Protein Expression and Purification in the Fast Lane

全基因组序列的可用性开始对结构生物学产生深远的影响。这是第一次有可能从成千上万的开放阅读框中选择目标，通过聚合酶链反应（PCR）的力量，任何一个都可以很容易地从其基因组中检索。可以理解的是，这种观点已经在很大程度上唤起了结构生物学的愿景，创造了一个被称为“结构基因组学”的新领域。然而，要将这一梦想变为现实，需要技术进步，以加快确定生物大分子三维结构的速度。该项目的目标是开发一种适合大规模结构生物学计划的蛋白质表达和纯化的综合策略。“高通量”一词经常与结构基因组学联系在一起。然而，我们认为从“高产出”的角度来考虑更为合适。毕竟，重要的不是做了多少实验，而是有多少成功的结果。输出是两个变量的函数：投入和效率，后者是衡量有多少目标成功地从管道的另一端出现。原则上，可以通过增加这两个变量中的任何一个来增加输出。我们的研究重点是尽量提高蛋白质生产的效率。如果蛋白质表达是一种完全偶然的冒险，这将是一场艰苦的战斗，但我们不相信情况是这样的。虽然我们认识到没有单一的方法会一直成功，但经验告诉我们，平均而言，一些方法始终比其他方法更有成效。我们的目标是将这些高概率方法合并成一个连贯的“最大可能性”蛋白质表达和纯化策略，使我们能够通过提高效率来增加产量，而不考虑项目的规模。我们方法的核心是自由使用基因工程亲和标签。亲和标签的优点是引人注目的：它们可以提高重组蛋白的产量，帮助保护其免受细胞内蛋白水解，增强其溶解度，并促进其纯化。我们认为，亲和标签的集体属性远远超过其潜在的缺点。此外，不可能想象不涉及亲和标签的重组蛋白生产的通用过程。在致力于使用标记方法之后，我们的目标是获得比关联标记所能获得的最大可能的好处。关联标签并不完全相同；有些人比其他人更好地执行某些任务，因此我们需要认识到各种标签的优点和缺点，以便最大限度地利用它们。我们对探索亲和标签对重组蛋白溶解度的影响特别感兴趣，因为不溶解度是蛋白质表达和纯化的主要问题，而蛋白质的重折叠似乎与高输出应用不相容。通过反复试验，我们发现一个特别的标签，即大肠杆菌麦芽糖结合蛋白（MBP），具有提高溶解度和促进其融合伙伴正确折叠的惊人能力。因为我们认为这是一种罕见而有价值的属性，所以我们采用了MBP作为我们的亲和标记策略的基石。我们正在进行的目标之一是了解溶解效应的潜在机制，以便我们能够更好地利用它。我们也希望通过对这个模型系统的详细研究，了解一些关于辅助蛋白质折叠过程的更基本和更普遍的性质。虽然MBP有一些强大的优势，但它并不是一个特别好的蛋白质纯化亲和标签。

The availability of complete genome sequences is beginning to have a profound impact on structural biology. For the first time, it is possible to select targets from among many thousands of open reading frames, any of which can easily be retrieved from its genome through the power of the polymerase chain reaction (PCR). Understandably, this perspective has conjured up visions of structural biology on a grand scale, creating a new field that has come to be known as "structural genomics". Yet, transforming this dream into reality will require technical advances that increase the speed with which the three-dimensional structures of biological macromolecules can be determined. The goal of this project is to develop an integrated strategy for protein expression and purification that will be suitable for large-scale structural biology initiatives. The term "high-throughput" is often used in connection with structural genomics. However, we believe it is more appropriate to think in terms of "high-output." After all, what matters in the end are not how many experiments that one does but how many successful outcomes one has to show for it. Output is a function of two variables: input and efficiency, the latter being a measure of how many targets that enter the pipeline successfully emerge from the other end. In principal, output can be augmented by increasing either of these variables. Our research focuses on trying to maximize the efficiency of protein production. This would be an uphill battle if protein expression were a completely haphazard venture, but we do not believe this is the case. While we recognize that no single method will succeed all of the time, experience has taught us that some approaches are, on average, consistently more productive than others. Our goal is to merge these high-probability approaches into a coherent strategy for "maximum likelihood" protein expression and purification that will enable us to increase output by improving efficiency, irrespective of the scale of the project.Central to our approach is the liberal use of genetically engineered affinity tags. The advantages of affinity tags are compelling: they can improve the yield of a recombinant protein, help protect it from intracellular proteolysis, enhance its solubility, and facilitate its purification. We believe that the collective attributes of affinity tags far outweigh their potential disadvantages. Moreover, it is impossible to imagine a generic process for the production of recombinant proteins that does not involve affinity tags. Having committed ourselves to the tagging approach, our objective is to derive the maximum possible benefit than we can from affinity tags. Affinity tags are not all the same; some perform certain tasks better than others do, and so we need to be cognizant of the advantages and disadvantages of various tags in order to use them to maximum advantage.We have been particularly interested in exploring the influence of affinity tags on the solubility of recombinant proteins, because insolubility is a major problem in protein expression and purification, and refolding of proteins seems incompatible with high-output applications. Through trial and error, we have discovered that one tag in particular, E. coli maltose-binding protein (MBP), has an amazing ability to improve the solubility and promote the proper folding of its fusion partners. Because we believe this to be a rare and valuable attribute, we have adopted MBP as the cornerstone of our affinity-tagging strategy. One of our ongoing objectives is to understand the underlying mechanism of the solubilizing effect so that we can better manipulate it to our advantage. We also hope to learn something of a more fundamental and general nature about the process of assisted protein folding by studying this model system in detail. Although MBP has some powerful advantages, it is not a particularly good affinity tag for protein purification.