Co- and post-translational translocation through the protein-conducting channel:analogous mechanisms at work?

Many proteins are translocated across, or integrated into, membranes. Both functions are fulfilled by the 'translocon/translocase', which contains a membrane-embedded proteinconducting channel (PCC) and associated soluble factors that drive translocation and insertion reactions using nucleotide triphosphates as fuel. This perspective focuses on reinterpreting existing experimental data in light of a recently proposed PCC model comprising a front-to-front dimer of SecY or Sec61 heterotrimeric complexes. In this new framework, we propose (i) a revised model for SRP-SR-mediated docking of the ribosome-nascent polypeptide to the PCC; (ii) that the dynamic interplay between protein substrate, soluble factors and PCC controls the opening and closing of a transmembrane channel across, and/or a lateral gate into, the membrane; and (iii) that co-and post-translational translocation, involving the ribosome and SecA, respectively, not only converge at the PCC but also use analogous mechanisms for coordinating protein translocation

SecA, a remarkable nanomachine

Biological cells harbor a variety of molecular machines that carry out mechanical work at the nanoscale. One of these nanomachines is the bacterial motor protein SecA which translocates secretory proteins through the protein-conducting membrane channel SecYEG. SecA converts chemically stored energy in the form of ATP into a mechanical force to drive polypeptide transport through SecYEG and across the cytoplasmic membrane. In order to accommodate a translocating polypeptide chain and to release transmembrane segments of membrane proteins into the lipid bilayer, SecYEG needs to open its central channel and the lateral gate. Recent crystal structures provide a detailed insight into the rearrangements required for channel opening. Here, we review our current understanding of the mode of operation of the SecA motor protein in concert with the dynamic SecYEG channel. We conclude with a new model for SecA-mediated protein translocation that unifies previous conflicting data

Polygenic risk modeling for prediction of epithelial ovarian cancer risk

Polygenic risk scores (PRS) for epithelial ovarian cancer (EOC) have the potential to improve risk stratification. Joint estimation of Single Nucleotide Polymorphism (SNP) effects in models could improve predictive performance over standard approaches of PRS construction. Here, we implemented computationally efficient, penalized, logistic regression models (lasso, elastic net, stepwise) to individual level genotype data and a Bayesian framework with continuous shrinkage, "select and shrink for summary statistics" (S4), to summary level data for epithelial non-mucinous ovarian cancer risk prediction. We developed the models in a dataset consisting of 23,564 non-mucinous EOC cases and 40,138 controls participating in the Ovarian Cancer Association Consortium (OCAC) and validated the best models in three populations of different ancestries: prospective data from 198,101 women of European ancestries; 7,669 women of East Asian ancestries; 1,072 women of African ancestries, and in 18,915 BRCA1 and 12,337 BRCA2 pathogenic variant carriers of European ancestries. In the external validation data, the model with the strongest association for non-mucinous EOC risk derived from the OCAC model development data was the S4 model (27,240 SNPs) with odds ratios (OR) of 1.38 (95% CI: 1.28-1.48, AUC: 0.588) per unit standard deviation, in women of European ancestries; 1.14 (95% CI: 1.08-1.19, AUC: 0.538) in women of East Asian ancestries; 1.38 (95% CI: 1.21-1.58, AUC: 0.593) in women of African ancestries; hazard ratios of 1.36 (95% CI: 1.29-1.43, AUC: 0.592) in BRCA1 pathogenic variant carriers and 1.49 (95% CI: 1.35-1.64, AUC: 0.624) in BRCA2 pathogenic variant carriers. Incorporation of the S4 PRS in risk prediction models for ovarian cancer may have clinical utility in ovarian cancer prevention programs

Non-standard errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants

Investigating the SecY plug movement at the SecYEG translocation channel

Protein translocation occurs across the energy-conserving bacterial membrane at the SecYEG channel. The crystal structure of the channel has revealed a possible mechanism for gating and opening. This study evaluates the plug hypothesis using cysteine crosslink experiments in combination with various allelic forms of the Sec complex. The results demonstrate that the SecY plug domain moves away from the center of the channel toward SecE during polypeptide translocation, and further show that the translocation-enhancing prlA3 mutation and SecG subunit change the properties of channel gating. Locking the plug in the open state preactivates the Sec complex, and a super-active translocase can be created when combined with the prlA4 mutation located in the pore of the channel. Dimerization of the Sec complex, which is essential for translocase activity, relocates the plug toward the open position. We propose that oligomerization may result in SecYEG cooperative interactions important to prime the translocon function

SecYEG assembles into a tetramer to form the active protein translocation channel

Translocase mediates preprotein translocation across the Escherichia coli inner membrane. It consists of the SecYEG integral membrane protein complex and the peripheral ATPase SecA. Here we show by functional assays, negative-stain electron microscopy and mass measurements with the scanning transmission microscope that SecA recruits SecYEG complexes to form the active translocation channel. The active assembly of SecYEG has a side length of 10.5 nm and exhibits an ∼5 nm central cavity. The mass and structure of this SecYEG as well as the subunit stoichiometry of SecA and SecY in a soluble translocase–precursor complex reveal that translocase consists of the SecA homodimer and four SecYEG complexes

Three-dimensional structure of the bacterial protein-translocation complex SecYEG

Transport and membrane integration of polypeptides is carried out by specific protein complexes in the membranes of all living cells. The Sec transport path provides an essential and ubiquitous route for protein translocation. In the bacterial cytoplasmic membrane, the channel is formed by oligomers of a heterotrimeric membrane protein complex consisting of subunits SecY, SecE and SecG. In the endoplasmic reticulum membrane, the channel is formed from the related Sec61 complex. Here we report the structure of the Escherichia coli SecYEG assembly at an in-plane resolution of 8 A. The three-dimensional map, calculated from two-dimensional SecYEG crystals, reveals a sandwich of two membranes interacting through the extensive cytoplasmic domains. Each membrane is composed of dimers of SecYEG. The monomeric complex contains 15 transmembrane helices. In the centre of the dimer we observe a 16 x 25 A cavity closed on the periplasmic side by two highly tilted transmembrane helices. This may represent the closed state of the protein-conducting channel