Optimization modeling of single-chain antibody against hepatoma based on similarity algorithm

Area of Science:

• Biotechnology
• Immunology
• Structural Biology

Background:

• Single-chain variable fragment (ScFv) antibodies are crucial in targeted cancer therapy.
• Optimizing linker peptides is essential for maintaining ScFv structural integrity and bioactivity.
• Understanding linker effects is key for developing effective anti-liver cancer therapeutics.

Purpose of the Study:

• To determine the optimal linker length for single-chain antibodies against liver cancer.
• To assess the impact of different linker lengths on antibody structure and bioactivity.
• To identify connecting peptides that minimize structural and functional alterations in VH and VL domains.

Main Methods:

• Modeling of variable heavy (VH) and variable light (VL) chains and ScFv constructs with varying linker lengths (n=0-7).
• Structural similarity analysis using a spatial spherical shell layer alignment algorithm.
• Stability assessment via MATLAB analysis of distances and diffusion radius.
• Evaluation of immunological activity using indirect ELISA and anti-cancer efficacy via MTT assay.

Main Results:

• Linker length n=4 yielded the highest structural similarity between VH/VL and their original forms.
• Linker length n=3 showed minimal impact on VH and VL stability.
• Antibody affinity and inhibition rates against liver cancer cells were highest at n=4, followed by n=3.
• Optimal structural stability and biological function were observed at n=4.

Conclusions:

• A linker length of n=4 is optimal for anti-liver cancer single-chain antibodies, balancing structural integrity and bioactivity.
• This finding provides a basis for designing and constructing improved single-chain antibodies for cancer therapy.
• The study highlights the critical role of linker optimization in ScFv-based therapeutics.