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(如囊性纤维化、阿尔茨海默病)与蛋白质错误折叠和不稳定有关。特别是一位 治疗这些疾病的有希望的方法是使用小分子，这种小分子被称为“校正器”，与受损的细胞结合。 蛋白质，并部分恢复其折叠。多个矫正剂已获得FDA批准(例如，异烟肼、他法马司、 米卡司他)，但还有成百上千的错误折叠疾病。快速发现的障碍是什么？ 更多的校正员？一个重要的障碍是，之前的校正者是通过 长时间的搜索，使用不够通用的专门(即特定于目标的)技术 跨许多感兴趣蛋白质(POI)。在此，我们提出了下一代差示扫描荧光法 (DSF)来填补这一空白。在典型的DSF实验中，POI在qPCR仪中加热，其展开是 通过与溶剂变色染料(如赛普罗橙，SO)的结合来监测。由此产生的温度与 然后使用荧光曲线来估计熔融转变(TM)，并通过以下方式确定假定的校正值 它们对该值的影响(DTM)。DSF是通用的，因为它不需要蛋白质标记或结构 知识。此外，与圆二色(CD)或差分扫描等可比平台不同 热分析(DSC)，DSF适用于384孔板格式，便于大规模的化学筛选。而当 DSF具有改变校正器发现的潜力，但仍有主要障碍需要克服。例如，DSF 经常失败是因为它不结合目标蛋白或它结合到天然状态上的疏水斑块， 遮挡了TM。此外，对于一些POI，温度-荧光曲线是复杂的，具有多个 转变，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~50种不同的蛋白质中，这些问题导致DSF在60%以上的情况下失败。我们建议解决这些问题 通过颠覆性创新解决的问题：(SA1)设计和合成下一代染料库， 显著扩展DSF和(SA2)理论和实验驱动的数据显著改进的范围 分析，由机器学习实现，并通过门户网站(DSFWorld)公开提供。 受到初步成功的鼓舞，我们还建议：(SA3)通过以下方式扩大DSF的应用范围 多蛋白质复合体和构象变化的开创性研究。重要的是，我们将以每一项为基准 这些创新与当前最先进的方法相比，重点是批判性地理解 优势和劣势。总之，这些研究有望极大地扩大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.