Breaking boundaries: TINTO in POKY for computer vision-based NMR walking strategies
Breaking boundaries: TINTO in POKY for computer vision-based NMR walking strategies
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DOI：
10.1007/s10858-023-00423-6
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发表时间：
2023-10-07
影响因子：
2.7
通讯作者：
Lee，Woonghee
中科院分区：
文献类型：
--
作者：
Giraldo，Andrea Estefania Lopez;Werner，Zowie;Lee，Woonghee
核磁共振是研究生物复合物的一项重要技术，因为它提供了原子水平上精确的结构和动态信息。然而，分配共振的过程可能非常耗时且具有挑战性，特别是在峰重叠或数据质量较差的情况下。在本文中，我们介绍了 TINTO（通过 CV/ML 进行 NMR sTrip 操作的二维和三维成像），这是一种用于 NMR 共振分配的先进半自动工具集。 TINTO 包含两个独立的工具，每个工具都针对二维或三维成像而定制。该工具集利用计算机视觉方法和机器学习方法，特别是结构相似性指数和主成分分析，来执行共振的视觉相似性搜索并快速定位相似的条带，从而克服与峰重叠相关的挑战，而无需拾峰。我们的工具提供了用户友好的界面，并且有可能提高 NMR 共振分配的效率和准确性，特别是在复杂的情况下。这一进展对于加深我们在分子水平上对生物系统的理解具有广阔的前景。 TINTO 预装在 POKY 套件中，可从 https://poky.clas.ucdenver.edu 获取。
Nuclear magnetic resonance is a crucial technique for studying biological complexes, as it provides precise structural and dynamic information at the atomic level. However, the process of assigning resonances can be time-consuming and challenging, particularly in cases where peaks overlap, or the data quality is poor. In this paper, we present TINTO (Two and three-dimensional Imaging for NMR sTrip Operation via CV/ML), an advanced semiautomatic toolset for NMR resonance assignment. TINTO comprises two separate tools, each tailored for either two-dimensional or three-dimensional imaging. The toolset utilizes a computer-vision approach and a machine learning approach, specifically structural similarity index and principal components analysis, to perform visual similarity searches of resonances and quickly locate similar strips, and in that way overcome the challenges associated with peak overlap without requiring peak picking. Our tool offers a user-friendly interface and has the potential to enhance the efficiency and accuracy of NMR resonance assignment, particularly in complex cases. This advancement holds promising implications for furthering our understanding of biological systems at the molecular level. TINTO is pre-installed in the POKY suite, which is available at https://poky.clas.ucdenver.edu.