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

Abstract

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 is also uniquely able to appraise each optimization and quantify the accuracy of modeled exchange rates ab initio using a novel autovalidation method based on a covariance matrix. Here, we address the noise-handling capabilities of HDXmodeller and demonstrate the effectiveness of the algorithm on self-inconsistent datasets. Reference intervals for experimental HDX-MS data are also derived, and this information is presented in an updated online workflow for HDXmodeller, allowing users to evaluate the consistency of their data. The development of a modified version of HDXmodeller is also discussed with enhanced noise-handling capability brought about through loss function optimization. Changes in optimizer accuracy with different loss functions are also demonstrated along with the effectiveness of HDXmodeller to select the most effective optimizer for different data using currently embedded autovalidation criteria.