Work

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

Year: 2024

Type: article

Abstract: Given the crucial role of specific matrix metalloproteinases (MMPs) in the extracellular matrix, an imbalance in the regulation of activation of matrix metalloproteinase-9 (MMP-9) zymogen and inhibition of the enzyme can result in various diseases, such as cancer, neurodegenerative, and gynecological diseases. Thus, developing novel therapeutics that target MMP-9 with single-chain antibody fragments (scFvs) is a promising approach. We used fluorescent-activated cell sorting (FACS) to screen a synthetic scFv antibody library displayed on yeast for enhanced binding to MMP-9. The screened scFv mutants demonstrated improved binding to MMP-9 compared to the natural inhibitor of MMPs, tissue inhibitor of metalloproteinases (TIMPs). To identify the molecular determinants of these engineered scFv variants that affect binding to MMP-9, we used next-generation DNA sequencing and computational protein structure analysis. Additionally, a deep-learning language model was trained on the screened scFv library of variants to predict the binding affinities of scFv variants based on their CDR-H3 sequences.

Source: Computational and Structural Biotechnology Journal

DOI: 10.1016/j.csbj.2024.10.005

Authors: Masoud Kalantar, Ifthichar Kalanther, Sachin Kumar, Elham Khorasani Buxton, Maryam Raeeszadeh-Sarmazdeh

Institutions: University of Nevada, Reno, University of Illinois at Springfield

Language: English

Topic: Monoclonal and Polyclonal Antibodies Research

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $3,230