Engineering of Proteins for Crystallography

描述（由申请人提供）：尽管结构生物学取得了巨大的成功，但它受到大分子结晶成功率低和许多样品在NMR中性能差的严重限制。许多有价值的、生物医学上重要的靶标逃避结晶尝试，并且结构测定的总体高成本主要是由于在蛋白质生产、结晶和初始HSQC光谱收集阶段的靶标的劳动和时间密集型筛选。我们的研究将解决这一瓶颈，通过蛋白质工程策略的发展，允许合理设计的目标变体，增强溶解度，稳定性和结晶性或NMR性能，并实现基于网络的，公开可用的服务器，促进这些策略的应用。在前一阶段，我们证明了蛋白质结晶可以通过基于表面熵降低（SER）的前提的表面工程来合理地诱导，即用小残基（例如Ala）诱变大的、极性的和溶剂暴露的氨基酸（例如Lys、Glu和Gln）。此外，我们设计并实现了第一代XtalPred和SERp服务器，该服务器提供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。这些工具已被全世界数千名研究人员成功使用，并帮助解决了近170种晶体结构，包括药物设计管道中的新型球状和膜蛋白、复合物和药物靶点的晶体结构。我们现在建议进行进一步的实验和计算研究的蛋白质表面的物理化学和其溶液性质之间的关系。具体来说，我们将研究表面熵减少如何影响蛋白质的溶解性和稳定性，以及蛋白质表面性质如何影响异相NMR光谱的质量。我们将设计和实施第二代XtalPred和SERp算法，具有许多新功能，以实现蛋白质结晶性预测的更高成功率，并设计具有更高结晶性，溶解性和NMR光谱性能增强的变体。这两个第二代服务器将与折叠预测服务器FFAS 03集成到一个可互操作的网络中，我们将设计一个蛋白质构建设计元服务器（PCDM），使其能够从一个单一的GUI访问整个工具箱，从预测到引物和/或合成基因的交互式设计，以表达目标。最后，为了验证这些方法，我们将使用选择的生物相关蛋白质靶标对其进行测试。

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.