Peptides are promising drug development frameworks thanks to their high target selectivity, tolerability and relatively low production cost. However, despite the fact that several thousand potentially therapeutic peptides reported, only sixty have arrived at the market. This concerning low proportion is partially explained by undesired properties such as peptide-induced hemolytic activity. Hence, we aim to get a better insight into the chemical space of hemolytic peptides using a novel approach based on network science and interactive data mining as an alternative to design more effective peptide drugs with low hemolytic activity. Metadata networks (METNs) were used to characterize and find general patterns associated to hemolytic peptides, whereas Half-Space Proximal Networks (HSPNs), created using five different two-way dissimilarity measures, represented the hemolytic peptide space. Then, using the best candidate HSPNs, we extracted various scaffolds that capture information of almost all the chemical space but avoiding peptide overrepresentation. Such scaffolds can have many applications, such as training accurate ML-based prediction models, constructing one-class multi-query similarity searching models and characterizing the diversity of hemolytic peptides using a manageable set of peptides. Finally, by means of an alignment-free approach, we reported 47 putative hemolytic motifs, which might provide hints about the mechanisms of hemolysis and can also be used as toxic signatures when developing novel peptide-based drugs.

The use of synthetic biology on yeasts has enhanced the production of commercially relevant chemicals, from biofuels to recombinant therapeutic proteins, to name just a few. Despite most of these advances had already been studied and described in Saccharomyces cerevisiae, during the last years the attention has turned to the use of alternative expression systems with a higher yield and quality such as non-conventional yeasts. Recently, there has been an increase in studies about non-conventional yeasts due to advantages based on their natural capacity to tolerate harsh conditions or the wide range of carbon sources they need during the generation of specific products. This chapter, therefore, aims to describe the current status of the most used non-conventional yeasts in metabolite production as well as the engineering behind them in order to optimize or regulate protein expression: Pichia pastoris, Kluyveromyces marxianus, Kluyveromyces lactis and Yarrowia lipolytica.