Differential Scanning Fluorimetry (DSF) Methods for Studying Protein Stability

研究蛋白质稳定性的差示扫描荧光 (DSF) 方法

• 批准号: 10626847
• 负责人: Jason E Gestwicki
• 金额: $ 37.41万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-05 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10626847
• 关键词: Algorithms; Alzheimer' s Disease; Benchmarking; Binding; Binding Proteins; Chemicals; Chemistry; Chromatin Remodeling Factor; Circular Dichroism; Clinical; Collaborations; Complex; Cystic Fibrosis; Data; Data Analyses; Dedications; Defect; Differential Scanning Calorimetry; Disease; Drug Targeting; Dyes; Equation; Failure; Fluorescence; Goals; Hydrophobicity; Individual; Kinetics; Knowledge; Label; Libraries; Ligand Binding; Link; Machine Learning; Measurement; Measures; Membrane; Methods; Modality; Monitor; Multiprotein Complexes; Neurodegenerative Disorders; Nucleic Acid Binding; Oranges; Pharmaceutical Chemistry; Pilot Projects; Process; Property; Protein Conformation; Proteins; Reporting; Research; Role; Scanning; Series; Specificity; Structure-Activity Relationship; System; Technology; Temperature; Testing; Therapeutic; Time; VX-770; Work; aerobic respiration control protein; cheminformatics; chromatin protein; conformational conversion; curve fitting; design; enzyme activity; experimental study; high throughput screening; high throughput technology; human disease; improved; innovation; instrument; interest; large datasets; melting; metalloenzyme; new technology; next generation; novel therapeutics; protein complex; protein folding; protein misfolding; public database; reconstitution; simulation; skills; small molecule; success; tau Proteins; theories; web portal

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.

抽象的。人们对测量蛋白质稳定性的技术非常感兴趣，因为许多毁灭性的 疾病（例如囊性纤维化，阿尔茨海默氏病）与蛋白质错误折叠和不稳定性有关。特别是一个 治疗这些疾病的有前途的方法是使用小分子，称为“校正器”，与受损 蛋白质并部分恢复其折叠。多个校正器已获得FDA批准（例如，ivacaftor，tafamadis， Migalastat），但还有数百种错误折叠疾病。快速发现的障碍是什么 其他校正器？一个重要的障碍是通过 长时间的搜索，使用专门的（即特定于目标的）技术，这些技术的用途不足以使用 在许多利益的蛋白质（POI）中。在这里，我们提出了下一代差异扫描荧光仪 （DSF）填补此空白。在典型的DSF实验中，在QPCR仪器中加热POI，其未折叠是 通过与溶剂染料的结合（例如Sypro Orange，SO）来监测。由此产生的温度与 然后，使用荧光曲线来估计熔融跃迁（TM），并通过假定的校正器确定 它们对这个值的影响（DTM）。 DSF用途广泛，因为它不需要蛋白质标记或结构 知识。此外，与可比平台不同，例如圆二色性（CD）或差分扫描 量热法（DSC），DSF适合384孔板格式，支持大规模的化学筛选。尽管 DSF有可能改变纠正措施，有重大障碍要克服。例如，DSF 通常会失败，因为因此不结合靶蛋白，也不会结合天然状态上的疏水斑块， 遮盖TM。此外，对于某些POI，温度荧光曲线很复杂，有多个 过渡，因此不容易使用标准方程进行分析或拟合。根据我们的初步屏幕 在约50种不同的蛋白质中，这些问题导致DSF在60％以上的病例中失败。我们建议解决这些 通过破坏性创新问题：（SA1）设计和合成下一代染料库的综合 显着扩大了DSF和（SA2）理论和实验驱动的数据范围的范围显着改善 分析，通过机器学习启用，并通过Web门户（DSFWorld）公开提供。 在初步成功的鼓励下，我们还建议：（SA3）扩大DSF申请的范围 多蛋白质复合物和构象变化的开创性研究。重要的是，我们每个人都会基准 在针对当前最新方法的这些创新中，重点是对 优势和劣势。总之，这些研究有望大大扩大DSF的范围 技术。

项目成果

Protocol for performing and optimizing differential scanning fluorimetry experiments.

• DOI: 10.1016/j.xpro.2023.102688
• 发表时间: 2023-12-15
• 期刊: STAR PROTOCOLS
• 作者: Wu, Taiasean;Hornsby, Michael;Zhu, Lawrence;Yu, Joshua C.;Shokat, Kevan M.;Gestwicki, Jason E.
• 通讯作者: Gestwicki, Jason E.