TPPU_DSF:利用 DSF 数据计算热力学参数的网络应用程序

IF 4.7 2区 生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY Journal of Molecular Biology Pub Date : 2024-09-01 DOI:10.1016/j.jmb.2024.168519
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引用次数: 0

摘要

这里我们介绍 TPPU_DSF (https://maciasnmr.net/tppu_dsf/)。这是一款免费开源的网络应用程序,可自动打开、转换、拟合和计算标准差示扫描荧光光谱仪(DSF)数据中的蛋白质展开热力学参数。该软件有多种应用。在筛选蛋白质结合剂的化合物库中,获得热力学参数提供了一种比单独检测熔化温度(Tm)变化更可靠的方法,从而有助于增加筛选活动中的阳性结果数量。此外,与 Tm 的变化相比,ΔGuo 的变化表明蛋白质在较低化合物浓度下对结合的反应,从而降低了与检测所需的蛋白质和化合物数量相关的成本。此外,通过在 Tm 比较中添加热力学信息,该软件还有助于优化蛋白质结构和缓冲条件,这是结构和功能项目的常见做法。
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TPPU_DSF: A Web Application to Calculate Thermodynamic Parameters Using DSF Data

Here we present TPPU_DSF (https://maciasnmr.net/tppu_dsf/). This is a free and open-source web application that opens, converts, fits, and calculates the thermodynamic parameters of protein unfolding from standard differential scanning fluorimetry (DSF) data in an automated manner. The software has several applications. In the context of screening compound libraries for protein binders, obtaining thermodynamic parameters provides a more robust approach to detecting hits than the changes in the melting temperature (Tm) alone, thereby helping to increase the number of positive hits in screening campaigns. Moreover, changes in ΔGuo indicate protein response to binding at lower compound concentrations than those in the Tm, thereby reducing the costs associated with the amounts of protein and compounds required for the assays. Also, by adding thermodynamic information to the Tm comparison, the software can contribute to the optimization of protein constructs and buffer conditions, a common practice before structural and functional projects.

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来源期刊
Journal of Molecular Biology
Journal of Molecular Biology 生物-生化与分子生物学
CiteScore
11.30
自引率
1.80%
发文量
412
审稿时长
28 days
期刊介绍: Journal of Molecular Biology (JMB) provides high quality, comprehensive and broad coverage in all areas of molecular biology. The journal publishes original scientific research papers that provide mechanistic and functional insights and report a significant advance to the field. The journal encourages the submission of multidisciplinary studies that use complementary experimental and computational approaches to address challenging biological questions. Research areas include but are not limited to: Biomolecular interactions, signaling networks, systems biology; Cell cycle, cell growth, cell differentiation; Cell death, autophagy; Cell signaling and regulation; Chemical biology; Computational biology, in combination with experimental studies; DNA replication, repair, and recombination; Development, regenerative biology, mechanistic and functional studies of stem cells; Epigenetics, chromatin structure and function; Gene expression; Membrane processes, cell surface proteins and cell-cell interactions; Methodological advances, both experimental and theoretical, including databases; Microbiology, virology, and interactions with the host or environment; Microbiota mechanistic and functional studies; Nuclear organization; Post-translational modifications, proteomics; Processing and function of biologically important macromolecules and complexes; Molecular basis of disease; RNA processing, structure and functions of non-coding RNAs, transcription; Sorting, spatiotemporal organization, trafficking; Structural biology; Synthetic biology; Translation, protein folding, chaperones, protein degradation and quality control.
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