Prediction of protein subcellular localization for prokaryotes, using experimentally-based criteria

在农业和医学中，人们越来越感兴趣的是通过计算预测细菌蛋白质的亚细胞定位，以从基因组序列数据中识别新的潜在可接近（表面暴露）的药物靶标和候选疫苗。在古细菌原核生物中鉴定特定定位的蛋白质也有越来越大的兴趣，因为这些蛋白质具有潜在的重要工业应用（即耐热酶）和环境效用（检测生态上重要的古细菌）。此前，我领导了PSORTb的开发，这是目前世界上最精确的细菌蛋白亚细胞定位预测器。这个程序，再加上我的团队开发的已知和计算预测的亚细胞定位蛋白质的数据库，现在被用来加速对特定医学，农业或环境利益的蛋白质的全基因组鉴定。然而，目前还没有这样的计算预测在这个水平的精度分析古菌，所以我建议开发PSORTa为此目的。此外，虽然细菌PSORTb的精确度/特异性很高，但我假设，通过扩展与该程序相关的已知定位蛋白质数据库，以及通过添加本提案中概述的新计算分析，可以提高该方法的召回率/灵敏度。我还建议，改进识别暴露在特定的细胞表面蛋白的序列是可能的，使用从实验室分析的参考细胞表面蛋白的结构产生的数据。由此产生的扩大家庭的亚细胞定位预测，古细菌（PSORTa）和细菌（PSORTb），可用于执行全球分析的蛋白质定位在所有原核生物，导致新的见解的进化蛋白质定位和细胞网络跨定位。这种改进的PSORT工具可以进一步加速对医学，农业，环境和工业应用感兴趣的原核蛋白的鉴定。

There is a growing interest in both agriculture and medicine in computationally predicting the subcellular localization of bacterial proteins, to identify new potentially accessible (surface-exposed) drug targets and vaccine candidates from genomic sequence data. There is also a growing interest in the identification of proteins of particular localizations in archaeal prokaryotes, as such proteins have potential significant industrial applications (i.e. thermotolerant enzymes) and environmental utility (detection of ecologically important archaea). Previously, I led the development of PSORTb, which is currently the world's most precise predictor of bacterial protein subcellular localization. This program, coupled with a database my group developed of proteins of known and computationally-predicted subcellular localization, is now being used to accelerate genome-wide identification of proteins of particular medical, agricultural or environmental interest. However, there is currently no such computational predictor at this level of precision for the analysis of archaea and so I propose developing PSORTa for this purpose. In addition, while bacterial PSORTb precision/specificity is high, I hypothesize that the recall/sensitivity of this method could be improved though an expansion of the database of proteins of known localization associated with this program, and through the addition of a new computational analyses outlined in this proposal. I also propose that improved identification of sequences exposed on particular cell surface proteins is possible, using data generated from laboratory analysis of the structure of a reference cell-surface protein. The resulting expanded family of subcellular localization predictors, for both archaea (PSORTa) and bacteria (PSORTb), could be used to perform global analyses of protein localization across all prokaryotes, leading to new insights regarding the evolution of protein localization and cell networks across localizations. This improved PSORT tool could further accelerate the identification of prokaryotic proteins that are of interest for their medical, agricultural, environmental and industrial applications.