Chemical Biology in Drug Discovery
Tài liệu tham khảo
Bucci, 2010, A Decade of Chemical Biology, Nat. Chem. Biol., 6, 847, 10.1038/nchembio.489
Cohen, 2007, A Clickable Inhibitor Reveals Context-Dependent Autoactivation of p90 RSK, Nat. Chem. Biol., 3, 156, 10.1038/nchembio859
Bunnage, 2013, Target Validation Using Chemical Probes, Nat. Chem. Biol., 9, 195, 10.1038/nchembio.1197
Bunnage, 2011, Getting Pharmaceutical R&D Back on Target, Nat. Chem. Biol., 7, 335, 10.1038/nchembio.581
Martinez Molina, 2013, Monitoring Drug Target Engagement in Cells and Tissues Using the Cellular Thermal Shift Assay, Science, 341, 84, 10.1126/science.1233606
Bunnage, 2015, Know Your Target, Know Your Molecule, Nat. Chem. Biol., 11, 368, 10.1038/nchembio.1813
Schirle, 2012, Mass Spectrometry-Based Proteomics in Preclinical Drug Discovery, Chem. Biol., 19, 72, 10.1016/j.chembiol.2012.01.002
Lee, 2013, Target Deconvolution Techniques in Modern Phenotypic Profiling, Curr. Opin. Chem. Biol., 17, 118, 10.1016/j.cbpa.2012.12.022
Zinn, 2012, Mass Spectrometry Approaches to Monitor Protein-Drug Interactions, Methods, 57, 430, 10.1016/j.ymeth.2012.05.008
Huang, 2009, Tankyrase Inhibition Stabilizes Axin and Antagonizes Wnt Signalling, Nature, 461, 614, 10.1038/nature08356
Dale, 2015, A Selective Chemical Probe for Exploring the Role of CDK8 and CDK19 in Human Disease, Nat. Chem. Biol., 11, 973, 10.1038/nchembio.1952
Ito, 2010, Identification of a Primary Target of Thalidomide Teratogenicity, Science, 327, 1345, 10.1126/science.1177319
Bantscheff, 2007, Quantitative Chemical Proteomics Reveals Mechanisms of Action of Clinical ABL Kinase Inhibitors, Nat. Biotechnol., 25, 1035, 10.1038/nbt1328
Médard, 2015, Optimized Chemical Proteomics Assay for Kinase Inhibitor Profiling, J. Proteome Res., 14, 1574, 10.1021/pr5012608
Bantscheff, 2011, Chemoproteomics Profiling of HDAC Inhibitors Reveals Selective Targeting of HDAC Complexes, Nat. Biotechnol., 29, 255, 10.1038/nbt.1759
Yang, 2015, Activity-Based Protein Profiling: Recent Advances in Probe Development and Applications, Chembiochem, 16, 712, 10.1002/cbic.201402582
Sanman, 2014, Activity-Based Profiling of Proteases, Annu. Rev. Biochem., 83, 249, 10.1146/annurev-biochem-060713-035352
Martell, 2014, Applications of Copper-Catalyzed Click Chemistry in Activity-Based Protein Profiling, Molecules, 19, 1378, 10.3390/molecules19021378
Rostovtsev, 2002, A Stepwise Huisgen Cycloaddition Process: Copper(I)-Catalyzed Regioselective “Ligation” of Azides and Terminal Alkynes, Angew. Chem. Int. Ed. Engl., 41, 2596, 10.1002/1521-3773(20020715)41:14<2596::AID-ANIE2596>3.0.CO;2-4
Arastu-Kapur, 2008, Identification of Proteases that Regulate Erythrocyte Rupture by the Malaria Parasite Plasmodium falciparum, Nat. Chem. Biol., 4, 203, 10.1038/nchembio.70
Leung, 2003, Discovering Potent and Selective Reversible Inhibitors of Enzymes in Complex Proteomes, Nat. Biotechnol., 21, 687, 10.1038/nbt826
Adibekian, 2012, Confirming Target Engagement for Reversible Inhibitors in vivo by Kinetically Tuned Activity-Based Probes, J. Am. Chem. Soc., 134, 10345, 10.1021/ja303400u
Lapinsky, 2015, Recent Developments and Applications of Clickable Photoprobes in Medicinal Chemistry and Chemical Biology, Future Med. Chem., 7, 2143, 10.4155/fmc.15.136
Smith, 2015, Photoaffinity Labeling in Target- and Binding-Site Identification, Future Med. Chem., 7, 159, 10.4155/fmc.14.152
Zuhl, 2016, Chemoproteomic Profiling Reveals that Cathepsin D Off-Target Activity Drives Ocular Toxicity of β-Secretase Inhibitors, Nat. Commun., 7, 13042, 10.1038/ncomms13042
Niphakis, 2015, A Global Map of Lipid-Binding Proteins and Their Ligandability in Cells, Cell, 161, 1668, 10.1016/j.cell.2015.05.045
Koutsoukas, 2011, From In Silico Target Prediction to Multi-Target Drug Design: Current Databases, Methods and Applications, J. Proteomics, 74, 2554, 10.1016/j.jprot.2011.05.011
Guney, 2016, Network-Based In Silico Drug Efficacy Screening, Nat. Commun., 7, 10.1038/ncomms10331
Lamb, 2007, The Connectivity Map: A New Tool for Biomedical Research, Nat. Rev. Cancer, 7, 54, 10.1038/nrc2044
Rognan, 2010, Structure-Based Approaches to Target Fishing and Ligand Profiling, Mol. Inf., 29, 176, 10.1002/minf.200900081
Keiser, 2009, Predicting New Molecular Targets for Known Drugs, Nature, 462, 175, 10.1038/nature08506
Nidhi, 2006, Prediction of Biological Targets for Compounds Using Multiple-Category Bayesian Models Trained on Chemogenomics Databases, J. Chem. Inf. Model., 46, 1124, 10.1021/ci060003g
Li, 2006, TarFisDock: A Web Server for Identifying Drug Targets With Docking Approach, Nucleic Acids Res., 34, W219, 10.1093/nar/gkl114
Cheng, 2011, Identifying Compound-Target Associations by Combining Bioactivity Profile Similarity Search and Public Databases Mining, J. Chem. Inf. Model., 51, 2440, 10.1021/ci200192v
Simon, 1996, Chemical Similarity Using Physiochemical Property Descriptors, J. Chem. Inf. Comput. Sci., 36, 118, 10.1021/ci950274j
Hull, 2001, Latent Semantic Structure Indexing (LaSSI) for Defining Chemical Similarity, J. Med. Chem., 44, 1177, 10.1021/jm000393c
Durant, 2002, Reoptimization of MDL Keys for Use in Drug Discovery, J. Chem. Inf. Comput. Sci., 42, 1273, 10.1021/ci010132r
Schuffenhauer, 2003, Similarity Metrics for Ligands Reflecting the Similarity of the Target Proteins, J. Chem. Inf. Comput. Sci., 43, 391, 10.1021/ci025569t
Roy, 2012, Electrotopological State Atom (E-State) Index in Drug Design, QSAR, Property Prediction and Toxicity Assessment, Curr. Comput. Aided Drug Des., 8, 135, 10.2174/157340912800492366
Kawabata, 2003, MATRAS: A Program for Protein 3D Structure Comparison, Nucleic Acids Res., 31, 3367, 10.1093/nar/gkg581
Lo, 2016, 3D Chemical Similarity Networks for Structure-Based Target Prediction and Scaffold Hopping, ACS Chem. Biol., 11, 2244, 10.1021/acschembio.6b00253
Hendlich, 2003, Relibase: Design and Development of a Database for Comprehensive Analysis of Protein-Ligand Interactions, J. Mol. Biol., 326, 607, 10.1016/S0022-2836(02)01408-0
Sheridan, 1994, Extending the Trend Vector: The Trend Matrix and Sample-Based Partial Least Squares, J. Comput. Aided Mol. Des., 8, 323, 10.1007/BF00126749
Miller, 1994, FLOG: A System to Select ‘quasi-flexible’ Ligands Complementary to a Receptor of Known Three-Dimensional Structure, J. Comput. Aided Mol. Des., 8, 153, 10.1007/BF00119865
Poroikov, 2002, How to Acquire New Biological Activities in Old Compounds by Computer Prediction, J. Comput. Aided Mol. Des., 16, 819, 10.1023/A:1023836829456
Geronikaki, 2004, Design of New Cognition Enhancers: From Computer Prediction to Synthesis and Biological Evaluation, J. Med. Chem., 47, 2870, 10.1021/jm031086k
Chen, 2001, Ligand-Protein Inverse Docking and Its Potential Use in the Computer Search of Protein Targets of a Small Molecule, Proteins, 43, 217, 10.1002/1097-0134(20010501)43:2<217::AID-PROT1032>3.0.CO;2-G
Deng, 2004, Structural Interaction Fingerprint (SIFt): A Novel Method for Analyzing Three-Dimensional Protein-Ligand Binding Interactions, J. Med. Chem., 47, 337, 10.1021/jm030331x
Sliwoski, 2014, Computational Methods in Drug Discovery, Pharmacol. Rev., 66, 334, 10.1124/pr.112.007336
Reker, 2014, Identifying the Macromolecular Targets of De Novo-Designed Chemical Entities Through Self-Organizing Map Consensus, Proc. Natl. Acad. Sci. U. S. A., 111, 4067, 10.1073/pnas.1320001111
Dunkel, 2008, SuperPred: Drug Classification and Target Prediction, Nucleic Acids Res., 36, W55, 10.1093/nar/gkn307
Gfeller, 2013, Shaping the Interaction Landscape of Bioactive Molecules, Bioinformatics, 29, 3073, 10.1093/bioinformatics/btt540
Yamanishi, 2014, DINIES: Drug-Target Interaction Network Inference Engine Based on Supervised Analysis, Nucleic Acids Res., 42, W39, 10.1093/nar/gku337
Xiao, 2015, iDrug-Target: Predicting the Interactions Between Drug Compounds and Target Proteins in Cellular Networking Via Benchmark Dataset Optimization Approach, J. Biomol. Struct. Dyn., 33, 2221, 10.1080/07391102.2014.998710
Patterson, 1996, Neighborhood Behavior: A Useful Concept for Validation of “Molecular Diversity” Descriptors, J. Med. Chem., 39, 3049, 10.1021/jm960290n
Engel, 2006, Basic Overview of Chemoinformatics, J. Chem. Inf. Model., 46, 2267, 10.1021/ci600234z
Yan, 2016, Chemical Structure Similarity Search for Ligand-Based Virtual Screening: Methods and Computational Resources, Curr. Drug Targets, 17, 1580, 10.2174/1389450116666151102095555
Cross, 2012, GRID-Based Three-Dimensional Pharmacophores I: FLAPpharm, a Novel Approach for Pharmacophore Elucidation, J. Chem. Inf. Model., 52, 2587, 10.1021/ci300153d
Bajorath, 2017, Molecular Similarity Concepts for Informatics Applications, Methods Mol. Biol., 1526, 231, 10.1007/978-1-4939-6613-4_13
Downs, 1995
Peter, 1998, Chemical Similarity Searching, J. Chem. Inf. Comput. Sci., 38, 983, 10.1021/ci9800211
Bajorath, 2001, Selected Concepts and Investigations in Compound Classification, Molecular Descriptor Analysis, and Virtual Screening, J. Chem. Inf. Comput. Sci., 41, 233, 10.1021/ci0001482
Cheng, 2002, Computation of the Physio-Chemical Properties and Data Mining of Large Molecular Collections, J. Comput. Chem., 23, 172, 10.1002/jcc.1164
Schuur, 1996, The Coding of the Three-Dimensional Structure of Molecules by Molecular Transforms and Its Application to Structure-Spectra Correlations and Studies of Biological Activity, J. Chem. Inf. Comput. Sci., 36, 334, 10.1021/ci950164c
Ginn, 2000
Huang, 2012, Proteochemometric Modeling of the Bioactivity Spectra of HIV-1 Protease Inhibitors by Introducing Protein-Ligand Interaction Fingerprint, PLoS One, 7, 10.1371/journal.pone.0041698
Humbeck, 2017, What Can We Learn From Bioactivity Data? Chemoinformatics Tools and Applications in Chemical Biology Research, ACS Chem. Biol., 12, 23, 10.1021/acschembio.6b00706
Koutsoukas, 2013, In Silico Target Predictions: Defining a Benchmarking Data Set and Comparison of Performance of the Multiclass Naïve Bayes and Parzen-Rosenblatt Window, J. Chem. Inf. Model., 53, 1957, 10.1021/ci300435j
Mervin, 2015, Target Prediction Utilising Negative Bioactivity Data Covering Large Chemical Space, J. Chem., 7, 51, 10.1186/s13321-015-0098-y
Gaulton, 2012, ChEMBL: A Large-Scale Bioactivity Database for Drug Discovery, Nucleic Acids Res., 40, D1100, 10.1093/nar/gkr777
Hudson, 1996, Parameter Based Methods for Compound Selection From Chemical Databases, Quant. Struct. Act. Relat., 15, 285, 10.1002/qsar.19960150402
Gobbi, 2003, DISE: Directed Sphere Exclusion, J. Chem. Inf. Comput. Sci., 43, 317, 10.1021/ci025554v
Pantoliano, 2001, High-Density Miniaturized Thermal Shift Assays as a General Strategy for Drug Discovery, J. Biomol. Screen., 6, 429, 10.1177/108705710100600609
Lo, 2004, Evaluation of Fluorescence-Based Thermal Shift Assays for Hit Identification in Drug Discovery, Anal. Biochem., 332, 153, 10.1016/j.ab.2004.04.031
Liu, 2012, Iniparib Nonselectively Modifies Cysteine-Containing Proteins in Tumor Cells and Is Not a Bona Fide PARP Inhibitor, Clin. Cancer Res., 18, 510, 10.1158/1078-0432.CCR-11-1973
Peppard, 2003, Development of a High-Throughput Screening Assay for Inhibitors of Aggrecan Cleavage Using Luminescent Oxygen Channeling (AlphaScreen), J. Biomol. Screen., 8, 149, 10.1177/1087057103252308
Eglen, 2008, The Use of AlphaScreen Technology in HTS: Current Status, Curr. Chem. Genomics, 1, 2, 10.2174/1875397300801010002
Almqvist, 2016, CETSA Screening Identifies Known and Novel Thymidylate Synthase Inhibitors and Slow Intracellular Activation of 5-Fluorouracil, Nat. Commun., 7, 11040, 10.1038/ncomms11040
Longley, 2003, 5-Fluorouracil: Mechanisms of Action and Clinical Strategies, Nat. Rev. Cancer, 3, 330, 10.1038/nrc1074
Savitski, 2014, Tracking Cancer Drugs in Living Cells by Thermal Profiling of the Proteome, Science, 346, 1255784, 10.1126/science.1255784
Gelot, 2013, Vemurafenib: An Unusual UVA-Induced Photosensitivity, Exp. Dermatol., 22, 297, 10.1111/exd.12119
Becher, 2016, Thermal Profiling Reveals Phenylalanine Hydroxylase as an Off-Target of Panobinostat, Nat. Chem. Biol., 12, 908, 10.1038/nchembio.2185
Reinhard, 2015, Thermal Proteome Profiling Monitors Ligand Interactions with Cellular Membrane Proteins, Nat. Methods, 12, 1129, 10.1038/nmeth.3652
Huber, 2015, Proteome-Wide Drug and Metabolite Interaction Mapping by Thermal-Stability Profiling, Nat. Methods, 12, 1055, 10.1038/nmeth.3590
Lea, 2011, Fluorescence Polarization Assays in Small Molecule Screening, Expert Opin. Drug Discovery, 6, 17, 10.1517/17460441.2011.537322
Parker, 2000, Development of High Throughput Screening Assays Using Fluorescence Polarization: Nuclear Receptor-Ligand-Binding and Kinase/Phosphatase Assays, J. Biomol. Screen., 5, 77, 10.1177/108705710000500204
Dubach, 2014, In Vivo Imaging of Specific Drug-Target Binding at Subcellular Resolution, Nat. Commun., 5, 3946, 10.1038/ncomms4946
Dubach, 2017, Quantitating Drug-Target Engagement in Single Cells in vitro and In Vivo, Nat. Chem. Biol., 13, 168, 10.1038/nchembio.2248
Kumar, 2011, FLIM FRET Technology for Drug Discovery: Automated Multiwell-Plate High-Content Analysis, Multiplexed Readouts and Application In Situ, Chemphyschem, 12, 609, 10.1002/cphc.201000874
Zimmermann, 2013, Small Molecule Inhibition of the KRAS-PDEδ Interaction Impairs Oncogenic KRAS Signalling, Nature, 497, 638, 10.1038/nature12205
Machleidt, 2015, NanoBRET—A Novel BRET Platform for the Analysis of Protein-Protein Interactions, ACS Chem. Biol., 10, 1797, 10.1021/acschembio.5b00143
Robers, 2015, Target Engagement and Drug Residence Time Can Be Observed in Living Cells With BRET, Nat. Commun., 6, 10.1038/ncomms10091
Ito, 2011, Real-Time Imaging of Histone H4K12-Specific Acetylation Determines the Modes of Action of Histone Deacetylase and Bromodomain Inhibitors, Chem. Biol., 18, 495, 10.1016/j.chembiol.2011.02.009
Kaniskan, 2015, A Potent, Selective and Cell-Active Allosteric Inhibitor of Protein Arginine Methyltransferase 3 (PRMT3), Angew. Chem. Int. Ed. Engl., 54, 5166, 10.1002/anie.201412154
Schulze, 2015, Cell-Based Protein Stabilization Assays for the Detection of Interactions Between Small-Molecule Inhibitors and BRD4, J. Biomol. Screen., 20, 180, 10.1177/1087057114552398
Butler, 2009, Proteomic Identification of Multitasking Proteins in Unexpected Locations Complicates Drug Targeting, Nat. Rev. Drug Discov., 8, 935, 10.1038/nrd2945
Lai, 2017, Induced Protein Degradation: An Emerging Drug Discovery Paradigm, Nat. Rev. Drug Discov., 16, 101, 10.1038/nrd.2016.211
Boutros, 2008, The Art and Design of Genetic Screens: RNA Interference, Nat. Rev. Genet., 9, 554, 10.1038/nrg2364
Cong, 2013, Multiplex Genome Engineering Using CRISPR/Cas Systems, Science, 339, 819, 10.1126/science.1231143
Haurwitz, 2010, Sequence- and Structure-Specific RNA Processing by a CRISPR Endonuclease, Science, 329, 1355, 10.1126/science.1192272
Wood, 2011, Targeted Genome Editing Across Species Using ZFNs and TALENs, Science, 333, 307, 10.1126/science.1207773
Sander, 2011, Selection-Free Zinc-Finger-Nuclease Engineering by Context-Dependent Assembly (CoDA), Nat. Methods, 8, 67, 10.1038/nmeth.1542
Schenone, 2013, Target Identification and Mechanism of Action in Chemical Biology and Drug Discovery, Nat. Chem. Biol., 9, 232, 10.1038/nchembio.1199
Zengerle, 2015, Selective Small Molecule Induced Degradation of the BET Bromodomain Protein BRD4, ACS Chem. Biol., 10, 1770, 10.1021/acschembio.5b00216
Kettle, 2016, Potent and Selective Inhibitors of MTH1 Probe Its Role in Cancer Cell Survival, J. Med. Chem., 59, 2346, 10.1021/acs.jmedchem.5b01760
Gad, 2014, MTH1 Inhibition Eradicates Cancer by Preventing Sanitation of the dNTP Pool, Nature, 508, 215, 10.1038/nature13181
Petrocchi, 2016, Identification of Potent and Selective MTH1 Inhibitors, Bioorg. Med. Chem. Lett., 26, 1503, 10.1016/j.bmcl.2016.02.026
