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Published on: August 21, 2016
Moment-based prediction of DNA-binding proteins
1Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka 820 8502, Fukuoka, Japan.
Journal of Molecular Biology
|August 18, 2004
Summary
Electric charge distribution moments, including net charge, dipole, and quadrupole moments, can accurately predict DNA-binding proteins. This simple biophysical approach enhances genome-wide identification of these crucial proteins.
Area of Science:
- Biophysics
- Structural Biology
- Bioinformatics
Background:
- DNA-binding proteins are crucial for cellular processes.
- Accurate identification of DNA-binding proteins is essential for genomic studies.
- Existing prediction models can be complex and computationally intensive.
Purpose of the Study:
- To investigate the utility of electric charge distribution moments in predicting DNA-binding proteins.
- To develop a simple yet accurate method for identifying DNA-binding proteins.
- To assess the feasibility of using coarse-grained atomic charges for prediction.
Main Methods:
- Calculated net charge, electric dipole moment, and quadrupole moment tensors for 78 DNA-binding proteins.
- Compared charge distribution moments between DNA-binding and non-binding protein datasets.
- Developed single-variable and hybrid predictors using these moments.
- Evaluated prediction accuracy with and without cross-validation.
- Assessed the impact of coarse-graining atomic charges onto C(alpha) atoms.
Main Results:
- Magnitudes of electric charge distribution moments significantly differed between DNA-binding and non-binding proteins.
- Single-variable predictors achieved accuracies of 82.6% (net charge), 77.4% (dipole), and 73.7% (quadrupole).
- Hybrid predictors combining charge and moment information reached 85.6% (without cross-validation) and 83.9% (with cross-validation) accuracy.
- Coarse-graining charges onto C(alpha) atoms minimally impacted prediction accuracy.
Conclusions:
- Electric charge distribution moments are effective simple descriptors for predicting DNA-binding proteins.
- This method offers competitive accuracy compared to more complex models.
- The approach is compatible with homology modeling, enabling genome-wide DNA-binding protein recognition.
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