ArticleJournal of chemical information and modeling2015
High Resolution Prediction of Calcium-Binding Sites in 3D Protein Structures Using FEATURE.
Article in Journal of chemical information and modeling, 2015. 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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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.
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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
- A Comprehensive Review of Computation-Based Metal-Binding Prediction Approaches at the Residue Level.BioMed research international · 2022Review
- A deep learning framework to predict binding preference of RNA constituents on protein surface.Nature communications · 2019Article
- Pocket similarity identifies selective estrogen receptor modulators as microtubule modulators at the taxane site.Nature communications · 2019Article
- Computational analysis of kinase inhibitor selectivity using structural knowledge.Bioinformatics (Oxford, England) · 2019Article
- Estimating probabilistic context-free grammars for proteins using contact map constraints.PeerJ · 2019Article
- A density functional theory investigation of the interaction of the tetraaqua calcium cation with bidentate carbonyl ligands.Journal of molecular modeling · 2017Article
- Minimal Functional Sites in Metalloproteins and Their Usage in Structural Bioinformatics.International journal of molecular sciences · 2016Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Metal-binding proteins are ubiquitous in biological systems ranging from enzymes to cell surface receptors. Among the various biologically active metal ions, calcium plays a large role in regulating cellular and physiological changes. With the increasing number of high-quality crystal structures of proteins associated with their metal ion ligands, many groups have built models to identify Ca(2+) sites in proteins, utilizing information such as structure, geometry, or homology to do the inference. We present a FEATURE-based approach in building such a model and show that our model is able to discriminate between nonsites and calcium-binding sites with a very high precision of more than 98%. We demonstrate the high specificity of our model by applying it to test sets constructed from other ions. We also introduce an algorithm to convert high scoring regions into specific site predictions and demonstrate the usage by scanning a test set of 91 calcium-binding protein structures (190 calcium sites). The algorithm has a recall of more than 93% on the test set with predictions found within 3 Å of the actual sites.
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Registered trials
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