Introducing the CSP Analyzer: A novel Machine Learning-based application for automated analysis of two-dimensional NMR spectra in NMR fragment-based screening
Introducing the CSP Analyzer: A novel Machine Learning-based application for automated analysis of two-dimensional NMR spectra in NMR fragment-based screening
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DOI：
10.1016/j.csbj.2020.02.015
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发表时间：
2020-01-01
影响因子：
6
通讯作者：
Popowicz, G. M.
中科院分区：
文献类型：
--
作者：
Fino, R.;Byrne, R.;Popowicz, G. M.
基于核磁共振的筛选，特别是基于片段的药物发现是早期药物发现的一种有价值的方法。在蛋白质检测的二维核磁共振实验中监测片段结合需要分析数百个光谱来检测筛选配体存在的化学位移扰动（CSPs）。计算工具可以简化二维核磁共振光谱中csp的跟踪。然而，据我们所知，还没有一种有效的自动化工具来评估和合并配体结合的多个光谱。我们提出了一种基于机器学习驱动的统计判别的二维HSQC光谱分析新方法。CSP分析器的特点是c#前端接口与Python ML分类器相连。该软件允许从大量光谱中快速评估2D筛选数据，减少评估中用户引入的偏差。CSP Analyzer软件包可在GitHub https://github.com/rubbs/CSP-Analyzer/releases/tag/v1.0上获得，遵循GPL许可证3.0，可免费用于学术和商业用途。(C) 2020作者。由Elsevier B.V.代表计算与结构生物技术研究网络出版。
NMR-based screening, especially fragment-based drug discovery is a valuable approach in early-stage drug discovery. Monitoring fragment-binding in protein-detected 2D NMR experiments requires analysis of hundreds of spectra to detect chemical shift perturbations (CSPs) in the presence of ligands screened. Computational tools are available that simplify the tracking of CSPs in 2D NMR spectra. However, to the best of our knowledge, an efficient automated tool for the assessment and binning of multiple spectra for ligand binding has not yet been described. We present a novel and fast approach for analysis of multiple 2D HSQC spectra based on machine-learning-driven statistical discrimination. The CSP Analyzer features a C# frontend interfaced to a Python ML classifier. The software allows rapid evaluation of 2D screening data from large number of spectra, reducing user-introduced bias in the evaluation. The CSP Analyzer software package is available on GitHub https://github.com/rubbs/CSP-Analyzer/releases/tag/v1.0 under the GPL license 3.0 and is free to use for academic and commercial uses. (C) 2020 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.