Determining key residues of engineered scFv antibody variants with improved MMP-9 binding using deep sequencing and...

Area of Science:

• Biochemistry
• Molecular Biology
• Immunology

Background:

• Matrix metalloproteinases (MMPs), particularly MMP-9, play a critical role in extracellular matrix regulation.
• Imbalances in MMP-9 activity are implicated in various diseases, including cancer, neurodegenerative disorders, and gynecological conditions.
• Targeting MMP-9 with novel therapeutics, such as single-chain variable fragments (scFvs), represents a promising therapeutic strategy.

Purpose of the Study:

• To develop and characterize novel single-chain antibody fragments (scFvs) with enhanced binding affinity for matrix metalloproteinase-9 (MMP-9).
• To identify the molecular basis for the improved binding of engineered scFvs to MMP-9.
• To explore the potential of deep-learning models for predicting scFv binding affinities.

Main Methods:

• Screening of a synthetic scFv antibody library displayed on yeast using fluorescent-activated cell sorting (FACS) for enhanced MMP-9 binding.
• Next-generation DNA sequencing and computational protein structure analysis to identify molecular determinants of scFv binding.
• Training a deep-learning language model on scFv variants to predict binding affinities based on CDR-H3 sequences.

Main Results:

• Engineered scFv mutants exhibited superior binding to MMP-9 compared to the natural inhibitor, tissue inhibitor of metalloproteinases (TIMPs).
• Identification of key molecular determinants responsible for the enhanced binding of scFv variants.
• Development of a deep-learning model capable of predicting scFv binding affinities from CDR-H3 sequences.

Conclusions:

• Engineered scFvs targeting MMP-9 demonstrate significant potential as therapeutic agents.
• Understanding the molecular basis of scFv-MMP-9 interaction facilitates rational drug design.
• Deep learning offers a powerful tool for accelerating the development of antibody-based therapeutics.