Learning to predict expression efficacy of vectors in recombinant protein production

Background: Recombinant protein production is a useful biotechnology to produce a large quantity of highly soluble proteins. Currently, the most widely used production system is to fuse a target protein into different vectors in Escherichia coli (E. coli). However, the production efficacy of different vectors varies for different target proteins. Trial-and-error is still the common practice to find out the efficacy of a vector for a given target protein. Previous studies are limited in that they assumed that proteins would be over-expressed and focused only on the solubility of expressed proteins. In fact, many pairings of vectors and proteins result in no expression. Results: In this study, we applied machine learning to train prediction models to predict whether a pairing of vector-protein will express or not express in E. coli. For expressed cases, the models further predict whether the expressed proteins would be soluble. We collected a set of real cases from the clients of our re...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Wen-Ching Chan, Po-Huang Liang, Yan-Ping Shih, Ueng-Cheng Yang, Wen-chang Lin, Chun-Nan Hsu
• Keywords: BMCBI 2010, Protein, Recombinant Protein Production, Target Protein