HPPD: ligand- and target-based virtual screening on a herbicide target

Integrated Virtual Screening and Validation toward Potential HPPD Inhibition Herbicide

4-Hydroxyphenylpyruvate dioxygenase (EC 1.13.11.27, HPPD) is one of the most widely studied herbicide targets and has gained significant attention. To identify potential effective HPPD inhibitors, a rational multistep virtual screening workflow was built, which included CBP models (based on the receptor-ligand interactions in the crystal complex), Hypogen models with activity prediction ability (according to the derivation of structure-activity relationships from a set of molecules with reported activity values), and a consensus docking procedure (consisting of LibDock, Glide, and CDOCKER). About 1 million molecules containing diketone or β-keto-enol substructures were filtered by Lipinski's rules, CBP model, and Hypogen model. A total of 12 compounds with similar docking postures were generated by consensus docking. Eventually, four molecules were screened based on the specific binding pattern and affinity of the HPPD inhibitor. The biological evaluation in vivo displayed that compounds III-1 and III-2 exhibited comparable herbicidal activity to isoxaflutole and possessed superior safety on various crops (wheat, rice, sorghum, and maize). The ADMET prediction (absorption, distribution, metabolism, excretion, and toxicity) showed that compound III possessed relatively good toxicological results. This work provides a theoretical basis and valuable reference for the virtual screening and molecular design of novel HPPD inhibition herbicides.

Discovery of novel HPPD inhibitors: Virtual screening, molecular design, structure modification and biological evaluation

4-Hydroxyphenylpyruvate dioxygenase (EC 1.13.11.27, HPPD, a Fe(II)/α-ketoglutarate dependent oxygenases), is a popular herbicide target. In this work, two pharmacophore models based on common molecular characteristics (HipHop) and receptor-ligand complex (CBP) were generated for virtual screening for HPPD inhibitors. About 1,000,000 molecules containing diketone structure from PubChem were filtered by Lipinski's rules to build a 3D database. Then the database was screened through combining HipHop model, CBP model, ADMET (absorption, distribution, metabolism, excretion and toxicity) prediction and molecular docking. Subsequently, based on the specific binding mode and affinity of HPPD inhibitors, 4 molecules with high -CDOCKER energy, good aqueous solubility and human safety predicative properties values were screened. From the screening results and combined with previous work, three novel HPPD inhibitors were designed and synthesized through fragment splicing and bioisosterism strategies. Compound IV-a exhibited similar inhibition of Arabidopsis thaliana HPPD (AtHPPD) and herbicidal activity as mesotrione. Crop selectivity showed that compound IV-a had better crop safety than mesotrione. Comparing the molecular properties, ADMET and molecular docking studies indicated that compounds IV-a exhibited better properties than mesotrione, which could be further modified as novel HPPD inhibitor herbicides.

Identification of Potent Small-Molecule PCSK9 Inhibitors Based on Quantitative Structure-Activity Relationship, Pharmacophore Modeling, and Molecular Docking Procedure

The leading cause of atherosclerotic cardiovascular disease (ASCVD) is elevated low-density lipoprotein cholesterol (LDL-C). Proprotein convertase subtilisin/kexin type 9 (PCSK9) attaches to the domain of LDL receptor (LDLR), diminishing LDL-C influx and LDLR cell surface presentation in hepatocytes, resulting in higher circulating LDL-C levels. PCSK9 dysfunction has been linked to lower levels of plasma LDLC and a decreased risk of coronary heart disease (CHD). Herein, using virtual screening tools, we aimed to identify a potent small-molecule PCSK9 inhibitor in compounds that are currently being studied in clinical trials. We first performed chemical absorption, distribution, metabolism, excretion, and toxicity (ADMET) filtering of 9800 clinical trial compounds obtained from the ZINC 15 database using Lipinski's rule of 5 and achieved 3853 compounds. Two-dimensional (2D) quantitative structure-activity relationship (QSAR) was initiated by computing molecular descriptors and selecting important descriptors of 23 PCSK9 inhibitors. Multivariate calibration was performed with the partial least square regression (PLS) method with 18 compounds for training to design the QSAR model and 5 compounds for the test set to assess the model. The best latent variables (LV) (LV=6) with the lowest value of Root-Mean-Square Error of Cross-Validation (RMSECV) of 0.48 and leave-one-out cross-validation correlation coefficient (R²CV) = 0.83 were obtained for the QSAR model. The low RMSEC (0.21) with high R²cal (0.966) indicates the probability of fit between the experimental data and the calibration model. Using QSAR analysis of 3853 compounds, 2635 had a pIC₅₀<1 and were considered for pharmacophore screening. The PHASE module (a complete package for pharmacophore modeling) designed the pharmacophore hypothesis through multiple ligands. The top 14 compounds (pIC₅₀>1) were defined as active, whereas 9 (pIC₅₀<1) were considered as an inactive set. Three five-point pharmacophore hypotheses achieved the highest score: DHHRR1, DHHRR2, and DHRRR1. The highest and best model with survival scores (5.365) was DHHRR1, comprising 1 hydrogen donor (D), 2 hydrophobic groups (H), and 2 rings of aromatic (R) features. We selected the molecules with a higher 1.5 fitness score (257 compounds) in pharmacophore screening (DHHRR1) for molecular docking screening. Molecular docking indicates that ZINC000051951669, with a binding affinity: of -13.2 kcal/mol and 2 H-bonds, has the highest binding to the PCSK9 protein. ZINC000011726230 with energy binding: -11.4 kcal/mol and 3 H-bonds, ZINC000068248147 with binding affinity: -10.7 kcal/mol and 1 H-bond, ZINC000029134440 with a binding affinity: -10.6 kcal/mol and 4 H-bonds were ranked next, respectively. To conclude, the archived molecules identified as inhibitory PCSK9 candidates, and especially ZINC000051951669 may therefore significantly inhibit PCSK9 and should be considered in the newly designed trials.

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

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

In vivo diagnostics of abiotic plant stress responses via in situ real-time fluorescence imaging

A hydroxyphenylpyruvate dioxygenase-targeted fluorescent biosensor enables the early diagnostics of abiotic stresses in plants.

Virtual screening based on pharmacophore model for developing novel HPPD inhibitors

4-Hydroxyphenylpyruvate dioxygenase (HPPD) is an important target for herbicide design. A multilayered virtual screening workflow was constructed by combining two pharmacophore models based on ligand and crystal complexes, molecular docking, molecular dynamics (MD), and biological activity determination to identify novel small-molecule inhibitors of HPPD. About 110, 000 compounds of Bailingwei and traditional Chinese medicine databases were screened. Of these, 333 were analyzed through docking experiments. Five compounds were selected by analyzing the binding pattern of inhibitors with amino acid residues in the active pocket. All five compounds could produce stable coordination with cobalt ion, and form favorable π-π interactions. MD simulation demonstrated that Phe381 and Phe424 made large contributions to the strength of binding. The enzyme activity experiment verified that compound-139 displayed excellent potency against AtHPPD (IC₅₀ = 0.742 μM), however, compound-5222 had inhibitory effect on human HPPD (IC₅₀ = 6 nM). Compound-139 exhibited herbicidal activity to some extent on different gramineous weeds. This work provided a strong insight into the design and development of novel HPPD inhibitor using in silico techniques.

Discovery of New Estrogen-Related Receptor α Agonists via a Combination Strategy Based on Shape Screening and Ensemble Docking

Estrogen-related receptor α (ERRα), a member of nuclear receptors (NRs), plays a role in the regulation of cellular energy metabolism and is reported to be a novel potential target for type 2 diabetes therapy. To date, only a few agonists of ERRα have been identified to improve insulin sensitivity and decrease blood glucose levels. Herein, the discovery of novel potent agonists of ERRα determined using a combined virtual screening approach is described. Molecular dynamics (MD) simulations were used to obtain structural ensembles that can consider receptor flexibility. Then, an efficient virtual screening strategy with a combination of similarity search and ensemble docking was performed against the Enamine, SPECS, and Drugbank databases to identify potent ERRα agonists. Finally, a total of 66 compounds were purchased for experimental testing. Biological investigation of promising candidates identified seven compounds that have activity against ERRα with EC50 values ranging from 1.11 to 21.70 μM, with novel scaffolds different from known ERRα agonists until now. Additionally, the molecule GX66 showed micromolar inverse activity against ERRα with an IC50 of 0.82 μM. The predicted binding modes showed that these compounds were anchored in ERRα-LBP via interactions with several residues of ERRα. Overall, this study not only identified the novel potent ERRα agonists or an inverse agonist that would be the promising starting point for further exploration but also demonstrated a successful molecular dynamics-guided approach applicable in virtual screening for ERRα agonists.

Identification of New Rofecoxib-Based Cyclooxygenase-2 Inhibitors: A Bioinformatics Approach

The cyclooxygenase-2 receptor is a therapeutic target for planning potential drugs with anti-inflammatory activity. The selective cyclooxygenase-2 (COX-2) inhibitor rofecoxib was selected as a pivot molecule to perform virtual ligand-based screening from six commercial databases. We performed the search for similarly shaped Rapid Overlay of Chemical Structures (ROCS) and electrostatic (EON) compounds. After, we used pharmacokinetic and toxicological parameters to determine the best potential compounds, obtained through the softwares QikProp and Derek, respectively. Then, the compounds proceeded to the molecular anchorage study, which showed promising results of binding affinity with the hCOX-2 receptor: LMQC72 (∆G = -11.0 kcal/mol), LMQC36 (∆G = -10.6 kcal/mol), and LMQC50 (∆G = -10.2 kcal/mol). LMQC72 and LMQC36 showed higher binding affinity compared to rofecoxib (∆G = -10.4 kcal/mol). Finally, molecular dynamics (MD) simulations were used to evaluate the interaction of the compounds with the target hCOX-2 during 150 ns. In all MD simulation trajectories, the ligands remained interacting with the protein until the end of the simulation. The compounds were also complexing with hCOX-2 favorably. The compounds obtained the following affinity energy values: rofecoxib: ΔGbind = -45.31 kcal/mol; LMQC72: ΔGbind = -38.58 kcal/mol; LMQC36: ΔGbind = -36.10 kcal/mol; and LMQC50: ΔGbind = -39.40 kcal/mol. The selected LMQC72, LMQC50, and LMQC36 structures showed satisfactory pharmacokinetic results related to absorption and distribution. The toxicological predictions of these compounds did not display alerts for possible toxic groups and lower risk of cardiotoxicity compared to rofecoxib. Therefore, future in vitro and in vivo studies are needed to confirm the anti-inflammatory potential of the compounds selected here with bioinformatics approaches based on rofecoxib ligand.

Quantitative Structure Activity Relationship Studies and Molecular Dynamics Simulations of 2-(Aryloxyacetyl)cyclohexane-1,3-Diones Derivatives as 4-Hydroxyphenylpyruvate Dioxygenase Inhibitors

4-Hydroxyphenylpyruvate dioxygenase (HPPD) is a significant enzyme in the biosynthesis of plastoquinone and tocopherol. Moreover, it is also a potential target to develop new herbicide. The technology of computer-aided drug design (CADD) is a useful tool in the efficient discovery of new HPPD inhibitors. Forty-three compounds with known activities were used to generate comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) models based on common framework and molecular docking. The structural contribution to the activity was determined, which provided further information for the design of novel inhibitors. Molecular docking was used to explain the changes in activity caused by the binding mode between ligand and protein. The molecular dynamics (MD) results indicated that the electrostatic energy was the major driving force for ligand–protein interaction and the Phe403 made the greatest contribution to the binding. The present work has provided useful information for the rational design of novel HPPD inhibitors with improved activity.

Identification of Novel Bisbenzimidazole Derivatives as Anticancer Vacuolar (H⁺)-ATPase Inhibitors

The vacuolar (H⁺)-ATPases (V-ATPases) are a family of ATP-driven proton pumps and they have been associated with cancer invasion, metastasis, and drug resistance. Despite the clear involvement of V-ATPases in cancer, the therapeutic use of V-ATPase-targeting small molecules has not reached human clinical trials to date. Thus, V-ATPases are emerging as important targets for the identification of potential novel therapeutic agents. We identified a bisbenzimidazole derivative (V) as an initial hit from a similarity search using four known V-ATPase inhibitors (I–IV). Based on the initial hit (V), we designed and synthesized a focused set of novel bisbenzimidazole analogs (2a–e). All newly prepared compounds have been screened for selected human breast cancer (MDA-MB-468, MDA-MB-231, and MCF7) and ovarian cancer (A2780, Cis-A2780, and PA-1) cell lines, along with the normal breast epithelial cell line, MCF10A. The bisbenzimidazole derivative (2e) is active against all cell lines tested. Remarkably, it demonstrated high cytotoxicity against the triple-negative breast cancer (TNBC) cell line, MDA-MB-468 (IC₅₀ = 0.04 ± 0.02 μM). Additionally, it has been shown to inhibit the V-ATPase pump that is mainly responsible for acidification. To the best of our knowledge the bisbenzimidazole pharmacophore has been identified as the first V-ATPase inhibitor in its class. These results strongly suggest that the compound 2e could be further developed as a potential anticancer V-ATPase inhibitor for breast cancer treatment.

4-Hydroxyphenylpyruvate Dioxygenase and Its Inhibition in Plants and Animals: Small Molecules as Herbicides and Agents for the Treatment of Human Inherited Diseases

This review mainly focuses on the physiological function of 4-hydroxyphenylpyruvate dioxygenase (HPPD), as well as on the development and application of HPPD inhibitors of several structural classes. Among them, one illustrative example is represented by compounds belonging to the class of triketone compounds. They were discovered by serendipitous observations on weed growth and were developed as bleaching herbicides. Informed reasoning on nitisinone (NTBC, 14), a triketone that failed to reach the final steps of the herbicidal design and development process, allowed it to become a curative agent for type I tyrosinemia (T1T) and to enter clinical trials for alkaptonuria. These results boosted the research of new compounds able to interfere with HPPD activity to be used for the treatment of the tyrosine metabolism-related diseases.

In silico target prediction for elucidating the mode of action of herbicides including prospective validation

The rapid emergence of pesticide resistance has given rise to a demand for herbicides with new mode of action (MoA). In the agrochemical sector, with the availability of experimental high throughput screening (HTS) data, it is now possible to utilize in silico target prediction methods in the early discovery phase to suggest the MoA of a compound via data mining of bioactivity data. While having been established in the pharmaceutical context, in the agrochemical area this approach poses rather different challenges, as we have found in this work, partially due to different chemistry, but even more so due to different (usually smaller) amounts of data, and different ways of conducting HTS. With the aim to apply computational methods for facilitating herbicide target identification, 48,000 bioactivity data against 16 herbicide targets were processed to train Laplacian modified Naïve Bayesian (NB) classification models. The herbicide target prediction model ("HerbiMod") is an ensemble of 16 binary classification models which are evaluated by internal, external and prospective validation sets. In addition to the experimental inactives, 10,000 random agrochemical inactives were included in the training process, which showed to improve the overall balanced accuracy of our models up to 40%. For all the models, performance in terms of balanced accuracy of≥80% was achieved in five-fold cross validation. Ranking target predictions was addressed by means of z-scores which improved predictivity over using raw scores alone. An external testset of 247 compounds from ChEMBL and a prospective testset of 394 compounds from BASF SE tested against five well studied herbicide targets (ACC, ALS, HPPD, PDS and PROTOX) were used for further validation. Only 4% of the compounds in the external testset lied in the applicability domain and extrapolation (and correct prediction) was hence impossible, which on one hand was surprising, and on the other hand illustrated the utilization of using applicability domains in the first place. However, performance better than 60% in balanced accuracy was achieved on the prospective testset, where all the compounds fell within the applicability domain, and which hence underlines the possibility of using target prediction also in the area of agrochemicals.

Current Challenges and Trends in the Discovery of Agrochemicals

Crop protection chemistry has come a long way from its “alchemic” beginnings in the late 19th century to a high-tech science that supports the sustainable production of food, feed, and fiber for a rapidly growing population. Cutting-edge developments in the design and synthesis of agrochemicals help to tackle today’s challenges of weed and pest resistance, higher regulatory safety margins, and higher cost of goods with the invention of selective, environmentally benign, low use rate, and cost-effective active ingredients.

New substituted 4H-chromenes as anticancer agents

As a continuation of our efforts to discover and develop small molecules as anticancer agents, we identified GRI-394837 as an initial hit from similarity search on RGD and its analogs. Based on GRI-394837, we designed and synthesized a focused set of novel chromenes (4a-e) in a single step using microwave method. All five compounds showed activity in the nanomolar range (IC(50): 7.4-640 nM) in two melanoma, three prostate and four glioma cancer cell lines. The chromene 4e is active against all the cell lines and particularly against the A172 human glioma cell line (IC(50): 7.4 nM). Interestingly, in vitro tubulin polymerization assay shows 4e to be a weak tubulin polymerization inhibitor but it shows very strong cytotoxicity in cellular assays, therefore there must be additional unknown mechanism(s) for the anticancer activity. Additionally, the strong antiproliferative activity was verified by one of the selected chromene (4a) by the NCI 60 cell line screen. These results strongly suggest that the novel chromenes could be further developed as a potential therapeutic agent for a variety of aggressive cancers.

A perspective on the role of quantitative structure-activity and structure-property relationships in herbicide discovery

BACKGROUND

For the last 15 years the agrochemical industry has focused on using genetic modification to put genes that confer resistance to existing commercial herbicides into crop plants rather than on discovering new herbicides with novel modes of action. The widespread appearance of weeds resistant to those herbicides is now causing the industry to revive their herbicide discovery programs.

RESULTS

Elucidation of quantitative structure-activity relationships (QSARs) played a major role in the discovery and development of existing commercial herbicides, but the advent of genetically modified crops has caused published work (at least) in the area to drift from the industrial arena into academic studies. The focus has also turned inward, to refining models for established herbicide targets instead of elucidating new ones.

CONCLUSION

This perspective highlights the importance of QSARs and quantitative structure-property relationships (QSPRs) to herbicide discovery in an historical context and provides some guidance as to how they might profitably be applied going forward.

Pharmacophore and QSAR studies to design novel histone deacetylase 2 inhibitors

One pharmacophore model and three quantitative structure-activity relationship models were developed on a series of benzimidazole and imidazole inhibitors of histone deacetylase 2. The goodness of hit score value of the best pharmacophore model was 0.756, which indicated that it is reliable to be used for virtual screening. The built pharmacophore model was used to search the NCI database. The hit compounds were subjected to molecular docking. The results showed that 25 compounds had high scores and strong interactions with histone deacetylase 2. In three-dimensional quantitative structure-activity relationship studies, good predictive models were obtained using comparative molecular field analysis, comparative molecular similarity indices analysis, and Topomer comparative molecular field analysis. Some putative active compounds were proposed based on compound no. 41. Twenty-six compounds had high scores and good interactions when they were docking into histone deacetylase 2.

Discovery of a novel and potent class of F. tularensis enoyl-reductase (FabI) inhibitors by molecular shape and electrostatic matching

Enoyl-acyl carrier protein (ACP) reductase, FabI, is a key enzyme in the bacterial fatty acid biosynthesis pathway (FAS II). FabI is an NADH-dependent oxidoreductase that acts to reduce enoyl-ACP substrates in a final step of the pathway. The absence of this enzyme in humans makes it an attractive target for the development of new antibacterial agents. FabI is known to be unresponsive to structure-based design efforts due to a high degree of induced fit and a mobile flexible loop encompassing the active site. Here we discuss the development, validation, and careful application of a ligand-based virtual screen used for the identification of novel inhibitors of the Francisella tularensis FabI target. In this study, four known classes of FabI inhibitors were used as templates for virtual screens that involved molecular shape and electrostatic matching. The program ROCS was used to search a high-throughput screening library for compounds that matched any of the four molecular shape queries. Matching compounds were further refined using the program EON, which compares and scores compounds by matching electrostatic properties. Using these techniques, 50 compounds were selected, ordered, and tested. The tested compounds possessed novel chemical scaffolds when compared to the input query compounds. Several hits with low micromolar activity were identified and follow-up scaffold-based searches resulted in the identification of a lead series with submicromolar enzyme inhibition, high ligand efficiency, and a novel scaffold. Additionally, one of the most active compounds showed promising whole-cell antibacterial activity against several Gram-positive and Gram-negative species, including the target pathogen. The results of a preliminary structure-activity relationship analysis are presented.