Engineering of Proteins for Crystallography

尽管结构生物学取得了巨大的成功，但它的成功率很低， 大分子结晶许多有价值的，生物医学上重要的目标逃避结晶尝试， 结构确定的总体高成本主要是由于劳动和时间密集的筛选， 针对蛋白质生产和结晶阶段。我们的研究将解决这个瓶颈， 通过开发允许合理设计靶变体的蛋白质工程策略， 增强的溶解性、稳定性和可结晶性，以及基于网络的公开可用服务器的实现 这有助于这些战略的实施。 在前一阶段，我们证明了蛋白质结晶可以合理地诱导表面 基于表面熵减少（SER）的前提的工程化，即大的、极性的和 暴露于溶剂的氨基酸，如Lys、Glu和Gln，具有小残基，如Ala。此外，我们还设计了 并实施了第一代XtalPred和SERp服务器，提供自动评估 蛋白质的结晶倾向和基于SER设计具有增强的结晶性的变体 战略这些工具已被世界各地数千名调查人员成功使用，并帮助解决了 近170种晶体结构，包括新的球状和膜蛋白，复合物和 药物设计管道中的药物靶点。我们现在建议进行进一步的实验和计算 研究蛋白质表面物理化学与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度和稳定性。我们将 设计和实现第二代XtalPred和SERp算法，具有许多新功能， 预测蛋白质结晶性和设计具有更高结晶度的变体的成功率更高。 结晶性和溶解性。最后，为了验证这些方法，我们将使用选择的生物学方法对其进行测试。 相关蛋白质靶点。

In spite of its enormous successes, structural biology is severely limited by the low success rates of macromolecular crystallization. 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 and crystallization. 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, 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 drugtargets 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. 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 and solubility. Finally, to validate the methods, we will test them using a selection of biologically relevant protein targets.