ArticleProteins2011
XANNpred: neural nets that predict the propensity of a protein to yield diffraction-quality crystals.
Article in Proteins, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 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
14 citing papers in PubMed.
- A roadmap for metagenomic enzyme discovery.Natural product reports · 2021Review
- The Dundee Resource for Sequence Analysis and Structure Prediction.Protein science : a publication of the Protein Society · 2020Article
- Critical evaluation of bioinformatics tools for the prediction of protein crystallization propensity.Briefings in bioinformatics · 2018Review
- Survey of Predictors of Propensity for Protein Production and Crystallization with Application to Predict Resolution of Crystal Structures.Current protein & peptide science · 2018Article
- Computational crystallization.Archives of biochemistry and biophysics · 2016Review
- Crysalis: an integrated server for computational analysis and design of protein crystallization.Scientific reports · 2016Article
- Protein stability: a crystallographer's perspective.Acta crystallographica. Section F, Structural biology communications · 2016Review
- Large-scale identification of membrane proteins with properties favorable for crystallization.Protein science : a publication of the Protein Society · 2015Article
- Analysis of crystallization data in the Protein Data Bank.Acta crystallographica. Section F, Structural biology communications · 2015Article
- Covering complete proteomes with X-ray structures: a current snapshot.Acta crystallographica. Section D, Biological crystallography · 2014Article
- Article
- The Protein Structure Initiative Structural Biology Knowledgebase Technology Portal: a structural biology web resource.Journal of structural and functional genomics · 2012Article
- Target selection for structural genomics based on combining fold recognition and crystallisation prediction methods: application to the human proteome.Journal of structural and functional genomics · 2012Article
- Computational approaches to selecting and optimising targets for structural biology.Methods (San Diego, Calif.) · 2011Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Production of diffracting crystals is a critical step in determining the three-dimensional structure of a protein by X-ray crystallography. Computational techniques to rank proteins by their propensity to yield diffraction-quality crystals can improve efficiency in obtaining structural data by guiding both protein selection and construct design. XANNpred comprises a pair of artificial neural networks that each predict the propensity of a selected protein sequence to produce diffraction-quality crystals by current structural biology techniques. Blind tests show XANNpred has accuracy and Matthews correlation values ranging from 75% to 81% and 0.50 to 0.63 respectively; values of area under the receiver operator characteristic (ROC) curve range from 0.81 to 0.88. On blind test data XANNpred outperforms the other available algorithms XtalPred, PXS, OB-Score, and ParCrys. XANNpred also guides construct design by presenting graphs of predicted propensity for diffraction-quality crystals against residue sequence position. The XANNpred-SG algorithm is likely to be most useful to target selection in structural genomics consortia, while the XANNpred-PDB algorithm is more suited to the general structural biology community. XANNpred predictions that include sliding window graphs are freely available from http://www.compbio.dundee.ac.uk/xannpred
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.