ENZYME CLASSIFICATION BY ELECTROSTATIC PROPERTIES

这个子项目是许多利用 由NIH/NCRR资助的中心赠款提供的资源。子项目和 研究者（PI）可能从另一个NIH来源获得了主要资金， 因此可以在其他CRISP条目中表示。所列机构为 中心，但不一定是研究者所在的机构。 我们正在开发一种方法，用于多分辨率比较生物分子的静电势，而不需要全局结构对齐的生物分子。底层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计算一个大的蛋白质链的静电相似性度量，具有不同程度的序列，结构和功能的相似性。对于校准，我们还计算这些链的相似性度量的一个更传统的方法的基础上的3D结构对齐和分析的Carbo相似性指数。此外，由于MACT方法不依赖于成对结构比对，其准确性和效率有望在未来跨结构多样性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类成功能相关的组，这些组显示出对配体结合位点的强烈依赖性。

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.