FlexOracle: predicting flexible hinges by identification of stable domains.
FlexOracle: predicting flexible hinges by identification of stable domains.
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DOI：
10.1186/1471-2105-8-215
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发表时间：
2007-06-22
影响因子：
3
通讯作者：
Gerstein, Mark B
中科院分区：
文献类型：
--
作者：
Flores, Samuel C;Gerstein, Mark B
蛋白质运动在催化和蛋白质-配体相互作用中发挥着重要作用，但很难直接观察。蛋白质运动的很大一部分涉及铰链弯曲。对于这些蛋白质，准确识别连接刚性域的柔性铰链将为运动提供重要的洞察力。 GNM 和 FIRST 等程序已经以较低的计算成本提供了全局灵活性预测，但并不是专门为寻找铰点而设计的。在这里，我们提出了新颖的 FlexOracle 铰链预测方法，该方法基于这样的想法：结构域内的能量相互作用比结构域之间的能量相互作用更强，并且通过在铰链位点切割蛋白质产生的片段是独立稳定的。我们将其作为大分子运动数据库 MolMovDB.org 中的工具来实现。对于给定的结构，我们通过扫描蛋白质链上所有可能的切割点来生成片段对，计算片段与未分割蛋白质相比的能量，并预测该数量最小的铰链。我们提出了这种方法的三种具体实现。首先，我们仅考虑通过在蛋白质链上的单个位置切割而生成的片段对，然后使用标准分子力学力场来计算两个片段的焓。在第二个中，我们以相同的方式生成碎片，但使用基于知识的力场来计算它们的自由能。第三步，我们通过在蛋白质链上的两个点切割来生成片段对，然后计算它们的自由能。定量结果证明我们的方法能够从大分子运动数据库中预测已知的铰链。
Protein motions play an essential role in catalysis and protein-ligand interactions, but are difficult to observe directly. A substantial fraction of protein motions involve hinge bending. For these proteins, the accurate identification of flexible hinges connecting rigid domains would provide significant insight into motion. Programs such as GNM and FIRST have made global flexibility predictions available at low computational cost, but are not designed specifically for finding hinge points. Here we present the novel FlexOracle hinge prediction approach based on the ideas that energetic interactions are stronger within structural domains than between them, and that fragments generated by cleaving the protein at the hinge site are independently stable. We implement this as a tool within the Database of Macromolecular Motions, MolMovDB.org. For a given structure, we generate pairs of fragments based on scanning all possible cleavage points on the protein chain, compute the energy of the fragments compared with the undivided protein, and predict hinges where this quantity is minimal. We present three specific implementations of this approach. In the first, we consider only pairs of fragments generated by cutting at a single location on the protein chain and then use a standard molecular mechanics force field to calculate the enthalpies of the two fragments. In the second, we generate fragments in the same way but instead compute their free energies using a knowledge based force field. In the third, we generate fragment pairs by cutting at two points on the protein chain and then calculate their free energies. Quantitative results demonstrate our method's ability to predict known hinges from the Database of Macromolecular Motions.