Identification of novel inhibitors of p-hydroxyphenylpyruvate dioxygenase using receptor-based virtual screening

A review of deep learning methods for ligand based drug virtual screening

Drug discovery is costly and time consuming, and modern drug discovery endeavors are progressively reliant on computational methodologies, aiming to mitigate temporal and financial expenditures associated with the process. In particular, the time required for vaccine and drug discovery is prolonged during emergency situations such as the coronavirus 2019 pandemic. Recently, the performance of deep learning methods in drug virtual screening has been particularly prominent. It has become a concern for researchers how to summarize the existing deep learning in drug virtual screening, select different models for different drug screening problems, exploit the advantages of deep learning models, and further improve the capability of deep learning in drug virtual screening. This review first introduces the basic concepts of drug virtual screening, common datasets, and data representation methods. Then, large numbers of common deep learning methods for drug virtual screening are compared and analyzed. In addition, a dataset of different sizes is constructed independently to evaluate the performance of each deep learning model for the difficult problem of large-scale ligand virtual screening. Finally, the existing challenges and future directions in the field of virtual screening are presented.

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.

Identification of potentially high drug-like VEGFR2/c-Met dual-target type II kinase inhibitors with symmetric skeletons based on structural screening

Vascular endothelial growth factor receptor 2 (VEGFR2) and c-Mesenchymal epithelial transition factor (c-Met) are tyrosine kinase receptors associated with the occurrence of malignant tumors. Studies have shown that inhibition of VEGFR2 promotes a feedback increase in c-Met, a mechanism linked to the emergence of resistance to VEGFR2 inhibitors. Therefore, treatment targeting both VEGFR2 and c-Met will have better application prospects. In this study, hierarchical virtual screening was performed on ZINC15, Molport and Mcule-ULTIMATE databases to identify potential VEGFR2/c-Met dual inhibitors. Firstly, the best pharmacophore model for each target was used to cross-screen the three databases, and the compounds that could match the two pharmacophore models were then retained based on the Fit Value of the respective crystal ligands. Compounds ZINC, MOL, and MLB named after their database sources were retained by binding pattern analysis and docking assessment. ADMET predictions indicated that ZINC had significantly higher oral bioavailability compared to the approved drug cabozantinib. This is likely due to ZINC's unique symmetrical backbone with less structure complexity, which may reduce the occurrence of adverse effects. Molecular dynamics simulations and binding free energy analysis showed that all three hit compounds were able to stably bind at the active site, but only ZINC could form high occupancy of hydrogen bonds with both VEGFR2 and c-Met, and also only ZINC had a higher binding free energy than crystal ligands, suggesting that ZINC was the most likely potential VEGFR2/c-Met dual-target inhibitor. This finding provides a promising starting point for the development of VEGFR2/c-Met dual-target inhibitors.Communicated by Ramaswamy H. Sarma.

Current Landscape on Development of Phenylalanine and Toxicity of its Metabolites - A Review

Phenylalanine, an essential amino acid, is the "building block" of protein. It has a tremendous role in different aspects of metabolic events. The tyrosine pathway is the prime one and is typically used to degrade dietary phenylalanine. Phenylalanine exceeds its limit in bodily fluids and the brain when the enzyme, phenylalanine decarboxylase, phenylalanine transaminase, phenylalanine hydroxylase (PAH) or its cofactor tetrahydrobiopterin (BH4) is deficient causes phenylketonuria, schizophrenia, attentiondeficit/ hyperactivity disorder and another neuronal effect. Tyrosine, an amino acid necessary for synthesizing the pigments in melanin, is produced by its primary metabolic pathway. Deficiency/abnormality in metabolic enzymes responsible for the catabolism pathway of Phenylalanine causes an accumulation of the active intermediate metabolite, resulting in several abnormalities, such as developmental delay, tyrosinemias, alkaptonuria, albinism, hypotension and several other undesirable conditions. Dietary restriction of the amino acid(s) can be a therapeutic approach to avoid such undesirable conditions when the level of metabolic enzyme is unpredictable. After properly identifying the enzymatic level, specific pathophysiological conditions can be managed more efficiently.

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.

G Protein-Coupled Receptor Interaction Prediction Based on Deep Transfer Learning

G protein-coupled receptors (GPCRs) account for about 40% to 50% of drug targets. Many human diseases are related to G protein coupled receptors. Accurate prediction of GPCR interaction is not only essential to understand its structural role, but also helps design more effective drugs. At present, the prediction of GPCR interaction mainly uses machine learning methods. Machine learning methods generally require a large number of independent and identically distributed samples to achieve good results. However, the number of available GPCR samples that have been marked is scarce. Transfer learning has a strong advantage in dealing with such small sample problems. Therefore, this paper proposes a transfer learning method based on sample similarity, using XGBoost as a weak classifier and using the TrAdaBoost algorithm based on JS divergence for data weight initialization to transfer samples to construct a data set. After that, the deep neural network based on the attention mechanism is used for model training. The existing GPCR is used for prediction. In short-distance contact prediction, the accuracy of our method is 0.26 higher than similar methods.

Liposomal Formulations Loaded with a Eugenol Derivative for Application as Insecticides: Encapsulation Studies and In Silico Identification of Protein Targets

A recently synthesized new eugenol derivative, ethyl 4-(2-methoxy-4-(oxiran-2-ylmethyl)phenoxy)butanoate, with a high insecticidal activity against Sf9 (Spodoptera frugiperda) insect cells, was encapsulated in the liposomal formulations of egg-phosphatidylcholine/cholesterol (Egg-PC:Ch) 70:30 and 100% dioleoylphosphatidylglycerol (DOPG), aiming at the future application as insecticides. Compound-loaded DOPG liposomes have sizes of 274 ± 12 nm, while Egg-PC:Ch liposomes exhibit smaller hydrodynamic diameters (69.5 ± 7 nm), high encapsulation efficiency (88.8 ± 2.7%), higher stability, and a more efficient compound release, thus, they were chosen for assays in Sf9 insect cells. The compound elicited a loss of cell viability up to 80% after 72 h of incubation. Relevantly, nanoencapsulation maintained the toxicity of the compound toward insect cells while lowering the toxicity toward human cells, thus showing the selectivity of the system. Structure-based inverted virtual screening was used to predict the most likely targets and molecular dynamics simulations and free energy calculations were used to demonstrate that this molecule can form a stable complex with insect odorant binding proteins and/or acetylcholinesterase. The results are promising for the future application of compound-loaded nanoliposome formulations as crop insecticides.

Computer-Aided and AILDE Approaches to Design Novel 4-Hydroxyphenylpyruvate Dioxygenase Inhibitors

4-Hydroxyphenylpyruvate dioxygenase (HPPD) is a pivotal enzyme in tocopherol and plastoquinone synthesis and a potential target for novel herbicides. Thirty-five pyridine derivatives were selected to establish a Topomer comparative molecular field analysis (Topomer CoMFA) model to obtain correlation information between HPPD inhibitory activity and the molecular structure. A credible and predictive Topomer CoMFA model was established by "split in two R-groups" cutting methods and fragment combinations (q² = 0.703, r² = 0.957, ONC = 6). The established model was used to screen out more active compounds and was optimized through the auto in silico ligand directing evolution (AILDE) platform to obtain potential HPPD inhibitors. Twenty-two new compounds with theoretically good HPPD inhibition were obtained by combining the high-activity contribution substituents in the existing molecules with the R-group search via Topomer search. Molecular docking results revealed that most of the 22 fresh compounds could form stable π-π interactions. The absorption, distribution, metabolism, excretion and toxicity (ADMET) prediction and drug-like properties made 9 compounds potential HPPD inhibitors. Molecular dynamics simulation indicated that Compounds Y12 and Y14 showed good root mean square deviation (RMSD) and root mean square fluctuation (RMSF) values and stability. According to the AILDE online verification, 5 new compounds with potential HPPD inhibition were discovered as HPPD inhibitor candidates. This study provides beneficial insights for subsequent HPPD inhibitor design.

Survey on the Recent Advances in 4-Hydroxyphenylpyruvate Dioxygenase (HPPD) Inhibition by Diketone and Triketone Derivatives and Congeneric Compounds: Structural Analysis of HPPD/Inhibitor Complexes and Structure-Activity Relationship Considerations

The serendipitous discovery of the HPPD inhibitors from allelopathic plants opened the way for searching new and effective herbicidal agents by application of classical hit-to-lead optimization approaches. A plethora of active and selective compounds were discovered that belong to three major classes of cyclohexane-based triketones, pyrazole-based diketones, and diketonitriles. In addition, to enhance inhibitory constant and herbicidal activity, many efforts were also made to gain broader weed control, crop safety, and eventual agricultural applicability. Moreover, HPPD inhibitors emerged as therapeutic agents for inherited and metabolic human diseases as well as vector-selective insecticides in the control of hematophagous arthropods. Given the large set of experimental data available, structure-activity relationship analysis could be used to derive suggestions for next generation optimized compounds.

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.

Design, synthesis, herbicidal activity and CoMFA of aryl-formyl piperidinone HPPD inhibitors

Isoxazole, nicotinic acid and benzoic acid are important components in many natural products and useful synthons to build macrostructures having valuable biological activities. In continuation of our effort to discover 4-hydroxyphenylpyruvate dioxygenase (HPPD, EC 1.13.11.27) inhibitors and search for active fragments from natural products, a series of substituted aryl-formyl piperidinone derivatives with natural product fragments was rationally designed, synthesized and tested for their herbicidal activity. Compound I-9 was considered the most effective candidate with an IC₅₀ value of 0.260 μM. The molecular docking results showed that the triketone group of compound I-9 forms a bidentate complex with a metal ion, and the benzene ring interacted with Phe424 and Phe381 via π-π stacking, which was similar to the mechanisms of mesotrione. The present work indicates that compound I-9 may serve as a potential lead compound for further development of green HPPD inhibitors.

Based on the Virtual Screening of Multiple Pharmacophores, Docking and Molecular Dynamics Simulation Approaches toward the Discovery of Novel HPPD Inhibitors

4-Hydroxyphenylpyruvate dioxygenase (HPPD) is an iron-dependent non-heme oxygenase involved in the catabolic pathway of tyrosine, which is an important enzyme in the transformation of 4-hydroxyphenylpyruvic acid to homogentisic acid, and thus being considered as herbicide target. Within this study, a set of multiple structure-based pharmacophore models for HPPD inhibitors were developed. The ZINC and natural product database were virtually screened, and 29 compounds were obtained. The binding mode of HPPD and its inhibitors obtained through molecular docking study showed that the residues of Phe424, Phe381, His308, His226, Gln307 and Glu394 were crucial for activity. Molecular-mechanics-generalized born surface area (MM/GBSA) results showed that the coulomb force, lipophilic and van der Waals (vdW) interactions made major contributions to the binding affinity. These efforts will greatly contribute to design novel and effective HPPD inhibitory herbicides.

Identification of key residues determining the binding specificity of human 4-hydroxyphenylpyruvate dioxygenase

4-Hydroxyphenylpyruvate dioxygenase (HPPD, EC 1.13.11.27) is the second enzyme of the tyrosine catabolic pathway. Its physiological function is to catalyze the conversion of 4-hydroxyphenylpyruvic acid to homogentisic acid, which displays different physiological effects in mammals and plants. Insights on the selective inhibition of human HPPD (hHPPD) by triketone inhibitors were furnished by the integrated application of molecular simulation and biological testing. The binding free energy of hHPPD and inhibitors was obtained through molecular dynamics (MD) simulations, and the result was in agreement with the inhibition experiment in vitro. The binding free energy contribution demonstrated that the formation of hHPPD-inhibitor complexes was mainly driven by van der Waals energy. Ser226, Asn241, Gln265, Phe336, Phe359 and Phe364 made great contributions to binding affinities of all the systems. Among the residues involved in the interaction between nitisinone (NTBC) and hHPPD, Tyr221 and Leu224, whose mutation into Ala caused significant decrease of NTBC binding ability, were two key residues in determining the selective binding affinity of inhibitor and hHPPD. This work provides valuable theoretical basis for rational design of highly selective inhibitors targeting hHPPD.

Combined 3D-quantitative structure-activity relationships and topomer technology-based molecular design of human 4-hydroxyphenylpyruvate dioxygenase inhibitors

Aim: 4-Hydroxyphenylpyruvate dioxygenase (HPPD) has attracted increasing attention as an important target against tyrosinemia type I. This paper aimed to explore the structure-activity relationship of HPPD inhibitors with pyrazole scaffolds and to design novel HPPD inhibitors. Methodology & results: The best 3D-quantitative structure-activity relationships model was established by two different strategies based on 40 pyrazole scaffold-based analogs. Screening of molecular fragments by topomer technology, combined with molecular docking, 14 structures were identified for potential human HPPD inhibitory activity. Molecular dynamics results demonstrated that all the compounds obtained bound to the enzyme and possessed a satisfactory binding free energy. Conclusion: The quantitative structure-activity relationship of HPPD inhibitors of pyrazole scaffolds was clarified and 14 original structures with potential human HPPD inhibitory activity were obtained.

Design, Synthesis, and Herbicidal Activity of Novel Diphenyl Ether Derivatives Containing Fast Degrading Tetrahydrophthalimide

To seek new protoporphyrinogen oxidase (PPO) inhibitors with better biological activity, a series of novel diphenyl ether derivatives containing tetrahydrophthalimide were designed based on the principle of substructure splicing and bioisomerization. PPO inhibition experiments exhibited that 6c is the most potential compound, with the half-maximal inhibitory concentration (IC₅₀) value of 0.00667 mg/L, showing 7 times higher activity than Oxyfluorfen (IC₅₀ = 0.0426 mg/L) against maize PPO and similar herbicidal activities to Oxyfluorfen in weeding experiments in greenhouses and field weeding experiments. In view of the inspected bioactivities, the structure-activity relationship (SAR) of this series of compounds was also discussed. Crop selection experiments demonstrate that compound 6c is safe for soybeans, maize, rice, peanuts, and cotton at a dose of 300 g ai/ha. Accumulation analysis experiments showed that the accumulation of 6c in some crops (soybeans, peanuts, and cotton) was significantly lower than Oxyfluorfen. Current work suggests that compound 6c may be developed as a new herbicide candidate in fields.