Aizner Yonatan, Sharabi Oz, Shirian Jason, Dakwar George R, Risman Marina, Avraham Orly, Shifman Julia
Department of Biological Chemistry, The Alexander Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem 91904, Israel.
Department of Biological Chemistry, The Alexander Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem 91904, Israel.
Structure. 2014 Apr 8;22(4):636-45. doi: 10.1016/j.str.2014.01.012. Epub 2014 Mar 6.
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 Kd, kon, and koff. 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.
我们对蛋白质进化的理解将极大地受益于结合图谱的绘制，即由于所有单个突变导致的蛋白质-蛋白质结合亲和力的变化。然而，由于存在大量可能的突变，通过实验生成这样的图谱是一项繁琐的任务。在这里，我们使用一种简单的计算方法来绘制两种同源高亲和力复合物的结合图谱，这两种复合物涉及来自两个不同物种的蛇毒素束丝菌素和乙酰胆碱酯酶。为了验证我们的计算预测，我们通过实验测量了25个束丝菌素突变体与这两种酶之间的结合。计算和实验结果均表明，束丝菌素序列在与其靶标相互作用时已接近最优状态，但仍有一些突变可以进一步改善解离常数（Kd）、结合速率常数（kon）和解离速率常数（koff）。我们的计算预测与实验结果吻合良好，并生成了与其他高亲和力蛋白质-蛋白质相互作用中观察到的分布相似的结果，证明了简单计算方法在捕捉实际结合图谱方面的潜力。
