A statistical model for improved membrane protein expression using sequence-derived features.

ABSTRACT: The heterologous expression of integral membrane proteins (IMPs) remains a major bottleneck in the characterization of this important protein class. IMP expression levels are currently unpredictable, which renders the pursuit of IMPs for structural and biophysical characterization challenging and inefficient. Experimental evidence demonstrates that changes within the nucleotide or amino acid sequence for a given IMP can dramatically affect expression levels, yet these observations have not resulted in generalizable approaches to improve expression levels. Here, we develop a data-driven statistical predictor named IMProve that, using only sequence information, increases the likelihood of selecting an IMP that expresses in Escherichia coli The IMProve model, trained on experimental da

SUBMITTER: Saladi SM 

PROVIDER: S-EPMC5880134 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature