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: 10184149
• 负责人: Jason E Gestwicki
• 金额: $ 39.05万
• 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/10184149
• 关键词: Algorithms; Alzheimer' s Disease; Benchmarking; Binding; Binding Proteins; CD69 antigen; Chemicals; Chemistry; Chromatin Remodeling Factor; Circular Dichroism; Clinical; Collaborations; Complex; Cystic Fibrosis; Data; Data Analyses; Databases; Defect; Differential Scanning Calorimetry; Disease; Drug Targeting; Dyes; Equation; Failure; Fluorescence; Goals; Gold; Hydrophobicity; Individual; Kinetics; Knowledge; Label; Libraries; Ligand Binding; Link; Machine Learning; Measurement; Measures; Membrane; Methods; Modality; Molecular Conformation; Monitor; Multiprotein Complexes; Neurodegenerative Disorders; Nucleic Acid Binding; Oranges; Pharmaceutical Chemistry; Pilot Projects; Process; Property; Proteins; Reporting; Research; Role; Scanning; Series; Specificity; Structure; Structure-Activity Relationship; System; Technology; Temperature; Testing; Therapeutic; Time; Work; aerobic respiration control protein; base; cheminformatics; conformational conversion; 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; reconstitution; simulation; 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.

摘要 人们对测量蛋白质稳定性的技术非常感兴趣，因为许多技术具有破坏性。 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有关，尤其是其中之一。 治疗这些疾病的有前途的方法是使用小分子，称为“校正剂”，与受损的细胞结合 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），但还有数百种其他错误折叠疾病的快速发现的障碍是什么。 一个重要的障碍是以前的校正器已经被发现了 长时间的搜索，使用通用性不够强的专门（即针对特定目标）技术 在这里，我们提出了下一代差示扫描荧光测定法。 (DSF) 来填补这一空白 在典型的 DSF 实验中，POI 在 qPCR 仪器中加热，然后将其展开。 通过其与溶剂化显色染料（例如 Sypro Orange，SO）的结合进行监测。 然后使用荧光曲线来估计熔化转变 (Tm)，并通过以下方式确定假定的校正器 它们对该值 (DTm) 的影响是多方面的，因为它不需要蛋白质标记或结构。 与圆二色性 (CD) 或差分扫描等同类平台不同。 量热法 (DSC)，DSF 适合 384 孔板格式，有利于大规模化学筛选。 DSF 有潜力改变校正器的发现，但有一些重大障碍需要克服，例如 DSF。 经常失败，因为 SO 不结合目标蛋白或者它结合到天然状态的疏水斑块， 模糊 Tm 此外，对于某些 POI，温度-荧光曲线很复杂，有多个。 转变，因此不容易根据我们的初步屏幕进行分析或拟合。 约 50 种不同的蛋白质，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。 通过颠覆性创新解决问题：（SA1）设计和合成下一代染料库， 显着扩展了 DSF 和 (SA2) 理论和实验驱动的范围，数据显着改进 分析，由机器学习支持，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) 扩大 DSF 的应用范围 重要的是，我们将对多蛋白质复合物和构象变化进行基准测试。 这些创新针对当前最先进的方法，重点是对 总之，这些研究预计将极大地扩大 DSF 的范围。 技术。