Protein structures are essential in modern biology yet experimental methods are far from being able to catch up with the rapid increase in available genomic data. Computational protein structure prediction methods aim to fill the gap while the role of protein structure refinement is to take approximate initial template-based models and bring them closer to the true native structure. Current methods for computational structure refinement rely on molecular dynamics simulations, related sampling methods, or iterative structure optimization protocols. The best methods are able to achieve moderate degrees of refinement but consistent refinement that can reach near-experimental accuracy remains elusive. Key issues revolve around the accuracy of the energy function, the inability to reliably rank multiple models, and the use of restraints that keep sampling close to the native state but also limit the degree of possible refinement. A different aspect is the question of what exactly the target of high-resolution refinement should be as experimental structures are affected by experimental conditions and different biological questions require varying levels of accuracy. While improvement of the global protein structure is a difficult problem, high-resolution refinement methods that improves local structural quality such as favorable stereochemistry and the avoidance of atomic clashes are much more successful.

中文翻译：

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计算蛋白质结构细化：即将实现，但仍有很长的路要走。


蛋白质结构在现代生物学中至关重要，但实验方法远远无法跟上可用基因组数据的快速增长。计算蛋白质结构预测方法旨在填补这一空白，而蛋白质结构细化的作用是采用近似的基于模板的初始模型，并使它们更接近真实的天然结构。当前计算结构细化的方法依赖于分子动力学模拟、相关采样方法或迭代结构优化协议。最好的方法能够实现中等程度的细化，但能够达到接近实验精度的一致细化仍然难以实现。关键问题围绕着能量函数的准确性、无法可靠地对多个模型进行排序，以及使用保持采样接近原始状态但也限制了可能的细化程度的约束。另一个方面是高分辨率细化的目标到底应该是什么的问题，因为实验结构受到实验条件的影响，并且不同的生物学问题需要不同程度的准确性。虽然改善整体蛋白质结构是一个难题，但改善局部结构质量（例如有利的立体化学和避免原子冲突）的高分辨率精修方法要成功得多。