一个结合的实验和计算策略来定义认知模块的蛋白质相互作用网络
Amy Hin Yan Tong1, Becky Drees, Giuliano Nardelli
1Banting and Best Department of Medical Research and Department of Molecular and Medical Genetics, University of Toronto, Toronto, Ontario, Canada M5G 1L6.
概括
我们开发了一种结合计算预测和实验测试的新方法,以确定蛋白质相互作用. 这种方法成功地绘制了酵母SH3域的相互作用图,揭示了像Las17这样的关键结合伙伴.
科学领域:
- 分子生物学分子生物学
- 生物化学 生物化学
- 系统生物学 系统生物学
背景情况:
- 体识别模块对于宏分子复合体组装至关重要.
- 鉴定这些模块的生理上相关的结合伙伴是具有挑战性的,因为它们的丰富.
研究的目的:
- 开发和应用一种用于预测和验证蛋白质与蛋白质相互作用的新策略.
- 为酵母SH3域生成全面的交互网络.
主要方法:
- 从菌体显示连接体共识序列与大规模酵母两杂交试验的综合计算预测.
- 利用图形理论分析来识别两个网络共同的高可信度交互.
主要成果:
- 产生了一种菌体显示网络,其中206种蛋白质之间有394种相互作用.
- 产生了一种酵母双杂交网络,其中145种蛋白质之间有233种相互作用.
- 在两个网络中确定了59个高可信度相互作用,包括Las17 (Bee1) 的多重相互作用.
结论:
- 综合计算和实验策略对于绘制蛋白质相互作用网络是有效的.
- 在体内验证了关键相互作用,证明了已识别的合作伙伴的生理相关性.
- 这些发现为SH3域的功能及其在细胞过程中的作用提供了洞察力.
相关概念视频
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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
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