MIB2
MIB2 predicts metal ion-binding sites and coordinating residues in protein structures using predicted models from the (PS)2 method and the AlphaFold Protein Structure Database combined with metal ion docking and a metal ion type-specific scoring function to support analysis of 18 metal ions.
Key Features:
- Structure-Based Prediction Enhancement: Uses predicted protein structures from the (PS)2 method and the AlphaFold Protein Structure Database to provide structural models when experimental data is unavailable.
- Metal Ion Docking and Binding Residue Prediction: Performs metal ion docking on predicted structures to identify potential binding sites and predict specific coordinating residues.
- Expanded Metal Ion Coverage: Supports prediction for 18 different metal ion types.
- Improved Prediction Performance: Incorporates additional residue templates and a metal ion type-specific scoring function to refine binding-site predictions.
Scientific Applications:
- Protein Engineering: Enables design and modification of proteins by identifying metal-binding sites and coordinating residues.
- Drug Discovery and Development: Facilitates assessment of metal ion interactions in target proteins for identification of modulators of metal-dependent activity.
- Structural Biology Studies: Provides structural hypotheses for protein–metal ion complexes to support mechanistic and functional interpretation.
Methodology:
MIB2 acquires predicted structures via the (PS)2 method and the AlphaFold Protein Structure Database, uses these structures as templates for metal ion docking to identify binding sites and predict coordinating residues, and applies a metal ion type-specific scoring function together with expanded residue templates to refine predictions.
Topics
Details
- Cost:
- Free of charge
- Tool Type:
- web application
- Operating Systems:
- Mac, Linux, Windows
- Added:
- 10/1/2022
- Last Updated:
- 11/24/2024
Operations
Publications
Lu C, Chen C, Yu C, Liu Y, Liu J, Wei S, Lin Y. MIB2: metal ion-binding site prediction and modeling server. Bioinformatics. 2022;38(18):4428-4429. doi:10.1093/bioinformatics/btac534. PMID:35904542.