Cu/Ag EAM Potential Optimized for Heteroepitaxial Diffusion from ab initio Data

A binary embedded-atom method (EAM) potential is optimized for Cu on Ag(111) by fitting to ab initio data. The fitting database consists of DFT calculations of Cu monomers and dimers on Ag(111), specifically their relative energies, adatom heights, and dimer separations. We start from the Mishin Cu-Ag EAM potential and first modify the Cu-Ag pair potential to match the FCC/HCP site energy difference then include Cu-Cu pair potential optimization for the entire database. The optimized EAM potential reproduce DFT monomer and dimer relative energies and geometries correctly. In trimer calculations, the potential produces the DFT relative energy between FCC and HCP trimers, though a different ground state is predicted. We use the optimized potential to calculate diffusion barriers for Cu monomers, dimers, and trimers. The predicted monomer barrier is the same as DFT, while experimental barriers for monomers and dimers are both lower than predicted here. We attribute the difference with experiment to the overestimation of surface adsorption energies by DFT and a simple correction is presented. Our results show that the optimized Cu-Ag EAM can be applied in the study of larger Cu islands on Ag(111).Comment: 15 pages, 7 figure

Drosophila Uri, a PP1α binding protein, is essential for viability, maintenance of DNA integrity and normal transcriptional activity

<p>Abstract</p> <p>Background</p> <p>Protein phosphatase 1 (PP1) is involved in diverse cellular processes, and is targeted to substrates via interaction with many different protein binding partners. PP1 catalytic subunits (PP1c) fall into PP1α and PP1β subfamilies based on sequence analysis, however very few PP1c binding proteins have been demonstrated to discriminate between PP1α and PP1β.</p> <p>Results</p> <p>URI (unconventional prefoldin RPB5 interactor) is a conserved molecular chaperone implicated in a variety of cellular processes, including the transcriptional response to nutrient signalling and maintenance of DNA integrity. We show that <it>Drosophila </it>Uri binds PP1α with much higher affinity than PP1β, and that this ability to discriminate between PP1c forms is conserved to humans. Most Uri is cytoplasmic, however we found some protein associated with active RNAPII on chromatin. We generated a <it>uri </it>loss of function allele, and show that <it>uri </it>is essential for viability in <it>Drosophila</it>. <it>uri </it>mutants have transcriptional defects, reduced cell viability and differentiation in the germline, and accumulate DNA damage in their nuclei.</p> <p>Conclusion</p> <p>Uri is the first PP1α specific binding protein to be described in <it>Drosophila</it>. Uri protein plays a role in transcriptional regulation. Activity of <it>uri </it>is required to maintain DNA integrity and cell survival in normal development.</p

Targeted knock-down of miR21 primary transcripts using snoMEN vectors induces apoptosis in human cancer cell lines

We have previously reported an antisense technology, 'snoMEN vectors', for targeted knock-down of protein coding mRNAs using human snoRNAs manipulated to contain short regions of sequence complementarity with the mRNA target. Here we characterise the use of snoMEN vectors to target the knock-down of micro RNA primary transcripts. We document the specific knock-down of miR21 in HeLa cells using plasmid vectors expressing miR21-targeted snoMEN RNAs and show this induces apoptosis. Knock-down is dependent on the presence of complementary sequences in the snoMEN vector and the induction of apoptosis can be suppressed by over-expression of miR21. Furthermore, we have also developed lentiviral vectors for delivery of snoMEN RNAs and show this increases the efficiency of vector transduction in many human cell lines that are difficult to transfect with plasmid vectors. Transduction of lentiviral vectors expressing snoMEN targeted to pri-miR21 induces apoptosis in human lung adenocarcinoma cells, which express high levels of miR21, but not in human primary cells. We show that snoMEN-mediated suppression of miRNA expression is prevented by siRNA knock-down of Ago2, but not by knock-down of Ago1 or Upf1. snoMEN RNAs colocalise with Ago2 in cell nuclei and nucleoli and can be co-immunoprecipitated from nuclear extracts by antibodies specific for Ago2

Elucidation of the ebola virus VP24 cellular interactome and disruption of virus biology through targeted inhibition of host-cell protein function

Viral pathogenesis in the infected cell is a balance between antiviral responses and subversion of host-cell processes. Many viral proteins specifically interact with host-cell proteins to promote virus biology. Understanding these interactions can lead to knowledge gains about infection and provide potential targets for antiviral therapy. One such virus is Ebola, which has profound consequences for human health and causes viral hemorrhagic fever where case fatality rates can approach 90%. The Ebola virus VP24 protein plays a critical role in the evasion of the host immune response and is likely to interact with multiple cellular proteins. To map these interactions and better understand the potential functions of VP24, label-free quantitative proteomics was used to identify cellular proteins that had a high probability of forming the VP24 cellular interactome. Several known interactions were confirmed, thus placing confidence in the technique, but new interactions were also discovered including one with ATP1A1, which is involved in osmoregulation and cell signaling. Disrupting the activity of ATP1A1 in Ebola-virus-infected cells with a small molecule inhibitor resulted in a decrease in progeny virus, thus illustrating how quantitative proteomics can be used to identify potential therapeutic targets

Molecular Targets for 17α-Ethynyl-5-Androstene-3β,7β,17β-Triol, an Anti-Inflammatory Agent Derived from the Human Metabolome

HE3286, 17α-ethynyl-5-androstene-3β, 7β, 17β-triol, is a novel synthetic compound related to the endogenous sterol 5-androstene-3β, 7β, 17β-triol (β-AET), a metabolite of the abundant adrenal steroid dehydroepiandrosterone (DHEA). HE3286 has shown efficacy in clinical studies in impaired glucose tolerance and type 2 diabetes, and in vivo models of types 1 and 2 diabetes, autoimmunity, and inflammation. Proteomic analysis of solid-phase HE3286-bound bead affinity experiments, using extracts from RAW 264.7 mouse macrophage cells, identified 26 binding partners. Network analysis revealed associations of these HE3286 target proteins with nodes in the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways for type 2 diabetes, insulin, adipokine, and adipocyte signaling. Binding partners included low density lipoprotein receptor-related protein (Lrp1), an endocytic receptor; mitogen activated protein kinases 1 and 3 (Mapk1, Mapk3), protein kinases involved in inflammation signaling pathways; ribosomal protein S6 kinase alpha-3 (Rsp6ka3), an intracellular regulatory protein; sirtuin-2 (Sirt2); and 17β-hydroxysteroid dehydrogenase 1 (Hsd17β4), a sterol metabolizing enzyme

Characterization of human Sec16B: indications of specialized, non-redundant functions

The endoplasmic reticulum (ER) represents the entry point into the secretory pathway and from here newly synthesized proteins and lipids are delivered to the Golgi. The selective cargo export from the ER is mediated by COPII-assembly at specific sites of the ER, the so-called transitional ER (tER). The peripheral membrane protein Sec16, first identified in yeast, localizes to transitional ER and plays a key role in organization of these sites. Sec16 defines the tER and is thought to act as a scaffold for the COPII coat assembly. In humans two isoforms of Sec16 are present, the larger Sec16A and the smaller Sec16B. Nevertheless, the functional differences between the two isoforms are ill-defined. Here we describe characterization of the localization and dynamics of Sec16B relative to Sec16A, provide evidence that Sec16B is likely a minor or perhaps specialized form of Sec16, and that it is not functionally redundant with Sec16A

Caspase cleavage of the Golgi stacking factor GRASP65 is required for Fas/CD95-mediated apoptosis

GRASP65 (Golgi reassembly and stacking protein of 65 KDa) is a cis-Golgi protein with roles in Golgi structure, membrane trafficking and cell signalling. It is cleaved by caspase-3 early in apoptosis, promoting Golgi fragmentation. We now show that cleavage is needed for Fas-mediated apoptosis: expression of caspase-resistant GRASP65 protects cells, whereas expression of membrane proximal caspase-cleaved GRASP65 fragments dramatically sensitises cells. GRASP65 coordinates passage through the Golgi apparatus of proteins containing C-terminal hydrophobic motifs, via its tandem PDZ type ‘GRASP' domains. Fas/CD95 contains a C-terminal leucine–valine pairing so its trafficking might be coordinated by GRASP65. Mutagenesis of the Fas/CD95 LV motif reduces the number of cells with Golgi-associated Fas/CD95, and generates a receptor that is more effective at inducing apoptosis; however, siRNA-mediated silencing or expression of mutant GRASP65 constructs do not alter the steady state distribution of Fas/CD95. We also find no evidence for a GRASP65–Fas/CD95 interaction at the molecular level. Instead, we find that the C-terminal fragments of GRASP65 produced following caspase cleavage are targeted to mitochondria, and ectopic expression of these sensitises HeLa cells to Fas ligand. Our data suggest that GRASP65 cleavage promotes Fas/CD95-mediated apoptosis via release of C-terminal fragments that act at the mitochondria, and we identify Bcl-XL as a candidate apoptotic binding partner for GRASP65

Developing an interatomic potential for martensitic phase transformations in zirconium by machine learning

Interatomic potentials: predicting phase transformations in zirconium Machine learning leads to a new interatomic potential for zirconium that can predict phase transformations. A team led by Hongxian Zong at Xi’an Jiaotong University, China, and Turab Lookman at Los Alamos National Laboratory, U.S.A, used a Gaussian-type machine learning approach to produce an interatomic potential that predicted phase transformations in zirconium. They expressed each atomic energy contribution via changes in the local atomic environment, such as bond length, shape, and volume. The resulting machine-learning potential successfully described pure zirconium’s physical properties. When used in molecular dynamics simulations, it predicted a zirconium phase diagram as a function of both temperature and pressure that agreed well with previous experiments and simulations. Developing learnt interatomic potentials in phase-transforming systems could help us better simulate complex systems