Other affiliations: Shanghai University, East China University of Science and Technology, ShanghaiTech University ...read more
Bio: Kaixian Chen is an academic researcher from Chinese Academy of Sciences. The author has contributed to research in topic(s): Virtual screening & Docking (molecular). The author has an hindex of 47, co-authored 380 publication(s) receiving 9209 citation(s). Previous affiliations of Kaixian Chen include Shanghai University & East China University of Science and Technology.
Papers published on a yearly basis
TL;DR: Different types of PPI networks have been effectively mapped with the proposed 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.
Abstract: 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.
01 Jul 2006-Nucleic Acids Research
TL;DR: TarFisDock may be a useful tool for target identification, mechanism study of old drugs and probes discovered from natural products, and a reverse ligand–protein docking program for seeking potential protein targets by screening an appropriate protein database.
Abstract: 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 http://www.dddc.ac.cn/tarfisdock/.
19 Feb 2008-BMC Bioinformatics
TL;DR: PDTD serves as a comprehensive and unique repository of drug targets and in conjunction with TarFisDock, PDTD can be used to identify binding proteins for small molecules and may be a valuable platform for the pharmaceutical researchers.
Abstract: Target 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. PDTD 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. PDTD 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/ .
TL;DR: The dynamic feature revealed by the long-time molecular dynamics simulations rationalized several experimental observations for Aβ40 aggregation and amyloid fibril formation and is beneficial to understanding the mechanism of amyloids formation and designing the compounds for inhibiting the aggregation of Aβ and amymbrane fibrils.
Abstract: The amyloid beta-peptides (Abetas), containing 39-43 residues, are the key protein components of amyloid deposits in Alzheimer's disease To structurally characterize the dynamic behavior of Abeta(40), 12 independent long-time molecular dynamics (MD) simulations for a total of 850 ns were performed on both the wide-type peptide and its mutant in both aqueous solution and a biomembrane environment In aqueous solution, an alpha-helix to beta-sheet conformational transition for Abeta(40) was observed, and an entire unfolding process from helix to coil was traced by MD simulation Structures with beta-sheet components were observed as intermediates in the unfolding pathway of Abeta(40) Four glycines (G(25), G(29), G(33), and G(37)) are important for Abeta(40) to form beta-sheet in aqueous solution; mutations of these glycines to alanines almost abolished the beta-sheet formation and increased the content of the helix component In the dipalmitoyl phosphatidylcholine (DPPC) bilayer, the major secondary structure of Abeta(40) is a helix; however, the peptide tends to exit the membrane environment and lie down on the surface of the bilayer The dynamic feature revealed by our MD simulations rationalized several experimental observations for Abeta(40) aggregation and amyloid fibril formation The results of MD simulations are beneficial to understanding the mechanism of amyloid formation and designing the compounds for inhibiting the aggregation of Abeta and amyloid fibril formation
TL;DR: It is suggested 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.
Abstract: 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 ...
01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.
01 Aug 2000
TL;DR: Assessment of medical technology in the context of commercialization with Bioentrepreneur course, which addresses many issues unique to biomedical products.
Abstract: BIOE 402. Medical Technology Assessment. 2 or 3 hours. Bioentrepreneur course. Assessment of medical technology in the context of commercialization. Objectives, competition, market share, funding, pricing, manufacturing, growth, and intellectual property; many issues unique to biomedical products. Course Information: 2 undergraduate hours. 3 graduate hours. Prerequisite(s): Junior standing or above and consent of the instructor.
01 Feb 2011-Natural Product Reports
TL;DR: This review covers the literature published in 2014 for marine natural products, with 1116 citations referring to compounds isolated from marine microorganisms and phytoplankton, green, brown and red algae, sponges, cnidarians, bryozoans, molluscs, tunicates, echinoderms, mangroves and other intertidal plants and microorganisms.
Abstract: This review covers the literature published in 2014 for marine natural products (MNPs), with 1116 citations (753 for the period January to December 2014) referring to compounds isolated from marine microorganisms and phytoplankton, green, brown and red algae, sponges, cnidarians, bryozoans, molluscs, tunicates, echinoderms, mangroves and other intertidal plants and microorganisms. The emphasis is on new compounds (1378 in 456 papers for 2014), together with the relevant biological activities, source organisms and country of origin. Reviews, biosynthetic studies, first syntheses, and syntheses that lead to the revision of structures or stereochemistries, have been included.
TL;DR: A number of substructural features which can help to identify compounds that appear as frequent hitters (promiscuous compounds) in many biochemical high throughput screens are described.
Abstract: This report describes a number of substructural features which can help to identify compounds that appear as frequent hitters (promiscuous compounds) in many biochemical high throughput screens. The compounds identified by such substructural features are not recognized by filters commonly used to identify reactive compounds. Even though these substructural features were identified using only one assay detection technology, such compounds have been reported to be active from many different assays. In fact, these compounds are increasingly prevalent in the literature as potential starting points for further exploration, whereas they may not be.
01 Jul 1981-Military Medicine