Computational crystallization.
Computational crystallization.
复制标题
DOI：
10.1016/j.abb.2016.01.004
复制
发表时间：
2016-07-15
影响因子：
3.9
通讯作者：
Snell EH
中科院分区：
文献类型：
--
作者：
Altan I;Charbonneau P;Snell EH
结晶是晶体学测定大分子结构的关键步骤。虽然可以对该过程进行可靠的理论处理，但由于系统的复杂性，实验过程在很大程度上仍然是反复试验的过程。在本文中，使用溶解度相图讨论了该领域的工作以及理论基础。先验知识已被用来开发通过计算预测结晶结果的工具，并定义增强结晶可能性的突变方法。大多数情况下，这些工具基于二元结果（晶体或无晶体），并且结晶筛选实验集合中包含的完整信息会丢失。通过示例说明了这种附加信息的潜力，在这些示例中可以获得新的生物学知识，并且可以对目标进行子分类以预测哪类试剂提供结晶驱动力。结晶的计算分析需要完整且格式正确的数据。虽然大规模的结晶筛选工作正在进行中，但许多研究中可用的数据很少。讨论了这些数据的潜力以及实现这种潜力所需的步骤。
Crystallization is a key step in macromolecular structure determination by crystallography. While a robust theoretical treatment of the process is available, due to the complexity of the system, the experimental process is still largely one of trial and error. In this article, efforts in the field are discussed together with a theoretical underpinning using a solubility phase diagram. Prior knowledge has been used to develop tools that computationally predict the crystallization outcome and define mutational approaches that enhance the likelihood of crystallization. For the most part these tools are based on binary outcomes (crystal or no crystal), and the full information contained in an assembly of crystallization screening experiments is lost. The potential of this additional information is illustrated by examples where new biological knowledge can be obtained and where a target can be sub-categorized to predict which class of reagents provides the crystallization driving force. Computational analysis of crystallization requires complete and correctly formatted data. While massive crystallization screening efforts are under way, the data available from many of these studies are sparse. The potential for this data and the steps needed to realize this potential are discussed.