RiSLnet: Rapid identification of smart mutant libraries using protein structure network. Application to thermal...

Area of Science:

• Protein Engineering
• Computational Biology
• Biophysics

Background:

• Protein stability is crucial for function and activity.
• Identifying stabilizing mutations requires targeted approaches.
• Current methods may lack efficiency in pinpointing optimal residues.

Purpose of the Study:

• To develop and validate RiSLnet (Rapid identification of Smart mutant Library using residue network) for efficient protein stability engineering.
• To identify specific residues for mutation that enhance protein thermal stability without compromising function.
• To reduce the number of candidate residues for mutation through computational analysis.

Main Methods:

• RiSLnet combines protein residue interaction network analysis, residue conservation identification, and relative solvent accessibility evaluation.
• The method was validated on T4 lysozyme, ribonuclease H, barnase, and cold shock protein B.
• Experimental validation was performed on lysine decarboxylase (CadA).

Main Results:

• RiSLnet achieved ~62% average accuracy in predicting beneficial mutations for thermal stability against the Protherm database.
• For lysine decarboxylase, RiSLnet demonstrated ~60% accuracy and reduced candidate residues by ~99%.
• A triple mutant generated using RiSLnet showed a twofold increase in half-life at 58°C compared to the wild-type.

Conclusions:

• RiSLnet is an effective computational tool for identifying residues that enhance protein thermal stability.
• The method significantly streamlines the protein engineering process by reducing the search space for mutations.
• RiSLnet facilitates the creation of more stable proteins with potential applications in biotechnology and medicine.