Plasma is the most studied human tissue due to its availability, potential for bio-marker discovery and central role in many illnesses including metabolic disease. Large scale human plasma studies have many unique challenges including large sample cohorts (>1000 samples) that may lead to issues with variability and batch effects. Secreted proteins undergo in vivo digestion events (maturation/processing/degradation) that can lead to proteins/peptides of interest not being detected. In our lab, we have begun to address these problems by creating a new FASTA database generation tool. This tool incorporates biological knowledge of in vivo digestion events and allows for specific detection of the “active” regions of secreted proteins. In addition, we have taken advantage of new mass spectrometry methods including zenoSWATH and scanningSWATH to reduce analytical time (<8min per run) without sacrificing sensitivity (>200 proteins quantified per sample) . Using our annotated database, we were able to significantly decrease search times compared to a semi-tryptic full database search, without causing a significant decrease in protein identifications of human plasma. This database could detect proteins that had been digested in vivo and was able to accurately quantify individual chains in a polyprotein such as insulin and IGF-1. Analysis using zenoSWATH allowed for the same detection sensitivity as scanningSWATH in 8 min but required 5 times less sample (2ug per injection). Together, these advancements in human plasma proteomic analysis have allowed for much faster analyses without reducing proteins identifications or sample reproducibility while also increasing our capability to detect secreted proteins and accurately quantify polyproteins.