Sharing DNA-binding information across structurally similar proteins enables accurate specificity determination
Joshua L Wetzel1,2, Mona Singh1,2
1The Lewis-Sigler Institute for Integrative Genomics, Princeton, NJ 08544, USA.
Nucleic Acids Research
|November 29, 2019
Summary
This study introduces a new computational framework to improve DNA-binding specificity inference. By analyzing structurally similar proteins together, accuracy is significantly enhanced, leading to better predictions.
Area of Science:
- Computational Biology
- Bioinformatics
- Molecular Biology
Background:
- Thousands of protein-DNA interactions are experimentally assayed.
- Current computational methods infer DNA-binding specificities independently for each protein.
- This approach overlooks valuable interaction information from structurally similar proteins.
Purpose of the Study:
- To develop a novel framework for inferring DNA-binding specificities.
- To leverage interaction data from groups of structurally similar proteins simultaneously.
- To improve the accuracy of DNA-binding specificity predictions.
Main Methods:
- Developed a framework considering protein-DNA interactions for groups of structurally similar proteins.
- Devised constrained optimization and label propagation algorithms.
- Balanced individual protein observations with dataset-wide consistency.
Main Results:
- Tested approaches on two large, independent Cys2His2 zinc finger protein-DNA interaction datasets.
- Jointly inferring specificities dramatically improved accuracy within each dataset.
- Increased agreement between datasets and with an external standard.
Conclusions:
- Sharing protein-DNA interaction information across structurally similar proteins enhances prediction accuracy.
- The proposed framework offers a powerful method for accurate DNA-binding specificity inference.
- This approach improves consistency and agreement in protein-DNA interaction data analysis.
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