Identifying Key Residues in Lysine Decarboxylase for Soluble Expression Using Consensus Design Soluble Mutant...

Area of Science:

• Biochemistry
• Protein Engineering
• Computational Biology

Background:

• Deep learning models predict protein solubility but experimental validation is challenging.
• Rapidly confirming computational predictions is crucial for protein engineering success.

Purpose of the Study:

• To develop a hybrid computational and experimental approach for predicting and validating protein solubility-improving mutations.
• To identify key residues that enhance soluble protein expression using a novel screening strategy.

Main Methods:

• Developed ConsenSing (Consensus design Soluble Mutant Screening), a hybrid approach using sequence-based analysis for hot spot prediction.
• Constructed a compact mutant library using Darwin assembly for efficient empirical screening.
• Utilized split GFP as a reporter system for validating soluble expression.

Main Results:

• Identified multiple mutants of *Escherichia coli* lysine decarboxylase (LdcC) with significantly increased soluble expression.
• Pinpointed a single critical residue responsible for enhanced LdcC soluble expression.
• Unveiled the mechanism by which a single mutation improves protein solubility and expression.

Conclusions:

• ConsenSing effectively links computational predictions with experimental validation for protein solubility enhancement.
• Following natural evolutionary paths can guide single-residue mutations to significantly improve protein solubility and expression.
• The study provides a validated framework for rapid protein engineering to boost soluble protein production.