Kalantar Masoud, Kalanther Ifthichar, Kumar Sachin, Buxton Elham Khorasani, Raeeszadeh-Sarmazdeh Maryam
bioRxiv. 2024 Jun 6:2024.06.04.597476. doi: 10.1101/2024.06.04.597476.
An imbalance in matrix metalloproteinase-9 (MMP-9) regulation can lead to numerous diseases, including neurological disorders, cancer, and pre-term labor. Engineering single-chain antibody fragments (scFvs) Targeting MMP-9 to develop novel therapeutics for such diseases is desirable. We screened a synthetic scFv antibody library displayed on the yeast surface for binding improvement to MMP-9 using FACS (fluorescent-activated cell sorting). The scFv antibody clones isolated after FACS showed improvement in binding to MMP-9 compared to the endogenous inhibitor. To understand molecular determinants of binding between engineered scFv antibody variants and MMP-9, next-generation DNA sequencing, and computational protein structure analysis were used. Additionally, a deep-learning language model was trained on the synthetic library to predict the binding of scFv variants using their CDR-H3 sequences.
基质金属蛋白酶-9（MMP-9）调节失衡可导致多种疾病，包括神经紊乱、癌症和早产。设计靶向MMP-9的单链抗体片段（scFv）以开发针对此类疾病的新型疗法是很有必要的。我们利用荧光激活细胞分选术（FACS）筛选了展示在酵母表面的合成scFv抗体文库，以改善其与MMP-9的结合。与内源性抑制剂相比，FACS后分离出的scFv抗体克隆与MMP-9的结合有所改善。为了解工程化scFv抗体变体与MMP-9之间结合的分子决定因素，我们使用了下一代DNA测序和计算蛋白质结构分析。此外，在合成文库上训练了一个深度学习语言模型，以利用scFv变体的互补决定区重链3（CDR-H3）序列预测其结合情况。
