Discovery of multiple hidden allosteric sites by combining Markov state models and experiments
Discovery of multiple hidden allosteric sites by combining Markov state models and experiments
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DOI：
10.1073/pnas.1417811112
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发表时间：
2015-03-03
影响因子：
11.1
通讯作者：
Marqusee, Susan
中科院分区：
文献类型：
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作者：
Bowman, Gregory R.;Bolin, Eric R.;Marqusee, Susan
药物样分子结合蛋白质中不存在于晶体结构中但对活性施加变构控制的口袋的发现已经在设计利用变构效应的药物方面产生了极大的兴趣。然而，只有少数的成功，所以这些口袋的治疗潜力-所谓的隐藏变构位点-仍然不清楚。评估其效用的一个挑战是，合理的药物设计方法需要预先知道靶位点，但大多数隐藏的变构位点只有在发现小分子稳定它们时才被发现。我们提出了一种方法解耦识别隐藏的变构位点的药物，结合他们的发现，利用马尔可夫状态建模，提供前所未有的访问微秒到毫秒的时间尺度波动的蛋白质的结构的新发展。可视化这些波动使我们能够识别潜在的隐藏变构位点，然后我们通过巯基标记实验进行测试。这些方法的应用揭示了一个重要的抗生素靶标TEM-1 β-内酰胺酶中多个隐藏的变构位点。这一结果支持了这一假设，即有许多尚未发现的隐藏的变构位点，并表明我们的方法可以识别这样的网站，为未来的药物设计工作提供了一个起点。更一般地说，我们的研究结果证明了使用马尔可夫状态模型来指导实验的力量。
The discovery of drug-like molecules that bind pockets in proteins that are not present in crystallographic structures yet exert allosteric control over activity has generated great interest in designing pharmaceuticals that exploit allosteric effects. However, there have only been a small number of successes, so the therapeutic potential of these pockets-called hidden allosteric sites-remains unclear. One challenge for assessing their utility is that rational drug design approaches require foreknowledge of the target site, but most hidden allosteric sites are only discovered when a small molecule is found to stabilize them. We present a means of decoupling the identification of hidden allosteric sites from the discovery of drugs that bind them by drawing on new developments in Markov state modeling that provide unprecedented access to microsecond-to millisecond-timescale fluctuations of a protein's structure. Visualizing these fluctuations allows us to identify potential hidden allosteric sites, which we then test via thiol labeling experiments. Application of these methods reveals multiple hidden allosteric sites in an important antibiotic target-TEM-1 beta-lactamase. This result supports the hypothesis that there are many as yet undiscovered hidden allosteric sites and suggests our methodology can identify such sites, providing a starting point for future drug design efforts. More generally, our results demonstrate the power of using Markov state models to guide experiments.