Elucidating key determinants of engineered scFv antibody in MMP-9 binding using high throughput screening and machine...

Area of Science:

• Biotechnology
• Immunology
• Protein Engineering

Background:

• Matrix metalloproteinase-9 (MMP-9) dysregulation is implicated in various diseases, including cancer and neurological disorders.
• Targeting MMP-9 with therapeutics offers a potential treatment strategy.
• Engineering single-chain variable fragments (scFvs) is a promising approach for therapeutic development.

Purpose of the Study:

• To engineer and improve single-chain antibody fragments (scFvs) that bind to matrix metalloproteinase-9 (MMP-9).
• To identify molecular determinants governing the binding affinity between scFvs and MMP-9.
• To develop a predictive model for scFv binding using deep learning.

Main Methods:

• Screening of a synthetic scFv yeast surface display library using fluorescence-activated cell sorting (FACS) for enhanced MMP-9 binding.
• Next-generation DNA sequencing to identify successful scFv clones.
• Computational protein structure analysis to elucidate binding mechanisms.
• Training a deep-learning language model on CDR-H3 sequences to predict scFv binding.

Main Results:

• Isolated scFv clones demonstrated improved binding to MMP-9 compared to endogenous inhibitors.
• Molecular determinants of scFv-MMP-9 binding were identified through sequencing and structural analysis.
• A deep-learning model successfully predicted scFv variant binding based on CDR-H3 sequences.

Conclusions:

• Engineered scFvs show enhanced binding to MMP-9, indicating therapeutic potential.
• Understanding binding determinants facilitates rational design of improved antibody fragments.
• Deep learning offers a novel approach for predicting and optimizing antibody-antigen interactions.