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Exploring Sequence Space to Identify Binding Sites for Regulatory RNA-Binding Proteins
Published on: August 9, 2019
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Probing binding hot spots at protein-RNA recognition sites
Amita Barik1, Chandran Nithin1, Naga Bhushana Rao Karampudi2
1Computational Structural Biology Laboratory, Department of Biotechnology, Indian Institute of Technology Kharagpur, Kharagpur-721302, India.
Nucleic Acids Research
|September 15, 2015
Summary
Protein-RNA interactions are guided by evolutionary conservation. Key binding residues are more conserved, especially at major grooves, enabling prediction of binding affinity.
Area of Science:
- Biochemistry
- Structural Biology
- Bioinformatics
Background:
- Protein-RNA interactions are crucial for cellular functions.
- Identifying key residues (binding hot spots) in these interactions is essential for understanding molecular recognition.
- Evolutionary conservation offers insights into functional importance.
Purpose of the Study:
- To investigate evolutionary conservation patterns at protein-RNA interfaces.
- To identify factors contributing to binding hot spots in protein-RNA recognition.
- To develop a predictive model for protein-RNA binding affinity.
Main Methods:
- Structure alignment of polypeptide sequences.
- Analysis of structural and physicochemical attributes of protein-RNA interfaces.
- Identification of evolutionary conservation patterns.
- Development of a Random Forests model for predicting binding hot spots.
Main Results:
- Evolutionary conservation varies among RNA-binding proteins.
- RNA-binding site residues are more conserved than solvent-exposed residues.
- Residues at major grooves are better conserved than at minor grooves.
- Multi-interface residues show higher conservation.
- Residues at water preservation sites are more conserved.
- A Random Forests model predicted 80% of experimental binding affinity changes (ΔΔG).
Conclusions:
- Evolutionary conservation is a reliable indicator of binding hot spots in protein-RNA interactions.
- Structural and physicochemical features can predict binding affinity.
- The developed model aids in engineering protein-RNA interfaces with tailored affinity.
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