Protein Model Refinement and Flexible Docking by Constrained Free Energy Minimization

该项目关注两个基本问题。第一个是开发灵活的蛋白质对接算法，用于涉及侧链和环区的结合发生实质性构象变化的情况。第二个问题是构象搜索算法的发展，用于从天然结构中提纯RMSD为6~10A的低分辨率蛋白质模型，该模型通过同源建模、折叠识别获得。或从头算结构预测。在这两种应用中，自由能势将作为目标函数，将分子力学与经验溶剂化和熵项相结合。已经证明，这种势在诱饵识别中比纯经验函数更有效。然而，即使使用归一化过程来降低van der Waals能量中的噪声，组合的势也表现出多分量/多频率行为，并且很难最小化。自由能被认为是三个函数的总和：包括静电、溶剂化和熵贡献的光滑分量，内(键)能项的中频分量，以及本质上是高频噪声的van der Waals项，它几乎不携带与本征状态距离的信息。大多数已建立的寻找多分量/多频势的全局最小值的方法会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来模仿原生折叠或联想途径。该方法的基本思想是对光滑的自由能分量进行最小化，但将搜索限制在构象空间中范德华能低的区域。由于这个小组已经证明组合的势具有很好的区分力，所以成功对接或模型改进的关键是能够产生足够的近自然构象。预计利用光滑自由能分量的梯度将大大提高采样效率，由此产生的方法将对广泛的分子生物学/生物化学界有用。

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.