Engineering of Proteins for Crystallography

描述(申请人提供)：尽管结构生物学取得了巨大的成功，但由于大分子结晶成功率低，以及许多样品在核磁共振中的表现不佳，结构生物学受到了严重限制。许多有价值的、生物医学上重要的靶点没有进行结晶尝试，结构确定的总成本很高，主要是由于在蛋白质生产、结晶和初始HSQC光谱收集阶段对靶标的劳动力和时间密集型筛选。我们的研究将解决这一瓶颈，通过开发蛋白质工程策略，允许合理设计具有增强的溶解性、稳定性和结晶性或核磁共振性能的目标变体，并实施基于网络的、公开可用的服务器，以促进这些策略的应用。在前一阶段，我们证明了在表面熵降低(SER)的前提下，表面工程可以合理地诱导蛋白质结晶，即突变暴露在大分子、极性和溶剂中的氨基酸，如Lys、Glu和Gln，以及小残基，如Ala。此外，我们设计并实现了第一代XtalPred和SERP服务器，它们提供对蛋白质结晶倾向的自动评估，并基于SER策略设计具有增强结晶性的变体。这些工具已被世界各地数以千计的研究人员成功使用，并帮助解决了近170个晶体结构，包括药物设计管道中的新型球状和膜蛋白、复合体和药物靶标的晶体结构。我们现在建议对蛋白质表面的物理化学和其溶液性质之间的关系进行进一步的实验和计算研究。具体地说，我们将研究表面熵降低如何影响蛋白质的溶解度和稳定性，以及蛋白质表面性质如何影响异核核磁共振谱的质量。我们将设计和实施具有众多新功能的第二代XtalPred和SERP算法，以实现更高的蛋白质结晶性预测成功率，并设计具有更高结晶性、更高溶解性和更高核磁共振谱性能的变体。这两个第二代服务器将与折叠预测服务器FFAS03集成到一个可互操作的网络中，我们将设计一个蛋白质结构设计元服务器(PCDM)，使其能够从单个图形用户界面访问整个工具箱，从预测到用于表达靶标的引物和/或合成基因的交互设计。最后，为了验证这些方法，我们将使用一系列生物相关的蛋白质靶标对它们进行测试。

DESCRIPTION (provided by applicant): In spite of its enormous successes, structural biology is severely limited by the low success rates of macromolecular crystallization, and by the poor performance of many samples in NMR. Many valuable, biomedically important targets elude crystallization attempts, and overall high costs of structure determination are due primarily to the labor and time-intensive screening of targets at the stages of protein production, crystallization and initial HSQC spectra collection. Our research will address this bottleneck, through development of protein engineering strategies that allow for rational design of target variants with enhanced solubility, stability, and crystallizability or performance in NMR, and implementation of web-based, publicly available servers that facilitate application of these strategies. During the previous phase, we demonstrated that protein crystallization can be rationally induced by surface engineering based on the premise of surface entropy reduction (SER), i.e. mutagenesis of large, polar and solvent exposed amino acids, such as Lys, Glu and Gln, with small residues, e.g. Ala. Further, we designed and implemented the first generation XtalPred and SERp servers, which offer automated evaluation of protein's propensity to crystallize and design of variants with enhanced crystallizability based on the SER strategy. These tools have been used successfully by thousands of investigators world-wide, and helped solve nearly 170 crystal structures, including those of novel globular and membrane proteins, complexes and drug- targets in drug design pipelines. We now propose to pursue further experimental and computational studies of the relationships between physical chemistry of the protein surface and its solution properties. Specifically, we will investigate how surface entropy reduction affects protein solubility and stability, and how protein surface properties impact on the quality of heteronuclear NMR spectra. We will design and implement second generation XtalPred and SERp algorithms, with numerous new features, to achieve higher success rates for prediction of protein crystallizability and for design of variants with higher crystallizability, solubility, and with enhanced performance in NMR spectroscopy. The two second generation servers will be integrated into an interoperable network with the fold-prediction server FFAS03, and we will design a Protein Construct Design Metaserver (PCDM) making it possible to access the entire toolbox from a single GUI, proceeding from prediction to interactive design of primers and/or synthetic genes for expression of targets. Finally, to validate the methods, we will test them using a selection of biologically relevant protein targets.