Computationally predicting prokaryotic protein subcellular localization, using experimentally-based criteria

使用基于实验的标准计算预测原核蛋白质亚细胞定位

• 批准号: 240644-2011
• 负责人: Brinkman, Fiona
• 金额: $ 4.42万
• 依托单位: Simon Fraser University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=570614
• 关键词: Computationally; predicting; prokaryotic; protein; subcellular

Computationally predicting proteins located on the surface of bacterial cells is of wide interest in both medicine and agriculture, to identify new potentially accessible (surface-exposed) drug targets, diagnostic markers, and vaccine candidates. Such analysis also aids identification of markers for environmental detection of microbes, characterization of proteins of industrial interest for their stability from thermo/cold tolerant Archaea, general genome annotation, and design of proteomic experiments. Previously, I led the development of PSORTb, which is currently the most precise predictor of bacterial and archaeal protein subcellular localization, including cell surface protein localization. However, PSORTb needs to be improved to better handle the diversity of prokaryotes and their different cell structures. In particular, certain very medically important bacteria like Mycobacteria spp., as well as Archaea with 'atypical' membrane structures, are particularly poorly predicted. In addition, more accurate prediction of the components of cell surface proteins that are exposed on the cell surface is needed, for vaccine discovery or diagnostic markers for medical or environmental use. Therefore, I propose to gather high quality protein subcellular localization data about select prokaryotic cell surface proteins, both from the literature and my own laboratory experimentation. This rich data set will play a key role in development a new version of PSORTb that will make more accurate cell surface protein predictions for more diverse prokaryotes, and in some cases predict cell surface protein topology. The laboratory analysis, including a novel study of constructed outer membrane protein variants, will also provide structural insights regarding an essential bacterial outer membrane protein that plays a key role in outer membrane protein assembly. The bioinformatics research will generate a powerful new predictor and associated database of protein localization which will be a significant aid to diverse researchers needing to identify bacterial and archaeal cell surface proteins, and protein subcellular localization in general.

计算预测位于细菌细胞表面的蛋白质在医学和农业中具有广泛的意义，以识别新的潜在可接近（表面暴露）的药物靶标，诊断标记物和候选疫苗。这样的分析还有助于鉴定用于微生物的环境检测的标记物、表征工业感兴趣的蛋白质的耐热/耐寒性、一般基因组注释和蛋白质组学实验的设计的稳定性。此前，我领导了PSORTb的开发，这是目前细菌和古细菌蛋白质亚细胞定位（包括细胞表面蛋白定位）的最精确预测因子。 然而，PSORTb需要改进以更好地处理原核生物的多样性及其不同的细胞结构。特别是某些医学上非常重要的细菌，如分枝杆菌属，以及具有“非典型”膜结构的微囊藻，预测特别差。此外，需要更准确地预测暴露在细胞表面上的细胞表面蛋白的组分，用于疫苗发现或用于医疗或环境用途的诊断标记。 因此，我建议从文献和我自己的实验室实验中收集关于选择原核细胞表面蛋白的高质量蛋白质亚细胞定位数据。这个丰富的数据集将在开发新版本的PSORTb中发挥关键作用，该版本将为更多样化的原核生物做出更准确的细胞表面蛋白预测，并在某些情况下预测细胞表面蛋白拓扑结构。实验室分析，包括构建外膜蛋白变体的新研究，也将提供关于在外膜蛋白组装中起关键作用的基本细菌外膜蛋白的结构见解。生物信息学研究将产生一个强大的新的预测和相关的蛋白质定位数据库，这将是一个重要的援助，不同的研究人员需要确定细菌和古细菌细胞表面蛋白，和蛋白质亚细胞定位一般。