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DNA Sequence Recognition by DNA Primase Using High-Throughput Primase Profiling
Published on: October 8, 2019
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DBPMod: a supervised learning model for computational recognition of DNA-binding proteins in model organisms
Upendra K Pradhan1, Prabina K Meher1, Sanchita Naha2
1Division of Statistical Genetics, ICAR-Indian Agricultural Statistics Research Institute, PUSA, New Delhi 110012, India.
Briefings in Functional Genomics
|August 31, 2023
Summary
A new computational method, DBPMod, accurately identifies species-specific DNA-binding proteins (DBPs) using machine learning and evolutionary features. This tool surpasses existing methods and aids in understanding crucial biological processes.
Area of Science:
- Molecular Biology
- Bioinformatics
- Computational Biology
Background:
- DNA-binding proteins (DBPs) are essential for fundamental biological processes like gene regulation and DNA repair.
- Accurate identification of DBPs is crucial for understanding molecular mechanisms.
- Existing computational methods often lack the specificity for identifying species-specific DBPs.
Purpose of the Study:
- To develop a novel computational method, DBPMod, for accurate identification of species-specific DNA-binding proteins.
- To improve the accuracy of DBP prediction by utilizing species-specific features and machine learning models.
Main Methods:
- Developed DBPMod, a machine learning-based computational method.
- Employed both shallow and deep learning algorithms for prediction.
- Utilized evolutionary features and sequence-derived features for model training.
- Validated performance across five model organisms: *C. elegans*, *D. melanogaster*, *E. coli*, *H. sapiens*, and *M. musculus*.
- Assessed accuracy using five-fold cross-validation and independent test sets, measuring area under the receiver operating characteristic curve (auROC) and area under the precision-recall curve (auPRC).
Main Results:
- Shallow learning models demonstrated higher accuracy compared to deep learning models.
- Evolutionary features proved more effective than sequence-derived features for prediction accuracy.
- DBPMod achieved high prediction accuracies, with auROC ranging from approximately 89-92% and auPRC from 89-95% across model organisms.
- DBPMod outperformed 12 existing state-of-the-art computational methods in DBP identification for all tested species.
- A publicly accessible web server for DBPMod was developed.
Conclusions:
- DBPMod provides a highly accurate and species-specific approach for identifying DNA-binding proteins.
- The method's reliance on evolutionary features enhances its predictive power.
- DBPMod serves as a valuable tool for researchers, complementing experimental and computational DBP discovery efforts.
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