Deciphering Membrane Protein Energetics using Deep Sequencing; Towards Robust Design and Structure Prediction of Membrane Proteins

Abstract

The energetics of membrane-protein interactions dictates protein topology and structure, which in turn determines the function and expression levels of all plasma membrane proteins. However, systematic and reliable quantification of membrane-protein energetics has been challenging. I recently developed a deep mutational scanning method, dsTβL(Elazar et al., 2016a) (deep-sequencing TOXCAT-β-lactamase), to monitor the effects of hundreds of point mutations on insertion and association within the bacterial inner membrane. The assay quantifies insertion-energy profiles for each amino acid residue across the membrane, revealing that the hydrophobicity of biological membranes is significantly higher than appreciated. In addition, a key feature of the dsTβL profiles is that they show asymmetries for Arg, Lys, and His, in agreement with the ‘positive-inside’ rule. The three profiles, however, are not identical: whereas Lys and Arg are favored by 2 kcal/mol near the cytoplasm compared to near the periplasm, the titratable amino acid His shows a more modest asymmetry of 1 kcal/mol; moreover, only Arg stabilizes the segment near the cytosol, whereas Lys and His are nearly neutral at the cytosol- membrane interface. Based on these findings, together with Jonathan Weinstein, we developed a novel graphical topology-prediction algorithm named TopGraph(Elazar et al., 2016b), based on a sequence search for minimum energy of insertion using the dsTβL experimental insertion scale rather than statistics derived from known structures. Unlike many existing predictors, TopGraph exhibits high accuracy even on large transporters with no structural homologues. Furthermore, results suggest that the ‘positive-inside’ rule, which is known to orient segments with respect to the membrane, can also drive insertion of marginally hydrophobic segments in large membrane domains. These insights may aid structure prediction, engineering, and design of membrane proteins.