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Computational Design of an Albumin Affibody-Inserted Antibody Fragment for the Prolonged Serum Half-Life

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Abstract
Although single-chain variable fragments (scFvs) are promising in cancer treatment, their clinical application is limited by extremely short serum half-life. Albumin affibody (ABD) is known to effectively extend the serum half-life of its fusion partner. In the study, we developed a novel ABD-fused scFv format having prolonged serum half-life. In previous studies, a terminal domain of scFv was frequently utilized for the fusion of various useful molecules like toxins or cytokines. Instead, we developed scFv with internally inserted ABD within the linker domain that was rarely used before. Using anti-HER2 4D5scFv as a model protein, we constructed two internally ABD-inserted 4D5scFv variants, namely 4D5-ABD, which have short (4D5-S-ABD) and long (4D5-L-ABD) linker length respectively. The model structures of these 4D5scFv and 4D5-ABD variants were predicted using the deep learning-based protein structure prediction program (AlphaFold2) and revealed high similarity to both original 4D5scFv and ABD structures, implying that the functionality would be retained. 4D5-ABD variants were expressed in the bacterial expression system and characterization showed a stable monomeric structure formation. Both 4D5-ABD variants showed anti-HER2 binding affinity comparable with 4D5scFv. The binding affinity of both 4D5-ABD variants against albumin was also comparable. In a pharmacokinetic study in mice, the 4D5-ABD variants showed a dramatically extended serum half-life of 34 h, 114 times longer than that of 4D5scFv. In conclusion, we have developed a versatile scFv platform with enhanced pharmacokinetic profiles with an aid of deep learning-based structure prediction.
Author(s)
Na Hyun Kwon
Issued Date
2023
Type
Thesis
URI
https://scholar.gist.ac.kr/handle/local/19023
Alternative Author(s)
권나현
Department
대학원 신소재공학부
Advisor
Kwon, Inchan
Degree
Master
Appears in Collections:
Department of Materials Science and Engineering > 3. Theses(Master)
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