A Residue-Resolved Bayesian Approach to Quantitative Interpretation of Hydrogen-Deuterium Exchange from Mass Spectrometry: Application to Characterizing Protein-Ligand Interactions.
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.
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.
Daniel J
DJ
Saltzberg
Centro de Investigación Lilly, SA , Avenida de la Industria 30, 28108 Alcobendas, Spain.
Howard B
HB
Broughton
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.
Riccardo
R
Pellarin
Lilly Research Laboratories, Eli Lilly and Company , Indianapolis, Indiana, United States.
Michael J
MJ
Chalmers
Centro de Investigación Lilly, SA , Avenida de la Industria 30, 28108 Alcobendas, Spain.
Alfonso
A
Espada
Lilly Research Laboratories, Eli Lilly and Company , Indianapolis, Indiana, United States.
Jeffrey A
JA
Dodge
Bioinformatics Core, The Scripps Research Institute-Scripps Florida , Jupiter, Florida, United States.
Bruce D
BD
Pascal
Department of Molecular Therapeutics, The Scripps Research Institute-Scripps Florida , Jupiter, Florida, United States.
Patrick R
PR
Griffin
Lilly Research Laboratories, Eli Lilly and Company , Indianapolis, Indiana, United States.
Christine
C
Humblet
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.
Andrej
A
Sali
D001499:Bayes Theorem; D041961:Deuterium Exchange Measurement; D008024:Ligands; D013058:Mass Spectrometry; D056004:Molecular Dynamics Simulation; D009010:Monte Carlo Method; D011506:Proteins / Q000737:chemistry*
D008024:Ligands; D011506:Proteins