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| Content Provider | World Health Organization (WHO)-Global Index Medicus |
|---|---|
| Author | Ravichandran, Sarangan Luke, Brian T. Collins, Jack R. |
| Description | Author Affiliation: Ravichandran S ( Advanced Biomedical Computing Center, Frederick National Laboratory for Cancer Research (FNLCR), P.O. Box B, Frederick, MD 21702, USA. Electronic address: ravichandrans@mail.nih.gov.); Luke BT ( Advanced Biomedical Computing Center, Frederick National Laboratory for Cancer Research (FNLCR), P.O. Box B, Frederick, MD 21702, USA.); Collins JR ( Advanced Biomedical Computing Center, Frederick National Laboratory for Cancer Research (FNLCR), P.O. Box B, Frederick, MD 21702, USA.) |
| Abstract | Cancer is a complex disease resulting from the uncontrolled proliferation of cell signaling events. Protein kinases have been identified as central molecules that participate overwhelmingly in oncogenic events, thus becoming key targets for anticancer drugs. A majority of studies converged on the idea that ligand-binding pockets of kinases retain clues to the inhibiting abilities and cross-reacting tendencies of inhibitor drugs. Even though these ideas are critical for drug discovery, validating them using experiments is not only difficult, but also in some cases infeasible. To overcome these limitations and to test these ideas at the molecular level, we present here the results of receptor-focused in-silico docking of nine marketed drugs to 19 different wild-type and mutated kinases chosen from a wide range of families. This investigation highlights the need for using relevant models to explain the correct inhibition trends and the results are used to make predictions that might be able to influence future experiments. Our simulation studies are able to correctly predict the primary targets for each drug studied in majority of cases and our results agree with the existing findings. Our study shows that the conformations a given receptor acquires during kinase activation, and their micro-environment, defines the ligand partners. Type II drugs display high compatibility and selectivity for DFG-out kinase conformations. On the other hand Type I drugs are less selective and show binding preferences for both the open and closed forms of selected kinases. Using this receptor-focused approach, it is possible to capture the observed fold change in binding affinities between the wild-type and disease-centric mutations in ABL kinase for Imatinib and the second-generation ABL drugs. The effects of mutation are also investigated for two other systems, EGFR and B-Raf. Finally, by including pathway information in the design it is possible to model kinase inhibitors with potentially fewer side-effects. |
| File Format | HTM / HTML |
| ISSN | 10933263 |
| e-ISSN | 18734243 |
| DOI | 10.1016/j.jmgm.2014.12.007 |
| Journal | Journal of Molecular Graphics and Modelling |
| Volume Number | 57 |
| Language | English |
| Publisher | Elsevier |
| Publisher Date | 2015-04-01 |
| Publisher Place | United States |
| Access Restriction | Open |
| Subject Keyword | Discipline Molecular Biology Models, Molecular Protein Kinase Inhibitors Pharmacology Receptor Protein-tyrosine Kinases Antagonists & Inhibitors Chemistry Amino Acid Sequence Cell Line, Tumor Crystallography, X-ray Databases, Protein Imatinib Mesylate Kinetics Ligands Molecular Docking Simulation Molecular Sequence Data Proto-oncogene Proteins B-raf Receptor, Epidermal Growth Factor Sequence Alignment Thermodynamics Research Support, N.i.h., Extramural |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Graphics and Computer-Aided Design Spectroscopy Materials Chemistry Physical and Theoretical Chemistry |
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