Integrated AI-Guided de novo CDR-H3 Design and Combinatorial Phage Display for Antibody Discovery

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ID: 337338
2026
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Abstract
Abstract Background Obtaining target-specific therapeutic antibodies is critical for maximizing efficacy, yet conventional library-based approaches cannot guarantee epitope specificity or enable straightforward humanization; computational de novo design of human antibodies targeting specific epitopes has therefore attracted increasing interest. Methods We developed SANDAN (Structure-Aware de Novo Design for ANtibody), an end-to-end, structure-driven workflow that uses the antigen structure and epitope residues without supplying a known binding antibody or its complex pose as design input. Starting from a human framework with diverse CDR-H2 and CDR-L3 variants, the workflow performs CDR-H3-focused design using the diffusion-based method DiffAb on diverse template complex structures predicted by Boltz-1, aiming to obtain antibodies that bind specifically to the target epitope without requiring known complex pose information. Targeting human TIGIT (hTIGIT), we selected approximately 1,000 CDR-H3 design sequences and constructed a phage display library (diversity approximately ${10}^6$) incorporating them. Results Screening yielded six hTIGIT-binding antibodies that competed with the reference antibody MG1131. Their CDR-H3 sequences differed from that reference antibody’s CDR-H3 sequence. Conclusions Combining AI-guided CDR-H3 design with phage display screening yielded hTIGIT-binding antibodies that competed with MG1131 from a combinatorial library containing the designed H3 sequences.
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Authors Kairi Furui, Shinji Soga, Rika Yamazaki, Shigenori Yagi, K. Uchikawa, Takatsugu Kosugi, Takuya Shimomura, Kana Gondo, Masahito Ohue
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Journal Antibody Therapeutics
Year 2026
DOI
10.1093/abt/tbag054
URL
https://doi.org/10.1093/abt/tbag054
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