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Post-translational modifications (PTMs) can be routinely located and characterized by tandem mass spectrometry, provided the researchers know the identity and mass of the modification they are looking for. In the spirit of discovery proteomics, it is desirable that both bottom-up and top-down datasets be screened for all possible chemical modifications, in order to avoid bias toward the known. Analysis of saliva proteins from a number of human subjects is starting to reveal significant diversity. This variability and its plasticity must be understood if we are to realize the potential of this accessible fluid as a reliable source of biomarkers. This session will feature discussion of unbiased manual vs. automated interpretation of top-down datasets from human saliva proteins on high-resolution mass spectrometers, revealing novel PTMs and protein sequence polymorphisms. The session will also introduce new software paradigms for unbiased mining of bottom-up datasets (Spectral Networks) from aging lens proteins and snake venoms. The approach takes advantage of spectral pairs—pairs of spectra obtained from overlapping (often non-tryptic) peptides or from unmodified and modified versions of the same peptide. Having a spectrum of a modified peptide paired with a spectrum of an unmodified peptide allows one to separate the prefix and suffix ladders, to greatly reduce the number of noise peaks, and to generate a small number of peptide reconstructions that are likely to contain the correct one. In addition to speed, our approach provides a new paradigm for identifying PTMs. We also will discuss the ubiquitous microheterogeneity introduced by spontaneous modification of asparagine and glutamine residues via deamidation and the analysis of their acidic isomers using electron-capture dissociation techniques.