Engineering of Proteins for Crystallography

尽管结构生物学取得了巨大的成功，但它的低成功率严重限制了结构生物学的发展 大分子结晶。许多有价值的、生物医学上重要的靶子没有进行结晶尝试， 结构确定的总体成本较高，主要是由于筛选的劳动力和时间密集型 目标是蛋白质生产和结晶阶段。我们的研究将解决这一瓶颈， 通过开发蛋白质工程策略，允许合理设计目标变体 提高溶解性、稳定性和结晶性，并实施基于Web的公开提供的服务器 为这些战略的应用提供便利。 在上一阶段，我们证明了表面可以合理地诱导蛋白质结晶。 基于表面熵降低(SER)前提的工程，即大的、极性的和 暴露在溶剂中的氨基酸，如赖氨酸、谷氨酸和谷氨酸，残留量小，如丙氨酸。此外，我们还设计了 并实现了第一代XtalPred和SERP服务器，它们提供了自动评估 蛋白质的结晶倾向和基于SER的具有增强结晶能力的变体的设计 策略。这些工具已经被世界各地数以千计的调查人员成功使用，并帮助解决了 近170个晶体结构，包括新的球状和膜蛋白、络合物和 药物设计流水线中的药物靶标。我们现在建议进行进一步的实验和计算 蛋白质表面物理化学与其溶液性质之间关系的研究。 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。我们会 设计和实施第二代XtalPred和SERP算法，具有许多新功能，以 获得更高的蛋白质结晶性预测成功率和更高的 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它们进行测试 相关的蛋白质靶标。

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.