Protein Model Refinement and Flexible Docking by Constrained Free Energy Minimization

通过约束自由能最小化进行蛋白质模型细化和灵活对接

• 批准号: 9904834
• 负责人: Sandor Vajda
• 金额: $ 55.64万
• 依托单位: Trustees of Boston University
• 依托单位国家: 美国
• 项目类别: Continuing grant
• 财政年份: 1999
• 资助国家: 美国
• 起止时间: 1999-10-01 至 2003-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9904834&HistoricalAwards=false
• 关键词: Protein; Model; Refinement; Flexible; Docking

This project focuses on two fundamental problems. The first is the development of flexible protein docking algorithms for the case in which the association occurs with substantial conformational change involving side chains and loop regions. The second problem is the development of con-formational search algorithms for refining low resolution protein models with 6 to 10 A RMSD from the native structure, obtained by homology modeling, fold recognition. or ab initio structure prediction. In both applications, free energy potentials, combining molecular mechanics with em-pirical solvation and entropic terms, will be used as target functions. It has been shown that such potentials can be more effective in decoy discrimination than purely empirical functions. However even with a normalization procedure to reduce the noise in the van der Waals energy, the combined potentials exhibit a multicomponent/multifrequency behavior, and are very difficult to minimize. The free energy is regarded as the sum of three functions: a smooth component that includes elec-trostatic, solvation, and entropic contributions, an intermediate frequency component of internal (bonded) energy terms, and the van der Waals term which is essentially a high frequency noise, and carries little information about the distance from the native state.Most established methods of finding the global minimum of a multicomponent/multifrequency potential generate a large number of trial conformations and then rank them according to their free energies. This project will use a radically different approach that mimics native folding or association pathways. The basic idea of the method is performing minimization of the smooth free energy components, but restricting the search to regions of the conformational space with low van der Waals energy. Since this group has already shown that the combined potential has good discriminatory power, the key to a successful docking or model refinement is the ability to generate enough near-native conformations. It is expected that exploiting the gradient of the smooth free energy components will substantially increase the efficiency of sampling, and the resulting methods will be useful to the broad molecular biology/biochemistry community.

这个项目着重于两个基本问题。第一个是灵活的蛋白质对接算法的发展的情况下，其中的协会发生大量的构象变化，涉及侧链和环区。第二个问题是构象搜索算法的发展，用于从天然结构中提炼具有6至10 A RMSD的低分辨率蛋白质模型，通过同源建模，折叠识别获得。或从头算结构预测。在这两个应用程序中，自由能势，结合分子力学与经验溶剂化和熵项，将被用作目标函数。它已被证明，这样的潜力可以更有效的诱饵歧视比纯粹的经验功能。然而，即使使用归一化程序来减少货车德瓦尔斯能量中的噪声，组合电势也表现出多分量/多频率行为，并且非常难以最小化。自由能被认为是三个函数之和：包括静电、溶剂化和熵贡献的平滑分量，内部（键合）能量项的中频分量，以及本质上是高频噪声的货车德瓦耳斯项，大多数已建立的寻找多组分/多组分系统全局最小值的方法，多频势产生大量的尝试构象，然后根据它们的自由能对它们进行排序。这个项目将使用一种完全不同的方法，模仿天然折叠或关联途径。该方法的基本思想是进行最小化的平滑自由能组件，但限制搜索的构象空间的区域与低货车德瓦尔斯能量。由于这一组已经表明，组合电位具有良好的区分能力，成功对接或模型改进的关键是能够产生足够的近天然构象。预计利用平滑自由能分量的梯度将大大提高采样效率，并且所得方法将对广泛的分子生物学/生物化学社区有用。