ArticleGenomics & informatics2017
Prediction of Metal Ion Binding Sites in Proteins from Amino Acid Sequences by Using Simplified Amino Acid Alphabets and Random Forest Model.
Article in Genomics & informatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Identification and Functional Annotation of Hypothetical Proteins of Pan-Drug-ResistantBioinformatics and biology insights · 2024Article
- mebipred: identifying metal-binding potential in protein sequence.Bioinformatics (Oxford, England) · 2022Article
- A Comprehensive Review of Computation-Based Metal-Binding Prediction Approaches at the Residue Level.BioMed research international · 2022Review
- Zincbindpredict-Prediction of Zinc Binding Sites in Proteins.Molecules (Basel, Switzerland) · 2021Article
- Prediction of Metal Ion Binding Sites of Transmembrane Proteins.Computational and mathematical methods in medicine · 2021Article
- A novel protein descriptor for the prediction of drug binding sites.BMC bioinformatics · 2019Article
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1 author.
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Abstract
Metal binding proteins or metallo-proteins are important for the stability of the protein and also serve as co-factors in various functions like controlling metabolism, regulating signal transport, and metal homeostasis. In structural genomics, prediction of metal binding proteins help in the selection of suitable growth medium for overexpression's studies and also help in obtaining the functional protein. Computational prediction using machine learning approach has been widely used in various fields of bioinformatics based on the fact all the information contains in amino acid sequence. In this study, random forest machine learning prediction systems were deployed with simplified amino acid for prediction of individual major metal ion binding sites like copper, calcium, cobalt, iron, magnesium, manganese, nickel, and zinc.
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