Biomedical Informatics, 251 Campus Drive, MSOB, Stanford University, Stanford, CA 94305, USA.
Genome Biol. 2010;11(3):R30. doi: 10.1186/gb-2010-11-3-r30. Epub 2010 Mar 12.
We systematically analyzed the relationships between gene fitness profiles (co-fitness) and drug inhibition profiles (co-inhibition) from several hundred chemogenomic screens in yeast. Co-fitness predicted gene functions distinct from those derived from other assays and identified conditionally dependent protein complexes. Co-inhibitory compounds were weakly correlated by structure and therapeutic class. We developed an algorithm predicting protein targets of chemical compounds and verified its accuracy with experimental testing. Fitness data provide a novel, systems-level perspective on the cell.
我们系统地分析了来自酵母数百个化学基因组筛选的基因适合度图谱(协同适合度)和药物抑制图谱(协同抑制)之间的关系。协同适合度预测了与其他测定方法不同的基因功能,并鉴定了条件依赖性蛋白质复合物。协同抑制化合物在结构和治疗类别上相关性较弱。我们开发了一种预测化学化合物蛋白靶标的算法,并通过实验测试验证了其准确性。适合度数据为细胞提供了一种新颖的系统水平视角。