Computationally predicting bacterial and archaeal protein subcellular localization, using experimentally-based criteria

计算预测位于细菌细胞表面的蛋白质在医学和农业中都有广泛的兴趣，以确定新的潜在可达的（表面暴露的）药物靶点、诊断标记物和候选疫苗。这种分析还有助于鉴定微生物的环境检测标记，鉴定耐热/耐寒古菌中工业感兴趣的蛋白质的稳定性，一般基因组注释和蛋白质组学实验的设计。在此之前，我领导了PSORTb的开发，这是目前最精确的细菌和古菌蛋白亚细胞定位的预测因子，包括细胞表面蛋白定位。然而，PSORTb需要改进，以更好地处理细菌和古细菌的多样性及其不同的细胞结构。特别是，某些具有非典型外膜或附属物样结构的医学上重要的细菌，以及一些古细菌膜蛋白，预测特别差。目前还没有为宏基因组学数据设计的预测器，这是一个显著的未满足的数据需求，随着应用的广泛而迅速增长。此外，需要更准确地预测暴露在细胞表面的细胞表面蛋白成分，以便发现疫苗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微生物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，该版本将对更多样化的细菌和古细菌进行更准确的细胞表面蛋白预测，首次从宏基因组数据进行预测，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，也将提供有关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蛋白质定位预测器和相关数据库，这将对需要识别细菌和古细菌细胞表面蛋白质以及一般的蛋白质亚细胞定位的各种研究人员有重要的帮助。**

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 bacteria and archaea and their different cell structures. In particular, certain medically important bacteria with atypical' outer membranes or appendage-like structures, and some archaeal membrane proteins, are particularly poorly predicted. There is no current predictor designed for metagenomics data a significant unmet need for data that is growing rapidly with very wide applications. 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 microbial 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 bacterial and archaea, make predictions from metagenomic data for the first time, 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. **