PDA-PreD
PDA-PreD predicts binding affinities of protein-DNA complexes from structural features to quantify determinants of binding free energy (ΔG).
Key Features:
- Data Collection: Uses a dataset of experimental binding free energy (ΔG) values for 391 protein-DNA complexes.
- Structural Feature Analysis: Derives structure-based features including interaction energy, contact potentials, volume and surface area of binding site residues, DNA base-step parameters, and atom-type contacts between proteins and DNA.
- Correlation with Binding Affinity: Identifies factors influencing binding affinity such as number of DNA strands, functional and structural classes of proteins, and binding-site properties (atom contacts, binding site volume, interaction energies, contact potentials).
- Predictive Modeling: Employs multiple regression equations tailored to different structural and functional classes of protein-DNA complexes.
- Performance Metrics: Reports an average correlation coefficient of 0.78 and a mean absolute error of 0.98 kcal/mol between experimental and predicted affinities as validated by jack-knife testing.
Scientific Applications:
- Recognition Mechanism Analysis: Elucidates structural determinants of protein-DNA recognition by relating structural features to binding free energy.
- Experimental Design and Intervention: Informs experimental design and interventions targeting gene regulation by predicting effects on binding affinity.
- Drug Discovery: Supports drug discovery efforts aimed at modulating protein-DNA interactions by providing affinity predictions for complexes of interest.
Methodology:
Collects experimental binding free energies (ΔG) for 391 protein-DNA complexes, extracts structure-based features (interaction energy, contact potentials, binding-site volume and surface area, DNA base-step parameters, atom-type contacts), analyzes feature–affinity relationships, trains regression models using machine learning techniques with multiple regression equations, and validates performance via jack-knife testing.
Topics
Details
- Cost:
- Free of charge
- Tool Type:
- web application
- Operating Systems:
- Mac, Linux, Windows
- Programming Languages:
- Python
- Added:
- 11/7/2023
- Last Updated:
- 11/24/2024
Operations
Publications
Harini K, Kihara D, Michael Gromiha M. PDA-Pred: Predicting the binding affinity of protein-DNA complexes using machine learning techniques and structural features. Methods. 2023;213:10-17. doi:10.1016/j.ymeth.2023.03.002. PMID:36924867. PMCID:PMC10563387.