Computational design of new protein structures and interactions

项目摘要/摘要 计算设计在生物技术中的应用具有创造新的蛋白质功能的巨大潜力， 生物学和医学。然而，尽管在设计具有从头结构的蛋白质方面取得了令人兴奋的进展，我们的 设计具有新功能的蛋白质的能力落后。造成这种差异的一个关键原因是函数 通常要求蛋白质的几何构型偏离从头设计的结构和 因此更难设计。我们工作的长期目标是推进计算设计 使更复杂功能的预测性设计成为可能。这项建议的具体目标是 解决通常未解决的设计结合新小分子配体的蛋白质的问题。一种特殊的 应用是设计新的传感器/致动器：可以检测用户定义的小分子信号的蛋白质 并触发生物反应(如蛋白质信号或基因表达)。的重要应用 这种传感器/致动器包括最大限度地生产代谢工程中具有工业价值的化学品， 创造精确的工具来剖析细胞信号中的生物过程，并实现对 新兴的癌症疗法。我们在前期项目中的工作已经具备了先进的装订场地设计方法 并将其应用于设计了第一个计算机设计的化学诱导蛋白质二聚系统， 它对活细胞中的新配体进行感知和反应；晶体结构证实了De的准确性 Novo设计了结合部位。尽管取得了这一关键进展，但要推广这一方法仍存在重大障碍。 设计新的配体结合部位的第一步通常是确定所需的结合部位几何形状 (配位配体的氨基酸侧链星座)。然后第二步是将这些 形成一种合适的蛋白质，称为“支架”。该方法受到可用几何形状的严重限制， 既用于结合部位，也用于容纳它们的支架。为了解决这些问题，我们提出了两个关键 方法论创新：目标1将建立并实验测试一种新的计算方法 产生数以百万计的可能的结合位点几何图形，可以构建到蛋白质中。目标2将会发展 并测试一种新的计算方法来构建“新折叠族”(一组定制形状的新折叠族 蛋白质)通过系统地改变要使用的给定折叠拓扑内的结构元件的几何形状 作为脚手架。可行性得到了两个目标的初步结果的支持；我们设计了新的结合位点 (上一期)，并解决了3个从头设计的折叠相同但不同的蛋白质的结构 几何图形。拟议的研究在创造新的方法和扩展的新分子方面都有所创新。 可设计的结构和功能，并克服现有方法存在的问题 几何图形。最终，这些研究将导致先进的计算设计方法，我们将 免费获得关于这些方法的优点和局限性的新知识，以推动进一步的发展， 以及在生物工程中控制细胞行为和探索基础生物学和疾病生物学的新工具。

PROJECT SUMMARY/ABSTRACT Computational design has immense potential to create new protein functions with applications in biotechnology, biology, and medicine. However, despite exciting progress in designing proteins with de novo structures, our ability to design proteins with new functions lags behind. A key reason for this discrepancy is that function typically requires protein geometries that deviate from the “idealized” folds of de novo designed structures and that are hence more difficult to design. The long-term objective of our work is to advance computational design to make predictive design of more complex functions possible. The specific objective of this proposal is to address the generally unsolved problem of designing proteins that bind new small molecule ligands. A particular application is the design of new sensor/actuators: proteins that can detect a user-defined small molecule signal and trigger a biological response (such as protein signaling or gene expression). Significant applications of such sensor/actuators include maximizing production of industrially valuable chemicals in metabolic engineering, creating precise tools for dissecting biological processes in cell signaling, and achieving tight regulation in emerging cancer therapies. Our work in the prior project period has advanced methods for binding site design and applied them to engineer the first computationally designed chemically-induced protein dimerization system, which senses and responds to a new ligand in living cells; a crystal structure confirmed the accuracy of the de novo designed binding site. Despite this key progress, there are significant barriers to generalize the approach. The first step in engineering new ligand binding sites is generally to identify desired binding site geometries (constellations of amino acid side chains coordinating the ligand). The second step is then to place those geometries into a suitable protein termed “scaffold”. This approach is critically limited by available geometries, both for binding sites and scaffolds to accommodate them. To address these problems, we propose two key methodological innovations: Aim 1 will establish and experimentally test a new computational method to generate millions of possible binding site geometries de novo that can be built into proteins. Aim 2 will develop and test a new computational approach to build “de novo fold families” (sets of custom-shaped de novo designed proteins) by systematically varying the geometries of structural elements within a given fold topology, to be used as scaffolds. Feasibility is supported by preliminary results for both aims; we have designed new binding sites (prior period), and have solved structures of 3 de novo designed proteins with the same fold but distinct geometries. The proposed studies innovate in creating both new methods and new molecules that expand designable structures and functions and overcome problems with current approaches limited by available geometries. Ultimately, these studies will lead to advanced computational design methods that we will make freely available, new knowledge on strengths and limitations of these methods to drive further developments, and new tools to control cellular behavior in biological engineering and to probe basic and disease biology.