Accurate Molecular Modeling in Structural Genomics

描述（由申请人提供）：本提案的总体目标是继续提高结构基因组学同源性建模的准确性。这将通过两个主要方向的进步来实现：(a)更好地对实验学家发现的蛋白质结构域进行分类；(b)更好地改进蛋白质模型以使其更接近实际结构。对发现的已知结构进行分类将使实验学家能够更好地评估他们的进展，并将其与其他小组的工作联系起来。它还将为同源建模和折叠识别提供一个有价值的精确多结构比对数据库。更好的改进将包括在详细的全原子水平上开发有利于天然蛋白质结构的能量函数，以及从初始模型向真实结构移动的更强大的方法。除了产生更精确的同源模型外，这两项进展将在分子功能的所有研究中具有深远的应用，包括配体结合的建模，蛋白质-蛋白质相互作用的建模以及更一般的蛋白质功能模拟。我们认为，要取得这种重大进展，最好的办法是成功完成下列具体目标：

DESCRIPTION (provided by applicant): The overall aim of this proposal is to continue to advance the accuracy of homology modeling for structural genomics. This will be done by advances in two main directions: (a) better classification of protein domains as they are discovered by experimentalists and (b) better methods to refine protein models to bring them closer to the actual structure. Classification of known structures as they are discovered will enable experimentalists to better evaluate their progress and relate it to the work of other groups. It will also provide a valuable data-base of accurate multiple structure alignments for use in both homology modeling and fold recognition. Better refinement will involve the development of energy functions that favor native protein structures at a detailed all-atom level as well as more powerful methods for moving from an initial model towards the real structure. Besides leading to more accurate homology models, both these advances will have far-reaching applications in all studies of molecular function including modeling of ligand binding, modeling of protein-protein interactions and more general simulation of protein function. We believe that such significant progress will be best achieved by successful completion of the following specific aims: (1) Develop a reliable method for domain classification based on our improved structure alignment method Structal, which performed surprisingly well in this study of six different methods. Data will be updated every week using the computer resources that we have available for this grant. (2) Use the regular Structal searches done as part of Aim (1) to produce multiple structural alignments that will be based on our methods for multiple structure superposition and alignment conflict resolution. (3) Use orthogonal normal modes in torsion angle and Cartesian space to generate thousands of near-native decoys. These decoy sets will be generated to have small local deformations but differ from the native structure by different extents of the global root mean square (RMS) deviation. (4) Use near-native decoys to test different molecular mechanics and knowledge-based energy functions. Successes and failures to discriminate structures closer to the native structure from those that are further will be used to systematically improve energy functions that can be used to refine near native structures.