ArticleBMC bioinformatics2006
Prediction of the functional class of metal-binding proteins from sequence derived physicochemical properties by support vector machine approach.
Article in BMC bioinformatics, 2006. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
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- Zincbindpredict-Prediction of Zinc Binding Sites in Proteins.Molecules (Basel, Switzerland) · 2021Article
- Identification of Disease-Related 2-Oxoglutarate/Fe (II)-Dependent Oxygenase Based on Reduced Amino Acid Cluster Strategy.Frontiers in cell and developmental biology · 2021Article
- The Metallome as a Link Between the "Omes" in Autism Spectrum Disorders.Frontiers in molecular neuroscience · 2021Review
- Recognizing Ion Ligand-Binding Residues by Random Forest Algorithm Based on Optimized Dihedral Angle.Frontiers in bioengineering and biotechnology · 2020Article
- Design of metalloproteins and novel protein folds using variational autoencoders.Scientific reports · 2018Article
- Computational approaches forBioscience reports · 2017Review
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- Prediction and analysis of canonical EF hand loop and qualitative estimation of Ca²⁺ binding affinity.PloS one · 2014Article
- A survey of computational intelligence techniques in protein function prediction.International journal of proteomics · 2014Review
- Bagging with CTD--a novel signature for the hierarchical prediction of secreted protein trafficking in eukaryotes.Genomics, proteomics & bioinformatics · 2013Article
- Calciomics: integrative studies of Ca2+-binding proteins and their interactomes in biological systems.Metallomics : integrated biometal science · 2013Review
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- Prediction of metal ion-binding sites in proteins using the fragment transformation method.PloS one · 2012Article
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- Metal-binding sites are designed to achieve optimal mechanical and signaling properties.Structure (London, England : 1993) · 2010Article
- Analysis and prediction of calcium-binding pockets from apo-protein structures exhibiting calcium-induced localized conformational changes.Protein science : a publication of the Protein Society · 2010Article
- Integration of Diverse Research Methods to Analyze and Engineer Ca-Binding Proteins: From Prediction to Production.Current bioinformatics · 2010Article
- Prediction of functional class of proteins and peptides irrespective of sequence homology by support vector machines.Bioinformatics and biology insights · 2009Article
- Enzyme classification with peptide programs: a comparative study.BMC bioinformatics · 2009Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Metal-binding proteins play important roles in structural stability, signaling, regulation, transport, immune response, metabolism control, and metal homeostasis. Because of their functional and sequence diversity, it is desirable to explore additional methods for predicting metal-binding proteins irrespective of sequence similarity. This work explores support vector machines (SVM) as such a method. SVM prediction systems were developed by using 53,333 metal-binding and 147,347 non-metal-binding proteins, and evaluated by an independent set of 31,448 metal-binding and 79,051 non-metal-binding proteins. The computed prediction accuracy is 86.3%, 81.6%, 83.5%, 94.0%, 81.2%, 85.4%, 77.6%, 90.4%, 90.9%, 74.9% and 78.1% for calcium-binding, cobalt-binding, copper-binding, iron-binding, magnesium-binding, manganese-binding, nickel-binding, potassium-binding, sodium-binding, zinc-binding, and all metal-binding proteins respectively. The accuracy for the non-member proteins of each class is 88.2%, 99.9%, 98.1%, 91.4%, 87.9%, 94.5%, 99.2%, 99.9%, 99.9%, 98.0%, and 88.0% respectively. Comparable accuracies were obtained by using a different SVM kernel function. Our method predicts 67% of the 87 metal-binding proteins non-homologous to any protein in the Swissprot database and 85.3% of the 333 proteins of known metal-binding domains as metal-binding. These suggest the usefulness of SVM for facilitating the prediction of metal-binding proteins. Our software can be accessed at the SVMProt server http://jing.cz3.nus.edu.sg/cgi-bin/svmprot.cgi.
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