MALDI-TOF (matrix-assisted laser desorption/ionization time-of-flight) mass spectrometry has found many notable uses in current life sciences investigations. Building on the pioneering work of Kris Jarman and Karen Wahl at the Pacific Northwest National Laboratories, AIBioTech has implemented and refined the use of MALDI-TOF/MS for the purpose of ‘molecular fingerprint analysis’ of proteinaceous components in biologics, vaccine preparations, and for detection and confirmation of specific proteinaceous components in laboratory samples.
At the heart of the experiment is creation of so-called ‘molecular signatures’ of the individual reference standard proteins (or peptides). A molecular signature consists of a set of statistically unique mass spectra which are absolutely characteristic of the protein under investigation. The mass spec reference fingerprints are then assembled into a database (‘master fingerprint libraries’) which is actually a linkage dendrogram showing the relationship between the reference standard fingerprints. Then, mass spectra are obtained from the test sample(s) which are used to query the master fingerprint library. The query returns a ‘degree of association’ (DA) of the test samples against the reference fingerprints. As in human fingerprint analysis, the higher the DA, the higher the certainty of identification of the test sample against the reference standard.
The molecular fingerprint of the reference standard consists of a set of degradation peptide masses, normalized for ion intensities and distribution across the mass spectrum. A library containing such MALDI-TOF/MS molecular fingerprints can be used to identify a target analyte in a complex mixture of non-relevant analytes. Furthermore, representative molecular fingerprints from a diverse number of analytes can be examined for possible correlations (linkage dendrogram). As applied to peptide mapping, a molecular fingerprint is created for each protein digest mixture and these fingerprints are used to identify digests of particular proteins individually and in mixtures. This library is used to detect, in automated fashion, the presence of these proteins in individual protein digest samples.
The fingerprinting method has the advantage of being automated and because it is statistically based, small differences among individual protein digest spectra (missing peaks, peaks of low ion intensity, etc.) tend to be ignored and are not used in generating the fingerprint. The technique used generates mass spectral fingerprints that consist of a set of peak locations and heights, the variability in each peak location and height, and the relative occurrence of each peak. This is done using replicate spectra as follows:
- For each spectrum, a table of peak locations and heights is generated
- Normalized peak heights are obtained by dividing each peak height by the maximum peak height
- Mass peak locations and normalized heights are estimated by taking an average for each peak across all replicates
- Any variability in peak location is estimated using the corresponding standard deviations
- The relative frequency of occurrence is estimated as the number of replicate spectra in which the peak appeared, divided by the total number of replicate spectra collected
- The fingerprints are visualized by plotting each fingerprint peak location and height, with a region enclosing their expected variation
With an unknown protein digest sample, a similar process is followed and the extracted fingerprint of the unknown sample is compared to the fingerprints in the library to assess the relative correlation value of a match.
The methods implemented by AIBioTech have been successfully applied to the identification of particular collagens in a vaccine preparation, to QC analysis of various plasma proteins, and for detection, identification and confirmation of specific protein and peptide anlaytes in environmental samples. These analyses are done using verified and validated SOPs, and the same protocols can be applied to analysis of your test samples.
For more information please contact AIBioTech.