Impact of Imperfect Data on the Performance of Algorithms to Compare Near-Ultraviolet Circular Dichroism Spectra

Impact of imperfect data on protein secondary structure estimates from Far-UV circular dichroism spectra

Circular dichroism [CD] is widely used to rapidly assess protein structure. Deconvolution of the far-UV CD spectrum is widely used to quantify the secondary structural elements [SSEs]. Multiple algorithms are available for this. Imperfections in the experimental CD spectra arising from spectral noise, instrument miscalibration, spectral offsets and non-linearity will impact on the accuracy and precision of derived secondary structure estimates. Analytical validation for use in regulated environments, such as biopharmaceuticals, requires that the impact of imperfect data on these estimates be understood. Limited information on the impact of poor data were available. A series of noise-free simulated spectral datasets with modified intensity, wavelength, noise and intensity linearity and offsets were created from entries in the Protein Circular Dichroism Data Bank. These datasets were analysed using the BeStSel, on-line resource to estimate secondary structure. Data imperfections caused significant change in SSEs, but the spectral range is also important. This study emphasises the importance of analytical method validation and justifiable estimates of uncertainty when reporting results. The datasets created are made available as a resource to validate other secondary structure estimation programs.

Spectral Similarity: Full-Wavelength Range Calibration of Circular Dichroism Spectroscopy

Circular dichroism (CD) measurements help characterize optically active molecules and higher-order biomolecular structures. The harmonization of interlaboratory CD measurements requires minimizing measurement uncertainties and determinate errors. Most CD measurements utilize a single-wavelength intensity measurement at a spectral peak to calibrate the intensities of the ultraviolet wavelength range. However, such a single-wavelength calibration is inherently less precise than using the CD instrument's spectrum over the entire measured spectral range. A more thorough and informative calibration can be achieved by remapping the spectrum into what we call a spectral similarity plot. This process allows a straightforward, quantitative evaluation of the shape congruence between two spectra over the full spectral range. While preliminary analyses suggest spectral similarity plots can be utilized with a variety of different spectrometry methods, here we illustrate the process applied to circular dichroism. Spectral similarity plots are highly sensitive to deviations due to differences in concentration, pathlength, source and detector properties, circular polarization balance, as well as wavelength nonlinearities and shifts. Deviations in these properties can be quantitated by a linear least squares fit of the remapped data. The remapping enables protocols to correct spectra toward congruence between two spectra. The spectra similarity comparison provides an objective, unambiguous test of the CD instrument quality when, for example, compared to a carefully calibrated system as shown in the examples described in the text.

Performance Comparison of Spectral Distance Calculation Methods

Circular dichroism (CD) spectroscopy is a widely used technique for assessing the higher-order structure (HOS) of biopharmaceuticals, including antibody drugs. Since the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use established quality control guidelines, objective evaluation of spectral similarity has been required in order to assess structural comparability. Several spectral distance quantification methods and weighting functions to increase sensitivity have been proposed, but not many reports have compared their performance for CD spectra. We constructed comparison sets that combine actual spectra and simulated noise and performed a comprehensive performance evaluation of each spectral distance calculation method and weighting function under conditions that consider spectral noise and fluctuations from pipetting errors. The results showed that using the Euclidean distance or Manhattan distance with Savitzky-Golay noise reduction is effective for spectral distance assessment. For the weighting function, it is preferable to combine the spectral intensity weighting function and the noise weighting function. In addition, the introduction of the external stimulus weighting function should be considered to improve the sensitivity. It is crucial to select the weighting function based on the balance between spectral changes and noise distributions for robust, sensitive antibody HOS similarity assessment.

Circular Dichroism Spectral Similarity Plots to Extend Validation and Correction to All Measured Wavelengths

Interlaboratory comparisons of circular dichroism (CD) spectra are useful for developing confidence in the measurements associated with optically active molecules. These measurements also help define the higher-order (secondary and tertiary) structure of biopolymers. Unfortunately, the extent of the validity of these measurements has been unclear. In this work, a method is described to extend CD validation over the entire observed wavelength range using what will be called spectral similarity plots. The method involves plotting, wavelength by wavelength, all measured spectral intensities of a sample at one concentration against the intensity values of the same material at a different concentration or pathlength. These spectral similarity plots validate the instrument in terms of spectral shape and whether the shape is shifted in intensity and/or in wavelength. This comparison tests the linearity of instrument's signal, the balance of its left and right polarizations, its wavelengths, and its spectral intensity scales. When the process is applied to materials with accepted and archived intensity values, the method can be linked to older single-wavelength and double-wavelength calibration techniques. Further, spectral similarity testing of CD spectra from samples with different concentrations run in different labs suggests that improved interlaboratory validation of CD data is possible. Since a database of archival CD measurements is available online, spectral similarity comparisons could possibly provide the ability to compare linearity, polarization balance, wavelength, and spectral intensity between all current CD instruments. If the preliminary results published here prove robust and transferable, then comparisons of full-wavelength range spectra to archived data using spectral similarity plots should become part of the standard process to validate and calibrate the performance of CD instruments.

The PCDDB (Protein Circular Dichroism Data Bank): A Bioinformatics Resource for Protein Characterisations and Methods Development

The Protein Circular Dichroism Data Bank (PCDDB) [https://pcddb.cryst.bbk.ac.uk] is an established resource for the biological, biophysical, chemical, bioinformatics, and molecular biology communities. It is a freely-accessible repository of validated protein circular dichroism (CD) spectra and associated sample and other metadata, with entries having links to other bioinformatics resources including, amongst others, structure (PDB) and sequence (UniProt) databases, as well as to published papers which produced the data and cite the database entries. It includes primary (unprocessed) and final (processed) spectral data, which are available in both text and pictorial formats, as well as detailed sample and validation information produced for each of the entries. Recently the metadata content associated with each of the entries, as well as the number and structural breadth of the protein components included, have been expanded. The PCDDB includes data on both wild-type and mutant proteins, and because CD studies primarily examine proteins in solution, it also contains examples of the effects of different environments on their structures, plus thermal unfolding/folding series. Methods for both sequence and spectral comparisons are included. The data included in the PCDDB complement results from crystal, cryo-electron microscopy, NMR spectroscopy, bioinformatics characterisations and classifications, and other structural information available for the proteins via links to other databases. The entries in the PCDDB have been used for the development of new analytical methodologies, for interpreting spectral and other biophysical data, and for providing insight into structures and functions of individual soluble and membrane proteins and protein complexes.