Department of Chemistry, University of Colorado Denver, Denver, CO, 80204, USA.
Department of Biology, University of Colorado Denver, Denver, CO, 80204, USA.
J Biomol NMR. 2023 Dec;77(5-6):217-228. doi: 10.1007/s10858-023-00423-6. Epub 2023 Oct 7.
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 .
核磁共振是研究生物复合物的关键技术，因为它可以在原子水平上提供精确的结构和动态信息。然而，峰重叠或数据质量差的情况下，分配共振的过程可能会很耗时且具有挑战性。在本文中，我们提出了 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 共振分配的高级半自动工具集。TINTO 由两个单独的工具组成，每个工具都针对二维或三维成像进行了定制。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，对共振进行视觉相似性搜索，并快速找到相似的条带，从而克服了峰重叠相关的挑战，而无需峰选择。我们的工具提供了用户友好的界面，有可能提高 NMR 共振分配的效率和准确性，特别是在复杂情况下。这一进展有望进一步深入了解分子水平的生物系统。TINTO 已预先安装在 POKY 套件中，可在 https://poky.clas.ucdenver.edu 获得。
