Ensemble generation and the influence of protein flexibility on geometric tunnel prediction in cytochrome P450 enzymes.
Ensemble generation and the influence of protein flexibility on geometric tunnel prediction in cytochrome P450 enzymes.
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DOI：
10.1371/journal.pone.0099408
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发表时间：
2014
期刊：
影响因子：
3.7
通讯作者：
Lill MA
中科院分区：
文献类型：
--
作者：
Kingsley LJ;Lill MA
蛋白质中配体进入和排出路径的计算预测已经成为计算生物学中的新兴课题，并且已经证明在诸如蛋白质工程和药物设计等领域是有用的。几何隧道预测程序，如Caver3.0和MolAxis，是计算效率高的方法，以确定潜在的配体进入和蛋白质的出口路线。尽管许多几何隧道程序被设计成适应单个输入结构，但越来越多地认识到蛋白质柔性在隧道形成和行为中的重要性，这导致蛋白质集合在隧道预测中的更广泛使用。然而，目前还没有尝试直接调查合奏的大小和组成对几何隧道预测的影响。在这项研究中，我们比较了在单晶结构中发现的隧道，以使用不同的方法在细胞色素P450酶CYP119，CYP2C9和CYP3A4的脱辅基和全息形式上产生的各种大小的集合。测试了几种蛋白质结构聚类方法，试图生成能够从较大集合再现数据的较小集合。最终，我们发现，通过包含来自apo和holo数据集的成员，我们可以产生包含少于15个成员的合奏，这与包含超过100个成员的apo或holo合奏相当。此外，我们发现，在缺乏载脂蛋白或全息晶体结构数据的情况下，伪载脂蛋白或全息系综（例如，在整个MD模拟过程中向载脂蛋白添加配体）可用于分别模拟相应载脂蛋白和全息系综的结构系综。我们的研究结果不仅进一步强调了在几何隧道预测中包括蛋白质灵活性的重要性，而且还表明，较小的集合可以像较大的集合一样能够以较低的计算成本捕获许多对隧道预测重要的蛋白质运动。
Computational prediction of ligand entry and egress paths in proteins has become an emerging topic in computational biology and has proven useful in fields such as protein engineering and drug design. Geometric tunnel prediction programs, such as Caver3.0 and MolAxis, are computationally efficient methods to identify potential ligand entry and egress routes in proteins. Although many geometric tunnel programs are designed to accommodate a single input structure, the increasingly recognized importance of protein flexibility in tunnel formation and behavior has led to the more widespread use of protein ensembles in tunnel prediction. However, there has not yet been an attempt to directly investigate the influence of ensemble size and composition on geometric tunnel prediction. In this study, we compared tunnels found in a single crystal structure to ensembles of various sizes generated using different methods on both the apo and holo forms of cytochrome P450 enzymes CYP119, CYP2C9, and CYP3A4. Several protein structure clustering methods were tested in an attempt to generate smaller ensembles that were capable of reproducing the data from larger ensembles. Ultimately, we found that by including members from both the apo and holo data sets, we could produce ensembles containing less than 15 members that were comparable to apo or holo ensembles containing over 100 members. Furthermore, we found that, in the absence of either apo or holo crystal structure data, pseudo-apo or –holo ensembles (e.g. adding ligand to apo protein throughout MD simulations) could be used to resemble the structural ensembles of the corresponding apo and holo ensembles, respectively. Our findings not only further highlight the importance of including protein flexibility in geometric tunnel prediction, but also suggest that smaller ensembles can be as capable as larger ensembles at capturing many of the protein motions important for tunnel prediction at a lower computational cost.
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