Presep: Predicting the Propensity of a Protein Being Secreted into the Supernatant when Expressed in Pichia pastoris

Abstract

Pichia pastoris is commonly used for the production of recombinant proteins due to its preferential secretion of recombinant proteins, resulting in lower production costs and increased yields of target proteins. However, not all recombinant proteins can be successfully secreted in P. pastoris. A computational method that predicts the likelihood of a protein being secreted into the supernatant would be of considerable value; however, to the best of our knowledge, no such tool has yet been developed. We present a machine-learning approach called Presep to assess the likelihood of a recombinant protein being secreted by P. pastoris based on its pseudo amino acid composition (PseAA). Using a 20-fold cross validation, Presep demonstrated a high degree of accuracy, with Matthews correlation coefficient (MCC) and overall accuracy (Q2) scores of 0.78 and 95%, respectively. Computational results were validated experimentally, with six β-galactosidase genes expressed in P. pastoris strain GS115 to verify Presep model predictions. A strong correlation (R2 = 0.967) was observed between Presep prediction secretion propensity and the experimental secretion percentage. Together, these results demonstrate the ability of the Presep model for predicting the secretion propensity of P. pastoris for a given protein. This model may serve as a valuable tool for determining the utility of P. pastoris as a host organism prior to initiating biological experiments. The Presep prediction tool can be freely downloaded at http://www.mobioinfor.cn/Presep.