AI驱动的靶向KLK2的人源化纳米抗体从头设计用于前列腺癌免疫治疗

Conventional monoclonal antibodies (mAbs) are widely used in cancer therapy but are limited by large size (~150 kDa), structural complexity, and high production costs, motivating the development of alternative antibody formats. Single-chain variable fragments (scFvs), derived from mAbs, retain antigen-binding specificity and affinity while offering a smaller molecular size (~25 kDa), enabling applications such as chimeric antigen receptor (CAR)-T/NK cells and/or bispecific T/NK cell engagers (BiTEs or NKCEs) for immunotherapy. However, scFvs face challenges including structural instability, linker-dependent functionality, and aggregation-prone hydrophobic residues, which limit their therapeutic efficacy. In contrast, single-domain antibodies (sdAbs), derived from the variable antigen-binding domain of heavy-chain-only antibodies (VHHs) and commonly known as nanobodies (~15 kDa), overcome these limitations. Importantly, nanobodies can access cryptic or conformational epitopes-such as deep cavities that are inaccessible to conventional mAbs or scFvs-providing a stable, highly versatile, and clinically attractive biotherapeutic modality for cancer immunotherapy. Traditional methods to develop nanobodies rely on targeting protein-immunized camelids or high-throughput experimental nanobody display systems, including bacteria, phage, ribosome, or yeast display. These approaches are time-consuming, labor-intensive, camelid-derived (with potential immunogenicity), and restricted in library diversity. Moreover, affinity maturation and specificity optimization require multiple iterative rounds of experimental selection-often taking months to years-and may still fail to produce high-affinity nanobodies against difficult targets. To address these challenges, we developed and integrated multiple artificial intelligence (AI) platforms for target-guided humanized nanobody backbone design, soluble protein sequence generation, and enhanced target-nanobody complex prediction and screening. These platforms enable de novo , conformational epitope-based nanobody design, generating diverse, high-affinity, and highly specific candidates entirely in silico, with experimental validation achievable within weeks to months depending on the target. As a proof-of-principle, we have applied our AI platforms to design humanized nanobodies targeting kallikrein-related peptidase 2 (KLK2), a clinically validated, prostate cancer-specific cell surface protein, and are experimentally engineering the top-designed candidates into T/NK cell engagers and CAR-T/NK cells to evaluate their ability to eliminate KLK2-expressing prostate cancer cells.