Quantitative spectral comparison by weighted spectral difference for protein higher order structure confirmation.

Previously, different approaches of spectral comparison were evaluated, and the spectral difference (SD) method was shown to be valuable for its linearity with spectral changes and its independence on data spacing (Anal. Biochem. 434 (2013) 153-165). In this note, we present an enhancement of the SD calculation, referred to as the "weighted spectral difference" (WSD), by implementing a weighting function based on relative signal magnitude. While maintaining the advantages of the SD method, WSD improves the method sensitivity to spectral changes and tolerance for baseline inclusion. Furthermore, a generalized formula is presented to unify further development of approaches to quantify spectral difference.

Comparison of quantitative spectral similarity analysis methods for protein higher-order structure confirmation.

Optical and vibrational spectroscopic techniques are important tools for evaluating secondary and tertiary structures of proteins. These spectroscopic techniques are routinely applied in biopharmaceutical development to elucidate structural characteristics of protein products, to evaluate the impact of processing and storage conditions on product quality, and to assess comparability of a protein product before and after manufacturing changes. Conventionally, the degree of similarity between two spectra has been determined visually. In addition to requiring a significant amount of analyst training and experience, visual inspection of spectra is inherently subjective, and any determination of comparability based on visual analysis of spectra is therefore arbitrary. Here, we discuss a general methodology for evaluating the suitability of numerical methods to calculate spectral similarity, and then we apply the methodology to compare four quantitative spectral similarity methods: the correlation coefficient, area of spectral overlap, derivative correlation algorithm, and spectral difference methods. While the most effective spectral similarity method may depend on the particular application, all four approaches are superior to visual evaluation, and each is suitable for assessing the degree of similarity between spectra.

Precision of protein aggregation measurements by sedimentation velocity analytical ultracentrifugation in biopharmaceutical applications.

Sedimentation velocity analytical ultracentrifugation (SV-AUC) is routinely applied in biopharmaceutical development to measure levels of protein aggregation in protein products. SV-AUC is free from many limitations intrinsic to size exclusion chromatography (SEC) such as mobile phase and column interaction effects on protein self-association. Despite these clear advantages, SV-AUC exhibits lower precision measurements than corresponding measurements by SEC. The precision of SV-AUC is influenced by numerous factors, including sample characteristics, cell alignment, centerpiece quality, and data analysis approaches. In this study, we evaluate the precision of SV-AUC in its current practice utilizing a multilaboratory, multiproduct intermediate precision study. We then explore experimental approaches to improve SV-AUC measurement precision, with emphasis on utilization of high quality centerpieces.