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The advancement of machine learning (ML), particularly deep learning (DL) and natural language processing (NLP) technologies, along with increased computing power, has further enhanced biotechnological applications, including protein design and engineering . These developments have led to the creation of Large Protein Language Models (LPLMs), which assist in discovering the evolutionary, structural, and functional properties across protein space by encoding amino-acid sequences into numeric vector representations we leverage pretrained LPLMs to extract features for antibody design specifically targeting ADAM17 (A Disintegrin and Metalloproteinase 17) and MMP-9cd (Matrix Metalloproteinase-9 catalytic domain). Both ADAM17 and MMP-9 play critical roles in pathological processes such as inflammation, cancer metastasis, and tissue remodeling, making them promising therapeutic targets.