The development of PMF workflows was a necessary step for the increasing demands of biomedical research, which was soon to become modern proteomics. Because of the nearly unrivaled simplicity of the actual workflow and the value of the acquired data, the indispensability of PMF has evolved to become a mere fact in many laboratories, ranging from pharmacological studies to biotechnology applications. The workflow of PMF routines is devided into two parts (as shown in Figure ). First, the target protein is exposed to an enzyme, which performs endoprotease cleavage. The most popular choice is trypsine, which is a serine endopeptidase (PDB-ID 2RA3 ) that cleaves the target protein after either K or R, except when the next residue is P. In the next step, the resulting peptides after cleavage are analysed by means of mass spectrometry, where the ionisation methods of choice are either ESI or MALDI. The peptide masses are then compared to relevant databases, which enables identification of the precursor protein. Noticably, the mass spectrum of the cleaved protein is of such specificity, that it is often possible to identify the protein without ambiguity. Consequential, the comprehensiveness and quality of the comparative database is of paramount importance, since both accuracy and speed of detection are highly correlated with these characteristics. The development of dynamic and capable databases is a complex task that involves both a robust understanding of proteomics research and mass spectrometry as well as expertise in data management and data science. In the next chapter, we will take a closer look at the inner workings of such databases using the MASCOT database as an example.