Prediction and experimental validation of enzyme substrate specificity in protein structures

Area of Science:

• Biochemistry
• Structural Biology
• Bioinformatics

Background:

• Structural Genomics aims to determine protein structures for functional annotation.
• Predicting enzyme substrates is challenging due to minor residue variations affecting activity and specificity.
• Limited sequence identity (<20%) often hinders functional prediction.

Purpose of the Study:

• To develop a method for predicting enzyme activity and substrate specificity using local residue similarity.
• To demonstrate that evolutionarily important surface residues can identify enzyme function.
• To validate the motif-based prediction approach in identifying a specific enzyme.

Main Methods:

• Analysis of evolutionary importance and surface location of protein residues.
• Selection of small motifs (five to six residues) for functional prediction.
• Large-scale controls and direct experimental validation, including enzyme assays and directed mutagenesis.

Main Results:

• A five-residue motif successfully predicted a previously uncharacterized Silicibacter sp. protein as a carboxylesterase for short fatty acyl chains.
• The identified enzyme showed similarity to hormone-sensitive-lipase-like proteins despite low sequence identity.
• Experimental validation confirmed the predicted activity and demonstrated the motif's essential role in catalysis and substrate specificity.

Conclusions:

• Combining evolutionary and structural information can create predictive motifs for enzyme identification.
• These motifs, comprising both catalytic and non-catalytic residues, can accurately determine enzyme activity and substrate specificity.
• This approach enhances functional resolution in Structural Genomics, particularly for enzymes with challenging substrate prediction.