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Isothermal Chemical Denaturation: Data Analysis, Error Detection, and Correction by PARAFAC2
Author(s) -
Dillen Augustijn,
Alina Kulakova,
Sujata Mahapatra,
Pernille Harris,
Åsmund Rinnan
Publication year - 2020
Publication title -
analytical chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.117
H-Index - 332
eISSN - 1520-6882
pISSN - 0003-2700
DOI - 10.1021/acs.analchem.9b05748
Subject(s) - chemistry , isothermal process , denaturation (fissile materials) , outlier , salt (chemistry) , analytical chemistry (journal) , analyte , chromatography , biological system , thermodynamics , nuclear chemistry , physics , artificial intelligence , biology , computer science
Characterization of a protein's conformational stability is a key step in the development of biotherapeutics, where protein unfolding leads to adverse properties, such as aggregation and loss of efficacy. Isothermal chemical denaturation (ICD) can be applied to determine chemical stability, aiming to identify the optimal solvent conditions, in terms of pH, salt concentration, and added excipients. For seven monoclonal antibodies, this study investigates the observed intrinsic protein fluorescence emission spectra as a function of denaturant concentration. Protein formulations are screened in two experimental series. We show how the peak shapes of folded and unfolded proteins are preserved under added salt (0-140 mM NaCl) and added excipients concentrations, as typically found in biotherapeutic formulations and that only minor effects in tryptophan fluorescence peak tailing are observed over a large pH range (5.5-9.0). The data of seven mAbs, where GuHCl was a suitable denaturant, are modeled using PARAFAC2. PARAFAC2, a linear decomposition method, is well suited for the data and yields robust, valid, and automated models that allow for the detection of erroneous measurements. Analysis of the errors show correlation with the well-based experimental setup, and differences in observed errors between the two experimental series. We additionally show a correction method for these outliers based on PARAFAC2 model scores, such that full transition curves can be retrieved, increasing the accuracy of any subsequent analysis.

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