SGER: From Activity to ORF

基因组测序计划的巨大成功使许多基因与它们编码的蛋白质相关联。然而，许多基因仍然不能被分配给已知的蛋白质，许多已知的酶活性不能被分配给一个基因。根据文献，即使使用创新的生物信息学工具，大多数完全测序的基因组的13%至60%的ORF（开放阅读框架，编码蛋白质的基因的那些部分）未被表征或未注释。为了开始“填补代谢图谱中的漏洞”，已经开发了一种策略，即从ORF尚未鉴定的蛋白质的已知活性开始，并继续发现相关基因。感兴趣的活性依赖于简单的传统生化纯化技术与质谱技术的创新使用相结合。使用质谱技术，通过将蛋白质比活性的增加与部分纯化级分中蛋白质量的增加相关联来鉴定候选基因。酵母菌株已经被制造出来，一次过表达6,000个左右酵母基因中的每一个。使用对应于每个候选基因的菌株，可以鉴定负责感兴趣的活性的基因。以这种方式，酵母基因的甲基乙二醛还原酶活性的鉴定相对较少的努力。然而，负责这种活性的蛋白质比大多数其他酵母蛋白质更丰富。利用最先进的质谱和复杂的数据库挖掘工具，该项目将确定允许蛋白质与其基因关联的丰度下限。如果成功的话，这种方法将为代谢工程、药物发现和理解代谢网络提供重要的信息。该项目将吸收包括妇女和少数民族在内的大学生和高中生参加。

The spectacular success of genome sequencing projects has led to associating many genes with the proteins they encode. However many genes remain that cannot be assigned a known protein and many known enzyme activities cannot be assigned to a gene. According to the literature, even with the use of innovative bioinformatics tools, 13 to 60% of the ORFs (open reading frames, those portions of genes that encode proteins) of most completely sequenced genomes are uncharacterized or miss annotated. To begin to "fill the holes" in metabolic maps, a strategy of beginning with the known activity of proteins whose ORF's have not been identified and proceeding to discover the associated gene has been developed. The activity of interest is dependent on simple, traditional biochemical purification techniques combined with innovative use of mass spectrometric technology. Using mass spectrometric techniques, the candidate genes are identified by correlating the increase in protein specific activity with the increase in the amount of protein in the partially purified fraction. Yeast strains have been made that over-express, one at a time, each of the 6,000 or so yeast genes. Using the strain corresponding to each candidate gene, the gene responsible for the activity of interest can be identified. In this way, the yeast gene for methylglyoxal reductase activity was identified with relatively little effort. However, the protein responsible for this activity is more abundant than are most other yeast proteins. Using state of the art mass spectrometry and sophisticated database mining tools, this project will determine the lower limit of abundance that will allow association of a protein with its gene. If successful, this methodology will provide information that is important for metabolic engineering, drug discovery and understanding metabolic networks. This project will incorporate undergraduate and high school students, including women and minorities into the project.