Search

Your search keyword '"Jong Young Joung"' showing total 13 results
Start Over Author "Jong Young Joung"
13 results on '"Jong Young Joung"'

2. A Cyclized Helix-Loop-Helix Peptide as a Molecular Scaffold for the Design of Inhibitors of Intracellular Protein-Protein Interactions by Epitope and Arginine Grafting

Author

Seiji Kodama, Ikuo Fujii, Masahiro Oguri, Toshio Nishihara, Ikuhiko Nakase, Kenji Kono, Sihyun Ham, Eiji Yuba, Haeri Im, Jong Young Joung, Sunhee Cho, Daisuke Fujiwara, Hidekazu Kitada, Kazunori Shiraishi, and Masataka Michigami

Subjects
Protein Conformation, alpha-Helical, Arginine, Chemical biology, Peptide, 010402 general chemistry, Peptides, Cyclic, 01 natural sciences, Catalysis, Epitope, Protein–protein interaction, Cell membrane, Cell Line, Tumor, Protein Interaction Mapping, medicine, Humans, Amino Acid Sequence, Protein Interaction Maps, chemistry.chemical_classification, 010405 organic chemistry, Chemistry, Proto-Oncogene Proteins c-mdm2, General Medicine, General Chemistry, 0104 chemical sciences, Molecular Docking Simulation, Cytosol, medicine.anatomical_structure, Biochemistry, Drug Design, Tumor Suppressor Protein p53, Intracellular
Abstract

The design of inhibitors of intracellular protein-protein interactions (PPIs) remains a challenge in chemical biology and drug discovery. We propose a cyclized helix-loop-helix (cHLH) peptide as a scaffold for generating cell-permeable PPI inhibitors through bifunctional grafting: epitope grafting to provide binding activity, and arginine grafting to endow cell-permeability. To inhibit p53-HDM2 interactions, the p53 epitope was grafted onto the C-terminal helix and six Arg residues were grafted onto another helix. The designed peptide cHLHp53-R showed high inhibitory activity for this interaction, and computational analysis suggested a binding mode for HDM2. Confocal microscopy of cells treated with fluorescently labeled cHLHp53-R revealed cell membrane penetration and cytosolic localization. The peptide inhibited the growth of HCT116 and LnCap cancer cells. This strategy of bifunctional grafting onto a well-structured peptide scaffold could facilitate the generation of inhibitors for intracellular PPIs.

Published
2016
Full Text
View/download PDF

3. Template based protein structure modeling by global optimization in CASP11

Author

Jong Young Joung, InSuk Joung, Juyong Lee, Sung Jong Lee, Seungryong Heo, Sun Young Lee, Jooyoung Lee, Qianyi Cheng, Jong Yun Kim, Keehyoung Joo, Balachandran Manavalan, Mikyung Nam, and In-Ho Lee

Subjects
0301 basic medicine, business.industry, Computer science, Protein structure prediction, 3D modeling, Machine learning, computer.software_genre, Biochemistry, 03 medical and health sciences, 030104 developmental biology, Software, Template, Structural Biology, Homology modeling, Artificial intelligence, business, CASP, Cluster analysis, Molecular Biology, Algorithm, Global optimization, computer
Abstract

For the template-based modeling (TBM) of CASP11 targets, we have developed three new protein modeling protocols (nns for server prediction and LEE and LEER for human prediction) by improving upon our previous CASP protocols (CASP7 through CASP10). We applied the powerful global optimization method of conformational space annealing to three stages of optimization, including multiple sequence-structure alignment, three-dimensional (3D) chain building, and side-chain remodeling. For more successful fold recognition, a new alignment method called CRFalign was developed. It can incorporate sensitive positional and environmental dependence in alignment scores as well as strong nonlinear correlations among various features. Modifications and adjustments were made to the form of the energy function and weight parameters pertaining to the chain building procedure. For the side-chain remodeling step, residue-type dependence was introduced to the cutoff value that determines the entry of a rotamer to the side-chain modeling library. The improved performance of the nns server method is attributed to successful fold recognition achieved by combining several methods including CRFalign and to the current modeling formulation that can incorporate native-like structural aspects present in multiple templates. The LEE protocol is identical to the nns one except that CASP11-released server models are used as templates. The success of LEE in utilizing CASP11 server models indicates that proper template screening and template clustering assisted by appropriate cluster ranking promises a new direction to enhance protein 3D modeling. Proteins 2016; 84(Suppl 1):221-232. © 2015 Wiley Periodicals, Inc.

Published
2015
Full Text
View/download PDF

4. Prediction of homo- and hetero-protein complexes by protein docking and template-based modeling: a CASP-CAPRI experiment

Author

Eichiro Ichiishi, Dmitri Beglov, Bernard Maigret, Gyu Rie Lee, Artem B. Mamonov, Shoshana J. Wodak, Jonathan C. Fuller, Dima Kozakov, Jong Young Joung, Petr Popov, Xiaofeng Yu, Keehyoung Joo, João P. G. L. M. Rodrigues, Anna Vangone, Koen M. Visscher, Xiaoqin Zou, Paul A. Bates, Andriy Kryshtafovych, Shourya S. Roy Burman, Daisuke Kihara, Romina Oliva, Efrat Ben-Zeev, Jeffrey J. Gray, Yang Shen, Li C. Xue, Sameer Velankar, Emilie Neveu, Shruthi Viswanath, Dina Schneidman-Duhovny, Juan Esquivel-Rodríguez, Mieczyslaw Torchala, Amit Roy, Alexandre M. J. J. Bonvin, David R. Hall, Tanggis Bohnuud, Xusi Han, David W. Ritchie, Ron Elber, Daisuke Kuroda, Zhiwei Ma, Joan Segura, Carlos A. Del Carpio, Nicholas A. Marze, Jong Yun Kim, Andrej Sali, Petras J. Kundrotas, Ezgi Karaca, Neil J. Bruce, Chaok Seok, Panagiotis L. Kastritis, Shen You Huang, Ilya A. Vakser, Lim Heo, Sanbo Qin, Raphael A. G. Chaleil, Adrien S. J. Melquiond, Miguel Romero-Durana, Anisah W. Ghoorah, Surendra S. Negi, Andrey Tovchigrechko, Françoise Ochsenbein, Narcis Fernandez-Fuentes, Liming Qiu, Miriam Eisenstein, Mehdi Nellen, Marie-Dominique Devignes, Lenna X. Peterson, Jinchao Yu, Minkyung Baek, Brian G. Pierce, Hasup Lee, Toshiyuki Oda, Rebecca C. Wade, Raphael Guerois, Juan Fernández-Recio, Iain H. Moal, Edrisse Chermak, Sergei Grudinin, Sangwoo Park, Ivan Anishchenko, Chengfei Yan, Thom Vreven, Kentaro Tomii, Bing Xia, Hyung Rae Kim, Chiara Pallara, Jooyoung Lee, Kazunori D. Yamada, Xianjin Xu, Kenichiro Imai, Zhiping Weng, Luigi Cavallo, Tyler M. Borrman, Jianlin Cheng, Marc F. Lensink, Huan-Xiang Zhou, Jilong Li, Gydo C. P. van Zundert, Brian Jiménez-García, Tsukasa Nakamura, Scott E. Mottarella, Sandor Vajda, Institut de Recherche Interdisciplinaire [Villeneuve d'Ascq] ( IRI ), Université de Lille, Sciences et Technologies-Université de Lille, Droit et Santé-Centre National de la Recherche Scientifique ( CNRS ), European Molecular Biology Laboratory, European Bioinformatics Institute, Genome Center [UC Davis], University of California at Davis, Research Support Computing [Columbia], University of Missouri-Columbia, Bioinformatics Consortium and Department of Computer Science [Columbia], Department of Bioengineering and Therapeutic Sciences, University of California [San Francisco] ( UCSF ), Department of Pharmaceutical Chemistry, Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, University of California [San Francisco] ( UCSF ) -California Institute for Quantitative Biosciences, GN7 of the National Institute for Bioinformatics (INB) and Biocomputing Unit, Centro Nacional de Biotecnología (CSIC), Institute of Biological, Environmental and Rural Sciences ( IBERS ), Institute for Computational Engineering and Sciences [Austin] ( ICES ), University of Texas at Austin [Austin], Department of Computer Science, Department of Chemistry, Algorithms for Modeling and Simulation of Nanosystems ( NANO-D ), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique ( Inria ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -Laboratoire Jean Kuntzmann ( LJK ), Université Pierre Mendès France - Grenoble 2 ( UPMF ) -Université Joseph Fourier - Grenoble 1 ( UJF ) -Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique ( CNRS ) -Université Grenoble Alpes ( UGA ) -Université Pierre Mendès France - Grenoble 2 ( UPMF ) -Université Joseph Fourier - Grenoble 1 ( UJF ) -Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique ( CNRS ) -Université Grenoble Alpes ( UGA ) -Institut National Polytechnique de Grenoble ( INPG ), Moscow Institute of Physics and Technology [Moscow] ( MIPT ), Seoul National University [Seoul], Florida State University [Tallahassee] ( FSU ), Computational Algorithms for Protein Structures and Interactions ( CAPSID ), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique ( Inria ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -Department of Complex Systems, Artificial Intelligence & Robotics ( LORIA - AIS ), Laboratoire Lorrain de Recherche en Informatique et ses Applications ( LORIA ), Institut National de Recherche en Informatique et en Automatique ( Inria ) -Université de Lorraine ( UL ) -Centre National de la Recherche Scientifique ( CNRS ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -Université de Lorraine ( UL ) -Centre National de la Recherche Scientifique ( CNRS ) -Laboratoire Lorrain de Recherche en Informatique et ses Applications ( LORIA ), Institut National de Recherche en Informatique et en Automatique ( Inria ) -Université de Lorraine ( UL ) -Centre National de la Recherche Scientifique ( CNRS ) -Université de Lorraine ( UL ) -Centre National de la Recherche Scientifique ( CNRS ), University of Mauritius, Biomolecular Modelling Laboratory, The Francis Crick Institute, Lincoln's Inn Fields Laboratory, G-INCPM, Weizmann Institute of Science, Chemical Research Support [Rehovot], Sealy Center for Structural Biology and Molecular Biophysics, The University of Texas Medical Branch ( UTMB ), Program in Bioinformatics and Integrative Biology [Worcester], University of Massachusetts Medical School [Worcester] ( UMASS ), Institut de Biologie Intégrative de la Cellule ( I2BC ), Université Paris-Saclay-Centre National de la Recherche Scientifique ( CNRS ) -Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ) -Université Paris-Sud - Paris 11 ( UP11 ), Bijvoet Center for Biomolecular Research [Utrecht], Utrecht University [Utrecht], Dalton Cardiovascular Research Center [Columbia], Department of Computer Science [Columbia], Informatics Intitute, Department of Biochemistry, University of Missouri, UNIVERSITY OF MISSOURI, Toyota Technological Institute at Chicago [Chicago] ( TTIC ), Department of Biological Sciences, Purdue University, Purdue University [West Lafayette], Department of Computer Science [Purdue], Bioinformatics and Computational Biosciences Branch, Rocky Mountain Laboratories, Molecular and Cellular Modeling Group, Heidelberg Institute of Theoretical Studies, Center for Molecular Biology ( ZMBH ), Universität Heidelberg [Heidelberg], Interdisciplinary Center for Scientific Computing ( IWR ), Department of Molecular Biosciences [Lawrence], University of Kansas [Lawrence] ( KU ), Computational Biology Research Center ( CBRC ), National Institute of Advanced Industrial Science and Technology ( AIST ), Graduate School of Frontier Sciences, The University of Tokyo, Joint BSC-CRG-IRB Research Program in Computational Biology, Barcelona Supercomputing Center - Centro Nacional de Supercomputacion ( BSC - CNS ), Center for In-Silico Protein Science, Korea Institute for Advanced Study ( KIAS ), Center for Advanced Computation, Department of Biomedical Engineering [Boston], Boston University [Boston] ( BU ), Institute of Biological Diversity, International Pacific Institute of Indiana, Drosophila Genetic Resource Center, Kyoto Institute of Technology, International University of Health and Welfare Hospital ( IUHW Hospital ), International University of Health and Welfare Hospital, Department of Chemical and Biomolecular Engineering [Baltimore], Johns Hopkins University ( JHU ), Program in Molecular Biophysics [Baltimore], King Abdullah University of Science and Technology ( KAUST ), University of Naples, J Craig Venter Institute, Structural Biology Research Center, VIB, 1050 Brussels, Belgium, Institut de Recherche Interdisciplinaire [Villeneuve d'Ascq] (IRI), Université de Lille, Sciences et Technologies-Université de Lille, Droit et Santé-Centre National de la Recherche Scientifique (CNRS), European Bioinformatics Institute [Hinxton] (EMBL-EBI), EMBL Heidelberg, University of California [Davis] (UC Davis), University of California (UC)-University of California (UC), University of Missouri [Columbia] (Mizzou), University of Missouri System, University of California [San Francisco] (UC San Francisco), Centro Nacional de Biotecnología [Madrid] (CNB-CSIC), Consejo Superior de Investigaciones Científicas [Madrid] (CSIC)-Consejo Superior de Investigaciones Científicas [Madrid] (CSIC), Institute of Biological, Environmental and Rural Sciences (IBERS), Institute for Computational Engineering and Sciences [Austin] (ICES), Algorithms for Modeling and Simulation of Nanosystems (NANO-D), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Laboratoire Jean Kuntzmann (LJK ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Moscow Institute of Physics and Technology [Moscow] (MIPT), Seoul National University [Seoul] (SNU), Florida State University [Tallahassee] (FSU), Computational Algorithms for Protein Structures and Interactions (CAPSID), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Complex Systems, Artificial Intelligence & Robotics (LORIA - AIS), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Biomolecular Modelling Laboratory [London], The Francis Crick Institute [London], Weizmann Institute of Science [Rehovot, Israël], The University of Texas Medical Branch (UTMB), University of Massachusetts Medical School [Worcester] (UMASS), University of Massachusetts System (UMASS)-University of Massachusetts System (UMASS), Institut de Biologie Intégrative de la Cellule (I2BC), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Assemblage moléculaire et intégrité du génome (AMIG), Département Biochimie, Biophysique et Biologie Structurale (B3S), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut de Biologie Intégrative de la Cellule (I2BC), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), University of Missouri System-University of Missouri System, Toyota Technological Institute at Chicago [Chicago] (TTIC), Department of Biological Sciences [Lafayette IN], Heidelberg Institute for Theoretical Studies (HITS ), Center for Molecular Biology (ZMBH), Universität Heidelberg [Heidelberg] = Heidelberg University, Interdisciplinary Center for Scientific Computing (IWR), University of Kansas [Lawrence] (KU), Computational Biology Research Center (CBRC), National Institute of Advanced Industrial Science and Technology (AIST), The University of Tokyo (UTokyo), Barcelona Supercomputing Center - Centro Nacional de Supercomputacion (BSC - CNS), Korea Institute for Advanced Study (KIAS), Boston University [Boston] (BU), International University of Health and Welfare Hospital (IUHW Hospital), Johns Hopkins University (JHU), King Abdullah University of Science and Technology (KAUST), University of Naples Federico II = Università degli studi di Napoli Federico II, J. Craig Venter Institute, VIB-VUB Center for Structural Biology [Bruxelles], VIB [Belgium], Centre National de la Recherche Scientifique (CNRS)-Université de Lille, Droit et Santé-Université de Lille, Sciences et Technologies, University of California-University of California, University of California [San Francisco] (UCSF), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL), University of Naples Federico II, Barcelona Supercomputing Center, NMR Spectroscopy, and Sub NMR Spectroscopy

Subjects
0301 basic medicine, Protein Conformation, alpha-Helical, Protein Folding, Computer science, International Cooperation, Amino Acid Motifs, Oligomer state, Homoprotein, DATA-BANK, computer.software_genre, Molecular Docking Simulation, Biochemistry, CAPRI Round 30, DESIGN, Structural Biology, ALIGN, Blind prediction, AFFINITY, Protein interaction, Enginyeria biomèdica [Àrees temàtiques de la UPC], ZDOCK, Oligomer State, computer.file_format, Articles, Protein structure prediction, Proteïnes--Investigació, 3. Good health, WEB SERVER, CASP, Thermodynamics, Data mining, CAPRI, Protein docking, Molecular Biology, Algorithms, INTERFACES, Protein Binding, [ INFO.INFO-MO ] Computer Science [cs]/Modeling and Simulation, Bioinformatics, STRUCTURAL BIOLOGY, Computational biology, Molecular Dynamics Simulation, Article, 03 medical and health sciences, [ INFO.INFO-BI ] Computer Science [cs]/Bioinformatics [q-bio.QM], Heteroprotein, Humans, Protein binding, Macromolecular docking, Protein Interaction Domains and Motifs, Homology modeling, ALGORITHM, Protein-protein docking, Internet, Binding Sites, Models, Statistical, 030102 biochemistry & molecular biology, Bacteria, Sequence Homology, Amino Acid, Computational Biology, Proteins, Protein Data Bank, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Protein Structure, Tertiary, 030104 developmental biology, Structural biology, Docking (molecular), Protein structure, Protein Conformation, beta-Strand, Protein Multimerization, oligomer state, blind prediction, protein interaction, protein docking, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], computer, Software
Abstract

We present the results for CAPRI Round 30, the first joint CASP-CAPRI experiment, which brought together experts from the protein structure prediction and protein–protein docking communities. The Round comprised 25 targets from amongst those submitted for the CASP11 prediction experiment of 2014. The targets included mostly homodimers, a few homotetramers, and two heterodimers, and comprised protein chains that could readily be modeled using templates from the Protein Data Bank. On average 24 CAPRI groups and 7 CASP groups submitted docking predictions for each target, and 12 CAPRI groups per target participated in the CAPRI scoring experiment. In total more than 9500 models were assessed against the 3D structures of the corresponding target complexes. Results show that the prediction of homodimer assemblies by homology modeling techniques and docking calculations is quite successful for targets featuring large enough subunit interfaces to represent stable associations. Targets with ambiguous or inaccurate oligomeric state assignments, often featuring crystal contact-sized interfaces, represented a confounding factor. For those, a much poorer prediction performance was achieved, while nonetheless often providing helpful clues on the correct oligomeric state of the protein. The prediction performance was very poor for genuine tetrameric targets, where the inaccuracy of the homology-built subunit models and the smaller pair-wise interfaces severely limited the ability to derive the correct assembly mode. Our analysis also shows that docking procedures tend to perform better than standard homology modeling techniques and that highly accurate models of the protein components are not always required to identify their association modes with acceptable accuracy. We are most grateful to the PDBe at the European Bioinformatics Institute in Hinxton, UK, for hosting the CAPRI website. Our deepest thanks go to all the structural biologists and to the following structural genomics initiatives: Northeast Structural Genomics Consortium, Joint Center for Structural Genomics, NatPro PSI:Biology, New York Structural Genomics Research Center, Midwest Center for Structural Genomics, Structural Genomics Consortium, for contributing the targets for this joint CASP-CAPRI experiment. MFL acknowledges support from the FRABio FR3688 Research Federation “Structural & Functional Biochemistry of Biomolecular Assemblies.”

Published
2016
Full Text
View/download PDF

5. Prediction Models of P-Glycoprotein Substrates Using Simple 2D and 3D Descriptors by a Recursive Partitioning Approach

Author

Hwan Mook Kim, Soon Kil Ahn, Hyoungjoon Kim, Jong Young Joung, Kyoung Tai No, and Ky Youb Nam

Subjects
biology, Drug development, Molecular descriptor, Test set, biology.protein, Recursive partitioning, General Chemistry, Function (mathematics), Computational biology, Predictability, P-glycoprotein, ADME
Abstract

E-mail: kyn@youai.co.krReceived October 31, 2011, Accepted December 21, 2011P-gp (P-glycoprotein) is a member of the ATP binding cassette (ABC) family of transporters. It transportsmany kinds of anticancer drugs out of the cell. It plays a major role as a cause of multidrug resistance (MDR).MDR function may be a cause of the failure of chemotherapy in cancer and influence pharmacokineticproperties of many drugs. Hence classification of candidate drugs as substrates or nonsubstrate of the P-gp isimportant in drug development. Therefore to identify whether a compound is a P-gp substrate or not, in silicomethod is promising. Recursive Partitioning (RP) method was explored for prediction of P-gp substrate. A setof 261 compounds, including 146 substrates and 115 nonsubstrates of P-gp, was used to training and validation.Using molecular descriptors that we can interpret their own meaning, we have established two models forprediction of P-gp substrates. In the first model, we chose only 6 descriptors which have simple physicalmeaning. In the training set, the overall predictability of our model is 78.95%. In case of test set, overallpredictability is 69.23%. Second model with 2D and 3D descriptors shows a little better predictability (overallpredictability of training set is 79.29%, test set is 79.37%), the second model with 2D and 3D descriptors showsbetter discriminating power than first model with only 2D descriptors. This approach will be used to reduce thenumber of compounds required to be run in the P-gp efflux assay.Key Words : ADME prediction, P-Glycoprotein, Recursive partitioning, 2D Descriptors, 3D DescriptorsIntroductionAbsorption, distribution, metabolism, excretion, and toxi-city (ADMET) properties are very important in the drugdiscovery.

Published
2012
Full Text
View/download PDF

6. Computational Drug Discovery Approach Based on Nuclear Factor-κB Pathway Dynamics

Author

Jong Young Joung, Kyoung Tai No, Young Uk Cho, Sin Moon Gang, Mi-Young Song, Ky Youb Nam, Won Seok Oh, Sun-Young Kim, Jaeseong Park, and Chul Hoon Kim

Subjects
Dynamic simulation, Quantitative structure–activity relationship, Drug discovery, Chemistry, Mechanism (biology), Stereochemistry, Dynamics (mechanics), General Chemistry, Computational biology, Nuclear factor κb, Signal transduction, Transcription factor
Abstract

The NF-κB system of transcription factors plays a crucial role in inflammatory diseases, making it an important drug target. We combined quantitative structure activity relationships for predicting the activity of new compounds and quantitative dynamic models for the NF-κB network with intracellular concentration models. GFA-MLR QSAR analysis was employed to determine the optimal QSAR equation. To validate the predictability of the IKKβ QSAR model for an external set of inhibitors, a set of ordinary differential equations and mass action kinetics were used for modeling the NF-κB dynamic system. The reaction parameters were obtained from previously reported research. In the IKKb QSAR model, good cross-validated q 2 (0.782) and conventional r 2 (0.808) values demonstrated the correlation between the descriptors and each of their activities and reliably predicted the IKKβ activities. Using a developed simulation model of the NF-κB signaling pathway, we demonstrated differences in IκB mRNA expression between normal and different inhibitory states. When the inhibition efficiency increased, inhibitor 1 (PS-1145) led to long-term oscillations. The combined computational modeling and NF-κB dynamic simulations can be used to understand the inhibition mechanisms and thereby result in the design of mechanism-based inhibitors.

Published
2011
Full Text
View/download PDF

7. Discovery of Novel Anti-prion Compounds Using In Silico and In Vitro Approaches

Author

Kyoung Tai No, Seong Soo A. An, Jeongmin Lee, Myung Koo Lee, Jong Young Joung, Yeong Seon Lee, Kyu Jam Hwang, Su Yeon Kim, Rajiv Gandhi Govindaraj, Ji-Won Choi, and Jae Wook Hyeon

Subjects
PrPSc Proteins, In silico, animal diseases, Plasma protein binding, Biology, Ligands, Article, Prion Diseases, Xenobiotics, Protein structure, Cell Line, Tumor, Drug Discovery, Animals, Humans, Computer Simulation, PrPC Proteins, Surface plasmon resonance, Virtual screening, Multidisciplinary, Drug discovery, Surface Plasmon Resonance, Molecular biology, In vitro, Protein Structure, Tertiary, nervous system diseases, Molecular Docking Simulation, Biochemistry, Protein Binding
Abstract

Prion diseases are associated with the conformational conversion of the physiological form of cellular prion protein (PrPC) to the pathogenic form, PrPSc. Compounds that inhibit this process by blocking conversion to the PrPSc could provide useful anti-prion therapies. However, no suitable drugs have been identified to date. To identify novel anti-prion compounds, we developed a combined structure- and ligand-based virtual screening system in silico. Virtual screening of a 700,000-compound database, followed by cluster analysis, identified 37 compounds with strong interactions with essential hotspot PrP residues identified in a previous study of PrPC interaction with a known anti-prion compound (GN8). These compounds were tested in vitro using a multimer detection system, cell-based assays and surface plasmon resonance. Some compounds effectively reduced PrPSc levels and one of these compounds also showed a high binding affinity for PrPC. These results provide a promising starting point for the development of anti-prion compounds.

Published
2015
Full Text
View/download PDF

8. Template based protein structure modeling by global optimization in CASP11

Author

Keehyoung, Joo, InSuk, Joung, Sun Young, Lee, Jong Yun, Kim, Qianyi, Cheng, Balachandran, Manavalan, Jong Young, Joung, Seungryong, Heo, Juyong, Lee, Mikyung, Nam, In-Ho, Lee, Sung Jong, Lee, and Jooyoung, Lee

Subjects
Models, Molecular, Internet, Protein Folding, Models, Statistical, Computational Biology, Proteins, Protein Structure, Secondary, Structural Homology, Protein, Humans, Thermodynamics, Computer Simulation, Protein Interaction Domains and Motifs, Amino Acid Sequence, Databases, Protein, Sequence Alignment, Algorithms, Software
Abstract

For the template-based modeling (TBM) of CASP11 targets, we have developed three new protein modeling protocols (nns for server prediction and LEE and LEER for human prediction) by improving upon our previous CASP protocols (CASP7 through CASP10). We applied the powerful global optimization method of conformational space annealing to three stages of optimization, including multiple sequence-structure alignment, three-dimensional (3D) chain building, and side-chain remodeling. For more successful fold recognition, a new alignment method called CRFalign was developed. It can incorporate sensitive positional and environmental dependence in alignment scores as well as strong nonlinear correlations among various features. Modifications and adjustments were made to the form of the energy function and weight parameters pertaining to the chain building procedure. For the side-chain remodeling step, residue-type dependence was introduced to the cutoff value that determines the entry of a rotamer to the side-chain modeling library. The improved performance of the nns server method is attributed to successful fold recognition achieved by combining several methods including CRFalign and to the current modeling formulation that can incorporate native-like structural aspects present in multiple templates. The LEE protocol is identical to the nns one except that CASP11-released server models are used as templates. The success of LEE in utilizing CASP11 server models indicates that proper template screening and template clustering assisted by appropriate cluster ranking promises a new direction to enhance protein 3D modeling. Proteins 2016; 84(Suppl 1):221-232. © 2015 Wiley Periodicals, Inc.

Published
2015

10. Identification of novel rab27a/melanophilin blockers by pharmacophore-based virtual screening

Author

Jae Sung Hwang, Jong Il Park, Kyoung Tai No, Byung Ha Chang, Jong Young Joung, Ky Youb Nam, Jee-Young Lee, and Ha Yeon Lee

Subjects
Stereochemistry, Bioengineering, Biology, Applied Microbiology and Biotechnology, Biochemistry, Chemical library, chemistry.chemical_compound, Structure-Activity Relationship, Myosin, Structure–activity relationship, Animals, Humans, Molecular Biology, Actin, Melanosome, Adaptor Proteins, Signal Transducing, Virtual screening, Melanosomes, General Medicine, Cell biology, chemistry, rab GTP-Binding Proteins, Melanophilin, Melanocytes, Pharmacophore, Biotechnology
Abstract

Melanocytes are unique cells that produce specific melanin-containing intracellular organelles called melanosomes. Melanosomes are transported from the perinuclear area of melanocytes toward the plasma membrane as they become more melanized in order to increase skin pigmentation. In this vesicular trafficking of melanosomes, Rab27a, melanophilin, and myosin Va play crucial roles in linking melanosomes to actin-based motors. To identify novel compounds to inhibit binding interface between Rab27a and melanophilin, a pharmacophore model was built based on a modeled 3D structure of the protein complex that describes the essential binding residues in the intermolecular interaction. A pharmacophore model was employed to screen a chemical library database. Finally, 25 virtual hits were selected for biological evaluations. The biological activities of 11 analogues were evaluated in a second assay. Two compounds were identified as having concentration-dependent inhibitory activity. By analyzing structure-activity relationships of derivatives of BMD-20, two hydroxyl functional groups were found to be critical for blocking the intermolecular binding between Rab27a and melanophilin.

Published
2013

11. PXR ligand classification model with SFED-weighted WHIM and CoMMA descriptors

Author

K.H. Cho, Jong Young Joung, S.L. Ma, Sehan Lee, and K.T. No

Subjects
Models, Molecular, Receptors, Steroid, Support Vector Machine, Moment analysis, Decision tree, Quantitative Structure-Activity Relationship, Bioengineering, Computational biology, 3d descriptors, Machine learning, computer.software_genre, Ligands, Artificial Intelligence, Drug Discovery, Pregnane X receptor, Drug discovery, business.industry, Chemistry, Decision Trees, Pregnane X Receptor, General Medicine, Ligand (biochemistry), Kohonen neural network, Support vector machine, Molecular Medicine, Artificial intelligence, Neural Networks, Computer, business, computer
Abstract

Understanding which type of endogenous and exogenous compounds serve as agonists for the nuclear pregnane X receptor (PXR) would be valuable for drug discovery and development, because PXR regulates a large number of genes related to xenobiotic metabolism. Although several models have been proposed to classify human PXR activators and non-activators, models with better predictability are necessary for practical purposes in drug discovery. Grid-weighted holistic invariant molecular (G-WHIM) and comparative molecular moment analysis (G-CoMMA) type 3D descriptors that contain information about the solvation free energy of target molecules were developed. With these descriptors, prediction models built using decision tree (DT)-, support vector machine (SVM)-, and Kohonen neural network (KNN)-based models exhibited better predictability than previously proposed models. Solvation free energy density-weighted G-WHIM and G-CoMMA descriptors reveal new insights into PXR ligand classification, and incorporation with machine learning methods (DT, SVM, KNN) exhibits promising results, especially SVM and KNN. SVM- and KNN-based models exhibit accuracy around 0.90, and DT-based models exhibit accuracy around 0.8 for both the training and test sets.

Published
2012

12. Ligand aligning method for molecular docking: alignment of property-weighted vectors

Author

Kwang-Hwi Cho, Kyoung Tai No, Jong Young Joung, and Ky Youb Nam

Subjects
General Chemical Engineering, Molecular Conformation, Library and Information Sciences, Ligands, Quantitative Biology::Subcellular Processes, Small Molecule Libraries, Structure-Activity Relationship, Molecular property, Drug Discovery, Humans, Databases, Protein, Quantitative Biology::Biomolecules, Binding Sites, Chemistry, Solvation, Proteins, Hydrogen Bonding, General Chemistry, Moment of inertia, Computer Science Applications, Molecular Docking Simulation, Crystallography, Searching the conformational space for docking, Docking (molecular), Energy density, Thermodynamics, Target protein, Biological system, Hydrophobic and Hydrophilic Interactions, Algorithms, Databases, Chemical, Protein Binding
Abstract

To reduce searching effort in conformational space of ligand docking positions, we propose an algorithm that generates initial binding positions of the ligand in a target protein, based on the property-weighted vector (P-weiV), the three-dimensional orthogonal vector determined by the molecular property of hydration-free energy density. The alignment of individual P-weiVs calculated separately for the ligand and the protein gives the initial orientation of a given ligand conformation relative to an active site; these initial orientations are then ranked by simple energy functions, including solvation. Because we are using three-dimensional orthogonal vectors to be aligned, only four orientations of ligand positions are possible for each ligand conformation, which reduces the search space dramatically. We found that the performance of P-weiV compared favorably to the use of principle moment of inertia (PMI) as implemented in LigandFit when we tested the abilities of the two approaches to correctly predict 205 protein-ligand complex data sets from the PDBBind database. P-weiV correctly predicted the alignment of ligands (within rmsd of 2.5 Å) with 57.6% reliability (118/205) for the top 10 ranked conformations and with 74.1% reliability (152/205) for the top 50 ranked conformations of Catalyst-generated conformers, as compared to 22.9% (47/205) and 31.2% (64/205), respectively, in the case of PMI with the same conformer set.

Published
2012

13. Discovery of Novel Anti-prion Compounds Using In Silico and In Vitro Approaches.

Author

Jae Wook Hyeon, Jiwon Choi, Su Yeon Kim, Govindaraj, Rajiv Gandhi, Kyu Jam Hwang, Yeong Seon Lee, Seong Soo A. An, Myung Koo Lee, Jong Young Joung, Kyoung Tai No, and Jeongmin Lee

Subjects
PRIONS, SURFACE plasmon resonance, SURAMIN, PROTEIN-tyrosine kinases, AMPHOTERICIN B, CLUSTER analysis (Statistics)
Abstract

Prion diseases are associated with the conformational conversion of the physiological form of cellular prion protein (PrPC) to the pathogenic form, PrPSc. Compounds that inhibit this process by blocking conversion to the PrPSc could provide useful anti-prion therapies. However, no suitable drugs have been identified to date. To identify novel anti-prion compounds, we developed a combined structure- and ligand-based virtual screening system in silico. Virtual screening of a 700,000-compound database, followed by cluster analysis, identified 37 compounds with strong interactions with essential hotspot PrP residues identified in a previous study of PrPC interaction with a known antiprion compound (GN8). These compounds were tested in vitro using a multimer detection system, cell-based assays, and surface plasmon resonance. Some compounds effectively reduced PrPSc levels and one of these compounds also showed a high binding affinity for PrPC. These results provide a promising starting point for the development of anti-prion compounds. [ABSTRACT FROM AUTHOR]

Published
2015
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results