Molecular code for transmembrane-helix recognition by the Sec61 translocon

By T. Hessa, Nadja M. Meindl-Beinker, Andreas Bernsel, Hyun Kim, Yoko Sato, M. Lerch-Bader, I. Nilsson, S. White, G. von Heijne. Nature, 2007

710 citations recorded by the source corpus.

Transmembrane α-helices in integral membrane proteins are recognized co-translationally and inserted into the membrane of the endoplasmic reticulum by the Sec61 translocon. A full quantitative description of this phenomenon, linking amino acid sequence to membrane insertion efficiency, is still lacking. Here, using in vitro translation of a model protein in the presence of dog pancreas rough microsomes to analyse a large number of systematically designed hydrophobic segments, we present a quantitative analysis of the position-dependent contribution of all 20 amino acids to membrane insertion efficiency, as well as of the effects of transmembrane segment length and flanking amino acids. The emerging picture of translocon-mediated transmembrane helix assembly is simple, with the critical sequence characteristics mirroring the physical properties of the lipid bilayer.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/f71a64780cf5a1bb88aa1f6604ec35061b40d55e

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Citations in the corpus

The TOPCONS web server for consensus prediction of membrane protein topology and signal peptides (2015, methodology): “In addition, the biological hydrophobicity scale (38) is used to predict the free energy of membrane insertion for a window of 21 amino acids centered on each position in the sequence.”
Computational modeling of membrane proteins (2014, methodology): “51 This was accomplished by inserting helix segments into a helical MP with glycosylation sites on either side of the segment.”
An Integrated Framework Advancing Membrane Protein Modeling and Design (2015, result): “This result matches previous experimental values [50,52,53] and occurs because the longer, positively charged side chain of arginine can snorkel further towards the interface region (Fig C in S1 File) and interact with charged lipid Fig 3.”
Peptides: A Package for Data Mining of Antimicrobial Peptides (2015, background): “, 2013) and free energy of binding (Hessa et al., 2007).”
Prediction of membrane-protein topology from first principles (2008, methodology): “As in TopPred G, there are only two parameters in SCAMPI that are optimized on the training data: the emission probability of Arg and Lys in the i module (pKR) and a constant ( Gshift) that is subtracted from Gapp in the M module before this energy is converted to the corresponding insertion probabi”
TOPCONS: consensus prediction of membrane protein topology (2009, methodology): “the distance to the membrane center) of each amino acid, and a scale describing the free-energy contributions of translocon-mediated membrane insertion (17) is used to predict a G value for a window of 21 amino acids centered around each position in the sequence.”