Biomolecular NMR spectroscopy has been a keystone in structural biology for decades. It can provide unique, atomic-level insights into protein dynamics, interactions, and conformational ensembles. However, its complex workflows and fragmented data analysis pipelines are often perceived as significant barriers to entry. This review highlights the POKY suite as a comprehensive solution that modernizes and streamlines the entire biomolecular NMR process. From spectral processing to structure calculation, POKY creates a single user-friendly cyberinfrastructure for a seamless and efficient NMR data analysis environment. A key aspect of its design is the integration of various artificial intelligence (AI) components to streamline complex tasks and reduce user burden, such as automation, unsupervised learning, and more. While recent advances within in silico AI prediction models have raised questions about the role of experimental data, POKY provides a clear answer. This ecosystem can create a powerful synergy between the experimental data with structure prediction. modernizing the experimental workflow, POKY makes NMR more accessible and powerful, reinforcing its vital role instructural biology.
Chiu et al. (Sun,) studied this question.