Examination of the relationship between essential genes in PPI network and hub proteins in reverse nearest neighbor topology

Abstract Background In many protein-protein interaction (PPI) networks, densely connected hub proteins are more likely to be essential proteins. This is referred to as the "centrality-lethality rule", which indicates that the topological placement of a protein in PPI network is connected with its biological essentiality. Though such connections are observed in many PPI networks, the underlying topological properties for these connections are not yet clearly understood. Some suggested putative connections are the involvement of essential proteins in the maintenance of overall network connections, or that they play a role in essential protein clusters. In this work, we have attempted to examine the placement of essential proteins and the network topology from a different perspective by determining the correlation of protein essentiality and reverse nearest neighbor topology (RNN). Results The RNN topology is a weighted directed graph derived from PPI network, and it is a natural representation of the topological dependences between proteins within the PPI network. Similar to the original PPI network, we have observed that essential proteins tend to be hub proteins in RNN topology. Additionally, essential genes are enriched in clusters containing many hub proteins in RNN topology (RNN protein clusters). Based on these two properties of essential genes in RNN topology, we have proposed a new measure; the RNN cluster centrality. Results from a variety of PPI networks demonstrate that RNN cluster centrality outperforms other centrality measures with regard to the proportion of selected proteins that are essential proteins. We also investigated the biological importance of RNN clusters. Conclusions This study reveals that RNN cluster centrality provides the best correlation of protein essentiality and placement of proteins in PPI network. Additionally, merged RNN clusters were found to be topologically important in that essential proteins are significantly enriched in RNN clusters, and biologically important because they play an important role in many Gene Ontology (GO) processes.http://deepblue.lib.umich.edu/bitstream/2027.42/78257/1/1471-2105-11-505.xmlhttp://deepblue.lib.umich.edu/bitstream/2027.42/78257/2/1471-2105-11-505-S1.DOChttp://deepblue.lib.umich.edu/bitstream/2027.42/78257/3/1471-2105-11-505.pdfPeer Reviewe

Orbital Magnetism of 2D Chaotic Lattices

We study the orbital magnetism of 2D lattices with chaotic motion of electrons withing a primitive cell. Using the temperature diagrammatic technique we evaluate the averaged value and rms fluctuation of magnetic response in the diffusive regime withing the model of non-interacting electrons. The fluctuations of magnetic susceptibility turn out to be large and at low temperature can be of the order of $\chi_{L} (k_{F}l)^{3/2}$, where $k_{F}$ is the Fermi wavevector, $l$ is the mean free path, and $\chi_{L}$ is the Landau susceptibility. In the certain region of magnetic fields the paramagnetic contribution to the averaged response is field independent and larger than the absolute value of Landau response.Comment: 6 pages, Latex file, figures available upon reques

Proteomics to go: Proteomatic enables the user-friendly creation of versatile MS/MS data evaluation workflows

We present Proteomatic, an operating system independent and user-friendly platform that enables the construction and execution of MS/MS data evaluation pipelines using free and commercial software. Required external programs such as for peptide identification are downloaded automatically in the case of free software. Due to a strict separation of functionality and presentation, and support for multiple scripting languages, new processing steps can be added easily

Extensive mass spectrometry-based analysis of the fission yeast proteome: the Schizosaccharomyces pombe PeptideAtlas

We report a high quality and system-wide proteome catalogue covering 71% (3,542 proteins) of the predicted genes of fission yeast, Schizosaccharomyces pombe, presenting the largest protein dataset to date for this important model organism. We obtained this high proteome and peptide (11.4 peptides/protein) coverage by a combination of extensive sample fractionation, high resolution Orbitrap mass spectrometry, and combined database searching using the iProphet software as part of the Trans-Proteomics Pipeline. All raw and processed data are made accessible in the S. pombe PeptideAtlas. The identified proteins showed no biases in functional properties and allowed global estimation of protein abundances. The high coverage of the PeptideAtlas allowed correlation with transcriptomic data in a system-wide manner indicating that post-transcriptional processes control the levels of at least half of all identified proteins. Interestingly, the correlation was not equally tight for all functional categories ranging from r(s) >0.80 for proteins involved in translation to r(s) <0.45 for signal transduction proteins. Moreover, many proteins involved in DNA damage repair could not be detected in the PeptideAtlas despite their high mRNA levels, strengthening the translation-on-demand hypothesis for members of this protein class. In summary, the extensive and publicly available S. pombe PeptideAtlas together with the generated proteotypic peptide spectral library will be a useful resource for future targeted, in-depth, and quantitative proteomic studies on this microorganism

The functional interactome landscape of the human histone deacetylase family

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/102187/1/msb201326-sup-0001.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/102187/2/msb201326.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/102187/3/msb201326.reviewer_comments.pd

Electronic structure and magnetic properties of the graphene/Fe/Ni(111) intercalation-like system

The electronic structure and magnetic properties of the graphene/Fe/Ni(111) system were investigated via combination of the density functional theory calculations and electron-spectroscopy methods. This system was prepared via intercalation of thin Fe layer (1 ML) underneath graphene on Ni(111) and its inert properties were verified by means of photoelectron spectroscopy. Intercalation of iron in the space between graphene and Ni(111) changes drastically the magnetic response from the graphene layer that is explained by the formation of the highly spin-polarized $3d_{z^2}$ quantum-well state in the thin iron layer.Comment: Manuscript and supplementary material

Interactions between the Nse3 and Nse4 Components of the SMC5-6 Complex Identify Evolutionarily Conserved Interactions between MAGE and EID Families

The SMC5-6 protein complex is involved in the cellular response to DNA damage. It is composed of 6-8 polypeptides, of which Nse1, Nse3 and Nse4 form a tight sub-complex. MAGEG1, the mammalian ortholog of Nse3, is the founding member of the MAGE (melanoma-associated antigen) protein family and Nse4 is related to the EID (E1A-like inhibitor of differentiation) family of transcriptional repressors.Using site-directed mutagenesis, protein-protein interaction analyses and molecular modelling, we have identified a conserved hydrophobic surface on the C-terminal domain of Nse3 that interacts with Nse4 and identified residues in its N-terminal domain that are essential for interaction with Nse1. We show that these interactions are conserved in the human orthologs. Furthermore, interaction of MAGEG1, the mammalian ortholog of Nse3, with NSE4b, one of the mammalian orthologs of Nse4, results in transcriptional co-activation of the nuclear receptor, steroidogenic factor 1 (SF1). In an examination of the evolutionary conservation of the Nse3-Nse4 interactions, we find that several MAGE proteins can interact with at least one of the NSE4/EID proteins.We have found that, despite the evolutionary diversification of the MAGE family, the characteristic hydrophobic surface shared by all MAGE proteins from yeast to humans mediates its binding to NSE4/EID proteins. Our work provides new insights into the interactions, evolution and functions of the enigmatic MAGE proteins

The PeptideAtlas project

The completion of the sequencing of the human genome and the concurrent, rapid development of high-throughput proteomic methods have resulted in an increasing need for automated approaches to archive proteomic data in a repository that enables the exchange of data among researchers and also accurate integration with genomic data. PeptideAtlas (http://www.peptideatlas.org/) addresses these needs by identifying peptides by tandem mass spectrometry (MS/MS), statistically validating those identifications and then mapping identified sequences to the genomes of eukaryotic organisms. A meaningful comparison of data across different experiments generated by different groups using different types of instruments is enabled by the implementation of a uniform analytic process. This uniform statistical validation ensures a consistent and high-quality set of peptide and protein identifications. The raw data from many diverse proteomic experiments are made available in the associated PeptideAtlas repository in several formats. Here we present a summary of our process and details about the Human, Drosophila and Yeast PeptideAtlas build