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Chen L.,Guangxi University | Xu P.,Beijing Institute of Radiation Medicine | Xu P.,National Center for Protein science Beijing | Shi D.,Guangxi University | Li X.,Guangxi University
Shengwu Gongcheng Xuebao/Chinese Journal of Biotechnology | Year: 2014

The development of female germ cell is the cornerstone for animal reproduction. Mammalian oocyte and early embryo have many distinct phenomena and mechanisms during their growth and development, involving series dynamic changes of protein synthesis/degradation and phosphorylation. Research on the regulatory mechanism of oocyte division, maturation, and developmental principle of pre-implantation embryo is an important topic in the field of animal developmental biology. Proteomics using all of proteins expressed by a cell or tissue as research object, systematically identify, quantify and study the function of all these proteins. With the rapid development of protein separation and identification technology, proteomics provide some new methods and the research contents on fields of oogenesis, differentiation, maturation and quality control, such as protein quantification, modification, location and interaction important information which other omics technology can not provide. These information will contribute to uncover the molecular mechanisms of mammalian oocyte maturation and embryonic development. And it is great significant for improving the culture system of oocyte in vitro maturation, the efficiency of embryo production in vitro, somatic cell clone and transgenic animal production. © 2014 Chin J Biotech, All rights reserved Source


Tang L.,Beijing Institute of Radiation Medicine | Tang L.,National Center for Protein science Beijing | Gong M.,Beijing Institute of Radiation Medicine | Zhang P.,Beijing Institute of Radiation Medicine | Zhang P.,National Center for Protein science Beijing
Biochemical and Biophysical Research Communications | Year: 2016

The CRISPR-Cas9 genome editing system has been widely used in multiple cells and organisms. Here we developed a CRISPR-Cas9 based in vitro large DNA vector editing system, using the Ad5-based vector as an example. We demonstrate use of this system to generate targeted mutations, in-frame gene deletion, and gene replacement. This in vitro CRISPR editing system exhibits high efficiency and accuracy. We believe this system can be applied in a variety of experimental settings. © 2016 Elsevier Inc. Source


Liu Z.,Beijing Institute of Radiation Medicine | Liu Z.,National Center for Protein science Beijing | Guo F.,Beijing Institute of Radiation Medicine | Guo F.,National Center for Protein science Beijing | And 13 more authors.
Bioinformatics | Year: 2015

Motivation: Anatomical Therapeutic Chemical (ATC) classification system, widely applied in almost all drug utilization studies, is currently the most widely recognized classification system for drugs. Currently, new drug entries are added into the system only on users' requests, which leads to seriously incomplete drug coverage of the system, and bioinformatics prediction is helpful during this process. Results: Here we propose a novel prediction model of drug-ATC code associations, using logistic regression to integrate multiple heterogeneous data sources including chemical structures, target proteins, gene expression, side-effects and chemical-chemical associations. The model obtains good performance for the prediction not only on ATC codes of unclassified drugs but also on new ATC codes of classified drugs assessed by cross-validation and independent test sets, and its efficacy exceeds previous methods. Further to facilitate the use, the model is developed into a userfriendly web service SPACE (Similarity-based Predictor of ATC CodE), which for each submitted compound, will give candidate ATC codes (ranked according to the decreasing probability-score predicted by the model) together with corresponding supporting evidence. This work not only contributes to knowing drugs' therapeutic, pharmacological and chemical properties, but also provides clues for drug repositioning and side-effect discovery. In addition, the construction of the prediction model also provides a general framework for similarity-based data integration which is suitable for other drug-related studies such as target, side-effect prediction etc. Availability and implementation: The web service SPACE is available at http://www.bprc.ac.cn/ space. © The Author 2015. Published by Oxford University Press. All rights reserved. Source


Cheng R.,Beijing Institute of Radiation Medicine | Cheng R.,Beijing University of Technology | Peng J.,Beijing Institute of Radiation Medicine | Peng J.,East China Normal University | And 14 more authors.
FEBS Letters | Year: 2014

We developed an adenovirus-based CRISPR/Cas9 system for gene editing in vivo. In the liver, we demonstrated that the system could reach the level of tissue-specific gene knockout, resulting in phenotypic changes. Given the wide spectrum of cell types susceptible to adenoviral infection, and the fact that adenoviral genome rarely integrates into its host cell genome, we believe the adenovirus-based CRISPR/Cas9 system will find applications in a variety of experimental settings. © 2014 Federation of European Biochemical Societies. Published by Elsevier B.V. All rights reserved. Source


Liu Z.,Beijing Institute of Radiation Medicine | Liu Z.,National Center for Protein science Beijing | Guo F.,Beijing Institute of Radiation Medicine | Guo F.,National Center for Protein science Beijing | And 12 more authors.
Molecular and Cellular Proteomics | Year: 2013

Self-interacting proteins, whose two or more copies can interact with each other, play important roles in cellular functions and the evolution of protein interaction networks (PINs). Knowing whether a protein can self-interact can contribute to and sometimes is crucial for the elucidation of its functions. Previous related research has mainly focused on the structures and functions of specific self-interacting proteins, whereas knowledge on their overall properties is limited. Meanwhile, the two current most common high throughput protein interaction assays have limited ability to detect self-interactions because of biological artifacts and design limitations, whereas the bioinformatic prediction method of self-interacting proteins is lacking. This study aims to systematically study and predict self-interacting proteins from an overall perspective. We find that compared with other proteins the self-interacting proteins in the structural aspect contain more domains; in the evolutionary aspect they tend to be conserved and ancient; in the functional aspect they are significantly enriched with enzyme genes, housekeeping genes, and drug targets, and in the topological aspect tend to occupy important positions in PINs. Furthermore, based on these features, after feature selection, we use logistic regression to integrate six representative features, including Gene Ontology term, domain, paralogous interactor, enzyme, model organism self-interacting protein, and between-ness centrality in the PIN, to develop a proteome-wide prediction model of self-interacting proteins. Using 5-fold cross-validation and an independent test, this model shows good performance. Finally, the prediction model is developed into a user-friendly web service SLIPPER (SeLf-Interacting Protein PrEdictoR). Users may submit a list of proteins, and then SLIPPER will return the probability-scores measuring their possibility to be self-interacting proteins and various related annotation information. This work helps us understand the role self-interacting proteins play in cellular functions from an overall perspective, and the constructed prediction model may contribute to the high throughput finding of self-interacting proteins and provide clues for elucidating their functions. © 2013 by The American Society for Biochemistry and Molecular Biology, Inc. Source

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