Protein structure refinement through effective sampling and scoring

描述（由申请人提供）：详细的结构信息对于理解生物学过程和为开发针对各种疾病的治疗策略提供基础至关重要。实验方法允许精确测定高分辨率结构，但它们受到重大努力和实验限制的阻碍。作为替代，现代计算方法可以预测蛋白质结构到一个很好的准确度。然而，计算模型往往达不到实验精度。计算结构精化的目的是改善初始模型的实验质量。成功的蛋白质结构优化需要能够产生天然样构象的采样方法和能够在不了解真实实验结构的情况下从一组候选物中识别最天然结构的评分方法。在过去的进展与结构细化方法的发展的基础上，开发新的方法，以进一步提高采样的质量和效率，并选择天然的结构。一个关键组成部分是使用中间分辨率模型PRIMO与完全原子模型。这些方法将被应用于可溶性和膜结合蛋白质的精制。本提案的中心目标是开发一个可通过网络服务提供给社区的实用协议。

DESCRIPTION (provided by applicant): Detailed structural information is essential for understanding biological processes and for providing the basis for the development of therapeutic strategies against a variety of diseases. Experimental methods allow the accurate determination of high-resolution structures, but they are encumbered by significant effort and experimental constraints. As an alternative, modern computational methods can predict protein structures to a good degree of accuracy. However, computational models often do not reach experimental accuracy. Computational structure refinement aims at improving initial models towards experimental quality. Successful protein structure refinement requires sampling methods that can generate native-like conformations and scoring methods that are able to identify the most native structures from a set of candidates without knowledge of the true experimental structure. Building on past progress with the development of structure refinement methods, new methods are developed to further improve the quality and efficiency of sampling and the selection of native-like structures. A key component is the use of the intermediate resolution model PRIMO in conjunction with fully atomistic models. These methods will be applied to the refinement of both soluble and membrane-bound proteins. The development of a practical protocol that can be made available to the community via a web service is a central goal of this proposal.