Optimal parameter estimation for crystallisation trials

结晶试验的最佳参数估计

• 批准号: BB/I015868/1
• 金额: $ 11.71万
• 依托单位: University of York
• 依托单位国家: 英国
• 项目类别: Training Grant
• 财政年份: 2011
• 资助国家: 英国
• 起止时间: 2011 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FI015868%2F1
• 关键词: Optimal; parameter; estimation; crystallisation; trials

Currently there is no a priori method to determine the optimum crystallization strategy for a particular protein and the process remains highly empirical. Several important variables, which often interact, must be tested in combination. Many different buffers are available to maintain a specific pH and various salts, polymers or organic solvents may be used as precipitants. Detergents and other additives may also be needed to keep a protein soluble during the crystallization process and an exhaustive search of all possible combinations is impossible. Scarcity of protein often makes it crucial that suitable conditions are found in the fewest possible experiments and various sampling techniques have been proposed to reduce the number of trials [1]. The use of specific information about the macromolecule in question and prior experience allows a more systematic approach to crystallization and a number of laboratories have set up in-house databases in order to develop crystallization strategies. In a global program, the Biological Macromolecule Crystallization Database (BMCD) provides information related to proteins and the conditions in which they have been crystallized. This information has been used to build prior distributions, based on the relative frequencies of success for different combinations of conditions and the results show a correlation between families of macromolecules and the conditions under which they crystallize [2]. The aim of this project is to utilize information concerning the variables involved in successful crystallization trials as well as prior knowledge of a particular protein's characteristics through advanced data mining methods. Certain parameters can be estimated prior to crystallization trials. For example, pH-dependent properties such as solubility and stability can be established in the purification protocol. Crystallographers routinely carry out a number of techniques for protein characterization in order to confirm the identity of the protein and ensure it is pure, folded and stable. PAGE gels, IEF gels and dynamic light scattering all provide insight into protein characteristics. Mass spectroscopy, ultra centrifugation and NMR are further techniques that are readily available. The BMCD reports as many as 53 parameters per crystallization entry, although submission of such data is not yet routine for crystallographers, and the database is still patchy. The first stage of this project will be to collate information from databases at AstraZeneca as well as the BMCD and York Structural Biology laboratory. The cooperation of a number of other laboratories will be sought for the rationalization of crystal growth screening. Given all the available information about a protein, the challenge is to determine the relationship between the known factors and the optimal conditions for crystallization. Such a relationship is likely to be a complex, non-linear relationship. Statistical analysis of the data may well indicate the form of the relationship but to extract the maximum information from the data available will require non-linear modelling. This project will investigate the use of neural networks [3] and statistical pattern processing methods to design a systematic procedure for choosing the initial conditions for crystallization trials. The most appropriate neural network architecture will be dependent on the characteristics of the data, and detailed specification of the network will therefore occur after exploratory analysis of the data. [1] Carter, C.W., Jr. and Carter, C.W. (1979) J.Biol. Chem., 254, 12219-12223. [2] Hennessy, D., Buchanan, B., Subramanian, D.,Wilkosz, P. and Rosenberg, J. (2000). Acta. Cryst., D56, 817-827. [3] Bishop, C. Neural networks and pattern recognition. Oxford, Clarendon Press, 1995.

目前还没有先验方法来确定特定蛋白质的最佳结晶策略，并且该过程仍然高度经验性。几个经常相互作用的重要变量必须结合起来进行测试。许多不同的缓冲液可用于维持特定的pH，并且各种盐、聚合物或有机溶剂可用作沉淀剂。在结晶过程中，可能还需要去污剂和其他添加剂来保持蛋白质的可溶性，并且不可能对所有可能的组合进行详尽的搜索。蛋白质的稀缺往往使得在尽可能少的实验中找到合适的条件变得至关重要，并且已经提出了各种采样技术来减少试验次数[1]。使用关于所讨论的大分子的特定信息和先前的经验允许更系统的结晶方法，并且许多实验室已经建立了内部数据库以开发结晶策略。生物大分子结晶数据库（BMCD）提供了与蛋白质及其结晶条件相关的信息。该信息已用于构建先验分布，基于不同条件组合的相对成功频率，结果显示大分子家族与其结晶条件之间存在相关性[2]。该项目的目的是通过先进的数据挖掘方法利用有关成功结晶试验所涉及的变量的信息以及特定蛋白质特性的先验知识。某些参数可以在结晶试验之前估计。例如，可以在纯化方案中建立pH依赖性性质如溶解度和稳定性。晶体学家通常进行一些蛋白质表征技术，以确认蛋白质的身份，并确保其纯度，折叠和稳定。PAGE凝胶，IEF凝胶和动态光散射都提供了深入了解蛋白质的特性。质谱、超离心和NMR是容易获得的进一步技术。BMCD报告每个结晶条目多达53个参数，尽管提交这些数据对晶体学家来说还不是常规的，数据库仍然是不完整的。该项目的第一阶段将是从阿斯利康以及BMCD和约克结构生物学实验室的数据库中整理信息。将寻求其他一些实验室的合作，以使晶体生长筛选合理化。考虑到所有关于蛋白质的可用信息，挑战在于确定已知因素与结晶的最佳条件之间的关系。这种关系可能是一种复杂的非线性关系。对数据的统计分析很可能表明这种关系的形式，但要从现有数据中提取最大限度的信息，就需要建立非线性模型。本项目将研究使用神经网络[3]和统计模式处理方法来设计一个系统的程序，用于选择结晶试验的初始条件。最合适的神经网络架构将取决于数据的特征，因此，在对数据进行探索性分析后，将对网络进行详细说明。[1]卡特，C.W.，Jr.卡特，C.W.（1979）J. Biol. Chem.，254，12219-12223。[2]Hennessy，D.，布坎南，B.，Subramanian，D.，Wilkosz，P. and Rosenberg，J.（2000）. Acta.水晶，D56，817-827。[3]毕晓普角神经网络与模式识别。牛津，克拉伦登出版社，1995年。