Surface functionalization with biological molecules, such as peptides or proteins, is a very promising method for developing new biomaterials with many potential applications. However, due to their chemical complexity, the characterization of biological materials is often a very challenging task. In this context, time-of-flight secondary ion mass spectrometry is a very helpful characterization tool due to its ability to provide very detailed spatially resolved chemical information of the topmost layer. The peculiar emission/ion formation mechanisms involved in ToF-SIMS analysis often do not allow the detection of the molecular ion of proteins and peptides, providing a rich fragmentation pattern, which is difficult to be related to the surface composition using a univariate approach, due to the relevant number of peaks in the SIMS spectra of peptides and proteins and the slight differences in intensities between different samples. Therefore, we used multivariate analysis to extract the information contained in the ToF-SIMS spectra of four peptides with high amino acid sequence similarity along the peptide chain. The reference peptide (TAT1) is a 12-unit sequence of six amino acids (GRKKRRQRRRPS). The other three peptides have been obtained by inserting a bAla-H dipeptide (carnosine) in three different positions inside the TAT1 chain, namely, GRKKRRQRRRPS-bAla-H (TAT1-Car), bAla-HGRKKRRQRRRPS (Car-TAT1), and GRKKRRQ-bAla-H-RRRPS (T-Car-T). We show that these peptides can be distinguished by ToF-SIMS combined with multivariate data analysis.

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