Differential hydrogen exchange-mass spectrometry (HX-MS) measurements are valuable for identification of differences in the higher order structures of proteins. Typically, the data sets are large with many differential HX values corresponding to many peptides monitored at several labeling times. To eliminate subjectivity and reliably identify significant differences in HX-MS measurements, a statistical analysis approach is needed. In this work, we performed null HX-MS measurements (i.e., no meaningful differences) on maltose binding protein and infliximab, a monoclonal antibody, to evaluate the reliability of different statistical analysis approaches. Null measurements are useful for directly evaluating the risk (i.e., falsely classifying a difference as significant) and power (i.e., failing to classify a true difference as significant) associated with different statistical analysis approaches. With null measurements, we identified weaknesses in the approaches commonly used. Individual tests of significance were prone to false positives due to the problem of multiple comparisons. Incorporation of Bonferroni correction led to unacceptably large limits of detection, severely decreasing the power. Analysis methods using a globally estimated significance limit also led to an overestimation of the limit of detection, leading to a loss of power. Here, we demonstrate a hybrid statistical analysis, based on volcano plots, that combines individual significance testing with an estimated global significance limit, that simultaneously decreased the risk of false positives and retained superior power. Furthermore, we highlight the utility of null HX-MS measurements to explicitly evaluate the criteria used to classify a difference in HX as significant.
Descriptions of materials, metrological methods, computational methods, and supplementary results. Figures of HDX-MS publications and citations versus publication year, histogram of peptide sequence lengths, sequence coverage maps, performance of instrumentsoftware configurations, repeatability plots, %E corrected peptide t HDX versus log 10 (t HDX ) for eight peptides. Tables of instrumentation,software, peptide search methodology, and operating conditions of proteolytic, chromatographic components, and effects of peptide charge on deuterium uptake (PDF)
PurposeTryptophan’s (Trp) unique hydrophobic and structural properties make it an important antigen binding motif when positioned in complementarity-determining regions (CDRs) of monoclonal antibodies (mAbs). Oxidation of Trp residues within the CDR can deleteriously impact antigen binding, particularly if the CDR conformation is altered. The goal of this study was to evaluate the conformational and functional impact of Trp oxidation for two mAb subtypes, which is essential in determining the structure-function relationship and establishing appropriate analytical control strategies during protein therapeutics development.MethodsSelective Trp oxidation was induced by 2,2′-Azobis(2-amidinopropane) dihydrochloride (AAPH) treatment in the presence of free methionine (Met). The native and chemically oxidized mAbs were characterized by hydrogen-deuterium exchange mass spectrometry (HDX-MS) for conformational changes and surface plasmon resonance (SPR) for antigen-antibody binding.ResultsTreatment of mAbs with AAPH selectively oxidized solvent accessible Trp residues. Oxidation of Trp within or in proximity of CDRs increased conformational flexibility in variable domains and disrupted antigen binding.ConclusionsTrp oxidation in CDRs can adversely impact mAbs’ conformation and antigen binding. Trp oxidation should be carefully evaluated as part of critical quality attribute assessments. Oxidation susceptible Trp should be closely monitored during process development for mAbs to establish appropriate analytical control for manufacturing of drug substance and drug product.Electronic supplementary materialThe online version of this article (10.1007/s11095-018-2545-8) contains supplementary material, which is available to authorized users.
Hydrogen exchange-mass spectrometry (HX-MS) is widely promoted for its ability to detect subtle perturbations in protein structure, but such perturbations will result in small differences in HX. However, the detection limit of HX-MS has not been widely investigated, nor is there a useful approach for defining the detection limit of HX-MS measurements. In this work, we designed a well-characterized structural variant spiking model to investigate the detection limit of conventional peptide-based HX-MS. The detection limit was challenged by spiking small fractions of a structural variant (modeled using maltose binding protein W169G mutant) into a reference protein (wild-type maltose binding protein). As little as 5% of the structural variant could be detected. The small structural perturbation was not resolvable by far UV circular dichroism, differential scanning calorimetry, or size exclusion chromatography. Furthermore, we validated the ability of the hybrid statistical analysis approach, presented in a companion paper (10.1021/acs.analchem.9b01325), to reliably identify small, significant differences in HX-MS measurements. With our structural variant spiking model, we demonstrate a benchmarking approach for determining a detection limit of HX-MS for detection of changes in higher-order structure that might be encountered in protein structural comparability and similarity assessment applications.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.