ReviewThe Journal of biological chemistry2026
Modernizing biomolecular NMR: The POKY suite.
Review in The Journal of biological chemistry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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 cyber infrastructure for a seamless and efficient NMR data analysis environment. A key aspect of its design is the integration of various artificial intelligence components to streamline complex tasks and reduce user burden, such as automation, unsupervised learning, and more. While recent advances within in silico artificial intelligence 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 in structural biology.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.