Significant enhancement of docking sensitivity using implicit ligand sampling.
Significant enhancement of docking sensitivity using implicit ligand sampling.
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DOI：
10.1021/ci100457t
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发表时间：
2011-03-28
影响因子：
5.6
通讯作者：
Lill MA
中科院分区：
文献类型：
--
作者：
Xu M;Lill MA
使用计算方法有效和准确地量化蛋白质-配体相互作用仍然是一项具有挑战性的任务。有两个因素导致对接方法无法准确预测结合自由能：没有充分考虑与配体结合的蛋白质柔性，以及在目前的评分方案中忽略了蛋白质-配体复合体的动力学。我们已经开发了一种新的方法学，名为“配体模型”的概念，以采样与结合结构不同的配体集合相关的蛋白质构象。在配体模型概念中，使用虚拟配体进行分子动力学(MD)模拟，该虚拟配体由一组与蛋白质结合的官能团表示，并在模拟过程中动态改变其形状和性质。配基模型实质上代表了与同一靶蛋白结合的不同化学物种的大集合。从分子动力学模拟中获得了具有代表性的蛋白质结构，并对接到这一蛋白质构象集合中。相似的绑定姿势被聚类，并利用平均得分对姿势进行重新排序。我们证明，与通过apo MD模拟生成的蛋白质结构的静态对接和系综对接模拟相比，配体模型方法在预测天然类结合姿势和量化结合亲和力方面有显着改进。
The efficient and accurate quantification of protein-ligand interactions using computational methods is still a challenging task. Two factors strongly contribute to the failure of docking methods to predict free energies of binding accurately: the insufficient incorporation of protein flexibility coupled to ligand binding and the neglected dynamics of the protein-ligand complex in current scoring schemes. We have developed a new methodology, named the ‘ligand-model’ concept, to sample protein conformations that are relevant for binding structurally diverse sets of ligands. In the ligand-model concept, molecular-dynamics (MD) simulations are performed with a virtual ligand, represented by a collection of functional groups that binds to the protein and dynamically changes its shape and properties during the simulation. The ligand model essentially represents a large ensemble of different chemical species binding to the same target protein. Representative protein structures were obtained from the MD simulation, and docking was performed into this ensemble of protein conformation. Similar binding poses were clustered, and the averaged score was utilized to re-rank the poses. We demonstrate that the ligand-model approach yields significant improvements in predicting native-like binding poses and quantifying binding affinities compared to static docking and ensemble docking simulations into protein structures generated from an apo MD simulation.
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影响因子：
7.3
作者：
Cozzini P;Kellogg GE;Spyrakis F;Abraham DJ;Costantino G;Emerson A;Fanelli F;Gohlke H;Kuhn LA;Morris GM;Orozco M;Pertinhez TA;Rizzi M;Sotriffer CA
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Sotriffer CA
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Friesner, RA
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Moitessier, Nicolas
影响因子：
13.8
作者：
Csermely, Peter;Palotai, Robin;Nussinov, Ruth
通讯作者：
Nussinov, Ruth