JEvTrace

JEvTrace implements the evolutionary trace (ET) method to identify functional sites in proteins by integrating multiple sequence alignments (MSAs), phylogenetic analyses, and structural data.

Key Features:

• Integration of Diverse Data Types: Integrates multiple sequence alignments (MSAs), phylogenetic analysis, and structural data to correlate evolutionary conservation with three-dimensional protein structure.
• MSA Coloring Data Structure: Implements an MSA coloring data structure that captures and stores evolutionary, biological, functional, and structural annotations for alignment positions.
• Functional Site Prediction: Extracts conserved residues across homologous proteins and maps them onto protein surfaces to predict functional interfaces and active sites, validated on modular signaling domains and DNA-binding domains.
• Evolutionary Analysis: Applies evolutionary trace principles and sequence conservation patterns to assess residue roles for site-directed mutagenesis and molecular recognition studies.

Scientific Applications:

• Functional annotation of uncharacterized proteins: Localizes functional interfaces in unannotated genes or proteins by mapping conserved residue clusters onto known structures.
• Structure–function and specificity studies: Predicts active sites and residues contributing to binding specificity in modular signaling domains and DNA-binding domains.
• Design and interpretation of mutagenesis experiments: Guides site-directed mutagenesis by identifying residues with evolutionary and structural significance.
• Protein–ligand interaction analysis: Assesses the energetic and structural significance of residue interactions within protein–ligand complexes through conservation and structural mapping.

Methodology:

Implements the evolutionary trace (ET) method by analyzing multiple sequence alignments (MSAs) and phylogenetic relationships to extract conserved residues, mapping those residues onto protein structures and clustering surface residues to identify functional interfaces, using an MSA coloring data structure and supported by mutational evolutionary analysis and structural homology.