PO.CH01.02 · 化学
AI加速发现靶向B7-H3和DLL3的环肽放射性配体:从文库设计到临床前验证
AI-accelerated discovery of B7-H3 and DLL3-targeted cyclic peptide radioligands: From library design to preclinical validation
作者与单位 Authors & Affiliations
摘要 Abstract
中文摘要
背景:放射性药物药物偶联物(RDC)代表了精准肿瘤学中一种变革性的范式,然而靶点特异性配体的发现仍是一个关键瓶颈。我们开发了一个集成的AI增强平台,以快速鉴定针对两个新兴RDC靶点的环肽结合物:B7-H3(免疫检查点)和DLL3(Notch配体)。
方法:构建了一个结构多样的噬菌体展示文库(容量1.5×10^11;8-17个氨基酸的大环),并通过NGS验证了其复杂度。重组4Ig-B7-H3(2Ig-B7-H3作为脱靶反筛)、DLL3(及反筛DLL1/DLL4)经过SPR和光谱位移测定(SPS)的正交生物物理表征。先导化合物三角定位采用:(1)深度测序驱动的共识基序分析,(2)肽-靶点复合物的AlphaFold3多聚体建模,以及(3)平行的SPR/光谱位移测定(10⁻⁷ M亲和力阈值)。顶级候选物用Cy5标记,以在工程化肿瘤细胞系中进行实时结合和内化动力学研究。
结果:发现:15/40个噬菌体克隆表现出靶点结合——AI优化:AlphaFold3预测揭示了7/15个顶级先导化合物中存在一个保守的β-转角基序,该基序锚定于靶点中的一个隐匿口袋——验证:先导肽被合成并显示:(i)KD为8.2×10⁻⁷ M(SPR),(ii)相对于同源靶蛋白具有>3倍选择性,(iii)在细胞上显示出结合。
结论:该平台通过为先导化合物发现、优化和验证提供简化的解决方案,解决了RDC开发中的关键挑战。AI驱动的结构预测与高通量实验验证的整合压缩了传统的先导化合物发现时间线。该平台将显著加速RDC开发管线——尤其是针对新肿瘤抗原或其他已验证的表面蛋白(如GPCR、转运蛋白)的先导结合物鉴定,从而利用环肽作为模态加速RDC药物发现。
查看英文原文 English abstract
Background: Radiopharmaceutical drug conjugates (RDCs) represent a transformative paradigm in precision oncology, yet target-specific ligand discovery remains a critical bottleneck. We developed an integrated AI-augmented platform to rapidly identify cyclic peptide binders for two emerging RDC targets: B7-H3 (immune checkpoint) and DLL3 (Notch ligand).
Methods: A structurally diverse phage display library (1.5×10 11 capacity; 8-17 aa macrocycles) was engineered with NGS-validated complexity. Recombinant 4Ig-B7-H3 (2Ig-B7-H3 as off-target counterscreens), DLL3 (and counterscreens DLL1/DLL4) underwent orthogonal biophysical characterization by SPR and Spectral shift assay (SPS). Hit triangulation employed: (1) Deep sequencing-driven consensus motif analysis, (2) AlphaFold3 multimer modeling of peptide-target complexes, and (3) Parallel SPR/spectral shift assays (10⁻⁷ M affinity threshold). Top candidates were Cy5-labeled for real-time binding and internalization kinetics in engineered tumor lines.
Results: Discovery: 15/40 phage clones demonstrated target binding - AI optimization: AlphaFold3 predictions revealed a conserved beta-turn motif in 7/15 top hits that anchors to a cryptic pocket in targets - Validation: Hit peptide were synthesized and showed: (i) KD 8.2×10 ⁻7 M (SPR), (ii) >3-fold selectivity over homologous target proteins (iii) showed binding on cells
Conclusions: This platform addresses critical challenges in RDC development by providing a streamlined solution for hit discovery, optimization, and validation. The integration of AI-driven structural prediction with high-throughput experimental validation compresses traditional hit discovery timelines This platform will significantly accelerate the RDC development pipeline-especially for hit binder identification for neo tumor antigens or other validated surface proteins like GPCRs, transporters, to accelerate RDC drug discovery using cyclic peptide as the modality.
利益披露 Disclosure
T. Bing, None.