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/.

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.

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/.

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.