The field of proteomics almost uniformly relies on peptide cation analysis, leading to an underrepresentation of acidic portions of proteomes, including relevant acidic post-translational modifications. Despite the many benefits negative mode proteomics can offer, peptide anion analysis remains in its infancy, due mainly to challenges with high pH reversed phase separations and a lack of robust fragmentation methods suitable for peptide anion characterization. Here we report the first implementation of activated ion negative electron transfer dissociation (AI-NETD) on the chromatographic timescale, generating 7601 unique peptide identifications from Saccharomyces cerevisiae in single-shot nLC-MS/MS analyses of tryptic peptides -- a greater than five-fold increase over previous results with NETD alone. These improvements translate to identification of 1106 proteins, making this work the first negative mode study to identify more than 1000 proteins in any system. We then compare the performance of AI-NETD for analysis of peptide generated by five proteases (trypsin, LysC, GluC, chymotrypsin, and AspN) for negative mode analyses, identifying as many as 5356 peptides (1045 proteins) with LysC and 4213 peptides (857 proteins) with GluC in yeast -- characterizing 1359 proteins in total. Finally, we present the first deep sequencing approach for negative mode proteomics, leveraging offline low pH reversed phase fractionation prior to online high pH separations and peptide fragmentation with AI-NETD. With this platform we identify 3467 proteins in yeast with trypsin alone and characterized a total of 3730 proteins using multiple proteases, or nearly 83% of the expressed yeast proteome. This work represents the most extensive negative mode proteomics study to date, establishing AI-NETD as a robust tool for large-scale peptide anion characterization and making the negative mode approach a more viable platform for future proteomic studies.