Predicted IL-18/IL-18R Binding Improvement Through Protein Interface Modification with Computer-Aided Design.

Area of Science:

• Biochemistry and Molecular Biology
• Immunology
• Computational Biology

Background:

• Cytokine immunotherapy, particularly using Interleukin-18 (IL-18), is a promising cancer treatment strategy.
• IL-18 enhances anti-tumor responses by boosting natural killer (NK) and cytotoxic T cell activity.
• Optimizing IL-18's interaction with its receptor is key to enhancing its therapeutic efficacy.

Purpose of the Study:

• To computationally design and predict mutations that increase the biological activity of IL-18.
• To investigate the effects of specific mutations on IL-18's receptor binding and stability.
• To explore a computer-aided design approach for developing more potent IL-18-based cancer therapies.

Main Methods:

• Structure-based computational energy calculations were employed to identify potential mutations.
• Molecular dynamic (MD) simulations were used to assess the stability and conformational changes of mutated IL-18.
• Analysis focused on electrostatic interactions and flexibility in key protein regions.

Main Results:

• Four candidate mutations (E6M, E6M+N111S+R131G, E6M+K129M+R131G, E6M+N111S+K129M+R131G) were predicted to enhance IL-18 receptor binding and stability.
• MD simulations indicated that mutations did not compromise overall protein stability but increased flexibility in the β8-β9 hairpin loop.
• The dynamic behavior of mutated IL-18 suggests potential for improved biological activity.

Conclusions:

• Computer-aided design is an effective strategy for enhancing IL-18 cytokine potency.
• The identified mutations offer a basis for developing improved IL-18 variants for cancer immunotherapy.
• Further simulations of the full IL-18-receptor complex are needed to fully validate these findings.