All living cells are subdivided into different compartments that are separated by membranes, which are essentially impermeable to water-soluble molecules. Since proteins are predominantly synthesized in the cytoplasm, specific sorting mechanisms and signals known as signal peptides are required to target them to other cellular compartments or the extracellular milieu. The research presented in this PhD thesis was focused on principles of protein sorting and secretion in bacteria, which were investigated with novel approaches that combined experimental analyses and computational tools. In particular, the studies addressed the bacterial cell factory Bacillus subtilis and the pathogens Porphyromonas gingivalis and Staphylococcus aureus. The results show how computational approaches can greatly enhance the experimental studies. In particular, this concerned predictions of subcellular protein localization with tailored tools that were developed for the different bacteria. These tools can enhance industrial and domestic applications of proteins produced with bacteria, or foster the identification of novel drugs or drug targets. In addition, the relationship between secretion efficiency and different features of signal peptides was investigated using a designed signal peptide library and an innovative high-throughput assay. The outcomes were used to generate a machine learning model that predicts signal peptide efficiency in directing protein secretion, and explains the relevant physico-chemical features of signal peptides. Importantly, the model allows de novo design of signal peptides that can be exploited in high-performing protein secretion systems. Altogether, the studies highlight the advantages of combined computational-experimental approaches and how they are best exploited in future biotechnological, pharmaceutical and biomedical applications.