HD) silico drug discovery.

The growing power of central processing units (CPU) has made it possible to use quantum mechanical (QM) calculations for in silico drug discovery. However, limited CPU power makes large-scale in silico screening such as virtual screening with QM calculations a challenge. Recently, general-purpose computing on graphics processing units (GPGPU) has offered an alternative, because of its significantly accelerated computational time over CPU. Here, we review a GPGPU-based supercomputer, TSUBAME2.0, and its promise for next generation in silico drug discovery, in high-density (HD) silico drug discovery.

Molecular modeling of vasopressin receptor and in silico screening of V1b receptor antagonists.

INTRODUCTION: G protein-coupled receptors (GPCRs) are integral membrane proteins which contain seven-transmembrane-spanning alpha-helices. GPCR-mediated signaling has been associated with various human diseases, positioning GPCRs as attractive targets in the drug discovery field. Recently, through advances in protein engineering and crystallography, the number of resolved GPCR structures has increased dramatically. This growing availability of GPCR structures has greatly accelerated structure-based drug design (SBDD) and in silico screening for GPCR-targeted drug discovery. AREAS COVERED: The authors introduce the current status of X-ray crystallography of GPCRs and what has been revealed from the resolved crystal structures. They also review the recent advances in SBDD and in silico screening for GPCR-targeted drug discovery and discuss a docking study, using homology modeling, with the discovery of potent antagonists of the vasopressin 1b receptor. EXPERT OPINION: Several innovative protein engineering techniques and crystallographic methods have greatly accelerated SBDD, not only for already-resolved GPCRs but also for those structures which remain unclear. These technological advances are expected to enable the determination of GPCR-fragment complexes, making it practical to perform fragment-based drug discovery. This paves the way for a new era of GPCR-targeted drug discovery.

4-Hydroxypyridazin-3(2H)-one derivatives as novel D-amino acid oxidase inhibitors.

D-Amino acid oxidase (DAAO) catalyzes the oxidation of d-amino acids including d-serine, a coagonist of the N-methyl-d-aspartate receptor. We identified a series of 4-hydroxypyridazin-3(2H)-one derivatives as novel DAAO inhibitors with high potency and substantial cell permeability using fragment-based drug design. Comparisons of complex structures deposited in the Protein Data Bank as well as those determined with in-house fragment hits revealed that a hydrophobic subpocket was formed perpendicular to the flavin ring by flipping Tyr224 in a ligand-dependent manner. We investigated the ability of the initial fragment hit, 3-hydroxy-pyridine-2(1H)-one, to fill this subpocket with the aid of complex structure information. 3-Hydroxy-5-(2-phenylethyl)pyridine-2(1H)-one exhibited the predicted binding mode and demonstrated high inhibitory activity for human DAAO in enzyme- and cell-based assays. We further designed and synthesized 4-hydroxypyridazin-3(2H)-one derivatives, which are equivalent to the 3-hydroxy-pyridine-2(1H)-one series but lack cell toxicity. 6-[2-(3,5-Difluorophenyl)ethyl]-4-hydroxypyridazin-3(2H)-one was found to be effective against MK-801-induced cognitive deficit in the Y-maze.

Agreement of drug discovery data with Benford's law.

The ever-increasing rate of drug discovery data has complicated data analysis and potentially compromised data quality due to factors such as data handling errors. Parallel to this concern is the rise in blatant scientific misconduct. Combined, these problems highlight the importance of developing a method that can be used to systematically assess data quality. Benford's law has been used to discover data manipulation and data fabrication in various fields. In the authors' previous studies, it was demonstrated that the distribution of the corresponding activity and solubility data followed Benford's law distribution. It was also shown that too intense a selection of training data sets of regression model can disrupt Benford's law. Here, the authors present the application of Benford's law to a wider range of drug discovery data such as microarray and sequence data. They also suggest that Benford's law could also be applied to model building and reliability for structure-activity relationship study. Finally, the authors propose a protocol based on Benford's law which will provide researchers with an efficient method for data quality assessment. However, multifaceted quality control such as combinatorial use with data visualization may also be needed to further improve its reliability.

Structural basis for telmisartan-mediated partial activation of PPAR gamma.

Telmisartan, a selective angiotensin II type 1 receptor blocker, has recently been shown to act as a partial agonist for peroxisome proliferator-activated receptor gamma (PPARγ). To understand how telmisartan partially activates PPARγ, we determined the ternary complex structure of PPARγ, telmisartan, and a coactivator peptide from steroid receptor coactivator-1 at a resolution of 2.18 Å. Crystallographic analysis revealed that telmisartan exhibits an unexpected binding mode in which the central benzimidazole ring is engaged in a non-canonical--and suboptimal--hydrogen-bonding network around helix 12 (H12). This network differs greatly from that observed when full-agonists bind with PPARγ and prompt high-coactivator recruitment through H12 stabilized by multiple hydrogen bonds. Binding with telmisartan results in a less stable H12 that in turn leads to attenuated coactivator binding, thus explaining the mechanism of partial activation.

Lead generation and examples opinion regarding how to follow up hits.

In fragment-based drug discovery (FBDD), not only identifying the starting fragment hit to be developed but also generating a drug lead from that starting fragment hit is important. Converting fragment hits to leads is generally similar to a high-throughput screening (HTS) hits-to-leads approach in that properties associated with activity for a target protein, such as selectivity against other targets and absorption, distribution, metabolism, excretion, and toxicity (ADME/Tox), and physicochemical properties should be taken into account. However, enhancing the potency of the fragment hit is a key requirement in FBDD, unlike HTS, because initial fragment hits are generally weak. This enhancement is presently achieved by adding additional chemical groups which bind to additional parts of the target protein or by joining or combining two or more hit fragments; however, strategies for effecting greater improvements in effective activity are needed. X-ray analysis is a key technology attractive for converting fragments to drug leads. This method makes it clear whether a fragment hit can act as an anchor and provides insight regarding introduction of functional groups to improve fragment activity. Data on follow-up chemical synthesis of fragment hits has allowed for the differentiation of four different strategies: fragment optimization, fragment linking, fragment self-assembly, and fragment evolution. Here, we discuss our opinion regarding how to follow up on fragment hits, with a focus on the importance of fragment hits as an anchor moiety to so-called hot spots in the target protein using crystallographic data.

Docking study and binding free energy calculation of poly (ADP-ribose) polymerase inhibitors.

Recently, the massively parallel computation of absolute binding free energy with a well-equilibrated system (MP-CAFEE) has been developed. The present study aimed to determine whether the MP-CAFEE method is useful for drug discovery research. In the drug discovery process, it is important for computational chemists to predict the binding affinity accurately without detailed structural information for protein/ligand complex. We investigated the absolute binding free energies for Poly (ADP-ribose) polymerase-1 (PARP-1)/inhibitor complexes, using the MP-CAFEE method. Although each docking model was used as an input structure, it was found that the absolute binding free energies calculated by MP-CAFEE are well consistent with the experimental ones. The accuracy of this method is much higher than that using molecular mechanics Poisson-Boltzmann/surface area (MM/PBSA). Although the simulation time is quite extensive, the reliable predictor of binding free energies would be a useful tool for drug discovery projects.

Unique "delta lock" structure of telmisartan is involved in its strongest binding affinity to angiotensin II type 1 receptor.

Angiotensin II type 1 receptor (AT1 receptor) blockers (ARBs) are one of the most popular anti-hypertensive agents. Control of blood pressure (BP) by ARBs is now a therapeutic target for the organ protection in patients with hypertension. Recent meta-analysis demonstrated the possibility that telmisartan was the strongest ARB for the reduction of BP in patients with essential hypertension. However, which molecular interactions of telmisartan with the AT1 receptor could explain its strongest BP lowering activity remains unclear. To address the issue, we constructed models for the interaction between commonly used ARBs and AT1 receptor and compared the docking model of telmisartan with that of other ARBs. Telmisartan has a unique binding mode to the AT1 receptor due to its distal benzimidazole portion. This unique portion could explain the highest molecular lipophilicity, the greatest volume distribution and the strongest binding affinity of telmisartan to AT1 receptor. Furthermore, telmisartan was found to firmly bind to the AT1 receptor through the unique "delta lock" structure. Our present study suggests that due to its "delta lock" structure, telmisartan may be superior to other ARBs in halting cardiovascular disease in patients with hypertension.

Selection of in silico drug screening results by using universal active probes (UAPs).

We developed a new method that uses a set of drug-like compounds to select reliable in silico drug screening results. If some active compounds are known, the screening results that rank these active compounds at the top should be reliable. If no active compound is known, how to select the result is in question. We propose a concept of a set of "universal active probes" (UAPs), which is a set of small active compounds that bind to different kinds of proteins. We found that the hit ratio of the true active compounds in in silico screening shows positive correlation to that of the UAPs, probably because UAPs form a set of drug-like compounds. Thus, if the UAPs were added to the compound library, the screening result that shows a high hit ratio of the UAPs could give reliable actual hit compounds for the target protein. We examined this method for several targets and found this idea useful.

Are there differences between launched drugs, clinical candidates, and commercially available compounds?

To clarify the differences between commercially available compounds, clinical candidates, and launched drugs with regard to distribution of physicochemical properties and to characterize the correlation between physicochemical properties, we conducted analyses on physicochemical descriptors of commercially available compounds, clinical candidates, and launched drugs. Initial analysis of the marginal distribution of each physicochemical property showed that the distribution of commercially available compounds obeys a more normal distribution than that of launched drugs and clinical candidates. In addition, we calculated correlation coefficient values between values of physicochemical properties and found little similarity between values of clinical candidates and those of commercially available compounds, while observing marked similarity between values of clinical candidates and those of launched drugs. We also analyzed joint distribution for two physicochemical properties, with results showing that, similar to marginal distribution, the joint distribution of commercially available compounds obeys a more normal distribution than that of launched drugs and clinical candidates. We then assessed items using the Nagahara method, originally developed by one of this study's authors. Results showed that the probability distribution of molecular weight and log P for commercially available compounds was much narrower than that of launched drugs and clinical candidates. In conclusion, clinical candidates are more similar to launched drugs than to commercially available compounds with regard to marginal distribution, joint distribution, and correlation coefficients. These findings provide deeper insight regarding the concept of "druglikeness".

Use of Benford's law in drug discovery data.

Benford's law states that the distribution of the first digit of many data sets is not uniform. The first digit of any random number will be 1 almost 30% of the time, and larger digits occur as the first digit with lower and lower frequency, to the point where 9 occurs as a first digit only 5% of the time. Here, we demonstrate that several data sets in the field of drug discovery follow Benford's distribution, whereas 'doctored' data do not. Our findings indicate the applicability of Benford's law in assessing data quality in the field of drug discovery. We also propose a useful index of evaluating data quality based on Benford's law.

Small-world phenomena in chemical library networks: application to fragment-based drug discovery.

A wide variety of networks in various fields have been characterized as small-world networks. In scale-free networks, a representative class of small-world networks, numbers of contacts (degree distributions) of nodes follow power laws. Although several examples of power-law distributions have been found in the field of chemoinformatics, the network structures of chemical libraries have not been analyzed. Here, we show that small-world phenomena are observed not only in existing chemical libraries but also in virtual libraries generated from structurally diverse fragments when represented as networks. On the basis of this observation, we propose that an efficient compound-prioritization method of fragment-based drug discovery (FBDD) would be to select those fragments as a starting point such that the linked compounds become hubs in the library and therefore allow identification of many similar compounds when all-to-all fragment linkings are performed. Moreover, our analyses indicated that the variety of linkers had a marked influence on the network structure and thus on the diversity of the compounds synthesized by linking fragment hits.

Optimization of (4,4-difluoro-1,2,3,4-tetrahydro-5H-1-benzazepine-5-ylidene)acetamide derivatives as arginine vasopressin V2 receptor agonists and discussion of their binding modes.

A series of (4,4-difluoro-1,2,3,4-tetrahydro-5H-1-benzazepine-5-ylidene)acetamide derivatives were optimized to achieve potent agonistic activity, both in vitro and in vivo, for the arginine vasopressin V(2) receptor, resulting in the eventual discovery of compound 1g. Molecular modeling of compound 1g with V(2) receptor was also examined to evaluate the binding mode of this series of compounds.

Novel 7H-pyrrolo[2,3-d]pyrimidine derivatives as potent and orally active STAT6 inhibitors.

Signal transducers and activators of transcription 6 (STAT6) is an important transcription factor in interleukin (IL)-4 signaling pathway and a key regulator of the type 2 helper T (Th2) cell immune response. Therefore, STAT6 may be an excellent therapeutic target for allergic conditions, including asthma and atopic diseases. Previously, we reported 4-aminopyrimidine-5-carboxamide derivatives as STAT6 inhibitors. To search for novel STAT6 inhibitors, we synthesized fused bicyclic pyrimidine derivatives and identified a 7H-pyrrolo[2,3-d]pyrimidine derivative as a STAT6 inhibitor. Optimization of the pyrrolopyrimidine derivatives led to identification of 2-[4-(4-{[7-(3,5-difluorobenzyl)-7H-pyrrolo[2,3-d]pyrimidin-2-yl]amino}phenyl)piperazin-1-yl]acetamide (24, AS1810722) which showed potent STAT6 inhibition and a good CYP3A4 inhibition profile. Compound 24 also inhibited in vitro Th2 differentiation without affecting type 1 helper T (Th1) cell differentiation and eosinophil infiltration in an antigen-induced mouse asthmatic model after oral administration.

Discovery of highly potent and selective biphenylacylsulfonamide-based beta3-adrenergic receptor agonists and molecular modeling based on the solved X-ray structure of the beta2-adrenergic receptor: part 6.

As an extension of research, we have investigated modification of left-hand side (LHS) of biphenyl analogues containing an acylsulfonamide moiety in the development of potent and selective human beta(3)-adrenergic receptor (AR) agonists. Result of structure-activity relationships (SAR) and cassette-dosing evaluation in dogs showed that the hydroxynorephedrine analogue 16 had an excellent balance of in vitro and in vivo potency with pharmacokinetic profiles. In addition, to facilitate structure-based drug design (SBDD), we also have performed a docking study of biphenyl analogues based on the X-ray structure of the beta(2)-adrenergic receptor.