Accounting for receptor flexibility and enhanced sampling methods in computer-aided drug design.
Accounting for receptor flexibility and enhanced sampling methods in computer-aided drug design.
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DOI：
10.1111/cbdd.12051
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发表时间：
2013-01
影响因子：
3
通讯作者：
McCammon JA
中科院分区：
文献类型：
--
作者：
Sinko W;Lindert S;McCammon JA
蛋白质的柔性在生物分子识别中起着重要作用。在许多情况下，分子结构在与其他分子缔合后将如何变化并不明显。在蛋白质中，这些变化可能是主要的，在整个骨架结构中具有较大的偏差，或者它们可以更微妙，如侧链旋转。无论哪种方式，预测生物分子缔合可重复性的算法都需要相对准确地预测结合结构，以准确评估缔合中涉及的能量。在这里，我们回顾了一些技术，已经提出了适应受体的灵活性，在模拟小分子结合蛋白质受体。我们调查修改标准的刚性受体对接算法，并探讨增强采样技术，自由能计算和增强采样技术的组合。对受体灵活性的理解和允许有助于使配体蛋白结合的计算机模拟更加准确。这些进展可能有助于提高药物发现和开发的效率。当我们开始看到为个体患者量身定制的个性化药物时，效率将是必不可少的，这意味着需要针对每个患者的基因组成的特定药物。
Protein flexibility plays a major role in biomolecular recognition. In many cases it is not obvious how molecular structure will change upon association with other molecules. In proteins these changes can be major, with large deviations in overall backbone structure, or they can be more subtle as in a side chain rotation. Either way the algorithms that predict the favorability of biomolecular association require relatively accurate predictions of the bound structure to give an accurate assessment of the energy involved in association. Here we review a number of techniques that have been proposed to accommodate receptor flexibility in the simulation of small molecules binding to protein receptors. We investigate modifications to standard rigid receptor docking algorithms, and also explore enhanced sampling techniques, and the combination of free energy calculations and enhanced sampling techniques. The understanding and allowance for receptor flexibility are helping to make computer simulations of ligand protein binding more accurate. These developments may help improve the efficiency of drug discovery and development. Efficiency will be essential as we begin to see personalized medicine tailored to individual patients, which means specific drugs are needed for each patient’s genetic makeup.
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影响因子：
7.3
作者：
Ferrari, AM;Wei, BQQ;Shoichet, BK
通讯作者：
Shoichet, BK
影响因子：
15
作者：
Gervasio, FL;Laio, A;Parrinello, M
通讯作者：
Parrinello, M
影响因子：
7.3
作者：
Durrant, Jacob D.;Urbaniak, Michael D.;McCammon, J. Andrew
通讯作者：
McCammon, J. Andrew
DOI：
10.1073/pnas.202427399
发表时间：
2002-10-01
影响因子：
11.1
作者：
Laio, A;Parrinello, M
通讯作者：
Parrinello, M
影响因子：
3
作者：
Fajer, Mikolai;Swift, Robert V.;McCammon, J. Andrew
通讯作者：
McCammon, J. Andrew