ENZYME CLASSIFICATION BY ELECTROSTATIC PROPERTIES

这个子项目是许多研究子项目中利用 资源由NIH/NCRR资助的中心拨款提供。子项目和 调查员(PI)可能从NIH的另一个来源获得了主要资金， 并因此可以在其他清晰的条目中表示。列出的机构是 该中心不一定是调查人员的机构。 我们正在开发一种生物分子静电势的多分辨率比较方法，而不需要生物分子的全局结构比对。基本的计算几何算法使用多分辨率属性轮廓树(MACT)来比较体积标量场的拓扑特征。我们应用MACT来计算一大组具有不同程度的序列、结构和功能相似的蛋白质链的静电相似性度量。对于校准，我们还计算了这些链的相似性度量，通过基于3D结构比对和Carbo相似性指数分析的更传统的方法。此外，由于MACT方法不依赖于两两结构比对，其准确性和效率有望在未来跨结构不同蛋白质组的大规模分类工作中表现良好。MACT方法在与Carbo相似性指数方法相当的水平上区分蛋白质链；即，它能够准确地将蛋白质聚集到功能相关的基团中，这些基团表现出对配体结合位点的强烈依赖。

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. We are developing a method for the multiresolution comparison of biomolecular electrostatic potentials without the need for global structural alignment of the biomolecules. The underlying computational geometry algorithm uses multiresolution attributed contour trees (MACTs) to compare the topological features of volumetric scalar fields. We apply the MACTs to compute electrostatic similarity metrics for a large set of protein chains with varying degrees of sequence, structure, and function similarity. For calibration, we also compute similarity metrics for these chains by a more traditional approach based upon 3D structural alignment and analysis of Carbo similarity indices. Moreover, because the MACT approach does not rely upon pairwise structural alignment, its accuracy and efficiency promise to perform well on future large-scale classification efforts across groups of structurally diverse proteins. The MACT method discriminates between protein chains at a level comparable to the Carbo similarity index method; i.e., it is able to accurately cluster proteins into functionally relevant groups which demonstrate strong dependence on ligand binding sites.