Solving the Membrane Protein Expression Problem
The expression and purification of integral membrane proteins remains a major bottleneck in the characterization of this important class. Expression levels are currently unpredictable, which renders the pursuit of these targets challenging and highly inefficient. Evidence demonstrates that small changes in the nucleotide or amino-acid sequence can dramatically affect membrane protein biogenesis; yet these observations have not resulted in generalizable approaches to improve expression. In this talk, I will present two different but related approaches we have used to address this issue. For the first, using as a model system the protein TatC, we monitor the effect of sequence changes on experimentally observed expression levels demonstrating a strong correlation with the simulated integration efficiency obtained from coarse-grained modeling, which is directly confirmed using an in vivo assay. For the second, we develop a computational model that predicts membrane protein expression in E. coli directly from sequence. The model, trained on experimental data, combines a set of sequence-derived variables resulting in a score that predicts the likelihood of expression. The overall outcome are tools that will enable further investigation of these difficult to study proteins.
© 2017 Elsevier B.V. Available online 3 February 2017. Meeting Abstract: 1616-Symp.