| Autor: |
Saltzberg DJ; Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, and California Institute for Quantitative Biosciences (QB3), University of California, San Francisco , San Francisco, California, United States., Broughton HB; Centro de Investigación Lilly, SA , Avenida de la Industria 30, 28108 Alcobendas, Spain., Pellarin R; Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, and California Institute for Quantitative Biosciences (QB3), University of California, San Francisco , San Francisco, California, United States.; Structural Bioinformatics Unit, Institut Pasteur, CNRS UMR 3528 , Paris, France., Chalmers MJ; Lilly Research Laboratories, Eli Lilly and Company , Indianapolis, Indiana, United States., Espada A; Centro de Investigación Lilly, SA , Avenida de la Industria 30, 28108 Alcobendas, Spain., Dodge JA; Lilly Research Laboratories, Eli Lilly and Company , Indianapolis, Indiana, United States., Pascal BD; Bioinformatics Core, The Scripps Research Institute-Scripps Florida , Jupiter, Florida, United States., Griffin PR; Department of Molecular Therapeutics, The Scripps Research Institute-Scripps Florida , Jupiter, Florida, United States., Humblet C; Lilly Research Laboratories, Eli Lilly and Company , Indianapolis, Indiana, United States., Sali A; Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, and California Institute for Quantitative Biosciences (QB3), University of California, San Francisco , San Francisco, California, United States. |
| Abstrakt: |
Characterization of interactions between proteins and other molecules is crucial for understanding the mechanisms of action of biological systems and, thus, drug discovery. An increasingly useful approach to mapping these interactions is measurement of hydrogen/deuterium exchange (HDX) using mass spectrometry (HDX-MS), which measures the time-resolved deuterium incorporation of peptides obtained by enzymatic digestion of the protein. Comparison of exchange rates between apo- and ligand-bound conditions results in a mapping of the differential HDX (ΔHDX) of the ligand. Residue-level analysis of these data, however, must account for experimental error, sparseness, and ambiguity due to overlapping peptides. Here, we propose a Bayesian method consisting of a forward model, noise model, prior probabilities, and a Monte Carlo sampling scheme. This method exploits a residue-resolved exponential rate model of HDX-MS data obtained from all peptides simultaneously, and explicitly models experimental error. The result is the best possible estimate of ΔHDX magnitude and significance for each residue given the data. We demonstrate the method by revealing richer structural interpretation of ΔHDX data on two nuclear receptors: vitamin D-receptor (VDR) and retinoic acid receptor gamma (RORγ). The method is implemented in HDX Workbench and as a standalone module of the open source Integrative Modeling Platform. |
