ArticleJournal of magnetic resonance (San Diego, Calif. : 1997)2008
Protein conformational flexibility prediction using machine learning.
Article in Journal of magnetic resonance (San Diego, Calif. : 1997), 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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
6 citing papers in PubMed.
- Protein Function Analysis through Machine Learning.Biomolecules · 2022Review
- Detection of chemical exchange in methyl groups of macromolecules.Journal of biomolecular NMR · 2019Article
- Protein flexibility in the light of structural alphabets.Frontiers in molecular biosciences · 2015Review
- The DynaMine webserver: predicting protein dynamics from sequence.Nucleic acids research · 2014Article
- PredyFlexy: flexibility and local structure prediction from sequence.Nucleic acids research · 2012Article
- An introduction to NMR-based approaches for measuring protein dynamics.Biochimica et biophysica acta · 2011Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Using a data set of 16 proteins, a neural network has been trained to predict backbone 15N generalized order parameters from the three-dimensional structures of proteins. The final network parameterization contains six input features. The average prediction accuracy, as measured by the Pearson's correlation coefficient between experimental and predicted values of the square of the generalized order parameter is >0.70. Predicted order parameters for non-terminal amino acid residues depends most strongly on the local packing density and the probability that the residue is located in regular secondary structure.
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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.