Exploiting the Propagation of Constrained Variables for Enhanced HDX-MS Data Optimization.

Nonlinear programming has found useful applications in protein biophysics to help understand the microscopic exchange kinetics of data obtained using hydrogen-deuterium exchange mass spectrometry (HDX-MS). Finding a microscopic kinetic solution for HDX-MS data provides a window into local protein stability and energetics allowing them to be quantified and understood. Optimization of HDX-MS data is a significant challenge, however, due to the requirement to solve a large number of variables simultaneously with exceptionally large variable bounds.

Characterization and Management of Noise in HDX-MS Data Modeling.

Quantification of hydrogen deuterium exchange (HDX) kinetics can provide information on the stability of individual amino acids in proteins by finding the degree to which the local backbone environment corresponds to that of a random coil. When characterized by mass spectrometry, extraction of HDX kinetics is not possible because different residue exchange rates become merged depending on the peptides that are formed during proteolytic digestion. We have recently developed an advanced programming tool called HDXmodeller, which enables the exchange rates of individual amino acids to be understood by optimization of low-resolution HDX-mass spectrometry (MS) data.

HDXmodeller: an online webserver for high-resolution HDX-MS with auto-validation.

The extent to which proteins are protected from hydrogen deuterium exchange (HDX) provides valuable insight into their folding, dynamics and interactions. Characterised by mass spectrometry (MS), HDX benefits from negligible mass restrictions and exceptional throughput and sensitivity but at the expense of resolution. Exchange mechanisms which naturally transpire for individual residues cannot be accurately located or understood because amino acids are characterised in differently sized groups depending on the extent of proteolytic digestion.