Role of YidC in folding of polytopic membrane proteins

YidC of Echerichia coli, a member of the conserved Alb3/Oxa1/YidC family, is postulated to be important for biogenesis of membrane proteins. Here, we use as a model the lactose permease (LacY), a membrane transport protein with a known three-dimensional structure, to determine whether YidC plays a role in polytopic membrane protein insertion and/or folding. Experiments in vivo and with an in vitro transcription/translation/insertion system demonstrate that YidC is not necessary for insertion per se, but plays an important role in folding of LacY. By using the in vitro system and two monoclonal antibodies directed against conformational epitopes, LacY is shown to bind the antibodies poorly in YidC-depleted membranes. Moreover, LacY also folds improperly in proteoliposomes prepared without YidC. However, when the proteoliposomes are supplemented with purified YidC, LacY folds correctly. The results indicate that YidC plays a primary role in folding of LacY into its final tertiary conformation via an interaction that likely occurs transiently during insertion into the lipid phase of the membrane

Expression Profiling of PBMC-based Diagnostic Gene Markers Isolated from Vasculitis Patients

Vasculitis (angiitis) is a systemic autoimmune disease that often causes fatal symptoms. We aimed to isolate cDNA markers that would be useful for diagnosing not only vasculitis but also other autoimmune diseases. For this purpose, we used stepwise subtractive hybridization and cDNA microarray analyses to comprehensively isolate the genes whose expressions are augmented in peripheral blood mononuclear cells (PBMCs) pooled from vasculitis patients. Subsequently, we used quantitative real-time polymerase chain reaction (qRT–PCR) to examine the mRNA levels of each candidate gene in individual patients. These analyses indicated that seven genes exhibit remarkably augmented expression in many vasculitis patients. Of these genes, we analyzed G0/G1 switch gene 2 (G0S2) further because G0S2 expression is also enhanced in the PBMCs of patients with systemic lupus erythematodes (SLE). We generated G0S2 transgenic mice that ubiquitously overexpress human G0S2. Although we did not observe any obvious vasculitis-related histopathologic findings in these mice, these mice are unhealthy as they produce only few offspring and showed elevated serum levels of two autoimmunity-related antibodies, anti-nuclear antibody, and anti-double strand DNA antibody. Thus, our large-scale gene profiling study may help finding sensitive and specific DNA markers for diagnosing autoimmune diseases including vasculitis and SLE

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

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery