Active site detection by spatial conformity and electrostatic analysis--unravelling a proteolytic function in shrimp...

Area of Science:

• Computational biology
• Structural bioinformatics
• Enzymology

Background:

• Identifying protein active sites is crucial for understanding enzyme function.
• Existing methods often rely on sequence conservation or structural information alone.
• Electrostatic potential calculations offer a refined approach to characterizing active sites.

Purpose of the Study:

• To introduce CataLytic Active Site Prediction (CLASP), a novel computational methodology for active site detection.
• To leverage structural and electrostatic conformity for accurate prediction of enzymatic functions.
• To validate CLASP's efficacy on well-characterized enzymes and explore novel activities in promiscuous enzymes.

Main Methods:

• CLASP utilizes a pipelined model incorporating physical 3D signatures of active sites.
• It analyzes electrostatic potential differences (PD) between analogous residue pairs to refine predictions.
• The method was tested on beta-lactamases, serine proteases, and alkaline phosphatases, including analysis of Catalytic Site Atlas (CSA) motifs.

Main Results:

• CLASP accurately predicted active sites for beta-lactamases and serine proteases.
• Analysis of Catalytic Site Atlas (CSA) motifs demonstrated CLASP's classification ability.
• CLASP predicted a novel protease function for shrimp alkaline phosphatase (SAP), which was experimentally validated in vitro.

Conclusions:

• CLASP provides an effective computational approach for identifying and characterizing protein active sites.
• The method enhances the understanding of enzymatic function through structural and electrostatic analysis.
• CLASP successfully predicted and validated a previously unknown protease activity in shrimp alkaline phosphatase.