N𝛼TA like many PTM’s are studied using chemical enrichment techniques followed by bottom up proteomics (BUP) acquisition, and search. Enrichment techniques by definition discard the proteotypic peptides which are crucial for accurate and precise abundance determination. As a thought experiment; a 2-fold change in a PTM could arise from a 2-fold change in protein abundance or a 2-fold change in PTM occupancy with no change in protein expression. In response to this, we developed OnePotN𝛼TA is an efficient label-based technique to quantify N-terminal acetylation which involves adding deuterated acetyl groups to all free N-termini prior to proteolytic digestion directly in a silica filter trap. After bottom-up proteomic analysis and database search / quantification, the ratio between the abundance of light and heavy acetyl groups can be used to measure the fraction of acetylation in the original termini without enrichment; relying on the high-density acquisition rates of current-gen LC-MS proteomics instruments. This preserves protein quantification data within the same injection/analysis. However, In normal BUP workflows we run into two main issues: (1) Many injections contain missing data, decreasing our statistical power in the subsequent analysis and resulting in false 0 and 100% acetylation values; and (2) Gratuitous Match-Between-Run (MBR) algorithms, which incorrectly assign identify to peaks even in cases where they are genetically deleted from the proteome. Incorporating Skyline into the data analysis workflow has substantially improved our results in several ways. Using the isotopic modification menu, we can group light/heavy peptides and ensure that the retention times for these peptide pairs are constrained to the same value. Skyline also allows for the integration of light/heavy peaks even when peptides were not actually identified in the database search, which reduces the number of missing values and improves the precision of our results. Comparing a workflow using commercial database-MBR search results vs MSFragger and Skyline combined, we see an improvement in the median %RSD of calculated percent acetylation from 34% to 13%. We can also view the XICs and visually compare light/heavy N-terminal peptides to manually eliminate false positives and incorrect identifications. Using OnePotN𝛼TA and Skyline we have characterized a newly identified N-terminal acetyltransferase (NAT) in pathogenic mycobacteria and identified approximately 40 new substrates.