LIBRA

LIBRA predicts the presence and identity of active sites and ligand binding sites in protein structural models to support functional annotation of proteins from structural genomics and ab initio molecular modeling efforts.

Key Features:

• Graph theory-based algorithm: Uses graph theory to identify the largest subset of similar residues between an input protein and a curated collection of known functional sites.
• Database utilization: Compares structures against two predefined databases: active sites derived from the Catalytic Site Atlas and ligand binding sites extracted from the Protein Data Bank (PDB).
• High predictive accuracy: Empirical tests report the correct binding or active site identified in 90% of analyzed cases, with the correct site ranked first in 90% of these instances.
• Superior performance: Outperforms other structure-based ligand binding site detection tools in comparative analyses.

Scientific Applications:

• Functional annotation of uncharacterized proteins: Predicts active and ligand binding sites to aid assignment of biochemical function for proteins of unknown function.
• Drug discovery target identification: Identifies potential ligand binding sites to support selection of targetable sites for small-molecule design.
• Annotation of modeled or newly discovered proteins: Supports annotation of proteins obtained from structural genomics and ab initio models by mapping known functional sites.
• Hypothesis generation for biochemical function: Generates hypotheses about protein function and interactions based on detected residue similarity to known active sites.

Methodology:

Compares an input protein structural model against databases of known active and ligand binding sites using a graph theory algorithm that identifies the largest subsets of similar residues.