National Institutes of Health, National Center for Advancing Translational Sciences, Division of Discovery Innovation, 9800 Medical Center Drive, Bethesda, MD 20892-3370, USA.
Expert Opin Drug Discov. 2013 Sep;8(9):1071-82. doi: 10.1517/17460441.2013.806479. Epub 2013 Jun 6.
Despite tremendous advances in the application of biophysical methods in drug discovery, the preponderance of instruments and techniques still require sophisticated analyses by dedicated personnel and/or large amounts of frequently hard-to-produce proteins. A technique which carries the promise of simplicity and relatively low protein consumption is the differential scanning fluorometry (DSF). This technique monitors protein through the use of environmentally sensitive fluorescent dye, in a temperature-ramp regime by observing the gradual exposure to the solvent of otherwise buried hydrophobic faces of protein domains.
This review describes recent developments in the field of DSF. This article pays a particular emphasis on the advances published during the 2010 - 2013 period.
There has been a significant diversification of DSF applications beyond initial small molecule discovery into areas such as protein therapeutic development, formulation studies and various mechanistic investigations. This serves as a further indication of the broad penetration of the technique. In the small molecule arena, DSF has expanded toward sophisticated co-dependency MOA tests, demonstrating the wealth of information which the technique can provide. Importantly, the first public deposition of a large screening dataset may enable the use of thermal stabilization data in refining in silico models for small molecule binding.
尽管在药物发现中应用生物物理方法取得了巨大进展,但大多数仪器和技术仍然需要专门人员进行复杂的分析,或者需要大量通常难以制备的蛋白质。一种具有简单性和相对低蛋白质消耗潜力的技术是差示扫描荧光法(DSF)。该技术通过在温度斜坡条件下使用环境敏感的荧光染料监测蛋白质,通过观察蛋白质结构域中原本埋藏的疏水面逐渐暴露于溶剂来实现。
本文描述了 DSF 领域的最新进展。本文特别强调了 2010 年至 2013 年期间发表的进展。
DSF 的应用已经从最初的小分子发现扩展到了蛋白质治疗开发、制剂研究和各种机制研究等领域,这进一步表明了该技术的广泛应用。在小分子领域,DSF 已经扩展到复杂的协同作用 MOA 测试,展示了该技术可以提供的丰富信息。重要的是,第一个大型筛选数据集的公开存储可能使我们能够在小分子结合的计算模型中使用热稳定性数据进行优化。