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Identification of multiple subcellular locations for proteins in budding yeast

机译:鉴定萌芽酵母中蛋白质的多个亚细胞位置

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摘要

Knowing the subcellular locations of a protein helps to explore its functions in vivo since a protein can only play its roles properly if and only if it is located at certain subcellular compartments. Since it is both time-consuming and costly to determine protein subcellular localization purely by means of the conventional biotechnology experiments, computational methods play an important complementary role in this regard. Although a number of computational methods have been developed for predicting protein subcellular localization, it remains a challenge to deal with the multiplex proteins that may simultaneously exist at, or move between, two or more different locations. Here, a new predictor called Sort-PLoc was developed to tackle such a difficult and challenging problem. The key step was to select protein domains to code the protein samples by Incremental Feature Selection method. In each prediction, a series of subcellular locations were sorted descendingly according to their likelihood to be the site where the query protein may reside. Based on the selected domain set, the importance of Gene Ontology (GO) terms and domains in the contribution to the prediction was analyzed that may provide useful insights to the relevant areas. For the convenience of the broad experimental scientists, a user-friendly web-server for Sort-PLoc was established that is freely accessible to the public at http://yscl.biosino.org/.
机译:了解蛋白质的亚细胞位置有助于探讨其体内功能,因为蛋白只能在某些亚细胞隔室位于某些亚细胞隔室时才才能正常发挥其角色。由于纯粹通过传统的生物技术实验来确定蛋白质亚细胞定位既耗时且昂贵,所以计算方法在这方面发挥着重要的互补作用。尽管已经开发了许多用于预测蛋白质亚细胞定位的计算方法,但是对处理可以同时存在于两个或多个不同位置之间的多重蛋白质来说仍然是一个挑战。在这里,开发了一种称为Sort-Ploc的新预测因素以解决这种困难和具有挑战性的问题。关键步骤是通过增量特征选择方法选择蛋白质结构域来编写蛋白质样本。在每次预测中,根据它们的可能性,将一系列亚细胞位置分类为查询蛋白可能存在的位置。基于所选择的域集,分析了基因本体(GO)术语和域在对预测贡献中的重要性,这可能为相关领域提供有用的见解。为了方便广泛的实验科学家,建立了一个用于Sort-Ploc的用户友好的Web-Server,可以在http://yscl.biosino.org/上自由访问。

著录项

  • 来源
    《Current Bioinformatics》 |2011年第1期|共10页
  • 作者单位

    Shanghai Key Laboratory of Bio-Energy Crops School of Life Sciences Shanghai University Shanghai 200444 China;

    Department of Chemistry College of Sciences Shanghai University Shanghai 200444 China;

    Key Laboratory of Systems Biology Shanghai Institutes for Biological Sciences Chinese Academy of Sciences Shanghai 200031 China;

    Institute of Systems Biology Shanghai University Shanghai 200444 China;

    Institute of Systems Biology Shanghai University Shanghai 200444 China;

    Department of Chemistry College of Sciences Shanghai University Shanghai 200444 China;

    Gordon Life Science Institute San Diego CA 92130 United States;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 生物科学;
  • 关键词

    Incremental feature selection; Multi subcellular locations; Sort-PLoc;

    机译:增量特征选择;多亚细胞位置;排序 - ploc;

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