ArticlePloS one2014
High precision prediction of functional sites in protein structures.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Protein functional site annotation using local structure embeddings.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- COLLAPSE: A representation learning framework for identification and characterization of protein structural sites.Protein science : a publication of the Protein Society · 2023Article
- Machine learning differentiates enzymatic and non-enzymatic metals in proteins.Nature communications · 2021Article
- ResiRole: residue-level functional site predictions to gauge the accuracies of protein structure prediction techniques.Bioinformatics (Oxford, England) · 2021Article
- High precision protein functional site detection using 3D convolutional neural networks.Bioinformatics (Oxford, England) · 2019Article
- Improving the explainability of Random Forest classifier - user centered approach.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2018Article
- 3D deep convolutional neural networks for amino acid environment similarity analysis.BMC bioinformatics · 2017Article
- Using support vector machines to improve elemental ion identification in macromolecular crystal structures.Acta crystallographica. Section D, Biological crystallography · 2015Article
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Authors and funding
5 authors.
Funding
Abstract
We address the problem of assigning biological function to solved protein structures. Computational tools play a critical role in identifying potential active sites and informing screening decisions for further lab analysis. A critical parameter in the practical application of computational methods is the precision, or positive predictive value. Precision measures the level of confidence the user should have in a particular computed functional assignment. Low precision annotations lead to futile laboratory investigations and waste scarce research resources. In this paper we describe an advanced version of the protein function annotation system FEATURE, which achieved 99% precision and average recall of 95% across 20 representative functional sites. The system uses a Support Vector Machine classifier operating on the microenvironment of physicochemical features around an amino acid. We also compared performance of our method with state-of-the-art sequence-level annotator Pfam in terms of precision, recall and localization. To our knowledge, no other functional site annotator has been rigorously evaluated against these key criteria. The software and predictive models are incorporated into the WebFEATURE service at http://feature.stanford.edu/wf4.0-beta.
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Registered trials
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