Synthesis and Biological Activity Evaluation of Benzoxazinone-Pyrimidinedione Hybrids as Potent Protoporphyrinogen IX Oxidase Inhibitor.

Protoporphyrinogen IX oxidase (PPO/Protox, E.C. 1.3.3.4) is recognized as one of the most important targets for herbicide discovery. In this study, we report our ongoing research efforts toward the discovery of novel PPO inhibitors. Specifically, we identified a highly potent new compound series containing a pyrimidinedione moiety and bearing a versatile building block-benzoxazinone scaffold. Systematic bioassays resulted in the discovery of compound 7af, ethyl 4-(7-fluoro-6-(3-methyl-2,6-dioxo-4-(trifluoromethyl)-3,6-dihydropyrimidin-1(2H)-yl)-3-oxo-2,3-dihydro-4H-benzo[b][1,4]oxazin-4-yl)butanoate, which exhibited broad-spectrum and excellent herbicidal activity at the dosage of 37.5 g a.i./ha through postemergence application. The inhibition constant (Ki) value of 7af to Nicotiana tabacum PPO (NtPPO) was 14 nM, while to human PPO (hPPO), it was 44.8 µM, indicating a selective factor of 3200, making it the most selective PPO inhibitor to date. Moreover, molecular simulations further demonstrated the selectivity and the binding mechanism of 7af to NtPPO and hPPO. This study not only identifies a candidate that showed excellent in vivo bioactivity and high safety toward humans but also provides a paradigm for discovering PPO inhibitors with improved performance through molecular simulation and structure-guided optimization.

Discovery of a Subnanomolar Inhibitor of Protoporphyrinogen IX Oxidase via Fragment-Based Virtual Screening.

Protoporphyrinogen IX oxidase (PPO, E.C. 1.3.3.4), a key functional enzyme existing in various organisms, is acknowledged to be one of the most important action targets in the development of herbicides due to its pivotal roles in chlorophyll and heme biosynthesis pathways. As our persistent research work on the discovery of novel PPO-inhibiting herbicides, a new compound methyl 2-((5-(3-chloro-4,5,6,7-tetrahydro-2H-indazol-2-yl)-6-fluorobenzo[d]thiazol-2-yl)thio)acetate (8aj, Ki = 16 nM) was screened out as a hit compound via a fragment-based virtual screening method performed in the Auto Core Fragment in silico Screening web server. Subsequently, through a fused process of "hit-to-lead" optimization guided by molecular simulation, a total of 30 3-chloro-4,5,6,7-tetrahydro-2H-indazol-benzo[d]thiazole derivatives were synthesized and characterized. The results of the enzymatic inhibition bioassay showed that more than half of the newly synthesized compounds displayed higher activity against Nicotiana tabacum PPO (NtPPO) than oxadiazon, a commercial PPO-inhibiting herbicide. In particular, compound 8ab, a subnanomolar inhibitor with a Ki value of 380 pM against NtPPO, was discovered, which showed to be 71-fold more active than the commercial control oxadiazon (Ki = 27 nM), and was proven to be the most potent PPO inhibitor so far. Furthermore, the greenhouse assay demonstrated that most of the synthetic compounds showed good herbicidal activity toward the tested weeds. Especially, compound 8ad (Ki = 670 pM) showed the most promising post-emergence herbicidal activity with a broad spectrum of weed control even at a concentration as low as 37.5 g a.i./ha and relatively safe to rice at a dosage of 150 g a.i./ha, indicating that 8ad has the greatest potential to be developed as a new herbicide for weed control in paddy fields. This work provides a paradigm for the rational design and discovery of a novel PPO-inhibiting herbicide guided by the fragment-based drug design.

ACFIS 2.0: an improved web-server for fragment-based drug discovery via a dynamic screening strategy.

Drug discovery, which plays a vital role in maintaining human health, is a persistent challenge. Fragment-based drug discovery (FBDD) is one of the strategies for the discovery of novel candidate compounds. Computational tools in FBDD could help to identify potential drug leads in a cost-efficient and time-saving manner. The Auto Core Fragment in silico Screening (ACFIS) server is a well-established and effective online tool for FBDD. However, the accurate prediction of protein-fragment binding mode and affinity is still a major challenge for FBDD due to weak binding affinity. Here, we present an updated version (ACFIS 2.0), that incorporates a dynamic fragment growing strategy to consider protein flexibility. The major improvements of ACFIS 2.0 include (i) increased accuracy of hit compound identification (from 75.4% to 88.5% using the same test set), (ii) improved rationality of the protein-fragment binding mode, (iii) increased structural diversity due to expanded fragment libraries and (iv) inclusion of more comprehensive functionality for predicting molecular properties. Three successful cases of drug lead discovery using ACFIS 2.0 are described, including drugs leads to treat Parkinson's disease, cancer, and major depressive disorder. These cases demonstrate the utility of this web-based server. ACFIS 2.0 is freely available at http://chemyang.ccnu.edu.cn/ccb/server/ACFIS2/.

Fragment-based drug discovery supports drugging 'undruggable' protein-protein interactions.

Protein-protein interactions (PPIs) have important roles in various cellular processes, but are commonly described as 'undruggable' therapeutic targets due to their large, flat, featureless interfaces. Fragment-based drug discovery (FBDD) has achieved great success in modulating PPIs, with more than ten compounds in clinical trials. Here, we highlight the progress of FBDD in modulating PPIs for therapeutic development. Targeting hot spots that have essential roles in both fragment binding and PPIs provides a shortcut for the development of PPI modulators via FBDD. We highlight successful cases of cracking the 'undruggable' problems of PPIs using fragment-based approaches. We also introduce new technologies and future trends. Thus, we hope that this review will provide useful guidance for drug discovery targeting PPIs.

Protein Kinases as Potential Targets Contribute to the Development of Agrochemicals.

Using agrochemicals against pest insects, fungi, and weeds plays a major part in maintaining and improving crop yields, which helps to solve the issue of food security. Due to the limited targets and resistance of agrochemicals, protein kinases are regarded as attractive potential targets to develop new agrochemicals. Recently, a lot of investigations have shown the extension of agrochemicals by targeting protein kinases, implying an increasing concern for this kind of method. However, few people have summarized and discussed the targetability of protein kinases contributing to the development of agrochemicals. In this work, we introduce the research on protein kinases as potential targets used in crop protection and discuss the prospects of protein kinases in the field of agrochemical development. This study may not only provide guidance for the contribution of protein kinases to the development of agrochemicals but also help nonprofessionals such as students learn and understand the role of protein kinases quickly.

Deciphering Nonbioavailable Substructures Improves the Bioavailability of Antidepressants by Serotonin Transporter.

Inadequate bioavailability is one of the most critical reasons for the failure of oral drug development. However, the way that substructures affect bioavailability remains largely unknown. Serotonin transporter (SERT) inhibitors are first-line drugs for major depression disorder, and improving their bioavailability may be able to decrease side-effects by reducing daily dose. Thus, it is an excellent model to probe the relationship between substructures and bioavailability. Here, we proposed the concept of "nonbioavailable substructures", referring to substructures that are unfavorable to bioavailability. A machine learning model was developed to identify nonbioavailable substructures based on their molecular properties and shows the accuracy of 83.5%. A more potent SERT inhibitor DH4 was discovered with a bioavailability of 83.28% in rats by replacing the nonbioavailable substructure of approved drug vilazodone. DH4 exhibits promising anti-depression efficacy in animal experiments. The concept of nonbioavailable substructures may open up a new venue for the improvement of drug bioavailability.

Discovery of Macrocycle-Based HPK1 Inhibitors for T-Cell-Based Immunotherapy.

Hematopoietic progenitor kinase 1 (HPK1) is a negative regulator of T-cell activation, and targeting HPK1 is considered a promising strategy for improving responses to antitumor immune therapies. The biggest challenge of HPK1 inhibitor design is to achieve a higher selectivity to GLK, an HPK1 homology protein as a positive regulator of T-cell activation. Herein, we report the design of a series of macrocycle-based HPK1 inhibitors via a conformational constraint strategy. The identified candidate compound 5i exhibited HPK1 inhibition with an IC50 value of 0.8 nM and 101.3-fold selectivity against GLK. Compound 5i also displayed good oral bioavailability (F = 27-49%) in mice and beagles and favorable metabolic stability (T1/2 > 186.4 min) in human liver microsomes. More importantly, compound 5i demonstrated a clear synergistic effect with anti-PD-1 in both MC38 (MSI) and CT26 (MSS) syngeneic tumor mouse models. These results showed that compound 5i has a great potential in immunotherapy.

Metabolic alteration of Tetrahymena thermophila exposed to CdSe/ZnS quantum dots to respond to oxidative stress and lipid damage.

CdSe/ZnS Quantum dots (QDs) are possibly released to surface water due to their extensive application. Based on their high reactivity, even small amounts of toxicant QDs will disturb water microbes and pose a risk to aquatic ecology. Here, we evaluated CdSe/ZnS QDs toxicity to Tetrahymena thermophila (T. thermophila), a model organism of the aquatic environment, and performed metabolomics experiments. Before the omics experiment was conducted, QDs were found to induce inhibition of cell proliferation, and reactive oxygen species (ROS) production along with Propidium iodide labeled cell membrane damage indicated oxidative stress stimulation. In addition, mitochondrial ultrastructure alteration of T. thermophila was also confirmed by Transmission Electron Microscope results after 48 h of exposure to QDs. Further results of metabolomics detection showed that 0.1 µg/mL QDs could disturb cell physiological and metabolic metabolism characterized by 18 significant metabolite changes, of which twelve metabolites improved and three decreased significantly compared to the control. Kyoto Encyclopedia of Genes and Genomes analysis showed that these metabolites were involved in the ATP-binding cassette transporter and purine metabolism pathways, both of which respond to ROS-induced cell membrane damage. In addition, purine metabolism weakness might also reflect mitochondrial dysfunction associated with energy metabolism and transport abnormalities. This research provides deep insight into the potential risks of quantum dots in aquatic ecosystems.

Unveiling toxicity profile for food risk components: A manually curated toxicological databank of food-relevant chemicals.

Rigorous risk assessment of chemicals in food and feed is essential to address the growing worldwide concerns about food safety. High-quality toxicological data on food-relevant chemicals are fundamental for risk modeling and assessment in the food safety area. The organization and analysis of substantial toxicity information can positively support decision-making by providing insight into toxicity trends. However, it remains challenging to systematically obtain fragmented toxicity data, and related toxicological resources are required to meet the current demands. In this study, we collected 221,439 experimental toxicity records for 5,657 food-relevant chemicals identified from extensive databases and literature, along with their information on chemical identification, physicochemical properties, environmental fates, and biological targets. Based on the aggregated data, a freely available web-based databank, Food-Relevant Available Chemicals Toxicology Databank (FRAC-TD) is presented, which supports multiple browsing ways and search criterions. Applying FRAC-TD for data-driven analysis, we revealed the underlying toxicity profiles of food-relevant chemicals in humans, mammals, and other species in the food chain. Expectantly, FRAC-TD could positively facilitate toxicological studies, toxicity prediction, and risk assessments in the food industry.

Exploring the kinase-inhibitor fragment interaction space facilitates the discovery of kinase inhibitor overcoming resistance by mutations.

Protein kinases play crucial roles in many cellular signaling processes, making them become important targets for drug discovery. But drug resistance mediated by mutation puts a barrier to the therapeutic effect of kinase inhibitors. Fragment-based drug discovery has been successfully applied to overcome such resistance. However, the complicate kinase-inhibitor fragment interaction and fragment-to-lead process seriously limit the efficiency of kinase inhibitor discovery against resistance caused by mutation. Here, we constructed a comprehensive web platform KinaFrag for the fragment-based kinase inhibitor discovery to overcome resistance. The kinase-inhibitor fragment space was investigated from 7783 crystal kinase-inhibitor fragment complexes, and the structural requirements of kinase subpockets were analyzed. The core fragment-based virtual screening workflow towards specific subpockets was developed to generate new kinase inhibitors. A series of tropomyosin receptor kinase (TRK) inhibitors were designed, and the most potent compound YT9 exhibits up to 70-fold activity improvement than marketed drugs larotrectinib and selitrectinib against G595R, G667C and F589L mutations of TRKA. YT9 shows promising antiproliferative against tumor cells in vitro and effectively inhibits tumor growth in vivo for wild type TRK and TRK mutants. Our results illustrate the great potential of KinaFrag in the kinase inhibitor discovery to combat resistance mediated by mutation. KinaFrag is freely available at http://chemyang.ccnu.edu.cn/ccb/database/KinaFrag/.

Web support for the more efficient discovery of kinase inhibitors.

Kinases have a crucial role in cell signaling and are important drug targets, given that aberrant kinase activity has been linked to most disease areas. Therefore, kinase inhibitors (KIs) have significant potential as new therapeutics. In recent years, an increasing amount of computational resources have been developed to design ideal scaffold and selective KIs more efficiently. Thus, in this review, we systematically examine the computational tools used in KI research, and discuss and compare the characteristics and limitations of these resources. Such a discussion will facilitate the design of new KIs and provide a learning platform for nonspecialists.

PTMdyna: exploring the influence of post-translation modifications on protein conformational dynamics.

Protein post-translational modifications (PTM) play vital roles in cellular regulation, modulating functions by driving changes in protein structure and dynamics. Exploring comprehensively the influence of PTM on conformational dynamics can facilitate the understanding of the related biological function and molecular mechanism. Currently, a series of excellent computation tools have been designed to analyze the time-dependent structural properties of proteins. However, the protocol aimed to explore conformational dynamics of post-translational modified protein is still a blank. To fill this gap, we present PTMdyna to visually predict the conformational dynamics differences between unmodified and modified proteins, thus indicating the influence of specific PTM. PTMdyna exhibits an AUC of 0.884 tested on 220 protein-protein complex structures. The case of heterochromatin protein 1α complexed with lysine 9-methylated histone H3, which is critical for genomic stability and cell differentiation, was used to demonstrate its applicability. PTMdyna provides a reliable platform to predict the influence of PTM on protein dynamics, making it easier to interpret PTM functionality at the structure level. The web server is freely available at http://ccbportal.com/PTMdyna.

Fragment-based drug design facilitates selective kinase inhibitor discovery.

Protein kinases (PKs) are important drug targets, but kinases selectivity poses a challenge to protein kinase inhibitors (PKIs) design. Fragment-based drug discovery (FBDD) has achieved great success in the discovery of highly specific PKIs. It makes full use of kinase-fragment interaction in target kinase subpockets to obtain promising selectivity. However, it's difficult to understand the complicated kinase-fragment interaction space, and systemic discussion of these interactions is still lacking. Herein, we introduce the advantages of the FBDD strategy in PKIs design. Key features of the selectivity of kinase-fragment interactions are summarized and analyzed. Some promising PKIs are introduced as case studies to help understand the fragment-to-lead (F2L) optimization process. Novel strategies and technologies for FBDD in PKIs discovery are also outlooked.

Computational Fragment-Based Design Facilitates Discovery of Potent and Selective Monoamine Oxidase-B (MAO-B) Inhibitor.

Parkinson's disease (PD) is one of the most common age-related neurodegenerative diseases. Inhibition of monoamine oxidase-B (MAO-B), which is mainly found in the glial cells of the brain, may lead to an elevated level of dopamine (DA) in patients. MAO-B inhibitors have been used extensively for patients with PD. However, the discovery of the selective MAO-B inhibitor is still a challenge. In this study, a computational strategy was designed for the rapid discovery of selective MAO-B inhibitors. A series of (S)-2-(benzylamino)propanamide derivatives were designed. In vitro biological evaluations revealed that (S)-1-(4-((3-fluorobenzyl)oxy)benzyl)azetidine-2-carboxamide (C3) was more potent and selective than safinamide, a promising drug for regulating MAO-B. Further studies revealed that the selectivity mechanism of C3 was due to the steric clash caused by the residue difference of Phe208 (MAO-A) and Ile199 (MAO-B). Animal studies showed that compound C3 could inhibit cerebral MAO-B activity and alleviate 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-induced dopaminergic neuronal loss.

Development of Highly Potent, Selective, and Cellular Active Triazolo[1,5- a]pyrimidine-Based Inhibitors Targeting the DCN1-UBC12 Protein-Protein Interaction.

The cullin-RING ubiquitin ligases (CRLs) are responsible for about 20% of cellular protein degradation and regulate diverse cellular processes, and the dysfunction of CRLs is implicated in human diseases. Targeting the CRLs has become an emerging strategy for the treatment of human diseases. Herein, we describe the discovery of a hit compound from our in-house library and further structure-based optimizations, which have enabled the identification of new triazolo[1,5- a]pyrimidine-based inhibitors targeting the DCN1-UBC12 interaction. Compound WS-383 blocks the DCN1-UBC12 interaction (IC50 = 11 nM) reversibly and shows selectivity over selected kinases. WS-383 exhibits cellular target engagement to DCN1 in MGC-803 cells. WS-383 inhibits Cul3/1 neddylation selectively over other cullins and also induces accumulation of p21, p27, and NRF2. Collectively, targeting the DCN1-UBC12 interaction would be a viable strategy for selective neddylation inhibition of Cul3/1 and may be of therapeutic potential for disease treatment in which Cul3/1 is dysregulated.

Probing the binding mechanism of substituted pyridine derivatives as effective and selective lysine-specific demethylase 1 inhibitors using 3D-QSAR, molecular docking and molecular dynamics simulations.

Lysine-specific demethylase 1 (LSD1) was regarded as a promising anticancer target for the novel drug discovery. In this work, we carried out a molecular modeling study on the substituted pyridine derivatives as LSD1 inhibitors using three-dimensional quantitative structure-activity relationship (3D-QSAR), molecular docking and molecular dynamics (MD) simulations. Molecular docking studies predicted the probable binding mode of ligands, and suggested Lys661 and Asp555 might be key residues. Our 3D-QSAR models exhibited satisfactory internal and external predicted capacity. For the comparative molecular field analysis (CoMFA) model, its training set had q2  of 0.595 and r2 of 0.959, while test set had q2 of 0.512 and r2 of 0.846. For the best comparative molecular similarity indices analysis (CoMSIA) model, its training set had q2 of 0.733 and r2 of 0.982, while test set had q2 of 0.695 and r2 of 0.922. MD simulations result revealed the detailed binding process and found an important conserved water-bridge motif between ligands and protein. The binding free energies calculation using Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA) approach coincided well with the experimental bioactivity and demonstrated that the electrostatic interaction was the major driving force for binding. The energy decomposition pointed out some significant residues (Asp555, Lys661, Trp695, Tyr761 and FAD) for the LSD1 potency increase. Based on these results, five new inhibitors were designed, and their activities were predicted using our 3D-QSAR models. Communicated by Ramaswamy H. Sarma.

Identifying the novel inhibitors of lysine-specific demethylase 1 (LSD1) combining pharmacophore-based and structure-based virtual screening.

Lysine-specific demethylase 1 (LSD1) has been reported to connect with a range of solid tumors. Thus, the exploration of LSD1 inhibitors has emerged as an effective strategy for cancer treatment. In this study, we constructed a pharmacophore model based on a series of flavin adenine dinucleotide (FAD)-competing inhibitors bearing triazole - dithiocarbamate scaffold combining docking, structure-activity relationship (SAR) study, and molecular dynamic (MD) simulation. Meanwhile, another pharmacophore model was also constructed manually, relying on several speculated substrate-competing inhibitors and reported putative vital interactions with LSD1. On the basis of the two pharmacophore models, multi-step virtual screenings (VSs) were performed against substrate-binding pocket and FAD-binding pocket, respectively, combining pharmacophore-based and structure-based strategy to exploit novel LSD1 inhibitors. After bioassay evaluation, four compounds among 21 hits with diverse and novel scaffolds exhibited inhibition activity at the range of 3.63-101.43 µM. Furthermore, substructure-based enrichment was performed, and four compounds with a more potent activity were identified. After that, the time-dependent assay proved that the most potent compound with IC50 2.21 µM inhibits LSD1 activity in a manner of time-independent. In addition, the compound exhibited a cellular inhibitory effect against LSD1 in MGC-803 cells and may inhibit cell migration and invasion by reversing EMT in cultured gastric cancer cells. Considering the binding mode and SAR of the series of compounds, we could roughly deem that these compounds containing 3-methylxanthine scaffold act through occupying substrate-binding pocket competitively. This study presented a new starting point to develop novel LSD1 inhibitors.

3D-QSAR (CoMFA, CoMSIA), molecular docking and molecular dynamics simulations study of 6-aryl-5-cyano-pyrimidine derivatives to explore the structure requirements of LSD1 inhibitors.

Recently, Histone Lysine Specific Demethylase 1 (LSD1) was regarded as a promising anticancer target for the novel drug discovery. And several small molecules as LSD1 inhibitors in different structures have been reported. In this work, we carried out a molecular modeling study on the 6-aryl-5-cyano-pyrimidine fragment LSD1 inhibitors using three-dimensional quantitative structure-activity relationship (3D-QSAR), molecular docking and molecular dynamics simulations. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were used to generate 3D-QSAR models. The results show that the best CoMFA model has q2=0.802, r2ncv=0.979, and the best CoMSIA model has q2=0.799, r2ncv=0.982. The electrostatic, hydrophobic and H-bond donor fields play important roles in the models. Molecular docking studies predict the binding mode and the interactions between the ligand and the receptor protein. Molecular dynamics simulations results reveal that the complex of the ligand and the receptor protein are stable at 300K. All the results can provide us more useful information for our further drug design.

[Morphological and quality difference of adult Anguilla japonica under three aquaculture models].

Anguilla japonica adults with a snout-vent length of (25.91 +/- 3.26) cm were randomly sampled from the ponds of monoculture A. japonica (M1) and polyculture A. japonica and Macrobrachium nipponense (M2) and the proliferation site in reservoir (M3) to compare the morphological and quality indices of the adults under the three aquaculture models. Discriminant analysis, cluster analysis, and factor analysis were applied to reveal the differences among the individuals of these three cultured populations. Among the test 21 biological traits and 23 morphological and quality indices, there were significant differences in 15 biological traits and 14 morphological and quality indices between M1 and M2, 19 and 18 between M1 and M3, and 11 and 8 between M2 and M3, respectively. The Euclidean distance between M1 and M2, M1 and M3, and M2 and M3 was 1.433, 3. 516, and 2. 167, respectively, and the differences were significant. The accumulative variance percentage of the first five principal components was 82.1%, and the eigenvalues of these components were all larger than 1. The principal components 1 and 2 could be regarded as fatness factor and movement factor, the other three principal components could be regarded as well-being factor, and the three populations could be clearly separated each other by principal component 1. In discriminant analysis, the five principal components, i. e., body width / anal length, body length / anal length, net volume coefficient, swim bladder coefficient, and liver coefficient, were served as independent variables to establish discriminant functions of the populations, which could clearly distinguish the populations, with the discriminant accuracy and synthetic discriminant accuracy being 100%.