Use of chemometrics in the selection of a Saccharomyces cerevisiae expression system for recombinant cyprosin B production

Two multivariate statistical methods, factor analysis (FA) and hierarchical cluster analysis (HCA), were applied to experimental data set to evaluate their usefulness in selecting the adequate expression system and optimal growth parameters for recombinant cyprosin B production. Using FA, the large data set was reduced to two factors representing 73.4% of variability. Factor 1, with 53.5% of variability, corresponds to recombinant cyprosin B expression and efficient secretion, while factor 2, accounting for 19.9% of variability, represents cell growth and physiological characteristics. FA and HCA allowed the establishment of correlations among different variables and the clusters obtained providing clear identification of the experimental parameters related to cyprosin B production, which results on more accurate scientific output and time saving when selection of an adequate expression system is concerned.