Differential Scanning Fluorimetry (DSF) Methods for Studying Protein Stability

抽象的。人们对测量蛋白质稳定性的技术有很大的兴趣，因为许多破坏性的 疾病（例如囊性纤维化、阿尔茨海默病）与蛋白质错误折叠和不稳定性有关。一个特别 治疗这些疾病的一种有希望的方法是使用小分子，称为“矫正剂”， 蛋白质并部分恢复其折叠。多种校正剂已获得FDA批准（例如，ivacaftor，tafamadis， migalastat），但还有数百种其他的错误折叠疾病。快速发现的障碍是什么 更多的修正者？一个重要的障碍是，以前的校正器是通过 使用通用性不强的专门（即针对特定目标）技术进行长时间搜索 在许多感兴趣的蛋白质（POI）中。在这里，我们提出了下一代差示扫描荧光法， (DSF)来填补这个空白。在典型的DSF实验中，POI在qPCR仪器中加热，并且其解折叠是在 通过其与溶剂化显色染料（例如Sypro橙子，SO）的结合来监测。结果温度与 然后使用荧光曲线来估计解链转变（Tm），推定的校正因子由以下鉴定： 其对该值的影响（DTm）。DSF是通用的，因为它不需要蛋白质标记或结构 知识此外，与可比较的平台（例如圆二色性（CD）或差示扫描）不同， DSF适合于384孔板格式，便于大规模化学筛选。而 DSF有潜力改变校正器的发现，但还有一些主要的障碍需要克服。例如，DSF 通常失败，因为SO不结合靶蛋白或它结合天然状态下的疏水补丁， 模糊了Tm。此外，对于一些POI，温度-荧光曲线是复杂的，具有多个温度-荧光曲线。 过渡，因此不容易使用标准方程进行分析或拟合。根据我们的初步筛选 在大约50种不同的蛋白质中，这些问题导致DSF在超过60%的情况下失败。我们建议解决这些问题 （SA 1）设计和合成下一代染料库， 显著扩大DSF的范围，（SA 2）理论和实验驱动的数据显著改善 通过机器学习进行分析，并通过门户网站（DSFWorld）公开提供。 在初步成功的鼓舞下，我们亦建议：（补充措施3）扩大数码港基金的应用范围， 多蛋白复合物和构象变化的开创性研究。重要的是，我们将对每个 这些创新对当前国家的最先进的方法，重点是批判性的理解， 优势和劣势。总之，这些研究预计将大大扩大DSF的范围 技术.

Abstract. There is great interest in technologies that measure protein stability, because many devastating diseases (e.g. cystic fibrosis, Alzheimer’s disease) are linked to protein misfolding and instability. One especially promising way to treat these diseases is to use small molecules, termed “correctors” that bind to the damaged protein and partially restore its folding. Multiple correctors have received FDA approval (e.g. ivacaftor, tafamadis, migalastat), but there are hundreds of additional misfolding diseases. What are the hurdles to the rapid discovery of additional correctors? One important barrier is that previous correctors have been uncovered through prolonged searches, using specialized (i.e. target-specific) technologies that are not versatile enough for use across many proteins-of-interest (POIs). Here, we propose next-generation Differential Scanning Fluorimetry (DSF) to fill this gap. In a typical DSF experiment, a POI is heated in a qPCR instrument and its un-folding is monitored by its binding to a solvatochromatic dye (e.g. Sypro Orange, SO). The resulting temperature vs. fluorescence curves are then used to estimate the melting transition (Tm), with putative correctors identified by their effect on this value (DTm). DSF is versatile because it does not require protein labeling or structural knowledge. Moreover, unlike comparable platforms, such as circular dichroism (CD) or differential scanning calorimetry (DSC), DSF is amenable to 384-well plate format, facilitating large-scale chemical screens. While DSF has the potential to transform corrector discovery, there are major hurdles to overcome. For example, DSF often fails because SO does not bind the target protein or it binds to hydrophobic patches on the native state, obscuring the Tm. Further, for some POIs, the temperature-fluorescence curves are complex, with multiple transitions, and therefore not readily analyzed or fit using standard equations. Based on our preliminary screens of ~50 different proteins, these issues cause DSF to fail in more than 60% of cases. We propose to solve these issues through disruptive innovations: (SA1) Design and synthesis of next-generation dye libraries that significantly expand the scope of DSF and (SA2) Theory- and experiment-driven, dramatic improvements in data analysis, enabled by machine learning and made publicly available through a web portal (DSFWorld). Encouraged by preliminary success, we also propose to: (SA3) Expand the scope of DSF applications by pioneering studies of multi-protein complexes and conformational changes. Importantly, we will benchmark each of these innovations against current state-of-the-art approaches, with a focus on a critical understanding of strengths and weaknesses. Together, these studies are expected to dramatically expand the scope of DSF technology.