Mapping of the Binding Landscape for a Picomolar Protein-Protein Complex through Computation and Experiment
Mapping of the Binding Landscape for a Picomolar Protein-Protein Complex through Computation and Experiment
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DOI：
10.1016/j.str.2014.01.012
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发表时间：
2014-04-08
期刊：
影响因子：
5.7
通讯作者：
Shifman, Julia
中科院分区：
文献类型：
--
作者：
Aizner, Yonatan;Sharabi, Oz;Shifman, Julia
我们对蛋白质进化的理解将极大地受益于结合景观的绘制，即由于所有单突变而导致的蛋白质-蛋白质结合亲和力的变化。然而，由于大量可能的突变，这种景观的实验生成是一项繁琐的任务。在这里，我们使用一个简单的计算方案来绘制两个同源高亲和力复合物的结合景观，涉及来自两个不同物种的蛇毒素束状蛋白和乙酰胆碱酯酶。为了验证我们的计算预测，我们通过实验测量了25个Fas突变体与这两种酶之间的结合。计算和实验结果都表明，Fas序列在与靶标相互作用时接近最佳，但一些突变可以进一步改善k -d、k（on）和k（off）。我们的计算预测与实验结果非常吻合，并且生成的分布与在其他高亲和PPIs中观察到的分布相似，证明了简单计算协议在捕获现实结合景观方面的潜力。
Our understanding of protein evolution would greatly benefit from mapping of binding landscapes, i.e., changes in protein-protein binding affinity due to all single mutations. However, experimental generation of such landscapes is a tedious task due to a large number of possible mutations. Here, we use a simple computational protocol to map the binding landscape for two homologous high-affinity complexes, involving a snake toxin fasciculin and acetylcholinesterase from two different species. To verify our computational predictions, we experimentally measure binding between 25 Fas mutants and the 2 enzymes. Both computational and experimental results demonstrate that the Fas sequence is close to the optimum when interacting with its targets, yet a few mutations could further improve K-d, k(on), and k(off). Our computational predictions agree well with experimental results and generate distributions similar to those observed in other high-affinity PPIs, demonstrating the potential of simple computational protocols in capturing realistic binding landscapes.