GASS-WEB

GASS-WEB applies the Genetic Active Site Search (GASS) evolutionary algorithm to identify enzyme active sites in protein structures for comparative active-site analyses and function inference.

Key Features:

• Evolutionary algorithm: GASS-WEB employs the Genetic Active Site Search (GASS) evolutionary algorithm to identify similar active sites across protein structures.
• Dual search scenarios: Supports inputting a protein to find matching active-site templates or providing an active-site template to search across a database of protein structures.
• Template flexibility: Accommodates variations and size differences beyond exact template matching.
• Catalog benchmarking: Correctly identified over 90% of catalogued active sites from the Catalytic Site Atlas.
• Performance on CASP 10: Achieved a Matthew correlation coefficient of 0.63 on the CASP 10 dataset.
• Comparative ranking: Ranked fourth out of 18 methods in comparative analyses.

Scientific Applications:

• Protein function prediction: Identification of conserved active sites to support inference of protein function.
• Structural biology: Comparative analysis of active-site configurations to investigate structure–function relationships.
• Enzyme engineering and drug design: Active-site identification and comparison to inform enzyme modification and inhibitor/activator design.

Methodology:

Uses the Genetic Active Site Search (GASS) evolutionary algorithm and implements two search scenarios—search by protein to find matching active-site templates or search by active-site template across a database of protein structures.