Kaixian Chen

A nonpeptidic agonist of glucagon-like peptide 1 receptors with efficacy in diabetic db/db mice

Peptidic mimics of the gut hormone glucagon-like peptide (GLP) 1, exemplified by the recently approved drug exenatide, show promise as therapies for type 2 diabetes. Such “incretin mimetics” regulate glucose appearance in the plasma and can restore glucose-stimulated insulin secretion without excess risk of hypoglycemia. The need for injection, which may limit the use of peptidic GLP-1 receptor (GLP-1R) agonists, has driven largely unsuccessful efforts to find smaller molecules. The failure to identify orally effective agonists has instead promoted the indirect approach of inhibiting the GLP-1-degrading enzyme dipeptidyl peptidase IV. Here we report a nonpeptidic GLP-1R agonist with sufficient activity to evoke effects in whole animals, including antidiabetic efficacy in db/db mice. Two substituted cyclobutanes (S4P and Boc5) were developed after screening a compound library against a cell line stably cotransfected with GLP-1R and a cAMP-responsive reporter. Each bound to GLP-1R and increased intracellular cAMP. Agonist effects were blocked by the GLP-1R antagonist exendin(9–39). Boc5 amplified glucose-stimulated insulin secretion in isolated rat islets. Both i.p. and oral administration of Boc5 dose-dependently inhibited food intake in mice, an effect that could be blocked by pretreatment with exendin(9–39). Daily injections of Boc5 into db/db mice reduced HbA1c to nondiabetic values, an effect not observed in ad libitum-fed or pair-fed diabetic controls. Thus, Boc5 behaved as a full GLP-1 mimetic in vitro and in vivo. The chemical genus represented by Boc5 may prompt the exploration of orally available GLP-1R agonists with potential utility in diabetes and obesity.

Predicting protein–protein interactions based only on sequences information

Protein–protein interactions (PPIs) are central to most biological processes. Although efforts have been devoted to the development of methodology for predicting PPIs and protein interaction networks, the application of most existing methods is limited because they need information about protein homology or the interaction marks of the protein partners. In the present work, we propose a method for PPI prediction using only the information of protein sequences. This method was developed based on a learning algorithm-support vector machine combined with a kernel function and a conjoint triad feature for describing amino acids. More than 16,000 diverse PPI pairs were used to construct the universal model. The prediction ability of our approach is better than that of other sequence-based PPI prediction methods because it is able to predict PPI networks. Different types of PPI networks have been effectively mapped with our method, suggesting that, even with only sequence information, this method could be applied to the exploration of networks for any newly discovered protein with unknown biological relativity. In addition, such supplementary experimental information can enhance the prediction ability of the method.

TarFisDock: a web server for identifying drug targets with docking approach

TarFisDock is a web-based tool for automating the procedure of searching for small molecule–protein interactions over a large repertoire of protein structures. It offers PDTD (potential drug target database), a target database containing 698 protein structures covering 15 therapeutic areas and a reverse ligand–protein docking program. In contrast to conventional ligand–protein docking, reverse ligand–protein docking aims to seek potential protein targets by screening an appropriate protein database. The input file of this web server is the small molecule to be tested, in standard mol2 format; TarFisDock then searches for possible binding proteins for the given small molecule by use of a docking approach. The ligand–protein interaction energy terms of the program DOCK are adopted for ranking the proteins. To test the reliability of the TarFisDock server, we searched the PDTD for putative binding proteins for vitamin E and 4H-tamoxifen. The top 2 and 10% candidates of vitamin E binding proteins identified by TarFisDock respectively cover 30 and 50% of reported targets verified or implicated by experiments; and 30 and 50% of experimentally confirmed targets for 4H-tamoxifen appear amongst the top 2 and 5% of the TarFisDock predicted candidates, respectively. Therefore, TarFisDock may be a useful tool for target identification, mechanism study of old drugs and probes discovered from natural products. TarFisDock and PDTD are available at .

PDTD: a web-accessible protein database for drug target identification

BackgroundTarget identification is important for modern drug discovery. With the advances in the development of molecular docking, potential binding proteins may be discovered by docking a small molecule to a repository of proteins with three-dimensional (3D) structures. To complete this task, a reverse docking program and a drug target database with 3D structures are necessary. To this end, we have developed a web server tool, TarFisDock (Tar get Fis hing Dock ing) http://www.dddc.ac.cn/tarfisdock, which has been used widely by others. Recently, we have constructed a protein target database, P otential D rug T arget D atabase (PDTD), and have integrated PDTD with TarFisDock. This combination aims to assist target identification and validation.DescriptionPDTD is a web-accessible protein database for in silico target identification. It currently contains >1100 protein entries with 3D structures presented in the Protein Data Bank. The data are extracted from the literatures and several online databases such as TTD, DrugBank and Thomson Pharma. The database covers diverse information of >830 known or potential drug targets, including protein and active sites structures in both PDB and mol2 formats, related diseases, biological functions as well as associated regulating (signaling) pathways. Each target is categorized by both nosology and biochemical function. PDTD supports keyword search function, such as PDB ID, target name, and disease name. Data set generated by PDTD can be viewed with the plug-in of molecular visualization tools and also can be downloaded freely. Remarkably, PDTD is specially designed for target identification. In conjunction with TarFisDock, PDTD can be used to identify binding proteins for small molecules. The results can be downloaded in the form of mol2 file with the binding pose of the probe compound and a list of potential binding targets according to their ranking scores.ConclusionPDTD serves as a comprehensive and unique repository of drug targets. Integrated with TarFisDock, PDTD is a useful resource to identify binding proteins for active compounds or existing drugs. Its potential applications include in silico drug target identification, virtual screening, and the discovery of the secondary effects of an old drug (i.e. new pharmacological usage) or an existing target (i.e. new pharmacological or toxic relevance), thus it may be a valuable platform for the pharmaceutical researchers. PDTD is available online at http://www.dddc.ac.cn/pdtd/.

A Series of α-Heterocyclic Carboxaldehyde Thiosemicarbazones Inhibit Topoisomerase IIα Catalytic Activity

A series of novel thiosemicarbazone derivatives bearing condensed heterocyclic carboxaldehyde moieties were designed and synthesized. Among them, TSC24 exhibited broad antiproliferative activity in a panel of human tumor cells and suppressed tumor growth in mice. The mechanism research revealed that TSC24 was not only an iron chelator but also a topoisomerase IIalpha catalytic inhibitor. Its inhibition on topoisomerase IIalpha was due to direct interaction with the ATPase domain of topoisomerase IIalpha which led to the block of ATP hydrolysis. Molecular docking predicted that TSC24 might bind at the ATP binding site, which was confirmed by the competitive inhibition assay. These results about the mechanisms involved in the anticancer activities of thiosemicarbazones will aid in the rational design of novel topoisomerase II-targeted drugs and will provide insights into the discovery and development of novel cancer therapeutics based on the dual activity to chelate iron and to inhibit the catalytic activity of topoisomerase IIalpha.

Halogen Bond: Its Role beyond Drug–Target Binding Affinity for Drug Discovery and Development

Halogen bond has attracted a great deal of attention in the past years for hit-to-lead-to-candidate optimization aiming at improving drug-target binding affinity. In general, heavy organohalogens (i.e., organochlorines, organobromines, and organoiodines) are capable of forming halogen bonds while organofluorines are not. In order to explore the possible roles that halogen bonds could play beyond improving binding affinity, we performed a detailed database survey and quantum chemistry calculation with close attention paid to (1) the change of the ratio of heavy organohalogens to organofluorines along the drug discovery and development process and (2) the halogen bonds between organohalogens and nonbiopolymers or nontarget biopolymers. Our database survey revealed that (1) an obviously increasing trend of the ratio of heavy organohalogens to organofluorines was observed along the drug discovery and development process, illustrating that more organofluorines are worn and eliminated than heavy organohalogens during the process, suggesting that heavy halogens with the capability of forming halogen bonds should have priority for lead optimization; and (2) more than 16% of the halogen bonds in PDB are formed between organohalogens and water, and nearly 20% of the halogen bonds are formed with the proteins that are involved in the ADME/T process. Our QM/MM calculations validated the contribution of the halogen bond to the binding between organohalogens and plasma transport proteins. Thus, halogen bonds could play roles not only in improving drug-target binding affinity but also in tuning ADME/T property. Therefore, we suggest that albeit halogenation is a valuable approach for improving ligand bioactivity, more attention should be paid in the future to the application of the halogen bond for ligand ADME/T property optimization.

A simple and convenient copper-catalyzed tandem synthesis of quinoline-2-carboxylates at room temperature.

We developed a simple and convenient copper-catalyzed method for the synthesis of quinoline-2-carboxylate derivatives through sequential intermolecular addition of alkynes onto imines and subsequent intramolecular ring closure by arylation. The efficiency of this system allowed the reactions to be carried out at room temperature.

Rapid and Efficient Pd-Catalyzed Sonogashira Coupling of Aryl Chlorides

An efficient and effective microwave-assisted cross-coupling of terminal alkynes with various aryl chlorides including sterically hindered, electron-rich, electron-neutral, and electron-deficient aryl chloride is developed. It proceeds faster and generally gives good to excellent yields and also can be extended successfully to the Suzuki coupling and Buchwald-Hartwig amination, as well as the Heck coupling with inert aryl chlorides. The short reaction times and simple reaction conditions coupling with a broad substrate scope render this method particularly attractive for the efficient preparation of biologically and medicinally interesting molecules.

Virtual Screening on Natural Products for Discovering Active Compounds and Target Information

Natural products, containing inherently large-scale structural diversity than synthetic compounds, have been the major resources of bioactive agents and will continually play as protagonists for discovering new drugs. However, how to access this diverse chemical space efficiently and effectively is an exciting challenge for medicinal chemists and pharmacologists. While virtual screening, which has shown a great promise in drug discovery, will play an important role in digging out lead (active) compounds from natural products. This review focuses on the strategy of virtual screening based on molecular docking and, with successful examples from our laboratory, illustrates the efficiency of virtual screening in discovering active compounds from natural products. On the other hand, the sequencing of the human genome and numerous pathogen genomes has resulted in an unprecedented opportunity for discovering potential new drug targets. Chemogenomics has appeared as a new technology to initiate target discovery by using active compounds as probes to characterize proteome functions. Natural products are the ideal probes for such research. Binding affinity fingerprint is a powerful chemogenomic descriptor to characterize both small molecules and pharmacologically relevant proteins. Therefore, this review also discusses binding affinity fingerprint strategy for identifying target information from the genomic data by using natural products as the probes.

Cinanserin Is an Inhibitor of the 3C-Like Proteinase of Severe Acute Respiratory Syndrome Coronavirus and Strongly Reduces Virus Replication In Vitro

ABSTRACT The 3C-like proteinase (3CLpro) of severe acute respiratory syndrome-associated coronavirus (SARS-CoV) is one of the most promising targets for anti-SARS-CoV drugs due to its crucial role in the viral life cycle. In this study, a database containing structural information of more than 8,000 existing drugs was virtually screened by a docking approach to identify potential binding molecules of SARS-CoV 3CLpro. As a target for screening, both a homology model and the crystallographic structure of the binding pocket of the enzyme were used. Cinanserin (SQ 10,643), a well-characterized serotonin antagonist that has undergone preliminary clinical testing in humans in the 1960s, showed a high score in the screening and was chosen for further experimental evaluation. Binding of both cinanserin and its hydrochloride to bacterially expressed 3CLpro of SARS-CoV and the related human coronavirus 229E (HCoV-229E) was demonstrated by surface plasmon resonance technology. The catalytic activity of both enzymes was inhibited with 50% inhibitory concentration (IC50) values of 5 μM, as tested with a fluorogenic substrate. The antiviral activity of cinanserin was further evaluated in tissue culture assays, namely, a replicon system based on HCoV-229E and quantitative test assays with infectious SARS-CoV and HCoV-229E. All assays revealed a strong inhibition of coronavirus replication at nontoxic drug concentrations. The level of virus RNA and infectious particles was reduced by up to 4 log units, with IC50 values ranging from 19 to 34 μM. These findings demonstrate that the old drug cinanserin is an inhibitor of SARS-CoV replication, acting most likely via inhibition of the 3CL proteinase.