Protein structure refinement through effective sampling and scoring

通过有效的采样和评分进行蛋白质结构细化 详细的结构信息对于理解生物过程和 为开发针对各种疾病的治疗策略提供了基础， 疾病实验方法允许高分辨率的准确测定 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， 良好的准确度。然而，计算模型往往无法达到 实验精度计算结构精化的目的是改善初始 模型实验质量。成功的蛋白质结构优化需要 可以产生天然样构象的取样方法和 能够从一组候选者中识别出最原生的结构， 了解真实的实验结构。在过去取得的进展的基础上， 随着结构细化方法的发展，新方法被开发以进一步 提高取样和选择类似本国结构的质量和效率。 一个关键组成部分是结合使用中间分辨率模型PRIMO 完全原子化的模型。这些方法将被应用于两者的细化 可溶性和膜结合蛋白质。制定一项切实可行的协议， 通过网络服务提供给社区是这项建议的一个中心目标。

Protein structure refinement through effective sampling and scoring 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.