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

点击翻译按钮获取中文摘要

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.