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/.

Structure-Based Design of 4-Hydroxyphenylpyruvate Dioxygenase Inhibitor as a Potential Herbicide for Cotton Fields.

4-Hydroxyphenylpyruvate dioxygenase (HPPD, EC 1.13.11.27) is one of the most promising herbicide targets for the development of agricultural chemicals owing to its unique mechanism of action in plants. We previously reported on the co-crystal structure of Arabidopsis thaliana (At) HPPD complexed with methylbenquitrione (MBQ), an inhibitor of HPPD that we previously discovered. Based on this crystal structure, and in an attempt to discover even more effective HPPD-inhibiting herbicides, we designed a family of triketone-quinazoline-2,4-dione derivatives featuring a phenylalkyl group through increasing the interaction between the substituent at the R1 position and the amino acid residues at the active site entrance of AtHPPD. Among the derivatives, 6-(2-hydroxy-6-oxocyclohex-1-ene-1-carbonyl)-1,5-dimethyl-3-(1-phenylethyl)quinazoline-2,4(1H,3H)-dione (23) was identified as a promising compound. The co-crystal structure of compound 23 with AtHPPD revealed that hydrophobic interactions with Phe392 and Met335, and effective blocking of the conformational deflection of Gln293, as compared with that of the lead compound MBQ, afforded a molecular basis for structural modification. 3-(1-(3-Fluorophenyl)ethyl)-6-(2-hydroxy-6-oxocyclohex-1-ene-1-carbonyl)-1,5-dimethylquinazoline-2,4(1H,3H)-dione (31) was confirmed to be the best subnanomolar-range AtHPPD inhibitor (IC50 = 39 nM), making it approximately seven times more potent than MBQ. In addition, the greenhouse experiment showed favorable herbicidal potency for compound 23 with a broad spectrum and acceptable crop selectivity against cotton at the dosage of 30-120 g ai/ha. Thus, compound 23 possessed a promising prospect as a novel HPPD-inhibiting herbicide candidate for cotton fields.

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.

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.

Distribution- and Metabolism-Based Drug Discovery: A Potassium-Competitive Acid Blocker as a Proof of Concept.

Conventional methods of drug design require compromise in the form of side effects to achieve sufficient efficacy because targeting drugs to specific organs remains challenging. Thus, new strategies to design organ-specific drugs that induce little toxicity are needed. Based on characteristic tissue niche-mediated drug distribution (TNMDD) and patterns of drug metabolism into specific intermediates, we propose a strategy of distribution- and metabolism-based drug design (DMBDD); through a physicochemical property-driven distribution optimization cooperated with a well-designed metabolism pathway, SH-337, a candidate potassium-competitive acid blocker (P-CAB), was designed. SH-337 showed specific distribution in the stomach in the long term and was rapidly cleared from the systemic compartment. Therefore, SH-337 exerted a comparable pharmacological effect but a 3.3-fold higher no observed adverse effect level (NOAEL) compared with FDA-approved vonoprazan. This study contributes a proof-of-concept demonstration of DMBDD and provides a new perspective for the development of highly efficient, organ-specific drugs with low toxicity.

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/.

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.

Web-Based Quantitative Structure-Activity Relationship Resources Facilitate Effective Drug Discovery.

Traditional drug discovery effectively contributes to the treatment of many diseases but is limited by high costs and long cycles. Quantitative structure-activity relationship (QSAR) methods were introduced to evaluate the activity of compounds virtually, which saves the significant cost of determining the activities of the compounds experimentally. Over the past two decades, many web tools for QSAR modeling with various features have been developed to facilitate the usage of QSAR methods. These web tools significantly reduce the difficulty of using QSAR and indirectly promote drug discovery. However, there are few comprehensive summaries of these QSAR tools, and researchers may have difficulty determining which tool to use. Hence, we systematically surveyed the mainstream web tools for QSAR modeling. This work may guide researchers in choosing appropriate web tools for developing QSAR models, and may also help develop more bioinformatics tools based on these existing resources. For nonprofessionals, we also hope to make more people aware of QSAR methods and expand their use.

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.

Bioinformatics toolbox for exploring protein phosphorylation network.

A clear systematic delineation of the interactions between phosphorylation sites on substrates and their effector kinases plays a fundamental role in revealing cellular activities, understanding signaling modulation mechanisms and proposing novel hypotheses. The emergence of bioinformatics tools contributes to studying phosphorylation network. Some of them feature the visualization of network, enabling more effective trace of the underlying biological problems in a clear and succinct way. In this review, we aimed to provide a toolbox for exploring phosphorylation network. We first systematically surveyed 19 tools that are available for exploring phosphorylation networks, and subsequently comparatively analyzed and summarized these tools to guide tool selection in terms of functionality, data sources, performance, network visualization and implementation, and finally briefly discussed the application cases of these tools. In different scenarios, the conclusion on the suitability of a tool for a specific user may vary. Nevertheless, easily accessible bioinformatics tools are proved to facilitate biological findings. Hopefully, this work might also assist non-specialists, students, as well as computational scientists who aim at developing novel tools in the field of phosphorylation modification.

Cloud 3D-QSAR: a web tool for the development of quantitative structure-activity relationship models in drug discovery.

Effective drug discovery contributes to the treatment of numerous diseases but is limited by high costs and long cycles. The Quantitative Structure-Activity Relationship (QSAR) method was introduced to evaluate the activity of a large number of compounds virtually, reducing the time and labor costs required for chemical synthesis and experimental determination. Hence, this method increases the efficiency of drug discovery. To meet the needs of researchers to utilize this technology, numerous QSAR-related web servers, such as Web-4D-QSAR and DPubChem, have been developed in recent years. However, none of the servers mentioned above can perform a complete QSAR modeling and supply activity prediction functions. We introduce Cloud 3D-QSAR by integrating the functions of molecular structure generation, alignment, molecular interaction field (MIF) computing and results analysis to provide a one-stop solution. We rigidly validated this server, and the activity prediction correlation was R2 = 0.934 in 834 test molecules. The sensitivity, specificity and accuracy were 86.9%, 94.5% and 91.5%, respectively, with AUC = 0.981, AUCPR = 0.971. The Cloud 3D-QSAR server may facilitate the development of good QSAR models in drug discovery. Our server is free and now available at http://chemyang.ccnu.edu.cn/ccb/server/cloud3dQSAR/ and http://agroda.gzu.edu.cn:9999/ccb/server/cloud3dQSAR/.

Graph attention convolutional neural network model for chemical poisoning of honey bees' prediction.

The impact of pesticides on insect pollinators has caused worldwide concern. Both global bee decline and stopping the use of pesticides may have serious consequences for food security. Automated and accurate prediction of chemical poisoning of honey bees is a challenging task owing to a lack of understanding of chemical toxicity and introspection. Deep learning (DL) shows potential utility for general and highly variable tasks across fields. Here, we developed a new DL model of deep graph attention convolutional neural networks (GACNN) with the combination of undirected graph (UG) and attention convolutional neural networks (ACNN) to accurately classify chemical poisoning of honey bees. We used a training dataset of 720 pesticides and an external validation dataset of 90 pesticides, which is one order of magnitude larger than the previous datasets. We tested its performance in two ways: poisonous versus non-poisonous and GACNN versus other frequently-used machine learning models. The first case represents the accuracy in identifying bee poisonous chemicals. The second represents performance advantages. The GACNN achieved ~6% higher performance for predicting toxic samples and more stable with ~7% Matthews Correlation Coefficient (MCC) higher compared to all tested models, demonstrating GACNN is capable of accurately classifying chemicals and has considerable potential in practical applications. In addition, we also summarized and evaluated the mechanisms underlying the response of honey bees to chemical exposure based on the mapping of molecular similarity. Moreover, our cloud platform (http://beetox.cn) of this model provides low-cost universal access to information, which could vitally enhance environmental risk assessment.

Monitoring fragmentation and oligomerization of a di-µ-methoxo bridged copper(ii) complex: structure, mass spectrometry, magnetism and DFT studies.

Analyses of the structural information of molecular fragments from the mass spectra of the solid-state products and their reaction solutions allow for the understanding of their formation and of their diverse properties. The reaction of CuCl2 and (1-methyl-1H-benzo[d]imidazole-2-yl)methanol (HL) led only to crystals containing molecular dimers of [Cu2(L)2Cl2] (Cu2). The CuII-CuII distance and Cu-OR-Cu angle in the structure are 3.044 Å and 104.8°, respectively. The magnetic susceptibility (3-400 K) is characterized by a very strong intradimer antiferromagnetic interaction of J = -465 and interdimer zj = -0.83 cm-1. But mass spectrometry of a dissolved single crystal in different source energies identifies both its fragmentation and oligomerization to [Cu] and [Cu]. DFT calculations give the relative stabilization energies of the fragments observed in ESI-MS to provide a formation process.

Ferromagnetic coupling in copper benzimidazole chloride: structural, mass spectrometry, magnetism, and DFT studies.

Herein, quasi-square planar CuII(Hmbm)Cl2 (CBC, Hmbm = (1-methyl-1H-benzo[d]imidazol-2-yl)methanol) was arranged in a pseudo orthogonal way to form Cl-bridged chains, and further ππ interactions resulted in distorted hexagonal layers. DFT calculations reveal a bond strength order of Cu-Cl > Cu-O/N ≫ CuCl. ESI-MS data reveal several small fragments from CBC, but oligomeric [Cu], [Cu], and [Cu] for non-zero in-source energies; MS data indicates the occurrence of several chemical processes, viz. splitting of the ligand, oligomerization, and redox reaction of alcohol to aldehyde and CuII to CuI. Gibbs free energies for the fragments were estimated using DFT. The magnetic susceptibility was modeled with the ferromagnetic coupling J(Cu-Cl2aCu) = +0.99(30) cm-1 and J'(ππ) = +0.35(16) cm-1 and g = 2.38(2). HF-EPR determined the anisotropic g-values, gx = 2.24, gy = 2.16, and gz = 2.09, and a hyperfine constant of Az = 450 G. DFT calculations from crystal structure data reveal a J(Cu-Cl2aCu) of +3.6 at 296 K and +4.1 cm-1 at 90 K that dominates the magnetic properties, whereas J'(ππ) = 0.04 cm-1 is negligibly small.

Hierarchical Assembly and Aggregation-Induced Enhanced Emission of a Pair of Isostructural Zn14 Clusters.

Information of solid-state and solution structures is crucial in the characterization of molecular clusters and in advancing the understanding of their diverse properties. [Et3NH]2[Zn14(hmq)8(OH)4X10] [X = Cl and Br; H2hmq = 2-(hydroxymethyl)quinolin-8-ol] consist of a peanut-shaped Zn10O12 core, in which the Zn atoms occupy the faces and corners of an octahedron and are protected by bonded halogen atoms and bulky organic ligands. Observation of the [Zn14(hmq)8(OH)4X10]2- fragment in electrospray ionization mass spectrometry (ESI-MS) suggests that the cluster is stable in solution. ESI-MS analyses from dissolved crystals and mother liquors reveal that Zn(hmq) self-assembles to Zn5(hmq)4Cl, then dimerizes through four [OH]- bridges to Zn10(hmq)8(OH)4Cl2, and progressively captures four ZnCl2 one-by-one to [Zn14(hmq)8(OH)4Cl10]2-. Because the supramolecular interactions between the anion and cation in the solid suppress the rotation/vibration of the halogen atoms and confine the movable organic ligands on the rigid Zn-O core, both crystal phases exhibit intense photoluminescence, much stronger than that in solution. This is the first coordination cluster to exhibit "aggregation-induced enhanced emission". In addition, preliminary tests indicate that these coordination clusters are promising for organic-light-emitting-diode applications.

Effects of tiletamine on the adenosine monophosphate-activated protein kinase signaling pathway in the rat central nervous system.

OBJECTIVE: The dissociative anesthetic tiletamine, which acts on the central nervous system (CNS), is widely used in veterinary medicine and animal experiments. Recent studies indicate that adenosine 5'-monophosphate activated protein kinase (AMPK) plays a key role in the analgesic action of tiletamine. In the present study, the effects of tiletamine on the AMPK signaling pathway in rats were investigated. METHODS: Sprague-Dawley rats were injected intraperitoneally with tiletamine and executed at 10, 20, 40 and 60min post injection. The cerebral cortex, hippocampus, thalamus, cerebellum and brainstem were immediately taken out to evaluate the mRNA and protein phosphorylation levels of liver kinase B1 (LKB1), AMPKα and eIF4E-binding protein 1 (4EBP1) using quantitative real-time polymerase chain reaction and western blot analysis. RESULTS: Tiletamine increased AMPK mRNA expression in the rat brain (P<0.01). Increased mRNA expression of AMPK was accompanied by an increase in phosphorylation of LKB1, resulting in significant decreases in the phosphorylation levels of 4EBP1 in the corresponding brain regions (P<0.01). CONCLUSION: In summary, the findings indicate that tiletamine regulates the mRNA expression and protein phosphorylation levels of LKB1, AMPK and 4EBP1 in the CNS, suggesting that the analgesic effect of the anesthetic is mediated, at least in part, by the AMPK signaling pathway.

Aspirin alleviates cardiac fibrosis in mice by inhibiting autophagy.

Aspirin (ASA) is a cardioprotective drug with anti-cardiac fibrosis action in vivo. This study was aimed to clarify the anti-cardiac fibrosis action of ASA and the underlying mechanisms. Two heart injury models (injection of isoproterenol and ligation of the left anterior descending branch) were used in mice to induce cardiac fibrosis. The animals were treated with ASA (10 mg·kg-1·d-1, ig) for 21 and 14 d, respectively. ASA administration significantly improved cardiac function, and ameliorated heart damage and fibrosis in the mice. The mechanisms underlying ASA's anti-fibrotic effect were further analyzed in neonatal cardiac fibroblasts (CFs) exposed to hypoxia in vitro. ASA (0.5-5 mmol/L) dose-dependently inhibited the proliferation and Akt phosphorylation in the CFs. In addition, ASA significantly inhibited CF apoptosis, and decreased the levels of apoptosis markers (cleaved caspase 3 and Parp1), which might serve as a side effect of anti-fibrotic effect of ASA. Furthermore, ASA dose-dependently inhibited the autophagy in the CFs, as evidenced by the reduced levels of autophagy marker LC3-II. The autophagy inhibitor Pepstatin A (PepA) promoted the inhibitory effect of ASA on CF proliferation, whereas the autophagy inducer rapamycin rescued ASA-caused inhibition of CF proliferation, suggesting an autophagy-dependent anti-proliferative effect of ASA. Both p38 inhibitor SB203580 and ROS scavenger N-acetyl-cysteine (NAC) significantly decreased Akt phosphorylation in CFs in the basal and hypoxic situations, but they both significantly increased LC3-II levels in the CFs. Our results suggest that an autophagy- and p38/ROS-dependent pathway mediates the anti-cardiac fibrosis effect of ASA in CFs. As PepA and SB203580 did not affect ASA-caused inhibition of CF apoptosis, the drug combination will enhance ASA's therapeutic effects.

Xylazine Activates Adenosine Monophosphate-Activated Protein Kinase Pathway in the Central Nervous System of Rats.

Xylazine is a potent analgesic extensively used in veterinary and animal experimentation. Evidence exists that the analgesic effect can be inhibited using adenosine 5'-monophosphate activated protein kinase (AMPK) inhibitors. Considering this idea, the aim of this study was to investigate whether the AMPK signaling pathway is involved in the central analgesic mechanism of xylazine in the rat. Xylazine was administrated via the intraperitoneal route. Sprague-Dawley rats were sacrificed and the cerebral cortex, cerebellum, hippocampus, thalamus and brainstem were collected for determination of liver kinase B1 (LKB1) and AMPKα mRNA expression using quantitative real-time polymerase chain reaction (qPCR), and phosphorylated LKB1 and AMPKα levels using western blot. The results of our study showed that compared with the control group, xylazine induced significant increases in AMPK activity in the cerebral cortex, hippocampus, thalamus and cerebellum after rats received xylazine (P < 0.01). Increased AMPK activities were accompanied with increased phosphorylation levels of LKB1 in corresponding regions of rats. The protein levels of phosphorylated LKB1 and AMPKα in these regions returned or tended to return to control group levels. However, in the brainstem, phosphorylated LKB1 and AMPKα protein levels were decreased by xylazine compared with the control (P < 0.05). In conclusion, our data indicates that xylazine alters the activities of LKB1 and AMPK in the central nervous system of rats, which suggests that xylazine affects the regulatory signaling pathway of the analgesic mechanism in the rat brain.