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Protein Pockets: Inventory, Shape, and Comparison

机译:蛋白质袋:库存,形状和比较

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摘要

The shape of the protein surface dictates what interactions are possible with other macromolecules, but defining discrete pockets or possible interaction sites remains difficult. First, there is the problem of defining the extent of the pocket. Second, one has to characterize the shape of each pocket. Third, one needs to make quantitative comparisons between pockets on different proteins. An elegant solution to these problems is to sort all surface and solvent points by travel depth and then collect a hierarchical tree of pockets. The connectivity of the tree is determined via the deepest saddle points between each pair of neighboring pockets. The resulting pocket surfaces tessellate the entire protein surface, producing a complete inventory of pockets. This method of identifying pockets also allows one to easily compute important shape metrics, including the problematic pocket volume, surface area, and mouth size. Pockets are also annotated with their lining residue lists and polarity and with other residue-based properties. Using this tree and the various shape metrics pockets can be merged, grouped, or filtered for further analysis. Since this method includes the entire surface, it guarantees that any pocket of interest will be found among the output pockets, unlike all previous methods of pocket identification. The resulting hierarchy of pockets is easy to visualize and aids users in higher level analysis. Comparison of pockets is done by using the shape metrics, avoiding the complex shape alignment problem. Example applications show that the method facilitates pocket comparison along mutational or time-dependent series. Pockets from families of proteins can be examined using multiple pocket tree alignments to see how ligand binding sites or how other pockets have changed with evolution. Our method is called CLIPPERS for complete liberal inventory of protein pockets elucidating and reporting on shape.
机译:蛋白质表面的形状决定了与其他大分子可能发生的相互作用,但是要确定离散的口袋或可能的相互作用位点仍然很困难。首先,存在限定凹穴的范围的问题。第二,必须表征每个口袋的形状。第三,需要对不同蛋白质的口袋进行定量比较。解决这些问题的一种好方法是按行进深度对所有表面和溶剂点进行排序,然后收集分层的口袋树。通过每对相邻口袋之间的最深鞍点确定树的连通性。产生的口袋表面会细分整个蛋白质表面,从而产生完整的口袋库存。这种识别口袋的方法还使人们能够轻松计算出重要的形状指标,包括有问题的口袋体积,表面积和嘴巴大小。口袋还带有衬里残渣列表和极性以及其他基于残渣的特性。使用该树,可以合并,分组或过滤各种形状度量值袋,以进行进一步分析。由于此方法包括整个表面,因此可以确保在输出口袋中找到任何感兴趣的口袋,这与以前的所有口袋识别方法不同。口袋的最终层次结构易于可视化,并有助于用户进行更高级别的分析。口袋的比较是通过使用形状度量完成的,从而避免了复杂的形状对齐问题。实例应用表明,该方法有助于沿突变或时间依赖性序列进行口袋比较。可以使用多个口袋树排列来检查蛋白质家族的口袋,以了解配体结合位点或其他口袋如何随着进化而变化。我们的方法称为CLIPPERS,用于完整自由地清点蛋白质袋,以阐明和报告形状。

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