Antib Ther, Vol. 7, Issue 3, Pages 256-265, 2024

PMID: 39262441

Recombinant antibodies (rAbs) offer solutions for improving antigen specificity, enhancing immunogenic potential, and enabling versatile functionalization for disease treatment. Single-chain variable fragments (scFvs) have advanced cancer and viral infection therapies due to their favorable pharmacokinetics and human compatibility. However, experimental antibody selection often requires iterative optimization, prompting a shift toward in silico methods. To streamline this process, rAbDesFlow, an open-source computational workflow, was developed. It integrates antigen selection, antibody library generation, structure modeling, interaction analysis, and consensus ranking to identify optimal rAb candidates for experimental validation. Demonstrated in designing rAbs for ovarian cancer antigen Mucin-16 (CA-125), this workflow provides a blueprint for targeting various disease-specific biomarkers.

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