The free energy landscape of small molecule unbinding.
The free energy landscape of small molecule unbinding.
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DOI：
10.1371/journal.pcbi.1002002
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发表时间：
2011-02
影响因子：
4.3
通讯作者：
Caflisch A
中科院分区：
文献类型：
--
作者：
Huang D;Caflisch A
采用显式水分子动力学模拟和网络分析研究了6个小配体从FKBP（FK 506结合蛋白）活性位点上的自发解离。配体具有四个（二甲基亚砜）和11（5-二乙氨基-2-戊酮）非氢原子，对FKBP的亲和力范围为20至0.2 mM。FKBP/配体复合物的构象沿着多个轨迹保存（每个配体在310 K下运行50次）根据一组分子间距离分组到网络的节点中，它们之间的直接过渡就是链接。网络分析表明，束缚态由几个子盆地组成，即，结合模式的特点是不同的分子间氢键和疏水接触。解离动力学显示简单的（即，单指数）时间依赖性，因为非结合势垒比束缚态中子盆地之间的势垒高得多。未结合过渡态由FKBP活性位点中配体的异质位置和取向组成，其对应于多个解离途径。对于FKBP的6个小配体，结合亲和力越弱越接近结合态（沿着分子间距离）的是过渡态结构，这是哈蒙德行为的一种新表现。研究片段与蛋白质结合的实验方法在时间和空间分辨率上有局限性。我们的网络分析从酶的小抑制剂的解结合模拟描绘了配体解结合的自由能景观（热力学和动力学）的清晰画面。大多数已知的用于对抗人类疾病的药物是与蛋白质，特别是与参与基本生物化学或生理过程的酶或受体强烈结合的小分子。由于原子对之间存在多个自由度和多个相互作用，结合过程非常复杂。在这里，我们展示了网络分析，一种用于研究从社会互动（例如，Facebook中的友谊链接）到代谢网络的大量复杂系统的数学工具，提供了对小分子与酶结合所涉及的自由能景观和途径的详细描述。使用分子动力学模拟样品的自由能景观，我们提供了强有力的证据，在原子的细节，小的配体可以有多个有利的位置和取向的活性位点。我们还观察到广泛的异质性（联合国）结合途径。研究片段与蛋白质结合的实验方法在空间和时间分辨率上有局限性。我们的网络分析的分子动力学模拟不受这些限制。它提供了结合过程的热力学和动力学的透彻描述。
The spontaneous dissociation of six small ligands from the active site of FKBP (the FK506 binding protein) is investigated by explicit water molecular dynamics simulations and network analysis. The ligands have between four (dimethylsulphoxide) and eleven (5-diethylamino-2-pentanone) non-hydrogen atoms, and an affinity for FKBP ranging from 20 to 0.2 mM. The conformations of the FKBP/ligand complex saved along multiple trajectories (50 runs at 310 K for each ligand) are grouped according to a set of intermolecular distances into nodes of a network, and the direct transitions between them are the links. The network analysis reveals that the bound state consists of several subbasins, i.e., binding modes characterized by distinct intermolecular hydrogen bonds and hydrophobic contacts. The dissociation kinetics show a simple (i.e., single-exponential) time dependence because the unbinding barrier is much higher than the barriers between subbasins in the bound state. The unbinding transition state is made up of heterogeneous positions and orientations of the ligand in the FKBP active site, which correspond to multiple pathways of dissociation. For the six small ligands of FKBP, the weaker the binding affinity the closer to the bound state (along the intermolecular distance) are the transition state structures, which is a new manifestation of Hammond behavior. Experimental approaches to the study of fragment binding to proteins have limitations in temporal and spatial resolution. Our network analysis of the unbinding simulations of small inhibitors from an enzyme paints a clear picture of the free energy landscape (both thermodynamics and kinetics) of ligand unbinding. Most known drugs used to fight human diseases are small molecules that bind strongly to proteins, particularly to enzymes or receptors involved in essential biochemical or physiological processes. The binding process is very complex because of the many degrees of freedom and multiple interactions between pairs of atoms. Here we show that network analysis, a mathematical tool used to study a plethora of complex systems ranging from social interactions (e.g, friendship links in Facebook) to metabolic networks, provides a detailed description of the free energy landscape and pathways involved in the binding of small molecules to an enzyme. Using molecular dynamics simulations to sample the free energy landscape, we provide strong evidence at atomistic detail that small ligands can have multiple favorable positions and orientations in the active site. We also observe a broad heterogeneity of (un)binding pathways. Experimental approaches to the study of fragment binding to proteins have limitations in spatial and temporal resolution. Our network analysis of the molecular dynamics simulations does not suffer from these limitations. It provides a thorough description of the thermodynamics and kinetics of the binding process.
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影响因子：
2.7
作者：
Huang, Danzhi;Caflisch, Amedeo
通讯作者：
Caflisch, Amedeo
影响因子：
15
作者：
Colizzi, Francesco;Perozzo, Remo;Cavalli, Andrea
通讯作者：
Cavalli, Andrea
影响因子：
7.3
作者：
Ekonomiuk, Dariusz;Su, Xun-Cheng;Caflisch, Amedeo
通讯作者：
Caflisch, Amedeo
影响因子：
8
作者：
Curcio, R;Caflisch, A;Paci, E
通讯作者：
Paci, E
影响因子：
3.4
作者：
Guarnera, Enrico;Pellarin, Riccardo;Caflisch, Amedeo
通讯作者：
Caflisch, Amedeo