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Updated: May 29, 2026
07:38
Mass Spectrometry-Based Proteomics Analyses Using the OpenProt Database to Unveil Novel Proteins Translated from Non-Canonical Open Reading Frames
Published on: April 11, 2019
iDNA-Prot: identification of DNA binding proteins using random forest with grey model
Wei-Zhong Lin1, Jian-An Fang, Xuan Xiao
1Information Science and Technology School, Donghua University, Shanghai, China.
Plos One
|September 22, 2011
Summary
A new computational tool, iDNA-Prot, accurately identifies DNA-binding proteins using amino acid sequences. This high-throughput method offers a faster and effective approach for genome annotation and protein analysis.
Area of Science:
- Genomics
- Proteomics
- Bioinformatics
Background:
- DNA-binding proteins are essential for cellular functions.
- Accurate identification of DNA-binding proteins is critical for genome annotation.
- Existing methods require enhancement for improved prediction power.
Purpose of the Study:
- To develop a novel, high-throughput computational tool for identifying DNA-binding proteins.
- To enhance the prediction accuracy of DNA-binding proteins using sequence information alone.
- To provide a user-friendly web server for public access.
Main Methods:
- Incorporated features from the 'grey model' into pseudo amino acid composition.
- Utilized the random forest algorithm for classification.
- Developed a stringent benchmark dataset with low sequence identity.
Main Results:
- The iDNA-Prot predictor achieved an overall success rate of 83.96% via jackknife tests.
- Demonstrated significantly shorter computational time compared to existing predictors.
- Validated performance on a robust, non-redundant dataset.
Conclusions:
- iDNA-Prot is an effective and efficient tool for identifying DNA-binding proteins.
- The predictor can aid in large-scale analysis and genome annotation.
- A publicly accessible web server facilitates its use by researchers.
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